CN116388302A

CN116388302A - Active-reactive power combined optimization method for power distribution network for coordinating network side resources

Info

Publication number: CN116388302A
Application number: CN202310070578.3A
Authority: CN
Inventors: 李勇; 凌锋; 乔学博; 钟俊杰; 曹一家; 刘敏
Original assignee: Hunan University
Current assignee: Hunan University
Priority date: 2023-01-17
Filing date: 2023-01-17
Publication date: 2023-07-04

Abstract

The invention provides a method for active-reactive power combined optimization of a self-energy-storage flexible interconnection power distribution network for coordinating network side resources, which comprises the steps of constructing a long-time active-reactive power combined optimization model for an on-load voltage regulating transformer, a discrete/continuous reactive power compensation device, a reconstruction switch and an intelligent energy storage soft switch in a day-ahead stage, and constructing a multi-target rolling optimization model in the day-ahead stage; determining a source load uncertainty fuzzy set based on KL divergence, solving a day-ahead two-stage distribution robust optimization model by adopting a column and constraint generation algorithm, wherein the distribution robust optimization model ensures the economy and the robustness of a day-ahead scheduling scheme by solving an operation scheme under the worst scene probability distribution of an uncertainty variable; the daily multi-target rolling optimization is converted based on an ideal point method, so that the network loss and the voltage optimization can be considered, and more accurate daily operation strategies of each quick speed regulating and controlling device can be obtained; and the model linearization processing is utilized, so that the calculation efficiency of the model can be greatly improved on the premise of minimizing errors.

Description

Active-reactive power combined optimization method for power distribution network for coordinating network side resources

Technical Field

The invention relates to the field of power distribution network optimization operation, in particular to an active-reactive combined optimization method of a self-energy-storage flexible interconnection power distribution network for coordinating network side resources.

Background

The scale of distributed power sources (Distributed Generation, DG) is rapidly evolving and high permeability renewable energy DG will become one of the important features of new power distribution systems. However, after a large number of DG are connected to the power distribution network, the randomness and fluctuation of the output of DG are very easy to cause a series of problems of bidirectional power flow, voltage out-of-limit, line overload and the like of the system. The active distribution network realizes system operation optimization through on-load tap changing (OLTC) adjustment, capacitor Bank (CB) switching, topology reconstruction optimization, source side DG active-reactive control and the like. An intelligent soft point (SOP) is used as a novel power electronic device capable of replacing a tie switch, and can rapidly realize functions of flow precise control between feeder lines, dynamic reactive compensation, flexible switching of power supply modes and the like, so that a novel technical approach is provided for operation control of a power distribution network.

The combined optimization of energy storage and SOP can fully cope with uncertainty of source load, and the running economy of the power distribution network is improved. The intelligent energy storage soft switch (soft open points with energy storage, E-SOP) has more flexible space-time adjustment capability and has smaller volume and cost than independently configuring energy storage and SOP. Considering that the cost of the current flexible interconnection device is still high, the coordination and optimization of flexible interconnection equipment and traditional regulation and control equipment are required to be researched so as to realize the multi-time-scale economic and safe operation of the power distribution network.

The existing uncertainty optimization method for the self-energy-storage flexible interconnection distribution network containing E-SOP is widely applied and mainly comprises random optimization and robust optimization. However, stochastic optimization faces the problem of difficult acquisition of uncertainty parameter probability distribution, while robust optimization typically uses worst scenario characterization uncertainty, and the computation results are too conservative. It is necessary to introduce distribution robustness optimization to find solutions under the worst probability distribution of uncertain parameters so as to ensure the economy and robustness of the flexible interconnection power distribution network optimization scheme.

The discretization, the speed characteristic and the E-SOP time sequence of the traditional network side regulation and control resources determine that the self-energy-storage flexible interconnected power distribution network optimization scheduling model is a multi-time Duan Jiang coupled mixed integer nonlinear programming model, and the model solving difficulty is further increased due to uncertainty of source load.

Disclosure of Invention

The invention provides an active-reactive combined optimization method of a self-energy-storage flexible interconnection power distribution network for coordinating network side resources, which aims to solve the problems that when the existing high-permeability distributed photovoltaic is connected into a power distribution network, coordination and optimization of flexible interconnection equipment and traditional regulation and control equipment are not fully considered, so that DPV (differential pressure swing) digestion capacity is low, and power distribution network economy and safety are poor.

The invention solves the problems by the following technical means:

an active-reactive combined optimization method for a self-energy-storage flexible interconnected power distribution network for coordinating network side resources comprises the following steps:

step S1, determining a prediction time sequence scene of distributed photovoltaic and load at a day front stage and a day inner stage with a plurality of preset time scales;

s2, establishing a daily long-time active-reactive combined optimization model by taking the minimum running cost of the power distribution network as a target, and establishing a daily multi-target rolling optimization model by taking the minimum network loss and voltage offset as targets, wherein model constraints of the daily long-time active-reactive combined optimization model and the daily multi-target rolling optimization model comprise load flow equation constraints, voltage and current constraints, distributed photovoltaic reactive power constraints, intelligent energy storage soft switch running constraints, network reconstruction constraints, on-load voltage regulating transformer running constraints, reactive power compensation device constraints and flexible load running constraints;

s3, determining a source load uncertainty fuzzy set based on KL divergence, converting a long-time active-reactive combined optimization model before the day into a two-stage distribution robust optimization model before the day based on a column and constraint generation algorithm, converting a multi-target rolling optimization model in the day based on an ideal point method, and linearizing nonlinear parts of the long-time active-reactive combined optimization model before the day and the multi-target rolling optimization model in the day based on a linear approximation method;

And S4, solving the daily-front long-time active-reactive combined optimization model and the daily multi-objective rolling optimization model in the step S3 based on the predicted time sequence scene to obtain the running cost of the power distribution network and the network side resource regulation strategy.

Preferably, the step S1 specifically includes:

determining historical data of distributed photovoltaic output and load for n hours, and reducing the historical data into a plurality of predicted time sequence scenes with preset time scales by using a time sequence scene analysis algorithm and a time sequence clustering algorithm; the time sequence scene analysis algorithm is as follows:

in the above formula, p represents load and DPV output;

represents the load at the t hour; />

Indicating the output of DPV at hour t; />

Represents the load of the nth hour; />

Indicating the DPV output for the nth hour.

Preferably, in the step S2, the objective function of the long-time active-reactive combined optimization model is:

C _ADN ＝min(C _P +C _Loss +C _FL +C _ESS ) (2)

wherein C is _ADN For the running cost of the distribution network, C _P For electricity purchasing cost, C _Loss For loss of network cost, C _FL For flexible load response cost, C _ESS Is the energy storage degradation cost;

the objective function of the daily multi-objective rolling optimization model is as follows:

wherein f ₁ And f ₂ Sub-object 1 and sub-object 2, respectively; v (V) _i,t And I _ij,t The voltage of the node i and the current of the branch ij are respectively; r is (r) _ij The resistor is a branch ij resistor;

the power is lost for intelligent energy storage soft switch; />

And->

Respectively storing energy charging and discharging power; η (eta) ^ESS,C And eta ^ESS,D Respectively storing energy, charging and discharging efficiency; Δt' is the time interval.

Preferably, in the step S2, the load flow equation constraint is a load flow equation constraint described by using a branch load flow model, and specifically is as follows:

wherein x is _ij Representing the reactance of branch ij; p (P) _ij,t,s And Q _ij,t,s Active and reactive power transfer of branch ij are shown respectively; p (P) _j,t,s And Q _j,t,s Respectively representing the active power and the reactive power of the injection node j;

and->

Respectively representing the active power and the reactive power after load reduction; />

And->

Respectively representing the active power and the reactive power output by the generator; />

And

active and reactive power of the distributed photovoltaic output are represented respectively; />

And->

Active power and reactive power of the intelligent energy storage soft switch injection node j are respectively represented; />

And->

Respectively representing reactive power output by the static reactive compensator and the capacitor bank; omega shape _l 、Ω _G 、Ω _DPV 、Ω _ESOP 、Ω _SVC And omega _CB Respectively representing a line set, a generator, a distributed photovoltaic, an intelligent energy storage soft switch, an SVC and a capacitor set access node set;

in the step S2, the constraint conditions of each branch current and each node voltage are as follows:

Wherein: v (V) _i ^max And V _i ^min Respectively representing an upper limit value and a lower limit value of the voltage of the node i;

representing the upper limit value of the current of branch ij.

Preferably, in the step S2, the distributed photovoltaic reactive constraint condition is:

in the above-mentioned method, the step of,

a per unit value representing the distributed photovoltaic output; />

Representing the distributed photovoltaic installation capacity of node i; />

Representing a minimum power factor of the operation of the distributed photovoltaic inverter;

the intelligent energy storage soft switch operation constraint conditions are as follows:

wherein:

representing the stored energy output power; />

The loss coefficient of the intelligent energy storage soft switch is represented; />

Representing the installation capacity of the intelligent soft switch; />

And->

Respectively representing the maximum charge and discharge power; />

Representing the energy storage residual quantity at the moment t; />

And->

Respectively representing the upper limit and the lower limit of the energy storage charge state; />

Representing the stored energy rated capacity.

Preferably, in the step S2, an energy storage power interval constraint is introduced:

wherein:

and->

Respectively representing the upper limit and the lower limit of the energy storage charge state at the t moment obtained by optimization;

and->

Respectively representing the upper limit and the lower limit of the energy storage charge state at the time t-1 obtained by optimization; />

And->

The value is a fixed value, and the minimum and maximum value intervals of the energy storage charge states at all times are respectively represented;

the distribution network needs to meet radial and connectivity constraints, and the network reconstruction constraints are as follows:

Wherein: alpha _ij Is 0-1 variable, representing line state, alpha _ij =1 is line closed, otherwise open; beta _ij Taking 1 when the node i is the father node of the node j and taking 0 when the node i is a 0-1 variable; setting that network reconstruction only occurs once in the day-ahead optimization stage;

adding line state variables alpha taking network reconfiguration into consideration _ij The flow equation constraint is then rewritten as follows:

preferably, in the step S2, the on-load tap changing transformer operation constraint is:

wherein: v (V) _m,t,s Representing the voltage of the virtual bus of the on-load tap changing transformer; k (k) _ij,t And K _ij,t The transformation ratio and tap position of the on-load voltage regulating transformer are respectively represented; Δk _oltc And

each gear adjusting step length and the maximum adjusting gear of the on-load voltage-regulating transformer are respectively represented; lambda (lambda) _k,t A variable of 0-1, which indicates whether the kth adjusting gear of the on-load voltage regulating transformer is changed; c (C) _OLTC Representing the maximum number of times the on-load tap changer can operate each day;

the reactive compensation device is constrained as follows:

wherein:

and->

Respectively denoted as SAn upper and lower limit at which VC can deliver reactive power; />

The number of capacitor bank groups put into use at time t is represented; />

Representing the capacity of a single set of capacitor banks; />

Representing the maximum number of capacitor banks that can be put into; />

Indicating whether the capacitor bank is operated at time t; c (C) _CB Indicating the maximum number of times the capacitor bank can be operated per day.

Preferably, in the step S2, the flexible load operation constraint is:

wherein:

active power representing load shedding; />

Representing that the threshold coefficient can be reduced, and the value is 0-1;

representing a set of curtailable periods; />

A reduction scaling factor representing the time t; />

Rate coefficient of cut-down representing flexible load allowance；/>

And->

The active power and the reactive power before load shedding are shown, respectively.

Preferably, in the step S3, a source load uncertainty fuzzy set based on KL divergence is constructed by counting distributed photovoltaic and load historical data, a long-time active-reactive combined optimization model before the day is converted into a two-stage distributed robust optimization model before the day based on a column and constraint generation algorithm, and decoupling is performed to obtain a main problem and a sub problem for iterative solution; converting a daily multi-target rolling optimization model based on an ideal point method; and replacing square terms of variables in the power distribution network daily long-time active-reactive combined optimization model and the daily multi-objective rolling optimization model by linear variables, introducing a large M method relaxation treatment to a variable multiplication form in the daily long-time active-reactive combined optimization model and the daily multi-objective rolling optimization model, performing second order cone relaxation treatment to quadratic form constraint, converting the quadratic form constraint into a plurality of inequality expression constraint by utilizing a polyhedral approximation method, and introducing absolute value constraint into two non-negative intermediate variables for replacement to linearize nonlinear parts of the daily long-time active-reactive combined optimization model and the daily multi-objective rolling optimization model.

Preferably, the step S3 specifically includes the following steps:

s31, constructing a source load uncertainty fuzzy set based on KL divergence; constructing a reference probability distribution by estimating a large amount of historical data

Characterization of the KL divergence>

And->

A measure of the distance between the reference probability distribution and the true probability distribution; when referencing probability distribution->

When the source load uncertainty fuzzy set is as follows:

wherein:

representation->

True probability distribution of D _KL A KL divergence value representing the difference between the true probability distribution and the reference probability distribution; lambda (lambda) ₀ Representing the error level;

s32, converting a daily distribution robust optimization model; decoupling the model into a main problem and a sub problem iteration solution by adopting a generation algorithm based on columns and constraints; the sub-problem is that the decision variable determined by the main problem optimization is taken as a known quantity, the most serious scene distribution probability in the uncertainty fuzzy set is returned to the main problem, and the model of the sub-problem is as follows:

wherein: ρ _s A probability representing scene s; s represents the number of scenes; d is KL divergence ambiguity set; i ^* A value of a first stage variable obtained in the main problem, which is constant in the sub-problem;

0-1 variables of the second-stage optimization model; p (P) _s Optimizing continuous variables of the model for the second stage; c (C) ^T Z, G, Q, h are constant coefficient matrices; the sub-problem is decoupled into two independent steps, the specific steps are as follows:

first, solving the lower S mixed integer linear programming models, as shown in formula (38):

wherein:

the optimal value obtained by the lower model is represented, and substituted into the upper model, and the objective function value obtained by optimization at the moment is the upper bound of the original problem;

through the two steps, the sub-problem can obtain the worst scene probability distribution

And returns it to the main question; uncertainty scene variable of distributed photovoltaic and load transmitted by main problem as sub-problem +.>

For the known quantity, carrying out self-energy-storage flexible interconnection power distribution network scheduling strategy solving, wherein a model of a main problem is shown as a formula (40); at this time, the value obtained by optimizing the main problem is the updated lower limit value of the original problem;

wherein: η is an intermediate variable representing an estimated value for the sub-problem; K. k represents the total number of outer layer cycles and the kth time, respectively;

the probability of the most serious scene distribution found in the kth iteration is determined; />

And->

Respectively are provided with0-1 variable and continuous variable at the kth iteration;

s33, converting a daily multi-objective optimization model; the ideal point method uses the optimal solution of each target to take the distance between the target function and the optimal solution as a new target function, as follows:

Wherein F represents a newly constructed objective function; f (f) _1,opt And f _2,opt Respectively expressed as optimal values of the two sub-targets; the ideal point method based on Euclidean distance is nonlinear, and the Manhattan distance is introduced to correct the solving equation, so that the method is obtained:

min F＝|f ₁ -f _1,opt |+|f ₂ -f _2,opt | (42)

step S34, square term variable replacement; constraint equations (5), (8) and (9) of the power flow equation, and the square term of the voltage and current contained in the safety constraint equation (10):

and->

By v _i,t,s And iota (iota) _ij,t,s Instead, the conversion is as follows:

step S35, performing second-order cone relaxation treatment on the constraint type (45) of the tide equation and the operation constraint type (13) and (14) of the intelligent energy storage soft switch, and linearizing the constraint type; the method can be obtained through relaxation treatment:

the equation (47) is equivalent to:

the above decomposition is into two rotation cone constraint formulas:

formulas (48), (49), (51) and (52) have the same general form, as follows:

wherein: alpha ₁ 、α ₂ And alpha ₃ Are all continuous variables; uniformly processing the linear expression into a linear expression by using a polyhedral approximation method:

wherein ζ and χ are intermediate variables; v is a parameter that determines the number of constraints and variables introduced into the linearization process; beta is a variable less than v;

step S36: linearizing the rest constraint conditions; the large M method is adopted to relax constraint formula (27) and is converted into:

-α _ij M ₁ ≤P _ij,t,s ≤α _ij M ₁ (57)

-α _ij M ₂ ≤Q _ij,t,s ≤α _ij M ₂ (58)

-α _ij M ₃ ≤I _ij,t,s ≤α _ij M ₃ (59)

Wherein M is ₁ 、M ₂ 、M ₃ And M ₄ Are positive numbers not less than 10000;

the on-load voltage regulating transformer operating constraints translate into the following form:

ν _i,t,s -ν _m,t,s ＝2(P _ij,t,s r _ij +Q _ij,t,s x _ij )-(r _ij ² +x _ij ² )ι _ij,t,s (62)

wherein: o (o) _j,t,s ＝b _ij,k,t ν _j,t,s Is a variable introduced for linearizing the voltage at the end node of the on-load tap-changing transformer at the network side;

adding (66) to linearize the capacitor bank action count constraint:

wherein mu _j,t A reactive power variation value for the capacitor bank;

for absolute value processing method, non-negative intermediate variable X is introduced ⁺ And X ^- The substitution variable X, namely:

compared with the prior art, the invention has the beneficial effects that at least:

according to the active-reactive combined optimization method for the self-energy-storage flexible interconnection power distribution network of the coordinated network side resources, which is provided by the invention, aiming at the running problem of the high-permeability distributed photovoltaic power distribution network, the space-time adjustment characteristics of an intelligent energy-storage soft switch (E-SOP) and traditional network side regulation and control equipment can be effectively utilized, various network side resources are optimally controlled from multiple time scales, and the running economy and safety of the power distribution network are improved; the method comprises the steps of fully utilizing the speed regulation characteristics of discrete and continuous regulation equipment, constructing a long-time active-reactive combined optimization model of an on-load regulating transformer, a discrete/continuous reactive compensation device, a reconstruction switch and an intelligent energy storage soft switch in a day-ahead stage, and constructing a multi-objective rolling optimization model in a day-ahead stage; determining a source load uncertainty fuzzy set based on KL divergence, solving a day-ahead two-stage distribution robust optimization model by adopting a column and constraint generation (C & CG) algorithm, and ensuring the economy and the robustness of a day-ahead scheduling scheme by solving an operation scheme under the probability distribution of the worst scene of an uncertainty variable by the distribution robust optimization model; the daily multi-target rolling optimization is converted based on an ideal point method, so that the network loss and the voltage optimization can be considered, and more accurate daily operation strategies of each quick speed regulating and controlling device can be obtained; and the model linearization processing is utilized, so that the calculation efficiency of the model can be greatly improved on the premise of minimizing errors.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 is a flow chart of an active-reactive combined optimization method of a self-energy-storage flexible interconnected power distribution network for coordinating network side resources according to an embodiment of the invention;

FIG. 2 is a specific flowchart of an active-reactive combined optimization method for coordinating network side resources according to an embodiment of the present invention;

FIG. 3 (a) is a graph of a predicted DPV scene graph at a day before, in accordance with an embodiment of the invention;

FIG. 3 (b) is a graph of a predicted load scenario prior to day in accordance with an embodiment of the present invention;

FIG. 4 is a graph of intra-day prediction source load scene according to an embodiment of the present invention;

FIG. 5 is a flowchart of a solution of a two-stage distributed robust optimization model in the early days of an embodiment of the present invention;

FIG. 6 is a diagram of an example rack configuration of an embodiment of the present invention;

fig. 7 is a cost comparison of

cases

3 and 4 under different optimization methods according to embodiments of the present invention;

fig. 8 is a graph showing the probability distribution change of

cases

3 and 4 according to the embodiment of the present invention.

Detailed Description

For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.

In the embodiment of the present application, the term "and/or" is merely an association relationship describing the association object, which indicates that three relationships may exist, for example, a and/or B may indicate: a exists alone, A and B exist together, and B exists alone.

The terms "first", "second" in the embodiments of the present application are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present application, the terms "comprise" and "have," along with any variations thereof, are intended to cover non-exclusive inclusions. For example, a system, article, or apparatus that comprises a list of elements is not limited to only those elements or units listed but may alternatively include other elements not listed or inherent to such article, or apparatus. In the description of the present application, the meaning of "plurality" means at least two, for example, two, three, etc., unless specifically defined otherwise.

Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the present application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.

When the high-permeability distributed photovoltaic is connected into a distribution network, the randomness and the fluctuation of the output of the distributed photovoltaic are very easy to cause a series of problems of bidirectional power flow, voltage out-of-limit, line overload and the like of the system, the coordination and optimization of flexible interconnection equipment and traditional regulation and control equipment are not fully considered in the existing active distribution network, so that the DPV (differential pressure valve) absorption capacity is low, and the economy and the safety of the distribution network are poor.

Therefore, the embodiment of the invention provides the active-reactive power combined optimization method for the self-energy-storage flexible interconnection power distribution network, which coordinates network side resources, can effectively utilize the space-time adjustment characteristics of an intelligent energy-storage soft switch (E-SOP) and traditional network side regulation and control equipment, optimally control various network side resources from multiple time scales, and improve the running economy and safety of the power distribution network. The following description and description will be made with reference to various embodiments.

The embodiment of the invention provides a method for active-reactive power combined optimization of a self-energy-storage flexible interconnection power distribution network for coordinating network side resources, which is shown in fig. 1 and 2 and comprises the following steps:

step S1, determining a predicted time sequence scene of Distributed Photovoltaic (DPV) and load of a plurality of preset time scales in the day-ahead and day-in stages;

a large amount of historical data of DPV output and load is imported, a time sequence scene analysis algorithm is shown as formula (1), a time sequence clustering algorithm is adopted to reduce the original data, and a plurality of day-ahead prediction time sequence scenes with 24 hours as time scales and a single day-ahead prediction time sequence scene with 96 time periods as time scales are generated, as shown in fig. 3 (a), 3 (b) and 4.

In the above formula, p represents load and DPV output;

represents the load at the t hour; />

Indicating the output of DPV at hour t; />

Represents the load of the nth hour; />

Indicating the DPV output for the nth hour.

S2, a long-time active-reactive combined optimization model before the day is built by taking the minimum running cost of the power distribution network as a target, a multi-target rolling optimization model in the day is built by taking the minimum network loss and voltage offset as targets, and model constraints of the long-time active-reactive combined optimization model before the day and the multi-target rolling optimization model in the day comprise load flow equation constraints, voltage and current constraints, distributed Photovoltaic (DPV) reactive power constraints, intelligent energy storage soft switch (E-SOP) running constraints, network reconfiguration constraints, on-load voltage regulating transformer (OLTC) running constraints, reactive compensation device constraints and Flexible Load (FL) running constraints;

Step S21, the objective function of the long-time active-reactive combined optimization model before the day is that the running cost of the power distribution network is minimum, as shown in the formula (2):

C _ADN ＝min(C _P +C _Loss +C _FL +C _ESS ) (2)

wherein C is _ADN For the running cost of the distribution network, C _P For electricity purchasing cost, C _Loss For loss of network cost, C _FL For flexible load response cost, C _ESS Is the energy storage degradation cost.

The objective function of the daily multi-objective rolling optimization model is that the net loss and the voltage offset are minimum, as shown in formulas (3) and (4):

the power is lost for intelligent energy storage soft switch; />

And->

S22, constructing constraint conditions of a long-time active-reactive combined optimization model before the day and a multi-objective rolling optimization model in the day;

the flow equation constraint is a flow equation constraint of describing a system by adopting a branch flow model, and is specifically as follows:

/>

and->

Respectively representing the active power and the reactive power after load reduction; / >

And->

And

representing the active and reactive power of the DPV output, respectively; />

And->

Active and reactive power of the E-SOP injection node j are respectively represented; />

And->

Reactive power output by SVC and CB are respectively represented; omega shape _l 、Ω _G 、Ω _DPV 、Ω _ESOP 、Ω _SVC And omega _CB Representing the line set, generator, DPV, E-SOP, SVC and CB access node sets, respectively.

In order to ensure the safety and the electric energy quality of the system, the current of each branch and the voltage of each node meet constraint conditions:

wherein: v (V) _i ^max And V _i ^min Respectively represent the upper limit value of the node i voltageAnd a lower limit value;

representing the upper limit value of the current of branch ij.

The reactive constraint conditions of the DPV are as follows:

in the above-mentioned method, the step of,

a per unit value representing DPV output; />

Representing the DPV installation capacity of node i; />

Representing a minimum power factor for operation of the DPV inverter;

meanwhile, the E-SOP operation constraint conditions are as follows:

/>

wherein:

representing the stored energy output power; />

Representing the loss factor of E-SOP; />

Representing SOP installation capacity; />

And->

Respectively representing the maximum charge and discharge power; />

Representing the energy storage residual quantity at the moment t; />

And

respectively representing the upper limit and the lower limit of the energy storage SOC; />

Representing the stored energy rated capacity.

In order to ensure that the energy storage operation strategy in the daily optimization stage can meet the full-time optimization, namely all source load uncertainty scenes, the energy storage power interval constraint is introduced in the part:

Wherein:

and->

Respectively representing the upper limit and the lower limit of the energy storage SOC at the t moment obtained by optimization; />

And->

Respectively representing the upper limit and the lower limit of the energy storage SOC at the t-1 moment obtained by optimization; />

And->

The value is a fixed value, and the minimum value interval and the maximum value interval of the energy storage SOC at each moment are respectively represented;

wherein:α _ij is 0-1 variable, representing line state, alpha _ij =1 is line closed, otherwise open; beta _ij And 1 is taken when the node i is the father node of the node j and is a variable of 0-1, otherwise, 0 is taken. The network reconfiguration at the optimization stage before the day is set to occur only once.

Adding line state variables alpha taking network reconfiguration into consideration _ij The flow equation constraint can then be rewritten as follows:

OLTC operating constraints are:

wherein: v (V) _m,t,s Representing the voltage of the OLTC virtual bus; k (k) _ij,t And K _ij,t Respectively representing the gear ratio of the OLTC and the tap gear; Δk _oltc And

each gear adjustment step size and the maximum adjustment gear of the OLTC are respectively represented; lambda (lambda) _k,t A variable of 0-1, indicating whether there is a change in the kth gear of OLTC; c (C) _OLTC Indicating the maximum number of times OLTC can be actuated per day.

The reactive compensation device is constrained as follows:

wherein:

and->

Respectively indicated as upper and lower limits where SVC is capable of delivering reactive power; / >

The number of CB groups put into use at the time t is shown; />

Representing the capacity of a single set of CBs; />

Indicating the maximum CB group number which can be input; />

Indicating whether CB operates at time t; c (C) _CB Indicating the maximum number of times a CB can be actuated per day.

The flexible load operation constraints are:

wherein:

active power representing load shedding; />

representing a set of curtailable periods; />

A reduction scaling factor representing the time t; />

A curtailment rate coefficient indicative of the flexible load allowance; />

And->

S3, determining a source load uncertainty fuzzy set based on KL divergence, converting a long-time active-reactive combined optimization model before the day into a two-stage distributed robust optimization model before the day based on a column and constraint generation algorithm (C & CG algorithm), converting a multi-target rolling optimization model in the day based on an ideal point method, and linearizing nonlinear parts of the long-time active-reactive combined optimization model before the day and the multi-target rolling optimization model in the day based on a linear approximation method;

the discretization, the speed characteristic and the E-SOP time sequence of the traditional network side regulation and control resources determine that the self-energy-storage flexible interconnected power distribution network optimization scheduling model is a multi-time Duan Jiang coupled mixed integer nonlinear programming model, and the model solving difficulty is further increased due to uncertainty of source load. Therefore, a probability distribution fuzzy set based on KL divergence is constructed, a C & CG algorithm is adopted to solve a day-front two-stage distribution robust optimization model, a day-front multi-target rolling optimization model is converted based on an ideal point method, second-order cone relaxation and linearization are carried out on power flow constraint and E-SOP constraint, linearization treatment is carried out on other constraint conditions, and therefore the model can quickly obtain an optimal solution;

And S31, constructing a fuzzy set based on the KL divergence. Constructing a reference probability distribution by estimating a large amount of historical data

Characterization of the KL divergence>

And->

A measure of the distance between the reference probability distribution and the true probability distribution. To->

For example, the fuzzy set is as follows:

wherein:

representation->

Is a true probability distribution of (c). D (D) _KL The smaller the distance is, the more similar the two distributions are; in particular, when D _KL When=0, the true probability distribution is the same as the reference probability distribution, and the distribution robust optimization model is degraded into a traditional stochastic programming model. />

Is similar to the analysis process of (a). Lambda (lambda) ₀ Indicating the error level.

And S32, converting a daily distribution robust optimization model. And (3) decoupling the model into a main problem and a sub problem by adopting a C & CG algorithm for iterative solution. The sub-problem is that the decision variable determined by the main problem optimization is taken as a known quantity, the most serious scene distribution probability in the uncertainty fuzzy set is returned to the main problem, and the model is as follows:

first solve the lower S mixed integer linear programming models as shown in equation (38)

/>

Wherein:

And returns it to the main question. Uncertainty scene variables of DPV and load transmitted by main question in sub-question +.>

And (4) solving a scheduling strategy of the self-energy-storage flexible interconnected power distribution network according to a known quantity, wherein a model is shown as a formula (40).

At this time, the value obtained by optimizing the main problem is the lower bound value updated for the original problem.

And->

The 0-1 variable and the continuous variable at the kth iteration, respectively.

The solving flow of the C & CG algorithm is shown in fig. 5.

And step S33, converting the daily multi-objective optimization model. The ideal point method uses the optimal solution of each target to take the distance between the target function and the optimal solution as a new target function, as follows:

wherein F represents a newly constructed objective function; f (f) _1,opt And f _2,opt Represented as optimal values for the two sub-targets, respectively. The ideal point method based on Euclidean distance is nonlinear, and the Manhattan distance is introduced to correct the solving equation, so that the method can be obtained:

minF＝|f ₁ -f _1,opt |+|f ₂ -f _2,opt | (42)

step S34, square term variable replacement. Constraint equations (5), (8) and (9) of the power flow equation, and the square term of the voltage and current contained in the safety constraint equation (10):

and->

By v _i,t,s And iota (iota) _ij,t,s Instead, the conversion is as follows:

/>

and S35, performing second-order cone relaxation processing on the tide equation constraint formulas (45) and E-SOP constraint formulas (13) and (14), and linearizing the constraint formulas. The method can be obtained through relaxation treatment:

formula (47) may be equivalently:

the above formula can be decomposed into two rotation cone constraint formulas:

formulas (48), (49), (51) and (52) have the same general form, as follows:

wherein: alpha ₁ 、α ₂ And alpha ₃ Are all continuous variables; it can be uniformly processed into a linear expression by a polyhedral approximation method:

/>

Step S36: and linearizing the rest constraint conditions. The relaxation constraint formula (27) by the large M method can be converted into:

-α _ij M ₁ ≤P _ij,t,s ≤α _ij M ₁ (57)

-α _ij M ₂ ≤Q _ij,t,s ≤α _ij M ₂ (58)

-α _ij M ₃ ≤I _ij,t,s ≤α _ij M ₃ (59)

OLTC operating constraints can be converted into the following form:

wherein: o (o) _j,t,s ＝b _ij,k,t ν _j,t,s Is a variable introduced to linearize the OLTC end node voltage.

Linearization CB action number constraint may add (66):

wherein mu _j,t A reactive power variation value for the capacitor bank;

for absolute value processing methods, a non-negative intermediate variable X may be introduced ⁺ And X ^- Replacement X, namely:

/>

and step S4, solving the day-ahead and day-in optimization models in the step S3 based on the predicted time sequence scene in the step S1 to obtain the running cost of the power distribution network and the network side resource regulation strategy.

Taking a 10kV system of a node of a certain rural power grid 51 as an example, fig. 6. The total load in the system is 6875+j2440kVA, and three feeders are all arranged. Assume that the DPV access location and capacity are as shown in table 1 and the inverter minimum power factor is 0.9; each end of the E-SOP is respectively connected with the

nodes

14, 32 and 48, the capacity is 600 kV.A, the loss coefficient is 0.02, the energy storage installation capacity is 1000kWh, the maximum charge and discharge power is 300kW, the charge and discharge efficiency is 70%, the discharge depth is 90%, the SOC interval width is 0.2-0.4, and the degradation cost coefficient is 0.5 yuan/kWh; OLTC tap adjustable range is ± 4 x 1.25%; assuming that the

nodes

11, 33, 47, 51 are flexible loads, the maximum slew rate is 1, and the slew rate coefficient is 0.3; the SVC installation positions are

nodes

14 and 48, and the adjustable range is-500 kVar; CB mounting positions are nodes 9 and 31, the unit capacity is 100kVar, and 5 groups are mounted in total; 5 links are provided, 2 of which are replaced by E-SOPs, as shown in FIG. 5. The electricity price of electricity purchasing and selling is shown in Table 2, and the FL compensation cost coefficient is 0.6 yuan/kWh.

TABLE 1

Access node	2	3	11	12	13	15	16	17	18	19
											Capacity (kW)	60	60	180	60	120	90	120	60	60	90
Access node	20	21	32	34	43	47	49	50	51	/
											Capacity (kW)	180	120	1000	1000	1000	1000	300	600	600	/

TABLE 2

Study traditional regulation and control equipment and flexible interconnection device coordinate the influence of optimizing to distribution network economic nature, optimize stage before the day and set up 4 cases and carry out simulation analysis: case 1: consider CB, SVC and E-SOP optimizations; case 2: consider CB, SVC, E-SOP optimization and network reconstruction; case 3: consider CB, SVC, E-SOP and OLTC optimizations; case 4: consider CB, SVC, E-SOP, OLTC optimization and network reconfiguration. The operation cost results are shown in table 3, the total cost of case 4 is the lowest, and the active-reactive combined optimization method for coordinating network side resources can fully exert the speed regulation characteristics of network side regulation equipment, effectively reduce the operation cost of the power distribution network and improve the operation economy of the power distribution network. In addition, the comparison results of case 3 and case 4 under the random optimization and the distributed robust optimization are shown in fig. 7, and as can be seen from fig. 7, the total cost corresponding to the random optimization method is smaller than that of the distributed robust optimization method, the electricity purchasing cost of the distributed robust optimization method is higher than that of the random optimization method, and the distributed robust optimization model enables the distribution network to purchase more electric energy to the upper-level power grid so as to cope with the uncertainty of the DPV and the load, so that the distributed robust optimization method has stronger robustness compared with the random optimization method. Fig. 8 shows the probability distribution change for a typical scenario for case 3 and case 4. As can be seen from fig. 8, the probabilities of the scenes 1 and 3 rise from 0.205 to 0.393 and 0.391, from 0.303 to 0.330 and 0.333, respectively, and the probability of the scene 2 is lower from 0.492 to 0.277 and 0.276, because the distributed robust optimization model makes the scene probability with lower running cost smaller and the scene probability with higher running cost correspondingly larger in order to find the scene corresponding to the worst probability and thus ensure the robustness of the optimization result after the uncertainty is considered. The running scheme of the distribution robust optimization model under the probability distribution of the worst scene of the uncertainty variable is solved, so that the economy and the robustness of the day-ahead scheduling scheme are guaranteed.

TABLE 3 Table 3

Cost of each item	Case 1	Case 2	Case 3	Case 4
					Cost of electricity purchase/ten thousand yuan	1.1826	1.1790	1.1548	1.1516
Net loss cost/ten thousand yuan	0.1961	0.1866	0.1135	0.1077
					FL compensation cost/ten thousand yuan	0.0725	0.0705	0.0739	0.0738
Energy storage degradation cost/ten thousand yuan	0.0210	0.0211	0.0208	0.0209
					Total running cost/ten thousand yuan	1.4722	1.4572	1.3630	1.3539

The validity of the multi-objective optimization method in the day is studied, single objective optimization is introduced for comparison verification, and the comparison result is shown in table 4. The system can be obtained by adopting the multi-objective optimization with the network loss value and the voltage deviation between the objective function values of the two single-objective optimization, and can be considered to optimize the network loss and the voltage of the system, so that the system has better economy and safety.

TABLE 4 Table 4

Type(s)	Loss value f ₁ (p.u.)	Voltage deviation value f ₂ (p.u.)
			Network loss single objective optimization	5.692	295.966
Voltage single target optimization	54.784	80.920
			Multi-objective optimization	15.593	93.214

The embodiment of the invention provides a self-energy-storage flexible interconnection power distribution network active-reactive joint optimization method for coordinating network side resources, which aims at the running problem of a high-permeability distributed photovoltaic power distribution network, can effectively utilize the space-time adjustment characteristics of an intelligent energy-storage soft switch and traditional network side regulation and control equipment, coordinate and control various network side resources from multiple time scales, and improve the economy and safety of a power distribution network running strategy; the method comprises the steps of fully utilizing the speed regulation characteristics of discrete and continuous regulation equipment, constructing a long-time active-reactive combined optimization model of an on-load regulating transformer, a discrete/continuous reactive compensation device, a reconstruction switch and an intelligent energy storage soft switch in a day-ahead stage, and constructing a multi-objective rolling optimization model in a day-ahead stage; determining a source load uncertainty fuzzy set based on KL divergence, solving a day-ahead two-stage distribution robust optimization model by adopting a column and constraint generation (C & CG) algorithm, and ensuring the economy and the robustness of a day-ahead scheduling scheme by solving an operation scheme under the probability distribution of the worst scene of an uncertainty variable by the distribution robust optimization model; the daily multi-target rolling optimization is converted based on an ideal point method, so that the network loss and the voltage optimization can be considered, and more accurate daily operation strategies of each quick speed regulating and controlling device can be obtained; and the model linearization processing is utilized, so that the calculation efficiency of the model can be greatly improved on the premise of minimizing errors.

Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. The active-reactive combined optimization method for the self-energy-storage flexible interconnected power distribution network for coordinating network side resources is characterized by comprising the following steps of:

2. The active-reactive power combined optimization method for the self-energy-storage flexible interconnection power distribution network for coordinating network side resources according to claim 1, wherein the step S1 specifically includes:

in the above formula, p represents load and DPV output;

Represents the load at the t hour; />

Indicating the output of DPV at hour t; />

Represents the load of the nth hour; />

Indicating the DPV output for the nth hour.

3. The method for active-reactive power combined optimization of the self-energy-storage flexible interconnection power distribution network for coordinating network side resources according to claim 2, wherein in the step S2, an objective function of a long-time active-reactive power combined optimization model before the day is:

C _ADN ＝min(C _P +C _Loss +C _FL +C _ESS ) (2)

the power is lost for intelligent energy storage soft switch; />

And->

4. The active-reactive power combined optimization method of the self-energy storage flexible interconnection power distribution network for coordinating network side resources according to claim 3, wherein in the step S2, the load flow equation constraint is a load flow equation constraint described by adopting a branch load flow model, and specifically comprises the following steps:

and->

And->

And->

And->

And->

representing the upper limit value of the current of branch ij.

5. The active-reactive power combined optimization method for the self-energy-storage flexible interconnection power distribution network for coordinating network side resources according to claim 4, wherein in the step S2, the distributed photovoltaic reactive power constraint condition is as follows:

in the above-mentioned method, the step of,

a per unit value representing the distributed photovoltaic output; / >

Representing the distributed photovoltaic installation capacity of node i;

wherein:

representing the stored energy output power; />

Representing the installation capacity of the intelligent soft switch; />

And->

Respectively representing the maximum charge and discharge power; />

Representing the energy storage residual quantity at the moment t; />

And->

Representing the stored energy rated capacity.

6. The active-reactive power combined optimization method for the self-energy-storage flexible interconnection power distribution network for coordinating network side resources according to claim 5, wherein in the step S2, energy storage power interval constraint is introduced:

wherein:

and->

Respectively representing the upper limit and the lower limit of the energy storage charge state at the t moment obtained by optimization; />

And->

And->

7. the active-reactive power combined optimization method of the self-energy-storage flexible interconnection power distribution network for coordinating network side resources according to claim 6, wherein in the step S2, the operation constraint of the on-load voltage regulating transformer is as follows:

the reactive compensation device is constrained as follows:

wherein:

and->

Respectively indicated as upper and lower limits where SVC is capable of delivering reactive power; />

The number of capacitor bank groups put into use at time t is represented; />

Representing the capacity of a single set of capacitor banks; />

Representing the maximum number of capacitor banks that can be put into; / >

8. The active-reactive power combined optimization method of the self-energy-storage flexible interconnection power distribution network for coordinating network side resources according to claim 7, wherein in the step S2, flexible load operation constraint is as follows:

wherein:

active power representing load shedding; />

Representing that the threshold coefficient can be reduced, and the value is 0-1; />

Representing a set of curtailable periods; />

A reduction scaling factor representing the time t; />

A curtailment rate coefficient indicative of the flexible load allowance; />

And->

9. The active-reactive joint optimization method of the self-energy-storage flexible interconnection power distribution network of the coordinated network side resource according to claim 1, wherein in the step S3, a source load uncertainty fuzzy set based on KL divergence is constructed by counting distributed photovoltaic and load historical data, a long-time active-reactive joint optimization model before the day is converted into a two-stage distribution robust optimization model before the day based on a column and constraint generation algorithm, and decoupling is a main problem iterative solution and a sub problem iterative solution; converting a daily multi-target rolling optimization model based on an ideal point method; and replacing square terms of variables in the power distribution network daily long-time active-reactive combined optimization model and the daily multi-objective rolling optimization model by linear variables, introducing a large M method relaxation treatment to a variable multiplication form in the daily long-time active-reactive combined optimization model and the daily multi-objective rolling optimization model, performing second order cone relaxation treatment to quadratic form constraint, converting the quadratic form constraint into a plurality of inequality expression constraint by utilizing a polyhedral approximation method, and introducing absolute value constraint into two non-negative intermediate variables for replacement to linearize nonlinear parts of the daily long-time active-reactive combined optimization model and the daily multi-objective rolling optimization model.

10. The active-reactive power combined optimization method for the self-energy-storage flexible interconnection power distribution network for coordinating network side resources according to claim 8, wherein the step S3 specifically comprises the following steps: