CN116384122A

CN116384122A - Target backbone network frame optimization method for power system restoration decision support

Info

Publication number: CN116384122A
Application number: CN202310368990.3A
Authority: CN
Inventors: 李少岩; 张友好; 顾雪平; 刘艳; 王涛; 刘雨濛; 赵会超
Original assignee: North China Electric Power University
Current assignee: North China Electric Power University
Priority date: 2023-04-07
Filing date: 2023-04-07
Publication date: 2023-07-04

Abstract

Some embodiments of the present application provide a target backbone network frame optimization method for power system restoration decision support, including: and circularly executing the following operations until the verification result of the target grid frame scheme to be verified is passing, obtaining a target backbone grid frame scheme based on a target grid frame optimization model, and obtaining the target grid frame scheme to be verified, wherein the target grid frame optimization model comprises the following components: an objective function and an objective constraint, the objective constraint comprising: grid connectivity constraints and linearized ac power flow constraints; checking the target net rack scheme to be checked to obtain the checking result; and when the verification result is confirmed to be failed, acquiring abnormal out-of-limit information in the target grid frame scheme to be verified, and updating the target constraint condition based on the abnormal out-of-limit information. Some embodiments of the present application may enable decision support for power system security restoration.

Description

Target backbone network frame optimization method for power system restoration decision support

Technical Field

The application relates to the technical field of power systems, in particular to a target backbone network frame optimization method for power system recovery decision support.

Background

Under the propulsion of a double-carbon target, the permeability of new energy is improved year by year, the uncertainty of the source and the load of an electric power system is increased, and meanwhile, various extreme meteorological events frequently occur, and large-area power failure accidents occur repeatedly in the world. According to the analysis of the power failure accident in the past, a reasonable accident emergency recovery scheme is formulated in advance, so that the accident response capability and recovery efficiency can be greatly improved, and the loss caused by the large power failure is remarkably reduced.

The network frame reconstruction is an important stage of recovering the system after the power system has a major outage, and the main task is to construct a backbone network frame for connecting each important station, hub power station and important load in the system. The target net rack generating method based on the network flow theory and the direct current power flow has low solving efficiency and is difficult to pass the alternating current verification rapidly in the face of a large-scale power system.

Therefore, how to provide a technical scheme of a target backbone network frame optimization method for efficient power system restoration decision support becomes a technical problem to be solved.

Disclosure of Invention

The aim of some embodiments of the present application is to provide a target backbone network frame optimization method for power system recovery decision support, by using the technical scheme of the embodiments of the present application, a better target backbone network frame scheme can be obtained by lifting, so as to achieve rapid and safe recovery of a power system, and the efficiency is higher.

In a first aspect, some embodiments of the present application provide a method for optimizing a target backbone network frame for power system restoration decision support, including: and circularly executing the following operations until the verification result of the target grid frame scheme to be verified is passing, so as to obtain the target backbone grid frame scheme, wherein the target backbone grid frame scheme is used for supporting the safe recovery of the power system: obtaining the target grid scheme to be checked based on a target grid optimization model, wherein the target grid optimization model comprises the following components: an objective function and an objective constraint, the objective constraint comprising: grid connectivity constraints and linearized ac power flow constraints; checking the target net rack scheme to be checked to obtain the checking result; and when the verification result is confirmed to be failed, acquiring abnormal out-of-limit information in the target grid frame scheme to be verified, and updating the target constraint condition based on the abnormal out-of-limit information.

According to some embodiments of the method, the target grid frame scheme to be checked is obtained through grid frame connectivity constraint and linearization alternating current power flow constraint serving as constraint of a target grid frame optimization model, when checking fails, abnormal out-of-limit information can be lifted, and the target constraint condition is updated to execute the operation again until the target backbone grid frame scheme is obtained. According to the method and the device, the grid connectivity constraint and the linearization alternating current power flow constraint are adopted, the output result of the target grid optimization model can be obtained rapidly, the current scheme can be adjusted rapidly through abnormal out-of-limit information extraction, the efficiency of obtaining a better target backbone grid scheme can be improved, and further rapid and safe recovery of a power system can be achieved.

In some embodiments, before the target rack optimization model is based on the target rack plan to be verified, the method further includes: generating the objective function associated with a backbone grid of the power system; determining the grid connectivity constraint and determining the linearized ac power flow constraint; the target net rack optimizing model is based on to obtain the target net rack scheme to be checked, which comprises the following steps: and solving the objective function by utilizing the grid connectivity constraint and the linearization alternating current power flow constraint to obtain the target grid scheme to be checked.

According to the method and the device, the objective function is solved through grid connectivity constraint and linearization alternating current power flow constraint, and a target grid scheme to be verified can be obtained. According to the embodiment of the application, the grid connectivity constraint and the linearization alternating current power flow constraint are adopted, so that the overall solving efficiency of the model can be improved.

In some embodiments, the determining the rack connectivity constraint comprises: and generating the grid connectivity constraint based on the power nodes and the power lines in the power system by using a step flow method.

Some embodiments of the present application can obtain grid connectivity constraints through a step flow method and power nodes and power lines in a power system, and the constraints are more suitable for states of the power nodes and the power lines, and have good constraint performance.

In some embodiments, the linearized ac power flow constraint comprises: power branch break flow constraints, power node phase angle difference constraints, power node power balance constraints, power unit output constraints, apparent power constraints, power node voltage magnitude square constraints, and power branch decision variable state consistency constraints.

Some embodiments of the method and the device form linearization alternating current power flow constraint through various types of constraint, and therefore solving accuracy of the model is guaranteed.

In some embodiments, the power branch break flow constraint relates to an active power flow, a reactive power flow, an active power delivery limit and a reactive power delivery limit of the power line; the power node phase angle difference constraint is related to a power line switching state; the power node power balance constraint is related to active power output, reactive power output, active power flow and reactive power flow of a power unit; the power unit output constraint is related to the active output and the reactive output; the apparent power constraint is related to an apparent power output limit; the square constraint of the voltage amplitude of the power node is related to the upper voltage limit value and the lower voltage limit value of the power node; the power branch decision variable state consistency constraint is related to a power line commissioning state.

According to the method and the device, different constraints are obtained through various parameters, and the solving accuracy of the model can be guaranteed.

In some embodiments, the apparent power output limit value is calculated by calculating a maximum value of power loss for a target rack of the power system in a first state and a maximum value of power loss in a second state.

Some embodiments of the present application ensure accuracy of constraints by determining the apparent power output limit value from the power loss values at different states.

In some embodiments, the verifying the target rack scheme to be verified, to obtain the verification result, includes: inputting the target net rack scheme to be checked into an alternating current power flow check feedback model to obtain a check value, wherein a target check function in the alternating current power flow check feedback model is related to a power relaxation variable and a voltage relaxation variable; if the check value is zero, the check result is passed; and if the check value is greater than zero, the check result is that the check value does not pass.

According to the method and the device for verifying the target grid frame scheme to be verified, the target grid frame scheme to be verified is verified through the alternating current power flow verification feedback model, verification results can be obtained rapidly, and efficiency is high.

In some embodiments, the obtaining the abnormal out-of-limit information in the target rack scheme to be checked includes: and screening out power branches and/or power nodes with out-of-limit voltage amplitude, which cause the out-of-limit power branches and/or power nodes with out-of-limit voltage amplitude, which do not pass through the verification result, from the target grid scheme to be verified based on an alternating current power flow verification feedback model, wherein the power branches with out-of-limit power branches and/or the power nodes with out-of-limit voltage amplitude form the abnormal out-of-limit information.

According to the method and the device for checking the target backbone network frame, the abnormal out-of-limit information which causes the checking result to be failed can be screened out from the target backbone network frame scheme to be checked through the alternating current power flow checking feedback model, data support is provided for a subsequent adjustment scheme, and the acquisition efficiency of the final target backbone network frame scheme is improved.

In some embodiments, the updating the target constraint based on the anomaly threshold information comprises: adjusting the power branch power transmission limit value and/or the power node voltage amplitude range based on the abnormal out-of-limit information to obtain an adjusted power branch power transmission limit value and/or an adjusted power node voltage amplitude range; and updating the target constraint condition by utilizing the adjusted power branch power transmission limit value and/or the adjusted power node voltage amplitude range.

According to the method and the device for achieving the target grid frame scheme, the related parameters are updated through the abnormal out-of-limit information, the updated target constraint conditions are obtained, the target grid frame scheme can be quickly adjusted, and the obtaining efficiency of the final target backbone grid frame scheme is improved.

In some embodiments, the method further comprises: and adding the target grid frame scheme to be verified, the verification result of which is not passed, to the infeasible cutting set.

Some embodiments of the present application make statistics on a target rack scheme to be checked that does not pass through, so as to avoid timely discarding when the same scheme is obtained next time, and repeated checking is not required.

Drawings

In order to more clearly illustrate the technical solutions of some embodiments of the present application, the drawings that are required to be used in some embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort to a person having ordinary skill in the art.

FIG. 1 is one of the flowcharts of a target backbone network frame optimization method for power system restoration decision support provided by some embodiments of the present application;

FIG. 2 is a second flowchart of a target backbone network frame optimization method for power system restoration decision support according to some embodiments of the present application;

fig. 3 is a schematic diagram of an electronic device according to some embodiments of the present application.

Detailed Description

The technical solutions in some embodiments of the present application will be described below with reference to the drawings in some embodiments of the present application.

It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only to distinguish the description, and are not to be construed as indicating or implying relative importance.

In the related art, after a power system has a major power failure, a black start unit starts a nearby non-black start unit by using initial black start power, and an initial small system is built. And the grid of the system is gradually expanded to continuously recover other units and key loads. The target net rack is an important guiding scheme for guiding the recovery process, and the system is guided to realize quick and safe recovery by defining the network topology recovered by the net rack and the optimal target state recovered by the source load. In order to achieve rapid recovery of the system, the target grid should communicate with the main power units, hub power stations and main loads in the system with as little operation as possible. While the target grid needs to have sufficient power carrying capacity to take into account that the system load will return to a comparable level when the reconfiguration is complete.

A great deal of research is made on target grid optimization in grid reconstruction by relevant experts. Based on the graph theory algorithm of Bellman-Ford, prim and the like. In the prior art, a topological network is established for the net rack to be recovered, and a high-efficiency optimization method for a power transmission path and a target net rack in a serial-parallel recovery stage in a reconstruction process is provided, but characteristics such as network power flow and the like cannot be calculated in topological optimization. Based on graph theory, the target net rack is divided into two stages for optimization by combining a genetic algorithm, and meanwhile, network power flow is brought into the constraint set of an optimization model. Along with the maturation of the artificial intelligent algorithm, an ant colony algorithm, a particle swarm algorithm, a bee colony algorithm and other artificial intelligent optimization algorithms are introduced into a target net rack optimization model, and a backbone net rack scheme with good indexes is obtained through iterative optimization aiming at different optimization targets. In addition, the grid differential planning and reinforcement for the pre-defense of the blackout is also a target grid optimization problem. In the prior art, an improved BBO (Biogeography-based optimization) algorithm and an improved particle swarm algorithm are adopted to solve the target net rack optimization problem respectively. However, the optimization method based on the intelligent algorithm still has the problems of stability, slower efficiency when facing a large-scale power grid and the like.

With the development and application of operational research, in recent years, attempts have been made to model the power grid decision optimization problem into characteristic forms such as mixed integer linear programming (mixed integer linear programming, MILP) and the like, and to determine an optimal solution by using a mature commercial solver. In the related art, a two-stage method is adopted, a direct current power flow model decision branch is firstly put into operation, and then an alternating current power flow is used for checking loads, but the optimal solution may be lost in a step-by-step solution method. Or, establishing connectivity constraint by adopting network flow theory, combining direct current power flow and alternating current verification, and finding a feasible scheme through infeasible cutset iteration, wherein the termination time of the iteration cannot be ensured, and effective information cannot be extracted from a failure scheme. If the alternating current power flow is directly adopted, although the feasibility of the power flow can be ensured, the model is changed into mixed integer nonlinear programming (Mixed integer nonlinear programming, MINLP), the solution is difficult, and the local optimization is possibly involved. On the other hand, with the increasing expansion of the power grid scale, the current connectivity constraint based on the network flow theory has the defects of higher solving difficulty and lower efficiency when processing a larger-scale grid frame.

In view of this, some embodiments of the present application provide a target backbone network frame optimization method for power system restoration decision support, where the method solves an objective function through network frame connectivity constraint and linearization ac power flow constraint to obtain a target network frame scheme to be checked, so that the solution efficiency of a model can be effectively improved. And when the target net rack scheme to be checked is checked, if the checking result is that the target net rack scheme to be checked fails, abnormal out-of-limit information can be acquired, the abnormal out-of-limit information is used as key information for adjusting the target net rack scheme to be checked, and the operation is circularly executed until the target backbone net rack scheme passing the checking is obtained. According to the method and the device for achieving the target grid optimization model, the solving efficiency of the target grid optimization model can be effectively improved, the alternating current testing efficiency of the target grid scheme to be tested is improved, the better target backbone grid scheme is obtained, and decision support is provided for guiding and achieving the rapid and safe recovery of the power system.

The implementation process of the target backbone network frame optimization method for power system restoration decision support provided in some embodiments of the present application is exemplarily described below with reference to fig. 1.

Referring to fig. 1, fig. 1 is a flowchart of a target backbone network frame optimization method for power system restoration decision support according to some embodiments of the present application, where the method includes:

and circularly executing the operations of S110 to S130 until the verification result of the target grid frame scheme to be verified is passing, and obtaining the target backbone grid frame scheme, wherein the target backbone grid frame scheme is used for supporting the safe recovery of the power system. It should be noted that, during the loop, the objective function is unchanged, and the objective constraint is changed.

The above-described process is exemplarily set forth below.

S110, obtaining the target grid frame scheme to be checked based on a target grid frame optimization model, wherein the target grid frame optimization model comprises: an objective function and an objective constraint, the objective constraint comprising: grid connectivity constraints and linearized ac power flow constraints.

For example, in some embodiments of the present application, it is first necessary to build a target grid optimization model associated with a power system, where the target grid optimization model includes an objective function and a target constraint.

In some embodiments of the present application, S110 includes:

s111, generating an objective function related to a backbone network frame of the power system.

For example, in some embodiments of the present application, target grid decision optimization modeling generally abstracts a power system into a power network (or power system, grid, simply system) characterized by nodes and branches, and recovery states of the nodes and branches are variables to be decided. The target net rack needs to cover all main power units and nodes where important loads are located, and the nodes of the hub transformer substation are also needed to be considered.

Specifically, a node contraction method is adopted to screen and sort the grid nodes of the system, so that key nodes which are required to be recovered are obtained. Except for key nodes, other nodes and branches should be reduced as much as possible to simplify the scale of the backbone target network frame and improve the recovery efficiency of the backbone network frame. Thus, the first term of the objective function is as in equation (1):

wherein: i. j represents the number of a power node (node for short) in the power system, and c represents the number of a return line of a power line (line for short). N is a node set in the net rack, N _obj And L is a line set of the system. m is m _i Indicating whether node i is restored, m _i Node i recovers when=1, whereas does not recover when 0. z _ijc Indicating whether the line i-j-c is put into operation, z _ijc Line i-j-c is put into operation when=1, whereas it is not put into operation when it is 0. Equation (1) represents minimizing the number of non-critical nodes and the total number of branches in the target rack.

On the basis of simplifying and limiting the scale of the target net rack, the problem of overvoltage possibly occurring in the recovery process is considered, and too many long lines should be avoided as much as possible, so that the charging reactive power of the lines is reduced, and the operation risk is reduced. Thus, the second term of the objective function is shown as equation (2):

wherein b is ^c _ijc Charge susceptance for line i-j-c, N _L The number of lines in the system is Bc, which is the sum of charging susceptances of all lines in the system. Equation (2) represents minimizing line charging reactive in the target rack.

When the reconstruction is completed, the target net rack bears a large amount of load to form part of the ring network, but the ring network is formed by ring closing operation. The loop closing operation in the recovery process is complex and time-consuming and has a certain failure probability, so that the target net rack should reduce the loop closing number and the operation risk, and the third term of the target function is the minimum loop closing operand shown in the formula (3):

the objective function is obtained by the formulas (1) to (3):

minf＝k _1,obj f ₁ +k _2,obj f ₂ +k _3,obj f ₃ (4)

k in _1,obj 、k _2,obj And k _3,obj The weight coefficient of each part is 0-1.

S112, determining grid connectivity constraints and determining linearization alternating current power flow constraints.

In some embodiments of the present application, S112 includes: and generating the grid connectivity constraint based on the power nodes and the power lines in the power system by using a step flow method.

For example, in some embodiments of the present application, based on heuristics of a minimum spanning tree model, the present application creatively proposes a step-flow method to form grid connectivity constraints that are more adaptive to both node and line states. The basic idea of the step flow method is to simulate the relationship between water flow and gravity: under the condition of no external force, the water flow always flows from high to low. Each node is regarded as a 'pool' with a certain height, and the height is recorded as h _i The method comprises the steps of carrying out a first treatment on the surface of the Regarding the line as "waterway", if waterway i-j-c exists, it is stated that the water flow flows from i to j, and further there is h _i >h _j . Assume that the node of the black start unit in the target net rack is 'valley point', and the height is 0. The heights of the other nodes are all more than 0, and the branch z forming the target net rack _ijc And waterway w _ijc The following 3 criteria are determined and complied with, namely:

(1) for any node except the valley point, if the node is in the target net rack, at least one waterway starts from the node and flows to the node with lower height;

(2) If a waterway corresponding to a certain branch in the target net rack decision space exists, the waterway can exist only in one direction;

(3) if there is water path w _ijc The height of node i is greater than the height of node j.

Based on the above, the mesh connectivity constraint based on the cascade flow method as shown in the formulas (5) to (7) is obtained by the idea based on the above criteria and the cascade flow method:

wherein equations (8) and (9) are the relationship constraints of the node live state and the line put into operation state.

Wherein L 'is the directed edge set of the branch set, namely if the branch i-j-c exists in the directed edge set, the branch i-j-c and the branch j-i-c exist in the directed edge set in the L'. w (w) _ijc Is a 0, 1 variable, representing whether a virtual "waterway" exists, w _ijc When=1, waterway i-j-c exists, w _ijc Absent when=0; m is M _T 、D _i Are all constant and can take a very large number, for example 1000, by reference to the large M method. However, considering that too large M will slow down the solving speed of the MILP solver, too small M will produce a numerical stability problem, thus M _T Grid key of value taking systemThe number of nodes in the full state is reduced by 1, D _i The value of (2) is taken as the degree of the corresponding node of the net rack under the normal operation condition.

In particular, equation (5) characterizes the above criterion (1), namely that for the charged nodes other than the valley point, at least one "waterway" flows from it and to the node lower than it is high. Equation (6) characterizes the basic criteria (2) representing the unidirectional nature of the waterway. The relation between the node height and the waterway in the describing rule (3) is represented by the formula (7), when w _ijc When=1, the height of node i is at least 1 greater than the height of node j, and when w _ijc When=0, equation (7) will be relaxed by a large M. Equations (8) and (9) are analytical representations of the node and branch state relationship.

The cascade flow method and the grid connectivity constraint based on the cascade flow method are creatively provided by the application, the calculation of network flow is eliminated, and the relation of more direct nodes and lines is reflected to the 'height' of the nodes. And adding a dimension of 'height' on the basis of the topological relation of the plane so as to restrict the communication of the net rack topology. On the other hand, because the number of nodes in the general net rack is generally less than the number of lines, a great amount of decision variables can be saved by replacing network traffic with node heights. In summary, connectivity constraints based on the cascade flow method have better model structures, and applicable conditions and scenes are consistent with network flow constraints.

In some embodiments of the present application, the linearized ac power flow constraint includes: power branch break flow constraints, power node phase angle difference constraints, power node power balance constraints, power unit output constraints, apparent power constraints, power node voltage magnitude square constraints, and power branch decision variable state consistency constraints.

Aiming at the problems that a target net rack optimization model adopting an alternating current power flow model is difficult to solve and a scheme obtained by adopting a direct current power flow model is difficult to pass through alternating current verification, some embodiments of the application provide a linearization alternating current power flow optimization model (as a specific example of linearization alternating current power flow constraint) based on a linearization alternating current power flow model, and a linearization alternating current power flow optimization model considering power loss and reactive power level margin is established.

Specifically, through the direct current power flow idea, the power loss on the line is ignored, and a linearized alternating current power flow optimization model suitable for generating a backbone target net rack is obtained:

in some embodiments of the present application, the power branch break flow constraints relate to the active power flow, reactive power flow, active power delivery limit and reactive power delivery limit of the power line.

Specific power branch switching-off flow constraints are as shown in formulas (10) to (15):

wherein P is _ijc And Q _ijc Representing the active and reactive power flows on lines i-j-c respectively,

representing the square of the voltage amplitude, θ, at node i _ij Representing the phase angle difference between node i and node j. P (P) _g And Q _g The active output and the reactive output of the unit g are respectively represented. />

Active power delivery limit for line i-j-c, < > >

For reactive power delivery limits of lines i-j-c,

the apparent power delivery limit for line i-j-c. P (P) _di And Q _di Active and reactive demand of node i, respectively, < >>

b _ijc And

the conductance, susceptance and charge capacitance of lines i-j-c, respectively. />

And->

Is the M value in the large M method. />

And->

The upper limit and the lower limit of the active output and the lower limit of the reactive output of the unit g are respectively. />

And->

Is the upper and lower voltage limits on node i.

In some embodiments of the present application, the power node phase angle difference constraint is related to a power line switching state.

The specific node phase angle difference constraint (i.e., the power node phase angle difference constraint) is equation (16):

in some embodiments of the present application, the power node power balance constraint relates to an active power output, a reactive power output, the active power flow, and the reactive power flow of the power unit.

Specific node power balancing constraints are formulas (17) and (18):

in some embodiments of the present application, the power unit output constraint is related to the active output and the reactive output.

Specific (electric) unit output constraints are formulas (19) and (20):

in some embodiments of the present application, the apparent power constraint is related to an apparent power output limit value.

A specific apparent power constraint is formula (21):

in some embodiments of the present application, the power node voltage magnitude square constraint is related to a voltage upper limit and a voltage lower limit of the power node.

The specific node voltage magnitude square constraint is equation (22):

in some embodiments of the present application, the power branch decision variable state consistency constraint relates to a power line commissioning state.

The specific power branch decision variable state consistency constraint is equation (23):

in addition, the mode of selecting the M value in the power branch switching-off tide constraint is as shown in formulas (24) and (25):

wherein formulae (10) to (12) represent the active power flow of the line i-j-c, and z is when the line is put into operation _ijc =1, formula (13) is relaxed, formulas (10), (11) will P _ijc Constrained to linearize active power flow, when z _ijc When=0, the formulas (10), (11) are relaxed, and the formula (12) will be P _ijc Constraint 0; and the same formulas (13) - (15) form linear expression of the reactive power flow of the line on whether the line is switched or not. Equation (16) is a constraint of phase angle difference considering the switching state of the line, and considering the power transmission stability of the transmission line, the phase angle difference of the line operation is generally limited within + -pi/3, and is more strictTo limit the range to within + -pi/4. According to the theoretical maximum phase angle difference and voltage amplitude difference, P can be deduced by combining formulas (10) and (13) _ijc And Q _ijc The large M value in the open-circuit power flow model obtained on the basis of the theoretical maximum value can be selected according to formulas (24) and (25).

The remaining equations in the model are linearly represented except for the line apparent power constraint represented by equation (21). Equation (21) is a typical convex set constraint, and the power circle constraint range can be approximated by a plurality of linear equations shown in equation (26). In the formula, k is a linear equation index, n is a linear equation number, and the larger the value of n is, the better the approximation effect is, but the larger the calculated amount is.

The specific values of the coefficients are shown in the formula (26) respectively.

Neglected line active loss term

And reactive loss term->

As shown in formula (27):

due to the problem of power flow and voltage out-of-limit caused by neglecting power loss, the neglected power loss term needs to be further processed. If margin space of line power loss is reserved in the linearization alternating current power flow model, the line is considered to be at the power limit

Update to minus margin +.>

Corresponding to the parameter +.>

The power transmission capacity of the transmission line under the linearization alternating current power flow model can be pulled down to the position below the margin line. When returning to the alternating current power flow verification, a buffer space is obtained, so that the alternating current power flow verification passing rate of the scheme is obviously improved.

Thus, in some embodiments of the present application, the apparent power output limit value is calculated by calculating a maximum value of power loss for a target rack of the power system in a first state and a maximum value of power loss in a second state.

For example, in some embodiments of the present application, the maximum power loss of the target rack is divided into two extreme cases, namely, the maximum power loss (i.e., the maximum power loss) of the rack under complete health (as a specific example of the first state) and the maximum power loss of the rack under the current line (as a specific example of the second state). The former calculates the maximum power loss of the line under normal operating conditions (i.e., the net rack is fully sound), and the latter calculates the maximum power loss of the line under the topology of the net rack (i.e., only the current line). The maximum power loss calculation model under the complete fitness of the net rack is shown in a formula (28), and the maximum power loss calculation model under the constraint of the net rack is not counted is shown in a formula (29).

Wherein the formulas (30) to (34) are respectively as follows:

from the above (28) and (29), and the corresponding constraint formulas, the apparent power limit (as a specific example of the apparent power output limit value) can be obtained

As shown in formula (35):

wherein V is _i For the voltage amplitude at node i, L ^C For the line for which the maximum power loss is to be calculated this time,

and->

The square of the maximum power loss of the line i-j-c resulting from the two calculations is calculated separately. />

And->

The loss coefficient is 0-1.

In addition, the influence of errors in the linearized alternating current power flow is not only reflected in power flow out-of-limit, but also possibly accompanied by voltage amplitude out-of-limit. The approximation of the power loss and trigonometric function neglected by linearizing the ac flow may result in a lower reactive balance level of the system as a whole. If the upper limit and the lower limit of the voltage amplitude of the set, the load and the important substation node are adjusted in the linearization alternating current power flow calculation, the voltage amplitude is further reduced within the range of the original upper limit and the original lower limit, so that the solution scheme has a higher reactive power balance level, and a certain voltage loss margin is provided when the scheme returns to the alternating current power flow verification. Since the linearization of the ac current ignores the power loss on the line, the actual line transmits the power S _real To linearize the transmission power S in alternating current power flow _Lin Large, set delta S _x ＝S _real –S _Lin Obtaining voltage loss DeltaU caused by power loss _x As shown in formula (36):

Δs in formula (36) _x The maximum power loss obtained by the method can be used for replacing, namely, the voltage loss caused by the power loss can be obtained, and the obtained voltage loss can be used as a reference for adjusting the upper and lower limit range adjustment quantity of the voltage of each node. In addition, the voltage regulating equipment in the system also has an influence on the trend feasibility of the target net rack, and can be further considered in a trend balance formula.

S120, checking the target net rack scheme to be checked, and obtaining the checking result.

In some embodiments of the present application, S110 may further include: and solving the objective function by using the grid connectivity constraint and the linearization alternating current power flow constraint to obtain a target grid scheme to be checked.

For example, in some embodiments of the present application, the objective function (4) is solved by the constraints of the formulas (5) - (36) above, to obtain the target rack scheme to be verified. The solution efficiency of the objective function can be improved through the constraint, and the practicability is good.

In some embodiments of the present application, S120 may include: inputting the target net rack scheme to be checked into an alternating current power flow check feedback model to obtain a check value, wherein a target check function in the alternating current power flow check feedback model is related to a power relaxation variable and a voltage relaxation variable; if the check value is zero, the check result is passed; and if the check value is greater than zero, the check result is that the check value does not pass.

Two main factors causing the power flow verification failure in the prior art, namely power flow out-of-limit and voltage out-of-limit, are closely related to the constraints (21), (34). If the power flow is out of limit, the apparent power constraint shown in the formula (21) is violated; if the voltage is out of limit, the voltage magnitude constraint indicated by trans (34) is violated. Thus, in some embodiments of the present application, new apparent power constraint and new voltage magnitude constraint equations (37) and (38) are derived by adding a relaxation variable in (21), (34).

Constructing an alternating current power flow verification feedback model by taking the square sum of the relaxation variables as an alternating current feedback objective function, wherein the alternating current power flow verification feedback model is shown as a formula (46):

in the method, in the process of the invention,

and->

An apparent power relaxation variable and a voltage amplitude relaxation variable, respectively.

It should be noted that, the balance of the branch power flow and the node is performed in the grid under the obtained target grid scheme to be checked. When the alternating current power flow check passes, the objective function f _C (as a specific example of a check value) is 0, i.e. the flow of power can pass without any slack. When the objective function f _C When the voltage is larger than 0, the current of the branch is out of limit or the voltage amplitude of the node is out of limit.

S130, when the verification result is confirmed to be failed, obtaining abnormal out-of-limit information in the target grid frame scheme to be verified, and updating the target constraint condition based on the abnormal out-of-limit information.

And S140, when the verification result of the target net rack scheme to be verified is passing, obtaining the target backbone net rack scheme.

The target grid scheme obtained under the linearization alternating current power flow model still possibly cannot pass the alternating current verification under extreme conditions. The previously adopted iteration method of the infeasible cutset only eliminates the infeasible scheme from the decision domain, but cannot extract key information which causes scheme verification failure from the failed scheme. Aiming at the problem, the power flow verification feedback mechanism based on the relaxation variable method is provided, key information is extracted from a scheme with verification failure and fed back to a next scheme to generate, and the probability that the subsequent scheme passes through alternating current verification is effectively improved.

The above-described process is exemplarily set forth below.

In some embodiments of the present application, S130 may include: and screening out power branches and/or power nodes with out-of-limit voltage amplitude, which cause the out-of-limit power branches and/or power nodes with out-of-limit voltage amplitude, which do not pass through the verification result, from the target grid scheme to be verified based on an alternating current power flow verification feedback model, wherein the power branches with out-of-limit power branches and/or the power nodes with out-of-limit voltage amplitude form the abnormal out-of-limit information.

For example, in some embodiments of the present application, an ac power flow verification feedback module, represented by equation (39), is performed, and the screening is performed

And->

The branch corresponding to the element with the larger absolute value (as a specific example of the power branch with the power flow out of limit) and/or the node (as a specific example of the power node with the voltage amplitude out of limit) can lock the key branch causing the power flow out of limit. By the embodiment of the application, the key line and the node which cause the scheme failure can be accurately found from the scheme with the failed verification, so that an effective power flow verification feedback mechanism can be formed, and the iteration solving efficiency of the scheme is greatly improved.

In some embodiments of the present application, S130 may include: adjusting the power branch power transmission limit value and/or the power node voltage amplitude range based on the abnormal out-of-limit information to obtain an adjusted power branch power transmission limit value and/or an adjusted power node voltage amplitude range; and updating the target constraint condition by utilizing the adjusted power branch power transmission limit value and/or the adjusted power node voltage amplitude range.

For example, in some embodiments of the present application, after a critical branch that causes a load flow violation is found, a corresponding out-of-limit line power transmission limit or node voltage amplitude range may be adjusted, so that updating the relevant constraint in the constraints of formulas (5) - (36) may be implemented, and an updated target constraint condition may be obtained. And then solving the objective function (4) again by using the updated objective constraint condition until a checked objective backbone net rack scheme is obtained.

In some embodiments of the present application, the method for optimizing a target backbone network frame for power system restoration decision support further includes: and adding the target grid frame scheme to be verified, the verification result of which is not passed, to the infeasible cutting set.

For example, in some embodiments of the present application, an infeasible solution is added to the infeasible cutset, avoiding the need for verification when solving the same solution next time, and reducing the time cost.

The following illustrates a specific process for target backbone grid optimization for power system restoration decision support provided by some embodiments of the present application in connection with fig. 2.

Referring to fig. 2, fig. 2 is a flowchart of a target backbone network frame optimization method for power system restoration decision support according to some embodiments of the present application, where the method includes:

s210, obtaining a target grid optimization model, and solving the target grid optimization model to obtain a target grid scheme to be checked.

In some embodiments of the present application, the target rack optimization model may be constructed based on the method embodiment provided in fig. 1 when determining the target rack solution. Alternatively, the target grid optimization model may be historically constructed and may be used directly. The embodiments of the present application are not specifically limited herein.

S220, checking the target grid frame scheme to be checked by using the alternating current power flow check feedback model, and obtaining a check result.

S230, judging whether the verification result passes or not, if so, executing S240, otherwise, executing S231.

S231, based on an alternating current power flow verification feedback model, abnormal out-of-limit information which causes the verification result to be failed is screened out from the target net rack scheme to be verified.

S232, adjusting the power branch power transmission limit value or the power node voltage amplitude range based on the abnormal out-of-limit information to obtain an adjusted power branch power transmission limit value and/or an adjusted power node voltage amplitude range.

And S233, updating target constraint conditions in the target grid optimization model by using the adjusted power branch power transmission limit value or the adjusted power node voltage amplitude range, adding the target grid to be verified to the infeasible cutset, and returning to S210.

S240, taking the target net rack to be checked as a target backbone net rack scheme, and outputting.

It should be noted that, specific implementation details of S210 to S240 may refer to the method embodiment provided in fig. 2, and detailed descriptions are omitted here as appropriate to avoid repetition.

According to some embodiments of the present application, the margin linearization ac power flow constraint provided by the present application is suitable for generating a target network frame in a state where system nodes and line states are all uncertain, and the margin is mainly that power loss caused by simplification is estimated in advance. In practical application, by taking a certain node system as an example, the maximum loss of each branch and the rated transmission power limit of the line can be obtained from the comparison result of the transmission power limit of the line and the rated transmission power limit after taking the loss margin into consideration, and the two loss upper-boundary calculation methods obtain the weighted sum of the results, wherein in practical application, the weighted sum can be obtained by adjusting

And->

The coefficient of (2) yields a suitable power loss margin. Therefore, the applicability of the embodiments of the present application is more extensive. Moreover, the grid connectivity constraint based on the step flow method can greatly improve the solving speed of the model, the alternating current verification passing rate of the scheme is effectively improved by taking the linearization alternating current power flow energy of the loss margin into consideration, and the iteration times of the scheme are reduced, so that the decision optimization efficiency of the target grid is further improved. According to the embodiment of the application, the target net rack can be rapidly generated, the target backbone net rack scheme is dynamically adjusted according to the actual recovery working condition, so that the target net rack has more guiding significance, the propulsion of the safety recovery process is accelerated, the network topology of net rack recovery and the optimal target state of source load recovery are clarified, and the system is guided to achieve rapid and safe recovery.

Some embodiments of the present application also provide a computer readable storage medium having stored thereon a computer program, which when executed by a processor, may implement operations of the method corresponding to any of the above-described methods provided by the above-described embodiments.

Some embodiments of the present application further provide a computer program product, where the computer program product includes a computer program, where the computer program when executed by a processor may implement operations of a method corresponding to any of the foregoing methods provided by the foregoing embodiments.

As shown in fig. 3, some embodiments of the present application provide an electronic device 300, the electronic device 300 comprising: memory 310, processor 320, and a computer program stored on memory 310 and executable on processor 320, wherein processor 320 may implement a method as in any of the embodiments described above when reading a program from memory 310 and executing the program via bus 330.

Processor 320 may process digital signals and may include various computing structures. Such as a complex instruction set computer architecture, a reduced instruction set computer architecture, or an architecture that implements a combination of instruction sets. In some examples, processor 320 may be a microprocessor.

Memory 310 may be used for storing instructions to be executed by processor 320 or data related to execution of the instructions. Such instructions and/or data may include code to implement some or all of the functions of one or more modules described in embodiments of the present application. The processor 320 of the disclosed embodiments may be configured to execute instructions in the memory 310 to implement the methods shown above. Memory 310 includes dynamic random access memory, static random access memory, flash memory, optical memory, or other memory known to those skilled in the art.

The foregoing is merely exemplary embodiments of the present application and is not intended to limit the scope of the present application, and various modifications and variations may be suggested to one skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application. It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures.

The foregoing is merely specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily think about changes or substitutions within the technical scope of the present application, and the changes and substitutions are intended to be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

It is 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.

Claims

1. The utility model provides a target backbone network frame optimization method for power system restoration decision support, which is characterized by comprising the following steps:

and circularly executing the following operations until the verification result of the target grid frame scheme to be verified is passing, so as to obtain the target backbone grid frame scheme, wherein the target backbone grid frame scheme is used for supporting the safe recovery of the power system:

obtaining the target grid scheme to be checked based on a target grid optimization model, wherein the target grid optimization model comprises the following components: an objective function and an objective constraint, the objective constraint comprising: grid connectivity constraints and linearized ac power flow constraints;

checking the target net rack scheme to be checked to obtain the checking result;

and when the verification result is confirmed to be failed, acquiring abnormal out-of-limit information in the target grid frame scheme to be verified, and updating the target constraint condition based on the abnormal out-of-limit information.

2. The method of claim 1, wherein prior to the deriving the target rack solution to be verified based on the target rack optimization model, the method further comprises:

generating the objective function associated with a backbone grid of the power system;

Determining the grid connectivity constraint and determining the linearized ac power flow constraint;

the target net rack optimizing model is based on to obtain the target net rack scheme to be checked, which comprises the following steps:

and solving the objective function by utilizing the grid connectivity constraint and the linearization alternating current power flow constraint to obtain the target grid scheme to be checked.

3. The method of claim 2, wherein the determining the rack connectivity constraint comprises: and generating the grid connectivity constraint based on the power nodes and the power lines in the power system by using a step flow method.

4. The method of any of claims 1-3, wherein the linearized ac power flow constraint comprises: power branch break flow constraints, power node phase angle difference constraints, power node power balance constraints, power unit output constraints, apparent power constraints, power node voltage magnitude square constraints, and power branch decision variable state consistency constraints.

5. The method of claim 4, wherein the power branch on-off flow constraint is related to an active flow, a reactive flow, an active power delivery limit and a reactive power delivery limit of the power line;

The power node phase angle difference constraint is related to a power line switching state;

the power node power balance constraint is related to active power output, reactive power output, active power flow and reactive power flow of a power unit;

the power unit output constraint is related to the active output and the reactive output;

the apparent power constraint is related to an apparent power output limit;

the square constraint of the voltage amplitude of the power node is related to the upper voltage limit value and the lower voltage limit value of the power node;

the power branch decision variable state consistency constraint is related to a power line commissioning state.

6. The method of claim 5, wherein the apparent power output limit is calculated by calculating a maximum power loss for a target rack of the power system in a first state and a maximum power loss in a second state.

7. The method as claimed in any one of claims 1 to 3, wherein said verifying the target rack scheme to be verified, obtaining the verification result, includes:

inputting the target net rack scheme to be checked into an alternating current power flow check feedback model to obtain a check value, wherein a target check function in the alternating current power flow check feedback model is related to a power relaxation variable and a voltage relaxation variable;

If the check value is zero, the check result is passed;

and if the check value is greater than zero, the check result is that the check value does not pass.

8. The method of any one of claims 1-3, wherein the obtaining anomaly out-of-limit information in the target rack scheme to be checked comprises:

and screening out power branches and/or power nodes with out-of-limit voltage amplitude, which cause the out-of-limit power branches and/or power nodes with out-of-limit voltage amplitude, which do not pass through the verification result, from the target grid scheme to be verified based on an alternating current power flow verification feedback model, wherein the power branches with out-of-limit power branches and/or the power nodes with out-of-limit voltage amplitude form the abnormal out-of-limit information.

9. The method of claim 8, wherein the updating the target constraint based on the anomaly threshold information comprises:

adjusting the power branch power transmission limit value and/or the power node voltage amplitude range based on the abnormal out-of-limit information to obtain an adjusted power branch power transmission limit value and/or an adjusted power node voltage amplitude range;

and updating the target constraint condition by utilizing the adjusted power branch power transmission limit value and/or the adjusted power node voltage amplitude range.

10. A method according to any one of claims 1-3, wherein the method further comprises: and adding the target grid frame scheme to be verified, the verification result of which is not passed, to an infeasible cutting set.