CN109638810B - Energy storage planning method and system based on transient stability of power system - Google Patents
Energy storage planning method and system based on transient stability of power system Download PDFInfo
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Classifications
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
Abstract
The application relates to an energy storage planning method and system based on transient stability of a power system, wherein an energy storage access position, generator output, node voltage amplitude and phase angle are used as optimization variables, and a pre-constructed transient stability risk mitigation model is input; determining an optimal configuration scheme according to an objective function and constraint conditions of the transient stability risk mitigation model; the transient stability risk mitigation model is constructed based on the output cost of the minimized energy storage and generator and a predefined transient stability risk index; and the transient stability risk index is formulated according to the fault severity and the fault occurrence probability of the uncertainty scene.
Description
Technical Field
The application relates to a method and a system, in particular to an energy storage planning method and a system based on transient stability of a power system.
Background
In recent years, with the wide application of the high-voltage ac/dc power transmission system, the operating point of the power system is pushed to the stable point boundary, and thus cascading failure and large-scale power failure may be caused. Therefore, a risk indicator for effectively quantifying the stability of an ac/dc power system is highly required, and then, based on the risk indicator, a preventive control that takes into account safety and economy can be achieved.
The risk-based transient stability index mainly considers the occurrence probability of faults and the severity of expected faults, and has been widely applied to safety evaluation and prevention control of power systems. The present scholars propose risk-based safety measures taking into account the probability of failure and the influence of instability, and additionally a linearization technique is used to calculate the risk-based dynamic safety measures. However, most transient stability risk studies do not consider direct current system access.
In addition to generator scheduling, research into energy storage for improving transient stability has gained widespread attention in recent years. The main research focus at present is to construct various controllers by utilizing the rapid power response capability of energy storage, rapidly stabilize power fluctuation when faults occur, provide system damping, and therefore, the emergency control system is free from transient instability. For example, there are studies on the construction of a fuzzy logic controller stabilization system using superconducting energy storage; and a learner also compares and researches the stabilizing effect of the flywheel energy storage system, the battery energy storage system and the static synchronous compensator in different fault scenes.
These studies mainly apply energy storage to emergency control, but there are few studies in terms of power system planning and scheduling, prevention and control considering economy and safety.
Disclosure of Invention
In order to solve the defects of the prior art, the application provides an energy storage planning method and system based on transient stability of a power system, which are used for verifying the feasibility of prevention and control of an alternating current-direct current series-parallel system and provide a powerful support for economic dispatch research considering transient stability constraint in the future.
In order to achieve the above purpose, the present application adopts the following technical scheme:
an energy storage planning method based on transient stability of a power system, the method comprising:
taking the energy storage access position, the generator output, the node voltage amplitude and the phase angle as optimization variables, and inputting a pre-constructed transient stability risk mitigation model;
determining an optimal configuration scheme according to an objective function and constraint conditions of the transient stability risk mitigation model;
the transient stability risk mitigation model is constructed based on the output cost of the minimized energy storage and generator and a predefined transient stability risk index;
and the transient stability risk index is formulated according to the fault severity and the fault occurrence probability of the uncertainty scene.
Preferably, the construction of the uncertainty scene includes:
performing numerical disturbance on random factors affecting transient stability, and determining uncertainty of the random factors;
combining uncertainty of each random factor with a pre-established historical fault data probability model to obtain an uncertainty scene;
the random factors affecting transient stability include: generator output, load level, and fault removal time;
the pre-established historical fault data probability model comprises the following steps: fault type, fault location, and probability of occurrence of a fault. Further, the uncertainty of the load level is determined by:
L＝L min +{r j *m}×(L max -L min ),L∈Ω m×1
in the formula, { r j * m is a sampling factor of a preferred point set, m is an uncertain scene number, L min And L max Respectively the lowest and highest value of the load level.
Further, the uncertainty of the generator output is determined by:
wherein G is gmax And G gmin The upper and lower limit values of the output of the generator g are respectively set.
Further, the uncertainty of the fault-removal time is determined by:
FCT＝FCT min +{r j *m}×(FCT max -FCT min ),FCT∈Ω m×1
in FCT max And FCT min The maximum and minimum values of fault-removal time, respectively.
Preferably, the severity of the fault of the uncertainty scenario is determined by:
SEV c ＝max{θ i -θ j }/π,
wherein c represents a fault, SEV c G is the generator set and θ is the generator power angle, which is the severity of fault c.
Preferably, the probability of occurrence of a fault in the uncertainty scenario is determined by:
wherein p is i For the average failure times of line i years, n f To account for the total number of lines, L is the set of lines.
Preferably, the formulating the transient stability risk indicator according to the fault severity and the fault occurrence probability of the uncertainty scene includes:
defining the mean value and standard deviation of products of fault occurrence probability and fault severity in all uncertain scenes as transient stability risk indexes; wherein,
the transient stability risk index is determined by the following formula;
TSR＝avg{P*SEV},TSD＝std{P*SEV}
P＝[P 1 ,...,P i ,...,P n ]
SEV＝[SEV 1 ,...,SEV i ,...,SEV n ]
wherein n is the number of uncertain scenes, P is a fault occurrence probability vector, SEV is a fault severity vector obtained by sampling in an expected fault set, and TSR and TSD are the mean value and standard deviation of the products of the fault occurrence probabilities and the fault severity of all uncertain scenes respectively.
Preferably, the objective function of the transient stability risk mitigation model is determined by:
minF＝[TSR,TSD,C cost ]
C cost ＝C ESS +C GEN
wherein C is cost For the total output cost of energy storage and generator, C ESS And C GEN The energy storage cost and the output cost of the generator are respectively; TSR and TSD are the mean and standard deviation of the product of failure occurrence probability and failure severity of all uncertain scenarios, respectively.
Further, the energy storage cost and the output cost of the generator are determined by:
C ESS ＝uP ESS T
wherein a is g 、b g And c g The output cost coefficients of the generators g are respectively, u is the investment cost of the energy storage unit capacity, and P g G is the power output of the generator, G is the power generationNumber of machines, P ESS And T is time domain simulation time for energy storage output.
Preferably, the optimization variables are determined by the following formula:
VAR opt ＝[P ESS ,LOC ESS ,U,θ,P G ] T
in LOC ESS For the energy storage access position, U and theta are node voltage amplitude and phase angle vector respectively, P G For the output vector of the generator, P ESS And T is time domain simulation time for energy storage output.
Preferably, the constraint condition of the transient stability risk mitigation model includes: ac-dc power flow constraints, node voltage constraints and line power flow constraints.
Further, the ac/dc power flow constraint is determined by:
wherein P is AC,i Active injection for ac side node, Q i Reactive power injection for AC side node, V AC I and V AC J is the voltage amplitude between the alternating current side nodes respectively; i. j are nodes, G AC,xy And B xy Real part and imaginary part of the x-th row and y-th column of the admittance matrix respectively; θ ij The phase angle difference between the nodes i and j; p (P) DC,i Active injection for DC side node, V DC,i And V DC,j Voltage amplitudes G of the direct current side nodes i and j, respectively DC,xy Is the value of the x-th row and y-th column of the admittance matrix.
Further, the node voltage constraint is determined by:
in the method, in the process of the application,and->The upper and lower voltage limit values of the AC side node, < ->And->The upper and lower voltage limit values of the DC node are respectively set.
Further, the line flow constraint is determined by:
in the method, in the process of the application,and->Respectively the upper and lower limit values of the power of the alternating current circuit, < + >>And->The upper and lower limit values of the power of the direct current line are respectively set.
Preferably, the determining the optimal configuration scheme includes:
selecting an energy storage access position according to the output working conditions of a generator and energy storage, generating an initial population NPOP, and calculating individual fitness of the initial population NPOP;
based on the initial population NPOP and a pre-established external set EPOP, establishing a combined population twoPOP= [ NPOP; EPOP ];
performing fitness allocation and environment selection operation on the combined population twoPOP to obtain a new external set, and performing selection, crossing and mutation operation on the new external set to obtain a child population NPOP';
when the sub-population NPOP' reaches the maximum evolution algebra, calculating the fitness of the current population and all individuals in the external set, and generating an optimal pareto set;
and sequencing the optimal pareto solution set by adopting an ordinal preference method based on information entropy to obtain an optimal configuration scheme.
An energy storage planning system based on transient stability of a power system, the system comprising:
the input module is used for taking the energy storage access position, the generator output, the node voltage amplitude and the phase angle as optimization variables and inputting a pre-constructed transient stability risk mitigation model;
the determining module is used for determining an optimal configuration scheme according to the objective function and the constraint condition of the transient stability risk mitigation model;
the construction module is used for constructing the transient stability risk relief model based on the output cost of the minimized energy storage and generator and a predefined transient stability risk index;
and the formulating module is used for formulating the transient stability risk index according to the fault severity and the fault occurrence probability of the uncertainty scene.
Preferably, the formulation module includes:
the uncertainty analysis unit is used for carrying out numerical disturbance on random factors affecting transient stability and determining uncertainty of the random factors;
and the uncertainty scene generating unit is used for combining the uncertainty of each random factor and a pre-established historical fault data probability model to obtain an uncertainty scene.
Further, the formulation module further includes: and the definition unit is used for defining the mean value and standard deviation of the products of the fault occurrence probability and the fault severity in all uncertain scenes as transient stability risk indexes.
Compared with the closest prior art, the application has the following beneficial effects:
according to the energy storage planning method and system based on the transient stability of the power system, the energy storage access position, the generator output, the node voltage amplitude and the phase angle are used as optimization variables, and a pre-constructed transient stability risk mitigation model is input; determining an optimal configuration scheme according to an objective function and constraint conditions of the transient stability risk mitigation model; and finally, obtaining the determined optimal configuration scheme by adopting a SPEA2 algorithm solution model. The SPEA2 algorithm can effectively solve the problem that the most fragile node of the energy storage device connected with the AC/DC series-parallel system can relieve the transient instability risk of the system through the obtained optimal configuration scheme, and when the energy storage device is connected with the AC/DC series-parallel system, the reasonable generator output plan can effectively relieve the transient instability risk of the system; therefore, the addressing constant volume optimization configuration of the energy storage system is realized, and a powerful support is provided for economic dispatch research considering transient stability constraint in the future.
The transient stability risk mitigation model is constructed based on the output cost of the minimized energy storage and generator and a predefined transient stability risk index;
and the transient stability risk index is formulated according to the fault severity and the fault occurrence probability of the uncertainty scene. The index quantifies the risk of transient stability under the current operating conditions. Defining an objective function and constraint conditions of a transient stability risk mitigation model according to the transient stability risk index; the multi-objective optimization strategy for relieving the transient stability risk by matching the energy storage system with the generator scheduling is studied in depth. The uncertainty scene is obtained through the sampling of the optimal point set, so that the uncertainty scene set generated by utilizing the sampling of the optimal point set is more uniform and comprehensive compared with the random sampling of Monte Carlo, and the sampling effect of the optimal point set and the random sampling is compared in detail from the numerical value through the information entropy quantization sampling effect.
Drawings
FIG. 1 is a flow chart of an energy storage planning method based on transient stability of a power system provided in an embodiment of the application;
FIG. 2 is a flowchart of a transient stability risk calculation method provided in an embodiment of the present application;
FIG. 3 is a schematic diagram of an improved IEEE39 node system architecture provided in an embodiment of the application;
FIG. 4 is a graph comparing the power output of 500 generators generated by the preferred point set sampling and the Monte Carlo random sampling provided in the embodiment of the present application; wherein, (a) is MC sampling and (b) is optimal point set sampling;
fig. 5 is a schematic diagram of quantization of sampling effects provided in an embodiment of the present application.
Detailed Description
The application is described in further detail below with reference to the accompanying drawings.
The application provides an energy storage planning method based on transient stability of a power system, which specifically comprises the following steps:
s1, taking an energy storage access position, generator output, node voltage amplitude and phase angle as optimization variables, and inputting a pre-constructed transient stability risk mitigation model;
s2, determining an optimal configuration scheme according to an objective function and constraint conditions of the transient stability risk mitigation model;
the transient stability risk mitigation model is constructed based on the output cost of the minimized energy storage and generator and a predefined transient stability risk index;
and the transient stability risk index is formulated according to the fault severity and the fault occurrence probability of the uncertainty scene.
The construction of the uncertainty scene comprises the following steps:
(1) Performing numerical disturbance on random factors affecting transient stability, and determining uncertainty of the random factors;
(2) Combining uncertainty of each random factor with a pre-established historical fault data probability model to obtain an uncertainty scene;
the random factors affecting transient stability include: generator output, load level, and fault removal time;
the pre-established historical fault data probability model comprises the following steps: fault type, fault location, and probability of occurrence of a fault.
In step (1), performing numerical perturbation on the random factors affecting transient stability includes:
and determining the current system operation condition, and importing basic power flow information such as initial output, load and the like of the generator.
And performing numerical disturbance on the output and load level of the generator by using the optimal point set sampling.
In the step (2), determining the uncertainty of the random factor, namely performing numerical disturbance on the output and load level of the generator through sampling a best point set, and constructing an uncertainty scene of the tide operation working condition.
And (3) for a pre-established historical fault model, performing numerical disturbance on possible fault removal time by using a best point set sampling, and constructing an uncertainty scene of the fault. Therefore, the power flow uncertainty scene and the fault uncertainty scene are combined, and a scene set with various uncertainty influences is constructed by considering a probability model of the fault based on historical data.
Wherein the uncertainty of the load level is determined by:
L＝L min +{r j *m}×(L max -L min ),L∈Ω m×1
in the formula, { r j * m is a sampling factor of a best point set, m is an uncertain scene number, L min And L max Respectively the lowest and highest value of the load level.
The uncertainty of the generator output is determined by:
wherein G is gmax And G gmin The upper and lower limit values of the output of the generator g are respectively set.
The uncertainty of the fault-removal time is determined by:
FCT＝FCT min +{r j *m}×(FCT max -FCT min ),FCT∈Ω m×1
in FCT max And FCT min The maximum and minimum values of fault-removal time, respectively.
As shown in fig. 2, a transient stability risk index is formulated according to the fault severity and the fault occurrence probability of the uncertainty scene; the index quantifies the risk of transient stability under the current operating conditions.
Wherein the severity of the fault of the uncertainty scenario is determined by:
SEV c ＝max{θ i -θ j }/π,
wherein c represents a fault, SEV c G is the generator set and θ is the generator power angle, which is the severity of fault c.
Determining the probability of occurrence of a fault in the uncertainty scene by:
wherein p is i For the average failure times of line i years, n f To account for the total number of lines, L is the set of lines.
The method for formulating the transient stability risk index comprises the following steps:
defining the mean value and standard deviation of products of fault occurrence probability and fault severity in all uncertain scenes as transient stability risk indexes; wherein,
the transient stability risk index is determined by the following formula;
TSR＝avg{P*SEV},TSD＝std{P*SEV}
P＝[P 1 ,...,P i ,...,P n ]
SEV＝[SEV 1 ,...,SEV i ,...,SEV n ]
wherein n is the number of uncertain scenes, P is a fault occurrence probability vector, SEV is a fault severity vector obtained by sampling in an expected fault set, and TSR and TSD are the mean value and standard deviation of the products of the fault occurrence probabilities and the fault severity of all uncertain scenes respectively.
According to the application, the transient stability risk index and the cost of energy storage and generator output are minimized by optimizing the energy storage capacity, the position and the generator output, and a transient stability risk relief model which does not contain energy storage and only optimizes the generator output is constructed at the same time for comparison.
In step S2, an objective function of the transient stability risk mitigation model is determined by:
minF＝[TSR,TSD,C cost ]
C cost ＝C ESS +C GEN
wherein C is cost For the total output cost of energy storage and generator, C ESS And C GEN The energy storage cost and the output cost of the generator are respectively.
The energy storage cost and the output cost of the generator are determined by:
C ESS ＝uP ESS T
wherein a is g 、b g And c g The output cost coefficients of the generators g are respectively, u is the investment cost of the energy storage unit capacity, and P g G is the output of the generator, G is the number of generators, P ESS And T is time domain simulation time for energy storage output.
The optimization variables in step S1 are determined by the following formula:
VAR opt ＝[P ESS ,LOC ESS ,U,θ,P G ] T
in LOC ESS For the energy storage access position, U and theta are node voltage amplitude and phase angle vector respectively, P G Is the output vector of the generator.
Constraints of the transient stability risk mitigation model include: ac-dc power flow constraints, node voltage constraints and line power flow constraints. In addition, the method comprises the following steps of traditional power balance equality constraint, energy storage output upper and lower limit constraint, generator output upper and lower limit constraint and steady state constraint of an alternating current-direct current system. All constraints are handled as penalty functions.
The traditional constraint is not repeated, and the steady-state constraint of the AC/DC system is as follows: ac-dc power flow constraints, node voltage constraints and line power flow constraints. Specifically, the ac/dc power flow constraint is determined by:
wherein P is AC,i Active injection for ac side node, Q i Reactive power injection for AC side node, V AC I and V AC J is the voltage amplitude between the alternating current side nodes respectively; i. j are nodes, G AC,xy And B xy Real part and imaginary part of the x-th row and y-th column of the admittance matrix respectively; θ ij The phase angle difference between the nodes i and j; p (P) DC,i Active injection for DC side node, V DC,i And V DC,j Voltage amplitudes G of the direct current side nodes i and j, respectively DC,xy Is admittanceThe value of the x-th row and y-th column of the matrix.
The node voltage constraint is determined by:
in the method, in the process of the application,and->The upper and lower voltage limit values of the AC side node, < ->And->The upper and lower voltage limit values of the DC node are respectively set.
Determining the line flow constraint by:
in the method, in the process of the application,and->Respectively the upper and lower limit values of the power of the alternating current circuit, < + >>And->The upper and lower limit values of the power of the direct current line are respectively set.
The determining of the optimal configuration scheme comprises the following steps:
the transient stability risk mitigation model provided by the application is a dynamic and nonlinear multi-objective optimization problem, and is difficult to solve through traditional numerical optimization. The application adopts the strength pareto evolution algorithm (Strength Pareto evolutionary algorithm, SPEA 2) to solve the model, and obtains the optimal configuration scheme. The method specifically comprises the following steps:
selecting an energy storage access position according to the output working conditions of a generator and energy storage, generating an initial population NPOP, and calculating individual fitness of the initial population NPOP;
based on the initial population NPOP and a pre-established external set EPOP, establishing a combined population twoPOP= [ NPOP; EPOP ], obtaining a combined population fitness twopa= [ Npa; epa ];
performing SPEA2 fitness allocation and environment selection operation on the combined population twoPOP to obtain a new external set, and performing selection, crossing and mutation operation on the new external set to obtain a child population NPOP';
when the sub-population NPOP' reaches the maximum evolution algebra, the current population is derived, the individual fitness is calculated, and the optimal pareto set pareto=EPOP is generated;
and sequencing the optimal pareto solution set by adopting an ordinal preference method based on information entropy to obtain an optimal configuration scheme.
In addition, the feasibility of applying the energy storage to transient stability risk alleviation is compared and studied by using a model which does not consider energy storage access. The transient stability mitigation steps without considering energy storage access are as follows:
step 1: and determining the optimal control variable as the generator output, wherein the control variable adopts real number coding.
Step 2: and determining the optimization targets as generator output cost and transient stability risk statistics indexes TSR and TSD.
Step 3: and randomly generating output working conditions of the two power generators in the power generator output, so as to generate an initial population NPOP.
Step 4: individual fitness is calculated. In the target value calculation, the output cost only considers the output cost of the generator, and TSR and TSD are calculated through a first layer step; the constraint condition of the transient stability risk relief model without energy storage is not considered, and each optimization target is added in a penalty function mode. And adding the individual target value and the constraint condition penalty function value to obtain the individual fitness. Population fitness is Npa.
Step 5: the SPEA2 external set EPOP and the adaptation Epa are set empty, and the sentence twoPOP= [ NPOP; EPOP merges populations NPOP and EPOP using the term twopa= [ Npa; epa to obtain the fitness of the combined population.
Step 6: performing SPEA2 fitness allocation and environment selection operation on the combined population twoPOP to obtain a SPEA2 new external set EPOP; and selecting, crossing and mutating the external set to obtain the offspring population NPOP.
Step 7: if the maximum evolution algebra is reached, stopping, deriving the current NPOP, EPOP and calculating the fitness Npa, epa; otherwise, repeating the steps 9 to 10,
Step 8: and merging the populations, performing fitness allocation and environment selection to obtain an optimal pareto set pareto=epop, and recording the fitness value of the pareto set.
Step 9: and sequencing the optimal pareto solution set by adopting an ordinal preference method (TOPSIS) based on information entropy to obtain a final optimal generator configuration strategy.
Examples:
the application verifies the feasibility of the method through an improved IEEE39 node system, and the reference power of the system is 100MW. The lines 4-14 are replaced with HVDC lines rated at 500kV and 500MW respectively. A modified IEEE39 node system is shown in fig. 3. The estimate based on historical statistics is shown in table 1, where both zero and negative sequence impedances in ground faults are 0.4p.u..
TABLE 1 set of expected incidents
Uncertain scene sampling:
the 500 generator output scenario is generated by the set of points sampling and monte carlo random sampling, as shown in fig. 4.
The application obtains information entropy curves of two sampling modes by gradually increasing the sampling quantity based on the information entropy quantization sampling effect, and the information entropy curves are shown in figure 5.
The present application verifies that the best point set sampling has four advantages over random sampling by comparing fig. 4 and 5: firstly, the output distribution of the generator sampled by the optimal point set is more uniform; secondly, the uncertainty scene covered by the optimal point set sampling is more comprehensive, and the situation of repeated sampling on a certain generator output combination scene can not occur; thirdly, the better point set sampling can reach higher information entropy under the lower sampling scene number, and the low sampling number can contain a large amount of information; fourth, with the increment of the sampling number, the information entropy of the sampling of the optimal point set is stable, no fluctuation occurs, and the sampling scene number is convenient to determine.
Model solving result:
solving a model proposed by the application through SPEA 2to obtain a pareto solution set;
the transient stability risk statistical index under the energy storage access is lower than the transient stability risk statistical index when the energy storage is not accessed, and the effectiveness and feasibility of the energy storage applied to the prevention control are verified.
The final pareto solutions were ranked by TOPSIS and the results are shown in table 2.
Table 2 pareto solution set for TOPSIS selection
As can be seen from table 2, the position with the greatest probability of occurrence and the greatest influence on the energy storage access accident can most effectively relieve the risk of transient stability, and the higher the energy storage output is, the lower the risk of transient stability is, but the higher the input cost is relatively.
The application provides basic research for a risk-based preventive control strategy and provides powerful support for realizing real-time scheduling application considering transient stability risk by utilizing energy storage in the future. Meanwhile, the embodiment of the application adopts an AC/DC system, and verifies the feasibility of applying the energy storage to relieving the safety risk of the AC/DC system.
Based on the same inventive concept, the application also provides an energy storage planning system based on transient stability of a power system, wherein the system comprises:
the input module is used for taking the energy storage access position, the generator output, the node voltage amplitude and the phase angle as optimization variables and inputting a pre-constructed transient stability risk mitigation model;
the determining module is used for determining an optimal configuration scheme according to the objective function and the constraint condition of the transient stability risk mitigation model;
the construction module is used for constructing the transient stability risk relief model based on the output cost of the minimized energy storage and generator and a predefined transient stability risk index;
and the formulating module is used for formulating the transient stability risk index according to the fault severity and the fault occurrence probability of the uncertainty scene.
Wherein, the formulation module includes:
the uncertainty analysis unit is used for carrying out numerical disturbance on random factors affecting transient stability and determining uncertainty of the random factors;
and the uncertainty scene generating unit is used for combining the uncertainty of each random factor and a pre-established historical fault data probability model to obtain an uncertainty scene.
Wherein the uncertainty analysis unit comprises a first calculation subunit for determining the uncertainty of the load level by:
L＝L min +{r j *m}×(L max -L min ),L∈Ω m×1
in the formula, { r j * m is a sampling factor of a preferred point set, m is an uncertain scene number, L min And L max Respectively the lowest and highest value of the load level.
A second calculation subunit for determining an uncertainty of the generator output by:
wherein G is gmax And G gmin The upper and lower limit values of the output of the generator g are respectively set.
A third calculation subunit configured to determine an uncertainty of the fault-removal time by:
FCT＝FCT min +{r j *m}×(FCT max -FCT min ),FCT∈Ω m×1
in FCT max And FCT min The maximum and minimum values of fault-removal time, respectively.
The uncertain scene generating unit includes: a first determining subunit configured to determine a fault severity of the uncertainty scenario by:
SEV c ＝max{θ i -θ j }/π,
wherein c represents a fault, SEV c G is the generator set and θ is the generator power angle, which is the severity of fault c.
A second determining subunit, configured to determine a probability of occurrence of a fault in the uncertainty scene by:
wherein p is i For the average failure times of line i years, n f To account for the total number of lines, L is the set of lines.
In addition, the formulation module further includes: and the definition unit is used for defining the mean value and standard deviation of the products of the fault occurrence probability and the fault severity in all uncertain scenes as transient stability risk indexes.
The input module comprises an optimization variable determining unit and is used for determining an optimization variable through the following formula:
VAR opt ＝[P ESS ,LOC ESS ,U,θ,P G ] T
in LOC ESS For the energy storage access position, U and theta are node voltage amplitude and phase angle vector respectively, P G Is the output vector of the generator.
The determining module comprises a third determining unit for determining an objective function of the transient stability risk mitigation model by:
minF＝[TSR,TSD,C cost ]
C cost ＝C ESS +C GEN
wherein C is cost For the total output cost of energy storage and generator, C ESS And C GEN The energy storage cost and the output cost of the generator are respectively.
A fourth determining unit, configured to determine an ac/dc power flow constraint by:
wherein P is AC,i Active injection for ac side node, Q i For communicationReactive power injection of side node, V AC I and V AC J is the voltage amplitude between the alternating current side nodes respectively; i. j are nodes, G AC,xy And B xy Real part and imaginary part of the x-th row and y-th column of the admittance matrix respectively; θ ij The phase angle difference between the nodes i and j; p (P) DC,i Active injection for DC side node, V DC,i And V DC,j Voltage amplitudes G of the direct current side nodes i and j, respectively DC,xy Is the value of the x-th row and y-th column of the admittance matrix.
A fifth determining unit for determining a node voltage constraint by:
in the method, in the process of the application,and->The upper and lower voltage limit values of the AC side node, < ->And->The upper and lower voltage limit values of the DC node are respectively set.
A sixth determining unit, configured to determine the line power flow constraint by:
in the method, in the process of the application,and->Respectively the upper and lower limit values of the power of the alternating current circuit, < + >>And->The upper and lower limit values of the power of the direct current line are respectively set.
The determining module further comprises an optimizing unit, and the optimizing unit is used for determining an optimal configuration scheme.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Claims (14)
1. An energy storage planning method based on transient stability of a power system, which is characterized by comprising the following steps:
taking the energy storage access position, the generator output, the node voltage amplitude and the phase angle as optimization variables, and inputting a pre-constructed transient stability risk mitigation model;
determining an optimal configuration scheme according to an objective function and constraint conditions of the transient stability risk mitigation model;
the transient stability risk mitigation model is constructed based on the output cost of the minimized energy storage and generator and a predefined transient stability risk index;
the transient stability risk index is formulated according to the fault severity and the fault occurrence probability of the uncertainty scene;
the severity of the fault of the uncertainty scenario is determined by:
SEV c ＝max{θ i -θ j }/π,
wherein c represents a fault, SEV c G is a generator set, and θ is a generator power angle;
determining the probability of occurrence of a fault in the uncertainty scene by:
wherein p is i For the average failure times of line i years, n f For the total number of lines to be considered, L is a line set;
the step of formulating transient stability risk indexes according to the fault severity and the fault occurrence probability of the uncertainty scene comprises the following steps:
defining the mean value and standard deviation of products of fault occurrence probability and fault severity in all uncertain scenes as transient stability risk indexes; wherein,
the transient stability risk index is determined by the following formula;
TSR＝avg{P*SEV},TSD＝std{P*SEV}
P＝[P 1 ,...,P i ,...,P n ]
SEV＝[SEV 1 ,...,SEV i ,...,SEV n ]
wherein n is the number of uncertain scenes, P is a fault occurrence probability vector, SEV is a fault severity vector obtained by sampling in an expected fault set, and TSR and TSD are the mean value and standard deviation of the products of the fault occurrence probabilities and the fault severity of all uncertain scenes respectively;
determining an objective function of the transient stability risk mitigation model by:
minF＝[TSR,TSD,C cost ]
C cost ＝C ESS +C GEN
wherein C is cost For the total output cost of energy storage and generator, C ESS And C GEN The energy storage cost and the output cost of the generator are respectively; TSR and TSD are respectively the fault occurrence probability and the fault occurrence probability of all uncertain scenesMean and standard deviation of fault severity product;
the energy storage cost and the output cost of the generator are determined by:
C ESS ＝uP ESS T
wherein a is g 、b g And c g The output cost coefficients of the generators g are respectively, u is the investment cost of the energy storage unit capacity, and P g G is the output of the generator, G is the number of generators, P ESS And T is time domain simulation time for energy storage output.
2. The method of claim 1, wherein the construction of the uncertainty scene comprises:
performing numerical disturbance on random factors affecting transient stability, and determining uncertainty of the random factors;
combining uncertainty of each random factor with a pre-established historical fault data probability model to obtain an uncertainty scene;
the random factors affecting transient stability include: generator output, load level, and fault removal time;
the pre-established historical fault data probability model comprises the following steps: fault type, fault location, and probability of occurrence of a fault.
3. The method of claim 2, wherein the uncertainty in the load level is determined by:
L＝L min +{r j *m}×(L max -L min ),L∈Ω m×1L
in the formula, { r j * m is a sampling factor of a preferred point set, m is an uncertain scene number, L min And L max Respectively the lowest and highest value of the load level.
4. A method according to claim 3, wherein the uncertainty in the generator output is determined by:
wherein G is gmax And G gmin The upper and lower limit values of the output of the generator g are respectively set.
5. The method of claim 4, wherein the uncertainty of the fault-removal time is determined by:
FCT＝FCT min +{r j *m}×(FCT max -FCT min ),FCT∈Ω m×1F
in FCT max And FCT min The maximum and minimum values of fault-removal time, respectively.
6. The method of claim 1, wherein the optimization variable is determined by:
VAR opt ＝[P ESS ,LOC ESS ,U,θ,P G ] T
in LOC ESS For the energy storage access position, U and theta are node voltage amplitude and phase angle vector respectively, P G For the output vector of the generator, P ESS And T is time domain simulation time for energy storage output.
7. The method of claim 1, wherein the constraints of the transient stability risk mitigation model include: ac-dc power flow constraints, node voltage constraints and line power flow constraints.
8. The method of claim 7, wherein the ac-dc power flow constraint is determined by:
wherein P is AC,i Active injection for ac side node, Q i Reactive power injection for AC side node, V AC I and V AC J is the voltage amplitude between the alternating current side nodes respectively; i. j are nodes, G AC,xy And B xy Real part and imaginary part of the x-th row and y-th column of the admittance matrix respectively; θ ij The phase angle difference between the nodes i and j; p (P) DC,i Active injection for DC side node, V DC,i And V DC,j Voltage amplitudes G of the direct current side nodes i and j, respectively DC,xy Is the value of the x-th row and y-th column of the admittance matrix.
9. The method of claim 7, wherein the node voltage constraint is determined by:
in the method, in the process of the application,and->The upper and lower voltage limit values of the AC side node, < ->And->The upper and lower voltage limit values of the DC node are respectively set.
10. The method of claim 7, wherein the line flow constraint is determined by:
in the method, in the process of the application,and->Respectively the upper and lower limit values of the power of the alternating current circuit, < + >>And->The upper and lower limit values of the power of the direct current line are respectively set.
11. The method of claim 1, wherein the determining the optimal configuration scheme comprises:
selecting an energy storage access position according to the output working conditions of a generator and energy storage, generating an initial population NPOP, and calculating individual fitness of the initial population NPOP;
based on the initial population NPOP and a pre-established external set EPOP, establishing a combined population twoPOP= [ NPOP; EPOP ];
performing fitness allocation and environment selection operation on the combined population twoPOP to obtain a new external set, and performing selection, crossing and mutation operation on the new external set to obtain a child population NPOP';
when the sub-population NPOP' reaches the maximum evolution algebra, calculating the fitness of the current population and all individuals in the external set, and generating an optimal pareto set;
and sequencing the optimal pareto solution set by adopting an ordinal preference method based on information entropy to obtain an optimal configuration scheme.
12. An energy storage planning system based on transient stability of a power system, the system comprising:
the input module is used for taking the energy storage access position, the generator output, the node voltage amplitude and the phase angle as optimization variables and inputting a pre-constructed transient stability risk mitigation model;
the determining module is used for determining an optimal configuration scheme according to the objective function and the constraint condition of the transient stability risk mitigation model;
the construction module is used for constructing the transient stability risk relief model based on the output cost of the minimized energy storage and generator and a predefined transient stability risk index;
the formulating module is used for formulating the transient stability risk index according to the fault severity and the fault occurrence probability of the uncertainty scene;
the severity of the fault of the uncertainty scenario is determined by:
SEV c ＝max{θ i -θ j }/π,
wherein c represents a fault, SEV c G is a generator set, and θ is a generator power angle;
determining the probability of occurrence of a fault in the uncertainty scene by:
wherein p is i For the average failure times of line i years, n f For the total number of lines to be considered, L is a line set;
the step of formulating transient stability risk indexes according to the fault severity and the fault occurrence probability of the uncertainty scene comprises the following steps:
defining the mean value and standard deviation of products of fault occurrence probability and fault severity in all uncertain scenes as transient stability risk indexes; wherein,
the transient stability risk index is determined by the following formula;
TSR＝avg{P*SEV},TSD＝std{P*SEV}
P＝[P 1 ,...,P i ,...,P n ]
SEV＝[SEV 1 ,...,SEV i ,...,SEV n ]
wherein n is the number of uncertain scenes, P is a fault occurrence probability vector, SEV is a fault severity vector obtained by sampling in an expected fault set, and TSR and TSD are the mean value and standard deviation of the products of the fault occurrence probabilities and the fault severity of all uncertain scenes respectively;
determining an objective function of the transient stability risk mitigation model by:
minF＝[TSR,TSD,C cost ]
C cost ＝C ESS +C GEN
wherein C is cost For the total output cost of energy storage and generator, C ESS And C GEN The energy storage cost and the output cost of the generator are respectively; TSR and TSD are the mean value and standard deviation of the products of the fault occurrence probability and the fault severity of all uncertain scenes respectively;
the energy storage cost and the output cost of the generator are determined by:
C ESS ＝uP ESS T
wherein a is g 、b g And c g The output cost coefficients of the generators g are respectively, u is the investment cost of the energy storage unit capacity, and P g G is the output of the generator, G is the number of generators, P ESS And T is time domain simulation time for energy storage output.
13. The system of claim 12, wherein the formulation module comprises:
the uncertainty analysis unit is used for carrying out numerical disturbance on random factors affecting transient stability and determining uncertainty of the random factors;
and the uncertainty scene generating unit is used for combining the uncertainty of each random factor and a pre-established historical fault data probability model to obtain an uncertainty scene.
14. The system of claim 12, wherein the formulation module further comprises: and the definition unit is used for defining the mean value and standard deviation of the products of the fault occurrence probability and the fault severity in all uncertain scenes as transient stability risk indexes.
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