CN107590564B - Transient stability constraint-based active power output adjustment method for power system - Google Patents
Transient stability constraint-based active power output adjustment method for power system Download PDFInfo
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Abstract
The invention discloses an active power output adjusting method of a power system based on transient stability constraint, which is characterized in that an expected fault set is verified on the current operating condition of the power system, when the power system is in transient instability, a plurality of scheduling sets containing adjustment quantities of generators participating in scheduling are generated according to an initial scheduling set, and each scheduling set is updated by adopting a particle swarm algorithm; calculating the current fitness value of each scheduling set by adopting an objective function added with a secondary penalty term; judging whether the current fitness value is smaller than the previous fitness value: if the current fitness value is smaller than the preset fitness value, updating a scheduling set containing the optimal adjustment quantity of the generator participating in scheduling by adopting the scheduling set corresponding to the current fitness value; otherwise, not updating; updating the active output scheduling set by adopting the minimum value in all scheduling sets containing the optimal adjustment quantity of the generators participating in scheduling; and when the updating times of the active output scheduling set are equal to the set updating times, outputting the optimal adjustment quantity of the generator participating in scheduling in the active output scheduling set updated for the last time.
Description
Technical Field
The invention relates to the field of power system scheduling, in particular to a power system active power output adjusting method based on transient stability constraint.
Background
At present, the problem of preventing and controlling the transient stability of the power system based on the active power output adjustment means is mostly that an optimal power flow method containing transient stability constraint is applied, and a great deal of favorable discussion is conducted on the method and the strategy for preventing and controlling the transient stability of the power system by starting from factors such as a target function, constraint conditions, control variables, solving technology and the like; however, the optimal power flow model has the following problems due to the addition of transient stability constraint conditions, and firstly, the complexity of the model is increased along with the introduction of different forms of transient stability constraint conditions; secondly, the solving method of the model is various and has advantages and disadvantages; some methods use a sensitivity function of an energy margin as transient stability constraint and utilize a multi-objective optimization technology to solve; in some methods, a generator rotor swing differential equation and a generator rotor relative swing angle stability limit are respectively used as equality and inequality constraints, and a modern interior point theory is combined to solve the equations; however, in the solving process of the model, the method needs to check whether the unmeasured optimization result meets the transient stability constraint condition through repeated time domain simulation calculation, the calculation amount is large, the solving speed is relatively slow, and the adjustment strategy of the active power output of the generator cannot be quickly obtained; therefore, the learners propose a method for solving the optimal power flow problem containing transient stability constraint conditions by using a track sensitivity method, although the track sensitivity method is high in calculation speed, the uncertainty of the transient stability criterion value can generate a large influence on an optimization result due to the linearization of the complex nonlinear transient stability constraint, and an accurate generator active power output adjustment strategy is difficult to obtain.
Disclosure of Invention
Aiming at the defects in the prior art, the active power output adjusting method of the power system based on the transient stability prediction machine constraint can quickly calculate the optimal adjusting quantity of the generator participating in dispatching when the power system is in transient instability.
In order to achieve the purpose of the invention, the invention adopts the technical scheme that:
performing expected fault set verification on the current operating condition of the power system, and judging the transient stability of the power system;
if the power system is transient instability, acquiring an initial scheduling set containing initial adjustment quantity of a generator participating in scheduling;
according to the initial scheduling set, adopting Latin hypercube sampling to generate a plurality of scheduling sets containing adjustment quantities participating in scheduling of the generator, and adopting a particle swarm algorithm to update each scheduling set;
calculating the current fitness value of each scheduling set by adopting an objective function which takes the minimum total cost of the active output adjustment of the generator added with a secondary penalty term as a target, wherein the secondary penalty term is a steady-state operation constraint condition and a transient stability constraint condition;
judging whether the current fitness value is smaller than the fitness value of the previous iteration:
if the current fitness value is smaller than the preset fitness value, updating a scheduling set containing the optimal adjustment quantity of the generator participating in scheduling by adopting the scheduling set corresponding to the current fitness value; otherwise, not updating the scheduling set containing the optimal adjustment quantity of the generator participating in scheduling;
updating the active power output scheduling set containing the optimal adjustment quantity of the generator participating in scheduling by adopting the minimum value in all the scheduling sets containing the optimal adjustment quantity of the generator participating in scheduling;
and when the updating times of the active output scheduling set are equal to the set updating times, outputting the optimal adjustment quantity of the generator participating in scheduling in the active output scheduling set updated for the last time.
The invention has the beneficial effects that: according to the scheme, when the electric power system is in transient instability, the optimal adjustment quantity of the generator participating in dispatching during dispatching of the electric power system is rapidly calculated through the combination of Latin hypercube sampling, the target function adding the secondary punishment term and the particle swarm algorithm and the rapid judgment of the transient stability of the system through the neural network by virtue of the initial adjustment quantity participating in dispatching of the generator active power, so that the complex differential algebraic equation calculation is avoided, and the rapid adjustment of the electric power system when the electric power system fails is ensured.
Drawings
Fig. 1 is a flowchart of an active power output adjustment method of a power system based on transient stability constraint.
Fig. 2 is a schematic structural diagram of a BP neural network.
Fig. 3 is a 39-node power system for the new england 10 machine.
Fig. 4 is active power output scene data of any two generators.
Fig. 5 shows the power angle trajectory of the generator in case of pre-control fault 6.
Fig. 6 is a schematic diagram of the active power output adjustment of the generator during the instability verification under a single fault.
Fig. 7 shows the power angle trajectory of the generator in case of a fault 6 after preventive control.
Fig. 8 is a verification result of an expected failure set during instability verification under a single failure.
Fig. 9 shows the power angle trajectory of the generator before and after preventive control under each fault.
Fig. 10 is a schematic diagram of the active power output adjustment of the generator during the instability verification under a single fault.
Fig. 11 is an expected failure set verification result in multi-failure instability verification.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate the understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and it will be apparent to those skilled in the art that various changes may be made without departing from the spirit and scope of the invention as defined and defined in the appended claims, and all matters produced by the invention using the inventive concept are protected.
Referring to fig. 1, fig. 1 shows a flow chart of a power system active power output adjustment method based on the method; as shown in fig. 1, the method 100 includes steps 101 to 108.
In step 101, active power output scene data of a generator in the power system is obtained, and transient stability of the power system is judged.
In implementation, the method for determining whether the power system is in transient stability includes:
the method comprises the steps of checking an expected fault set of the current operation working conditions (the active output level and the load level of a generator) of the power system to obtain a power angle value of the generator after the fault is removed; calculating the power angle difference between any two generators; when the power angle difference between any two generators is smaller than 180 degrees, the power system is transient stable; when the power angle difference between any two generators is larger than 180 degrees, the power system is transient unstable.
In step 102, if the power system is transient unstable, acquiring an initial scheduling set including an initial adjustment amount of a generator participating in scheduling; if the power system is transient stability, the process continues to step 101.
And generating n particles in an m-dimensional search space by utilizing a Latin hypercube sampling technology, wherein m represents the number of generators participating in scheduling, and the particles represent a scheduling set containing the adjustment quantity of the generators participating in scheduling.
In step 103, according to the initial scheduling set, a plurality of scheduling sets including adjustment quantities participating in scheduling of the generator are generated by adopting Latin hypercube sampling, and each scheduling set is updated by adopting a particle swarm algorithm.
In an embodiment of the present invention, a specific refinement implementation method for updating each scheduling set by using a particle swarm algorithm comprises:
calculate the position of the ith scheduling set (search direction, corresponding to the positive or negative of the adjustment amount):
Xi(0)＝Gi1,Gi2,...,Gim,i＝1,2,...,n
calculate the initial speed (step size, equivalent to adjustment) at the update of the ith scheduling set:
Vi(0)＝Vi1,Vi2,...,Vim,i＝1,2,...,n
meanwhile, initializing the maximum iteration number and scheduling set P containing the optimal adjustment quantity participating in scheduling the generator during the initial iterationbAnd an active output dispatching set G containing optimal adjustment quantity of the generator participating in dispatchingb。
In the searching process, each particle can search for P according to the particle itselfbAnd G found by all particlesbUpdating a scheduling set:
wherein the content of the first and second substances,
and
respectively the speed and position of the ith particle at the kth iteration; c. C1And c2Respectively tracking the weight coefficients of a scheduling set containing the optimal adjustment quantity of the generator participating in scheduling and an active power output scheduling set containing the optimal adjustment quantity of the generator participating in scheduling for the particles; pbi kSearching a scheduling set containing the optimal adjustment quantity of the generator participating in scheduling for the particles i after k iterations; gb kFor the active power output dispatching set containing the optimal adjustment quantity of the generator participating in dispatching after k iterations, ξ and η are [0,1 ]]Random numbers uniformly distributed in the interval; ω represents the inertial weight coefficient of the particle to maintain the original velocity.
In order to expand the initial search range of the particle swarm optimization algorithm, the approximate position of an active power output scheduling set containing the optimal adjustment quantity participating in scheduling of a generator is firstly found out globally and quickly, then fine search is carried out locally, so that the convergence speed is accelerated, and the inertial weight coefficient adopts a linear decreasing strategy:
wherein, ω ismaxAnd ωminMaximum and minimum inertial weights, respectively; k is a radical ofnAnd kmaxCurrent and maximum number of iterations respectively.
In step 104, a current fitness value of each scheduling set is calculated by using an objective function with the minimum total cost of the active output adjustment of the generator added with secondary penalty terms, wherein the secondary penalty terms are a steady-state operation constraint condition and a transient stability constraint condition.
In an embodiment of the present aspect, a calculation formula of the objective function with the minimum total cost of the generator active power output adjustment as a target is as follows:
wherein, CUiThe up-spinning standby cost of the ith generator is saved; Δ GUiAllocating an up-regulation quantity for the i-th generator active power output; cDiThe down-spinning standby cost of the ith generator is saved; Δ GDiRedistributing a down-regulation quantity for the active power output of the ith generator; lambda [ alpha ]TA penalty factor for transient stability constraint violation; TSIiThe transient stability index of the ith generator is obtained; ε is a defined stability limit; lambda [ alpha ]GA penalty factor for the out-of-limit active output of the generator; gRiThe regulated active power output of the ith generator is obtained; gRi limIs the active output limit; lambda [ alpha ]PA penalty factor for branch power out-of-limit; plIs the active power flow of branch l; pl limThe active power flow limit value of the branch I; sGIs a collection of adjustable generators; sLIs a set of adjustable branches; and | is an absolute value.
In implementation, the optimal steady-state operation constraint conditions of the scheme are as follows:
Pl min≤Pl≤Pl maxl∈SL
wherein G isi maxThe upper limit of active output of the generator is set; gi minThe lower limit of active output of the generator is set; pl maxIs the branch power upper limit; pl minThe branch power lower limit.
Wherein the transient stability constraint is:
ε+Δε≤Φ(GO1+ΔG1,GO2+ΔG2,...,GOi+ΔGi)
wherein ε is definedA stability limit of (d); delta epsilon is a stable adjustment; Δ GiScheduling the adjustment amount of the generator for the ith participant; gOiThe original active power output of the ith participating scheduling generator; gOi+ΔGi＝GRi(ii) a Phi is BP neural network.
In an embodiment of the present invention, when the scheduling set updated by the particle swarm optimization does not satisfy the transient stability constraint condition, the scheduling set is deleted. By the method, the operation amount of the subsequent fitness value calculation and comparison process can be greatly reduced, so that the scheduling set with better adjustment amount is gradually reserved.
In step 105, it is determined whether the current fitness value is less than the fitness value of the last iteration.
In step 106, if the current fitness value is smaller than the optimal fitness value, updating a scheduling set containing the optimal adjustment quantity of the generator participating in scheduling by adopting the scheduling set corresponding to the current fitness value; otherwise, the scheduling set containing the optimal adjustment quantity participating in scheduling the generator is not updated.
When a plurality of scheduling sets containing the adjustment quantity of the generators participating in scheduling are generated by adopting Latin hypercube sampling, each scheduling set containing the adjustment quantity of the generators participating in scheduling is endowed with a scheduling set containing the optimal adjustment quantity of the generators participating in scheduling, all the generated scheduling sets have an active output scheduling set containing the optimal adjustment quantity of the generators participating in scheduling, and the active output scheduling set is the minimum value of all the scheduling sets containing the optimal adjustment quantity of the generators participating in scheduling.
In step 107, the active power output scheduling set including the optimal adjustment amount of the generator participating in the scheduling is updated by using the minimum value of all the scheduling sets including the optimal adjustment amount of the generator participating in the scheduling.
In step 108, when the number of updates of the active power dispatching set is equal to the set number of updates, the optimal adjustment amount of the generator participating in dispatching in the active power dispatching set updated for the last time is output.
In practice, the active output limit GRi limActive power flow limit P of sum branch ll limRespectively as follows:
in one embodiment of the present invention, the method for constructing the objective function with the minimum total cost of the generator active power output adjustment added with the secondary penalty term as the target is as follows:
acquiring operation scene data and transient stability index data of a power system as sample data for training a BP neural network
The active power output of the generator is used as an input vector of the BP neural network, and the minimum value of the transient stability index under each operation scene is selected as an output vector of the BP neural network;
optimizing the neural network by using a genetic algorithm:
s.t.w∈Rn×p,v∈Rp×m,θ∈Rn×p,r∈Rp×m
wherein E is the output error,
to desired output, ykOutputting the output of the neural network output layer; w is the weight from the input layer to the hidden layer; theta is the threshold from the input layer to the hidden layer; v is the weight from the hidden layer to the output layer; r is a real number set, n is the number of nodes of an input layer, p is the number of nodes of a hidden layer, R is a threshold value from the hidden layer to an output layer, and m is the number of nodes of the output layer.
Adopting a neural network to construct a transient stability constraint condition:
ε+Δε≤Φ(GO1+ΔG1,GO2+ΔG2,...,GOi+ΔGi)
wherein ε is a defined stability limit; delta epsilon is a stable adjustment; Δ GiScheduling the adjustment amount of the generator for the ith participant; gOiThe original active power output of the ith participating scheduling generator; gOi+ΔGi＝GRi(ii) a Phi is a BP neural network;
based on a rotating standby market, the minimum total cost of the active power output adjustment of the generator is established as an objective function:
adding the steady-state operation constraint condition and the transient stability constraint condition as secondary penalty terms into an objective function:
the operation scene data is generated by Latin hypercube sampling. Aiming at the active output range and the load capacity of each node of each adjustable generator in the power system, respectively generating sampling matrixes with the following forms:
wherein X is the active output or node load of the generator; n is the number of input random variables, and N is the sampling scale. Because the sampling values of the random variables in the preliminarily formed sampling matrix are arranged randomly, a certain arrangement method is required to reduce the correlation between the sampling values of each random variable.
The scheme utilizes a correlation coefficient matrix rhon×nTo represent matrix Xn×NCorrelation between rows:
ρ＝{ρiji＝1,2,…,n；j＝1,2,…,n}；
where ρ isijRepresents the correlation coefficient between the ith and jth rows:
wherein E isiAnd EjOther than the mathematical expectation of the ith and jth rows. Introducing a square root of a correlation coefficient matrix to analyze the correlation degree between rows of the matrix, wherein the correlation degree is expressed as follows:
rearranging the elements in the sampling matrix according to the permutation matrix generated by Cholesky decomposition method by constructing an approximately orthogonal permutation matrix LnNThe correlation is reduced by rearranging the position of the elements in the sampling matrix, while the size of each element is unchanged.
Permutation matrix LKNIs a matrix of order N × N, the element values of each row of which represent the sampling matrix XnNCorresponding to the arrangement position of the line elements. The method for constructing the arrangement matrix by using the Cholesky decomposition method comprises the following steps:
(1) initializing permutation matrix LnNEach row of which consists of a random arrangement of integers 1,2,3, …, N;
(2) let an arrangement matrix LnNIs rho, the matrix of correlation coefficients between the rows ofLIt is possible to prove ρLThe method is a positive definite symmetric matrix, so that a real nonsingular lower triangular matrix D can be obtained by decomposing the positive definite symmetric matrix by using a Cholesky decomposition method, and the following equation is satisfied:
ρL＝{ρLiji＝1,2,…,n；j＝1,2,…,n}＝DDT
since D is the inverse of the non-singular, the initial permutation matrix L is combinednNA matrix with less column correlation can be constructed as follows:
GnN＝D-1LnN
the resulting matrix GnNThe row correlation is less than the matrix LnNThus using GnNThe arrangement order of the medium elements indicates the arrangement position of the elements in the sampling matrix, and the process is repeated until the column correlation of the arrangement matrix is less than a predetermined value.
After the elements in the sampling matrix are rearranged by the arrangement matrix obtained by the method, corresponding elements in each sampling matrix are extracted to form different operation scene data.
By using the Latin hypercube sampling method, the expansion space of the sample points can be effectively expanded, and the operation scene covers the operation condition of the power system as much as possible, so that the sample quality is improved, and the trained BP neural network has better generalization capability.
Transient Stability Index (TSI) data is obtained through checking of an expected fault set, a TSI based on a power angle of a generator is selected to describe the Stability or the severity of a fault of a power system, and the indexes are defined as follows:
wherein, deltamaxRepresenting the maximum power angle difference between any two generators.
The active power output of the generator is selected as an input vector of the BP neural network, and the fault with the lowest or the most serious stability degree under each scene, namely the smallest TSI value, is selected as an output vector. The BP neural network obtained by training can directly map the nonlinear relation between the active power output and the transient stability index of the generator, and when the output scene of the generator changes, the TSI value of the scene with the most serious fault can be quickly estimated through the trained BP neural network, so that the transient stability of the power system is judged, and the aim of pre-fault set verification is fulfilled.
As shown in fig. 2, it shows a schematic structural diagram of a neural network; the relationship between the input and the output of the BP neural network is as follows:
wherein x isiInputting a training sample, namely a generator active output sample; h isjIs the hidden layer output; y iskOutputting for an output layer, namely TSI index samples; w is aijAnd thetaijRespectively input layer to hidden layerWeight and threshold value with layers, vjkAnd rjkRespectively, the weight and the threshold from the hidden layer to the output layer. Optimizing the neural network using a genetic algorithm yields the following mathematical model:
s.t.w∈Rn×p,v∈Rp×m,θ∈Rn×p,r∈Rp×m
in the formula, E is the output error,
to desired output, ykAnd outputting the output of the neural network output layer.
The BP neural network is optimized by adopting a genetic algorithm, so that the learning efficiency of the neural network can be improved, and the estimation precision is improved.
The effect of the method of the present solution is described below with reference to specific examples:
as shown in fig. 3, a study is performed by taking a 39-node power system of a new england 10 machine as an example, wherein the power system comprises 10 generators, 39 buses and 46 lines, and a number 39 node is a balance node.
And performing time domain simulation by using PSD-BPA software, and performing Latin hypercube sampling in a range of 85-115% of an initial operating point of the power system according to the load level and the active output of each generator to form a pre-fault power system operating mode of a sample, taking the active output of the generator as an example.
Fig. 4 is active power output scene data of any two generators, and it can be seen that the latin hypercube method can effectively expand the span-forming space of sample points, which is helpful for improving the generalization ability of a neural network. The generator out line (excluding the balancing nodes) and lines 16-17 are selected as the expected failure set, as shown in table 1.
Table 1 list of expected failure sets
The fault is set as a three-phase short-circuit fault occurring in the middle of the line, and the fault clearing time is correspondingly set according to the Critical Clearing Time (CCT) of each line. And (3) carrying out expected fault set verification on different power system operation scenes, calculating a transient stability index under each fault, and finally generating 1241 samples which contain the active power output of the generator under different operation scenes and the TSI value under each expected fault.
And (3) constructing a mapping relation between the active power output of the generator and the TSI aiming at the most serious expected fault by adopting the BP neural network, and taking the mapping relation as a transient stability constraint condition of the power system. The accuracy of the trained BP neural network is 96.58%, compared with the estimation accuracy of 94.31% of a simple BPNN, the BP neural network trained after parameter optimization by using a genetic algorithm has a better estimation effect.
The following is a detailed analysis of the preventive control effect of the method provided herein, taking two cases of single-fault instability and multiple-fault instability as examples.
Instability under single fault
In a new england 39 node testing power system, an initial operation scene of the power system is randomly generated, an expected fault set is verified for the power system (step 101 is implemented), and the result shows that after a fault 4 occurs, the power system is unstable, a generator is out of synchronization, and a power angle trajectory of the power system has a large deviation relative to an inertia center (center of inertia, COI), as shown in fig. 5. The transient stability index TSI calculation result of the initial operation point of the power system under the fault 6 is-96.77.
To ensure that the power system has sufficient stability margin, ε is taken to be equal to 10. By adopting the method, the power generation re-scheduling control strategy (the optimal adjustment quantity of the power generators participating in scheduling in the active output scheduling set updated for the last time) obtained by optimization solution is applied to the initial operating point of the power system, and the active output adjustment quantity of each power generator is shown in fig. 6.
The power angle locus of the generator when the power system operating point adjusted by the preventive control strategy encounters the fault 4 is shown in fig. 7, and it can be obviously seen that the power angle locus deviation gradually decreases with time, the power system tends to be stable, and the TSI index at this time is 56.54. The verification results for the set of expected faults are shown in fig. 8, where the gray and black parts represent the stability of the power system after each expected fault occurred before and after taking preventive control measures, respectively. It can be seen that the prevention control strategy formulated by the method of the scheme not only makes the original unstable fault 4 return to the stable boundary again, but also ensures that the transient stability under other expected faults is not affected. The generator adjustment cost under the condition of single fault instability is 7968.9 yuan, and the time spent on optimization solution is 26 s.
Instability under multiple faults
In anticipation of the situation that a plurality of faults are unstable during fault set verification, an effective prevention control means is required to adjust the operation point of the power system to a stable range, so that the power system can stably operate after each fault occurs.
In a new england 39 node testing power system, an initial operation scene of the power system is randomly generated, an expected fault set verification (transient stability judgment) is carried out on the power system, the verification result of the expected fault set shows that the power system is unstable after faults 1, 5, 6, 7, 8 and 9 respectively occur, and the TSI indexes of the power system are-94.00, -94.88, -94.73, -92.64, -63.92 and 52.96 respectively. The power angle trajectories of the generator after the faults 1, 5 and 6 occur are respectively shown in fig. 9(a), (e) and (g), while the power system can keep transient stability after the fault 2 occurs, the power angle trajectories are shown in fig. 9(c), and fig. 9(a), (c), (e) and (g) are the power angle trajectories of the generator when the fault occurs; 9(b), (d), (f) and (h) are all the power angle rails of the generator adjusted by the method after the faults occur.
The generator rescheduling control strategy under the condition of multiple fault instability is obtained by the same solving method, and the adjustment result of each generator is shown in fig. 10. The power angle locus of the generator under partial expected failure after the adjustment of the preventive control strategy is shown in fig. 9(b), (d), (f) and (h), and it can be seen that after the control measures are taken, the states of the power system after the failures 1, 5 and 6 occur are changed from instability to stability, while the transient stability can be maintained after the failure 2 occurs, and the fluctuation range of the power angle of the generator is reduced. Fig. 11 shows the expected failure set verification results of the power system before and after the prevention control, and it can be seen that the adjustment strategy can not only pull back the multi-failure instability condition to be stable, but also does not cause adverse effects on the transient stability under the remaining failures, so that the power system has a sufficient stability margin.
The TSI values of faults 1, 5, 6, 7, 8 and 9 after the power system takes preventive control measures are 39.75, 36.36, 37.46, 40.19, 48.07 and 50.49 respectively, and the corresponding generator adjustment cost is 9880.0 yuan. Compared with the instability condition under a single fault, the corresponding adjustment cost is higher because the stability requirement under more faults is required to be met. The optimal solution time in this case is 31s, and compared with a method based on trajectory sensitivity, the method has a faster solution speed under the condition that multiple fault instability can be handled.
It can be seen from the above results that the prevention control strategy (the last updated optimal adjustment amount of the active output scheduling concentrated participating in scheduling of the generator) obtained by the method of the present solution can ensure that the adjusted operating point of the power system is stable after each expected fault occurs, and has a fast solving speed and a certain economic benefit.
Claims (7)
1. The method for adjusting the active power output of the power system based on the transient stability constraint is characterized by comprising the following steps of:
performing expected fault set verification on the current operating condition of the power system, and judging the transient stability of the power system;
if the power system is transient instability, acquiring an initial scheduling set containing initial adjustment quantity of a generator participating in scheduling;
according to the initial scheduling set, adopting Latin hypercube sampling to generate a plurality of scheduling sets containing adjustment quantities participating in scheduling of the generator, and adopting a particle swarm algorithm to update each scheduling set;
calculating the current fitness value of each scheduling set by adopting an objective function which takes the minimum total cost of the active output adjustment of the generator added with a secondary penalty term as a target, wherein the secondary penalty term is a steady-state operation constraint condition and a transient stability constraint condition;
judging whether the current fitness value is smaller than the fitness value of the previous iteration:
if the current fitness value is smaller than the preset fitness value, updating a scheduling set containing the optimal adjustment quantity of the generator participating in scheduling by adopting the scheduling set corresponding to the current fitness value; otherwise, not updating the scheduling set containing the optimal adjustment quantity of the generator participating in scheduling;
updating the active power output scheduling set containing the optimal adjustment quantity of the generator participating in scheduling by adopting the minimum value in all the scheduling sets containing the optimal adjustment quantity of the generator participating in scheduling;
and when the updating times of the active output scheduling set are equal to the set updating times, outputting the optimal adjustment quantity of the generator participating in scheduling in the active output scheduling set updated for the last time.
2. The power system active power output adjustment method based on transient stability constraint according to claim 1, wherein the calculation formula of the objective function with the objective of minimizing the generator active power output adjustment total cost is as follows:
wherein, CUiThe up-spinning standby cost of the ith generator is saved; Δ GUiAllocating an up-regulation quantity for the i-th generator active power output; cDiThe down-spinning standby cost of the ith generator is saved; Δ GDiRedistributing a down-regulation quantity for the active power output of the ith generator; lambda [ alpha ]TA penalty factor for transient stability constraint violation; TSIiThe transient stability index of the ith generator is obtained; ε is a defined stability limit; lambda [ alpha ]GA penalty factor for the out-of-limit active output of the generator; gRiThe regulated active power output of the ith generator is obtained; gRi limIs the active output limit; lambda [ alpha ]PA penalty factor for branch power out-of-limit; plIs the active power flow of branch l; pl limThe active power flow limit value of the branch I; sGIs a collection of adjustable generators; sLIs a set of adjustable branches; and | is an absolute value.
3. The transient stability constraint-based power system active power output adjustment method according to claim 2, wherein the steady-state operation constraint conditions are:
Pl min≤Pl≤Pl maxl∈SL
wherein G isi maxThe upper limit of active output of the generator is set; gi minThe lower limit of active output of the generator is set; pl maxIs the branch power upper limit; pl minIs the branch power lower limit;
the transient stability constraints are:
ε+Δε≤Φ(GO1+ΔG1,GO2+ΔG2,...,GOi+ΔGi)
wherein ε is a defined stability limit; delta epsilon is a stable adjustment; Δ GiScheduling the adjustment amount of the generator for the ith participant; gOiThe original active power output of the ith participating scheduling generator; gOi+ΔGi＝GRi(ii) a Phi is BP neural network.
4. The transient stability constraint-based active power output adjustment method for the power system as recited in claim 3, wherein the scheduling set is deleted when the scheduling set updated by the particle swarm optimization does not satisfy the transient stability constraint condition.
5. The transient stability constraint-based active power output adjustment method for an electric power system according to claim 3, wherein the active power output limit G is set toRi limActive power flow limit P of sum branch ll limRespectively as follows:
6. the transient stability constraint-based power system active power output adjusting method according to claim 1, wherein the method for determining whether the power system is in transient stability comprises:
the method comprises the steps of checking an expected fault set of a current operation condition of the power system to obtain a power angle value of a generator after the fault is removed;
calculating the power angle difference between any two generators;
when the power angle difference between any two generators is smaller than 180 degrees, the power system is in transient stability;
when the power angle difference between any two generators is larger than 180 degrees, the power system is in transient instability.
7. The power system active output adjusting method based on transient stability constraint of claim 2, wherein the objective function with the minimum total cost of generator active output adjustment added with the secondary penalty term as a target is constructed by:
acquiring operation scene data and transient stability index data of the power system as sample data for training a BP neural network;
the active power output of the generator is used as an input vector of the BP neural network, and the minimum value of the transient stability index under each operation scene is selected as an output vector of the BP neural network;
optimizing the neural network by using a genetic algorithm:
s.t.w∈Rn×p,v∈Rp×m,θ∈Rn×p,r∈Rp×m
wherein E is the output error,
to desired output, ykOutputting the output of the neural network output layer; w is the weight from the input layer to the hidden layer; theta is the threshold from the input layer to the hidden layer; v is the weight from the hidden layer to the output layer; r is a real number set, n is the number of nodes of an input layer, p is the number of nodes of a hidden layer, R is a threshold value from the hidden layer to an output layer, and m is the number of nodes of the output layer;
adopting a neural network to construct a transient stability constraint condition:
ε+Δε≤Φ(GO1+ΔG1,GO2+ΔG2,...,GOi+ΔGi)
wherein ε is a defined stability limit; delta epsilon is a stable adjustment; Δ GiScheduling the adjustment amount of the generator for the ith participant; gOiThe original active power output of the ith participating scheduling generator; gOi+ΔGi＝GRi(ii) a Phi is a BP neural network;
based on a rotating standby market, the minimum total cost of the active power output adjustment of the generator is established as an objective function:
adding the steady-state operation constraint condition and the transient stability constraint condition as secondary penalty terms into an objective function:
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Non-Patent Citations (4)
| Title |
|---|
| Novel pair-wise relative energy function for transient stability analysis and real-time emergency control;Jing Gou 等;《IET Generation,Transmission & Distribution》;20170821;第11卷(第18期);4565-4575 * |
| 多预想故障暂态稳定约束最优潮流;韦化 等;《中国电机工程学报》;20041031;第24卷(第10期);91-96 * |
| 暂态稳定预防控制和优化新进展;甘德强 等;《电力系统自动化》;20040525;第28卷(第10期);1-5 * |
| 电力系统暂态稳定预防控制算法综述;吕晓祥 等;《电力系统保护与控制》;20131016;第41卷(第20期);144-153 * |
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