A method for finding the optimal leader node of voltage control based on proxy optimization algorithm

Abstract

The present invention discloses a voltage control leader node optimization method based on a proxy optimization algorithm, which belongs to the field of power grid operation and control. The voltage control leader node optimization method based on the proxy optimization algorithm comprises: first inputting flow data, partition information and a secondary voltage control algorithm; then initializing a point in each area through sampling, and calculating the voltage control target function at the initialization point as a sample for the first step of fitting; finally, based on the fitted initial sample, finding a new fitting sample, adding the previous sample to form a new sample, looping until the number limit is reached, outputting the optimal value of the fitting function and the optimal sample as the optimal leader node combination, and effectively improving the quality of voltage control.

Description

Technical Field

[0001] The present invention belongs to the field of power grid operation and control, and in particular relates to a voltage control leader node optimization method based on an agent optimization algorithm. Background Art

[0002] At present, the load characteristics and fluctuations in the power system are constantly increasing, which brings new challenges to the voltage stability of the power grid. Voltage stability mainly relies on voltage control, and the selection of pilot nodes determines the quality of voltage control. Traditional voltage control pilot node selection technology not only has a long operation time but also makes it difficult to find the optimal pilot node, which will be detrimental to the voltage stability of the power grid. Summary of the Invention

[0003] In view of the shortcomings of the existing technology, the purpose of the present invention is to provide a voltage control leader node optimization method based on a proxy optimization algorithm, which improves the selection efficiency of the leader node while ensuring the quality of voltage control.

[0004] The purpose of the present invention can be achieved through the following technical solutions:

[0005] A method for optimizing a voltage control leader node based on a proxy optimization algorithm comprises the following steps:

[0006] S1, data input; the input data includes power flow data, partition information, and secondary voltage control algorithm;

[0007] S2, initialization of sampling and fitting; through sampling, a point is initialized in each region, and the voltage control objective function at the initialization point is calculated as the sample for the first step of fitting;

[0008] S3, fitting loop; according to the initial sample of the fitting, find a new fitting sample, and add the previous sample to form a new sample, loop until the upper limit of the number of times is reached, output the best value of the fitting function and the best sample as the best leader node combination.

[0009] Furthermore, in S1, the secondary voltage control algorithm is as follows:

[0010]

[0011] Where, ΔV p is the leader node voltage deviation, ΔV c is the voltage correction value of the controlled generator terminal, Q c is the reactive output of the controlled generator, α and β are weight factors;

[0012] Converting formula (3-1) into the least squares form is as follows:

[0013]

[0014]

[0015] C p =dQ g dV g (i PQG ,i PQG )C pv =dQ g dV g (i AVR ,i PQG )

[0016] C pqv =dV g dV l (i PLT ,i PQG )C pq =dV g dV l (i LOD ,i PQG )

[0017] C1=C pqv C2=C p1 -C p2 (3-3)

[0018]

[0019] A 2u =C pq A 2l =-C pq

[0020] A 3u =C pv A 3l =-C pv

[0021] A 4u =C p A 4l =-C p (3-4)

[0022]

[0023]

[0024] b 3u(k) =(Q GmaxAVR -Q GAVR(k) ) / S base b 3l(k) =(Q GAVR(k) -Q GmaxAVR ) / Sbase

[0025] b 4u(k) =Q GmaxPQG / S base -Q p(k) b 4l(k) =Q p(k) -Q GminPQG / S base (3-5)

[0026] d1=freq·(V PLT0 -V PLT(k) ),d2=Q p2 -Q p1 (3-6)

[0027] A=[A 1u A 1l A 2u A 2l A 3u A 3l A 4u A 4l ]

[0028] b=[b 1u(k) b 1l(k) b 2u(k) b 2l(k) b 3u(k) b 3l(k) b 4u(k) b 4l(k) ] (3-7)

[0029]

[0030]

[0031] Among them, dQ g dV g _1 is the reactive-voltage sensitivity matrix between generators after disturbance and before voltage control, dV g dV l_1 is the sensitivity matrix between the generator voltage and the node voltage after the disturbance and before voltage control, Q max_PQG and Q min_PQG are the upper and lower limit vectors of reactive power output of non-PQ node generators, 1 represents a matrix or vector whose elements are all 1, 0 represents a matrix or vector whose elements are all 0, I represents the unit matrix, V hi and V low Respectively represent the upper and lower limits of the node voltage, V PQG , V PLT , V LOD , V BUSrepresents the set of voltages of non-PQ generator nodes, pilot nodes, all load nodes, and all nodes during the control algorithm cycle, V PLT_0 is the voltage amplitude of the leading node before the disturbance occurs, the subscript of i represents the index of the node of different group types, i PQG represents a non-PQ generator node; i PLT Indicates the leading load node, which must be a PQ node; i LOD It represents the PQ load node of the non-leader node, and freq is the control step size.

[0032] Furthermore, formula (3-1) obeys the following conditions:

[0033]

[0034]

[0035] Among them, V PQ is the voltage of the non-leading load node, Q PV is the reactive power output of the uncontrolled PV generator node.

[0036] Furthermore, the node voltage of the uncontrolled PV generator remains unchanged during the control process.

[0037] Furthermore, the freq takes a value of 0.1.

[0038] Furthermore, in S2, sampling is performed using Latin hypercube sampling.

[0039] Furthermore, the power flow data includes line impedance, node voltage and node phase angle

[0040] Beneficial effects of the present invention:

[0041] 1. Through the heuristic algorithm, a more scientific and reasonable pilot node can be obtained than the sensitivity method, ensuring the quality of voltage control;

[0042] 2. Based on the agent optimization algorithm, its essence is a neural network fitting, which is faster than the traditional pure integer programming and can reduce the running time. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0044] Figure 1 It is the algorithm flow chart of the present invention. DETAILED DESCRIPTION

[0045] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0046] like Figure 1 As shown, a voltage control leader node optimization method based on a proxy optimization algorithm includes the following steps:

[0047] S1, data input;

[0048] The input data includes power flow data (line impedance, node voltage, node phase angle), partition information (node numbers contained in each area), and the secondary voltage control algorithm;

[0049] Among them, the algorithm of secondary voltage control is as follows:

[0050]

[0051]

[0052]

[0053] Where, ΔV p is the leader node voltage deviation, ΔV c is the voltage correction value of the controlled generator terminal (optimization variable), Q c is the reactive output of the controlled generator, V PQ is the voltage of the non-leading load node, Q PV is the reactive power output of the uncontrolled PV generator node (the terminal voltage is kept constant during the control process), α and β are weight factors;

[0054] Converting formula (3-1) to the least squares form is as follows:

[0055]

[0056]

[0057] C p =dQ g dV g (i PQG ,i PQG )C pv =dQ g dV g (i AVR ,i PQG )

[0058] C pqv =dV g dV l (i PLT ,i PQG )C pq =dV g dV l (i LOD ,i PQG )

[0059] C1=C pqv C2=C p1 -C p2 (3-3)

[0060]

[0061] A 2u =C pq A 2l =-C pq

[0062] A 3u =C pv A 3l =-C pv

[0063] A 4u =C p A 4l =-C p (3-4)

[0064]

[0065]

[0066] b 3u(k) =(Q GmaxAVR -Q GAVR(k) ) / S base b 3l(k) =(Q GAVR(k) -Q GmaxAVR ) / S base

[0067] b 4u(k) =Q GmaxPQG / S base -Q p(k) b 4l(k) =Q p(k) -Q GminPQG / S base (3-5)

[0068] d1=freq·(V PLT0 -V PLT(k)),d2=Q p2 -Q p1 (3-6)

[0069] A=[A 1u A 1l A 2u A 2l A 3u A 3l A 4u A 4l ]

[0070] b=[b 1u(k) b 1l(k) b 2u(k) b 2l(k) b 3u(k) b 3l(k) b 4u(k) b 4l(k) ] (3-7)

[0071]

[0072]

[0073] Among them, dQ g dV g _1 is the reactive-voltage sensitivity matrix between generators after disturbance and before voltage control, dV g dV l_1 is the sensitivity matrix between the generator voltage and the node voltage after the disturbance and before voltage control, Q max_PQG and Q min_PQG are the upper and lower limit vectors of reactive power output of non-PQ node generators, 1 represents a matrix or vector whose elements are all 1, 0 represents a matrix or vector whose elements are all 0, I represents the unit matrix, V hi and V low Respectively represent the upper and lower limits of the node voltage, V PQG , V PLT , V LOD , V BUS represents the set of voltages of non-PQ generator nodes, pilot nodes, all load nodes, and all nodes during the control algorithm cycle, V PLT_0 is the voltage amplitude of the leading node before the disturbance occurs, the subscript of i represents the index of the node of different group types, i PQG represents a non-PQ generator node; i PLT Indicates the leading load node, which must be a PQ node; i LOD It represents the PQ load node that is not the leader node. freq is the control step size, which is generally 0.1.

[0074] S2, initialization of sampling and fitting;

[0075] Using methods such as Latin hypercube sampling, a point is initialized in each region, and the voltage control objective function at the initialization point is calculated as the sample for the first step of fitting.

[0076] S3, fitting cycle;

[0077] Based on the initial fitting sample, new fitting samples are found according to certain rules, and the previous samples are added to form new samples. The cycle is repeated until the upper limit of the number of times is reached, and the optimal value and optimal sample of the fitting function are output as the optimal leading node combination.

[0078] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0079] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention, and such changes and modifications fall within the scope of the invention as claimed.

Claims

1. A voltage control pilot node optimization method based on a proxy optimization algorithm, characterized in that: The following steps are involved: S1, data input; the input data includes power flow data, partition information, and secondary voltage control algorithm; S2, initialization of sampling and fitting; through sampling, a point is initialized in each region, and the voltage control objective function at the initialization point is calculated as the sample for the first step of fitting; S3, fitting loop; based on the initial sample of the fitting, find a new fitting sample and add the previous sample to form a new sample, loop until the number of times reaches the upper limit, output the best value of the fitting function and the best sample as the best leader node combination; In S1, the secondary voltage control algorithm is as follows: Where, ΔV p is the leader node voltage deviation, ΔV c is the voltage correction value of the controlled generator terminal, Q c is the reactive output of the controlled generator, α and β are weight factors; Converting formula (3-1) into the least squares form is as follows: Among them, dQ g dV g _1 is the reactive-voltage sensitivity matrix between generators after disturbance and before voltage control, dV g dV l_1 is the sensitivity matrix between the generator voltage and the node voltage after the disturbance and before voltage control, Q max_PQG and Q min_PQG are the upper and lower limit vectors of reactive power output of non-PQ node generators, 1 represents a matrix or vector whose elements are all 1, 0 represents a matrix or vector whose elements are all 0, I represents the unit matrix, V hi and V low Respectively represent the upper and lower limits of the node voltage, V PQG , V PLT , V LOD , V BUS represents the set of voltages of non-PQ generator nodes, pilot nodes, all load nodes, and all nodes during the control algorithm cycle, V PLT_0 is the voltage amplitude of the leading node before the disturbance occurs, the subscript of i represents the index of the node of different group types, i PQG represents a non-PQ generator node; i PLT Indicates the leading load node, which must be a PQ node; i LOD It represents the PQ load node of the non-leader node, and freq is the control step size.

2. The voltage control leader node optimization method based on the proxy optimization algorithm according to claim 1 is characterized in that: Formula (3-1) obeys the following conditions: Among them, V PQ is the voltage of the non-leading load node, Q PV is the reactive power output of the uncontrolled PV generator node.

3. The voltage control leader node optimization method based on the proxy optimization algorithm according to claim 2 is characterized in that: The node voltage of the uncontrolled PV generator remains unchanged during the control process.

4. The voltage control leader node optimization method based on the proxy optimization algorithm according to claim 1 is characterized in that: The freq value is 0.

1.

5. The voltage control leader node optimization method based on the proxy optimization algorithm according to claim 1 is characterized in that: In S2, sampling is performed using the Latin hypercube sampling method.

6. The voltage control leader node optimization method based on the proxy optimization algorithm according to claim 1, characterized in that: The power flow data in S1 includes line impedance, node voltage and node phase angle.

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