An AGC collaborative control optimization method and system considering tie-line power stability

Abstract

The present disclosure belongs to the technical field of power systems, and provides an AGC collaborative control optimization method and system considering tie-line power stability, including the following steps: obtaining the original data of the power grid and the parameter information of the tie-line, and constructing an interconnected system power flow model considering AGC power distribution; based on the constructed interconnected system power flow model, taking the minimum amplitude of power fluctuation on the tie-line as the objective function, establishing a multi-region stability control model based on robust optimization; performing optimization solution of the multi-region stability control model based on robust optimization to achieve AGC collaborative control optimization.

Description

Technical Field

[0001] The present disclosure belongs to the technical field of power systems, and particularly relates to an AGC collaborative control optimization method and system considering tie-line power stability. Background Art

[0002] The statements in this part merely provide background technical information related to the present disclosure and do not necessarily constitute prior art.

[0003] With the continuous increase in the penetration ratio of new energy, the uncertainty of new energy further exacerbates the fluctuation amplitude of tie-line power between regions. Moreover, there are problems of incomplete and untimely exchange of operating state information between power grids in each region, forming an "information barrier" to a certain extent, which poses a great challenge to the control of tie-line power between multiple regions. When the exchange power fluctuates too much, the operating conditions of the adjacent regional power grids will fluctuate greatly, which will further lead to excessive power exchange borne by the sending and receiving-end power grids, and it is difficult to evacuate the power flow, and ultimately may cause safety and stability problems. In particular, when the power distribution of multiple tie-lines is unbalanced, and the fluctuation amplitude of the power of the heavily loaded tie-line is too large, exceeding the limit value, or even forced to stop operation, the power flow transfer between tie-lines may trigger a chain of failures, resulting in a large power outage. Automatic generation control can not only ensure the power balance within the region, but also better stabilize the fluctuation of section exchange power and ensure the safe and stable operation of the interconnected system. Therefore, automatic generation control (AGC for short) can effectively address the problem of large fluctuations in tie-line power between regions.

[0004] In order to suppress the tie-line power fluctuation caused by the grid connection of the new energy system, a large number of related studies have been carried out by domestic and foreign scholars. The existing research results mainly consider maximizing the economic indicators of regional interconnection, while the research on the power fluctuation stability of tie-lines between regions is still in the exploratory stage and there are few research results. Regarding the research on tie-line power control, relevant literature has proposed a multi-region power flow external characteristic stability control model, but it does not consider the influence of the branch impedance on the regional tie-line, which will cause the reference phase angle between regions to shift with the power transfer. At present, the actual operation in the power system adopts non-optimized control, optimizing the AGC control scheme inside the power grid, which belongs to indirectly controlling the tie-line power rather than directly controlling it; currently, the problem of the interconnection of local regional power grids is still in the theoretical research stage. Summary of the Invention

[0005] To solve the above problems, the present disclosure proposes an AGC collaborative control optimization method and system considering tie-line power stability, introduces AGC control into the power flow equation, and constructs a power flow model of an interconnected system considering AGC power distribution. Based on this, a robust optimization model with tie-line power stability as the objective function and a relaxation evolution solution algorithm based on a multi-population genetic algorithm are established. This method can ensure the stability of tie-line power under the worst conditions, and the solution algorithm has a fast calculation speed and high solution efficiency, providing a theoretical basis for ensuring the safe and stable operation of the power grid.

[0006] According to some embodiments, the first solution of the present disclosure provides an AGC collaborative control optimization method considering tie-line power stability, and adopts the following technical solutions:

[0007] An AGC collaborative control optimization method considering tie-line power stability includes the following steps:

[0008] Obtain the original data of the power grid and the parameter information of the tie-line, and construct a power flow model of an interconnected system considering AGC power distribution;

[0009] Based on the constructed power flow model of the interconnected system, with the minimum amplitude of power fluctuation on the tie-line as the objective function, establish a multi-region stability control model based on robust optimization;

[0010] Carry out the optimization solution of the multi-region stability control model based on robust optimization to realize the AGC collaborative control optimization.

[0011] As a further technical limitation, the parameter information of the tie-line at least includes iteration convergence conditions, the number of iterations, tie-line node voltage, and line resistance.

[0012] As a further technical limitation, in the process of constructing a power flow model of an interconnected system considering AGC power distribution, set the active power exchange between each sub-region and the external region as a fixed value, regard the regulation effect of AGC as the collaborative action of multiple balance machines in the system to distribute the unbalanced power according to the distribution factor, then introduce the unbalanced power distribution coefficient into the conventional power flow model to obtain the power balance equation and the section power flow equation, and combine the obtained power balance equation and the section power flow equation to obtain a power flow model of an interconnected system considering AGC power distribution.

[0013] As a further technical limitation, the constraint conditions of the multi-region stability control model based on robust optimization include power balance constraint, upper and lower limits of generator output, generator output ramp constraint, upper and lower limits of new energy output, short-term change range constraint of new energy, and line power constraint.

[0014] Further, when all the constraint conditions are satisfied simultaneously, the objective function is calculated to minimize the amplitude of power fluctuations on the tie line.

[0015] As a further technical limitation, a multi - population genetic algorithm relaxation evolution algorithm is used to solve the established multi - area stability control model based on robust optimization.

[0016] Further, the power balance constraint and line power constraint in the established multi - area stability control model based on robust optimization are linearized, and then the objective function is relaxed. The multi - population genetic algorithm is used to improve the relaxation evolution algorithm, and the improved evolution algorithm is used to repeatedly solve the established multi - area stability control model based on robust optimization until the objective function reaches a stable optimal solution.

[0017] According to some embodiments, the second solution of the present disclosure provides an AGC cooperative control optimization system considering tie - line power stability, adopting the following technical solution:

[0018] An AGC cooperative control optimization system considering tie - line power stability, comprising:

[0019] A modeling module, configured to obtain the original data of the power grid and the parameter information of the tie line, construct an interconnected system power flow model considering AGC power distribution; based on the constructed interconnected system power flow model, with the minimum amplitude of power fluctuations on the tie line as the objective function, establish a multi - area stability control model based on robust optimization;

[0020] An optimization module, configured to perform optimization and solution of the multi - area stability control model based on robust optimization to achieve AGC cooperative control optimization.

[0021] According to some embodiments, the third solution of the present disclosure provides a computer - readable storage medium, adopting the following technical solution:

[0022] A computer - readable storage medium, on which a program is stored, and when the program is executed by a processor, it implements the steps in the AGC cooperative control optimization method considering tie - line power stability as described in the first aspect of the present disclosure.

[0023] According to some embodiments, the fourth solution of the present disclosure provides an electronic device, adopting the following technical solution:

[0024] An electronic device, comprising a memory, a processor, and a program stored on the memory and executable on the processor, and when the processor executes the program, it implements the steps in the AGC cooperative control optimization method considering tie - line power stability as described in the first aspect of the present disclosure.

[0025] Compared with the prior art, the beneficial effects of the present disclosure are as follows:

[0026] Under the condition of high proportion of new energy accessing the power grid, based on the robust model and algorithm with the smallest tie-line power fluctuation, without the interconnection of regional information, the regulation of AGC is considered and the deviation of the calculation result caused by the traditional model ignoring the influence of the branch impedance on the regional tie-line is avoided; the power grid safety and stability are enhanced with the smallest tie-line power fluctuation, and the proposed solution algorithm is simple, easy to implement and has good stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The accompanying drawings forming a part of this disclosure are used to provide a further understanding of the present disclosure. The schematic embodiments and descriptions thereof of the present disclosure are used to explain the present disclosure and do not constitute an improper limitation to the present disclosure.

[0028] Figure 1 It is a flowchart of the AGC collaborative control optimization method considering tie-line power stability in the first embodiment of the present disclosure;

[0029] Figure 2 It is a schematic structural diagram of the power flow change of the regional interconnected network in the first embodiment of the present disclosure;

[0030] Figure 3 It is a flowchart of the solution algorithm of the robust optimization model of the interchange power of the interconnected power grid considering the uncertainty of new energy in the first embodiment of the present disclosure;

[0031] Figure 4 It is a structural block diagram of the AGC collaborative control optimization system considering tie-line power stability in the second embodiment of the present disclosure. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0032] The present disclosure will be further described below in conjunction with the accompanying drawings and embodiments.

[0033] It should be noted that the following detailed description is illustrative and is intended to provide further explanation of the present disclosure. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present disclosure belongs.

[0034] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present disclosure. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or combinations thereof.

[0035] Without conflict, the embodiments in the present disclosure and the features in the embodiments may be combined with each other.

[0036] Embodiment 1

[0037] Embodiment 1 of the present disclosure introduces an AGC collaborative control optimization method considering tie-line power stability.

[0038] As Figure 1 and Figure 3 shown, an AGC collaborative control optimization method considering tie-line power stability includes the following steps:

[0039] Step S01: Obtain the original data of the power grid and the parameter information of the tie-line, and construct an interconnected system power flow model considering AGC power distribution;

[0040] Step S02: Based on the constructed interconnected system power flow model, with the minimum amplitude of power fluctuation on the tie-line as the objective function, establish a multi-region stability control model based on robust optimization;

[0041] Step S03: Conduct optimization and solution of the multi-region stability control model based on robust optimization to achieve AGC collaborative control optimization.

[0042] In step S01, input the original data of the power grid and the relevant parameter information of the tie-line, set the iterative convergence condition, initialize the number of iterations, node voltage, and line resistance; then establish an interconnected system power flow model considering AGC power distribution, specifically as follows:

[0043] Assume that the active power exchange between each sub-region and the external region is a fixed value, and regard the regulation effect of AGC as the collaborative action of multiple balanced machines in the system according to a ratio (distribution factor) to share the unbalanced power, as shown in the following formula:

[0044] μ = [μ 1 μ 2 L μ D T (1)

[0045] where D is the total number of sub-regions; μ i represents the total active power obtained by the i-th sub-region from other sub-regions, with power received being positive and power sent being negative.

[0046] The total number of all buses in the multi-region power grid is represented by n, and the participation factor matrix can be obtained as shown in the following formula:

[0047]

[0048] where, α ik ​The element corresponding to the \(i\)-th row (area number) and the \(k\)-th column (bus node number) of the matrix, which is the participation factor of the \(k\)-th sub-area and the \(i\)-th bus in AGC. If the \(i\)-th bus is not in the \(k\)-th sub-area, then \(\alpha\) ik = 0; if the \(i\)-th bus is in the \(k\)-th sub-area but does not participate in AGC regulation, then \(\alpha\) ik = 0; if the \(i\)-th bus is in the \(k\)-th sub-area and participates in AGC regulation, then \(\alpha\) ik ≠ 0.

[0049] The elements of each column of the matrix, that is, the AGC distribution factors within each area, satisfy the following relationship:

[0050]

[0051] Furthermore, use \(\beta\) i to represent the adjustment amount of the active power output of the generator at bus \(i\) for the unbalanced power, as shown in the following formula:

[0052]

[0053] Introduce the unbalanced power distribution coefficient into the conventional power flow model to obtain the power balance equation:

[0054]

[0055] where \(m\) is the number of all PQ buses in the multi-area network; \(V\) and \(\theta\) are the vector sets of the voltage amplitudes and phase angles of all buses; \(P\) j and \(Q\) i are the injected active power and reactive power; \(f\) represents the active and reactive power balance equations; and are specifically expressed as shown in the following formula:

[0056]

[0057] where \(G\) ij and \(B\) ij are the mutual conductance and mutual susceptance between bus \(i\) and bus \(j\) respectively; \(\theta\) ij = \(\theta\) i - \(\theta\) j is the voltage phase angle difference between bus \(i\) and bus \(j\).

[0058] The cross-section control of the inter-area power flow is completed through AGC to form the cross-section power flow equation.

[0059]

[0060] where \(g = [g\) 1 , \(g\) 2 , …, \(g\) D ​T ; L is the total number of inter - area tie - lines; the power flow section equation of the d - th sub - area is shown as follows:

[0061]

[0062] where, is a D×L matrix. P l t is the active power flowing through the l - th tie - line, and the specific expression is shown as formula (9):

[0063] P l t =V i 2 G l -V i V j (G l cosθ ij +B l sinθ ij ) (9)

[0064] corresponding to the element in the d - th row and l - th column of the matrix is shown as formula (10):

[0065]

[0066] By combining formula (5) and formula (7), the unified multi - area power flow model under AGC control is shown as formula (11):

[0067]

[0068] X = [V, θ, μ] (11)

[0069] The transmission power between area A and area B can be linearly represented as shown in formula (12):

[0070]

[0071] where, X 1 、X 2 、X 3 and P 1 、P 2 、P 3 are the reactance values and the active powers flowing through the boundary tie - lines A1 - B1, A2 - B2, A3 - B3 respectively; θ A 、θ B are the reference node phase angles in area A and area B respectively; θ A1 、θ A2 、θA3 and θ B1 、θ B2 、θ B3 are the phase angle differences between the boundary nodes of regions A and B and the reference nodes within the regions, respectively.

[0072] It can be seen from formula (12) that the power flow fluctuations on the regional tie lines are mainly affected by the phase angle changes of the boundary nodes within the sub-regions. As Figure 2 shown, when the phase angle in region B fluctuates, it will also cause the phase angle in region A to change. When performing multi-region calculations, considering the cases of information confidentiality or information transfer lag between intervals, for a certain sub-region (taking region A as an example), the power angle change information of the boundary nodes outside the region (region B) cannot be obtained. Based on this, this paper first ignores the power angle changes of the nodes outside the region and sets them as constants when performing independent calculations within the sub-region, and combines the boundary phase angle adjustment strategy to gradually reduce the error and achieve the progressive matching of multi-region power flow.

[0073] Therefore, when calculating in this embodiment, the electrical parameters of region A are taken as unknowns, and let Δθ A1 = 0. At this time, the calculation formula for the magnitude of the power change on the tie line is as shown in formula (13):

[0074]

[0075] In step S02, the power flow model of the interconnected system considering AGC power distribution established in step S01 is combined with the robust optimization problem to establish a multi-region stability control model based on robust optimization. At this time, the objective function of the robust optimization problem is:

[0076]

[0077] In the formula, ΔP t is the change in the active power flowing on all tie lines between the sub-region and other sub-regions; P r is the possible change in the new energy generation output within the studied sub-region; the matrix α is the participation factor matrix of all generators participating in AGC within the region.

[0078] When studying a certain sub-region, it is assumed that there is no power disturbance in other systems outside the region, and let Δθ A = 0 in formula (13); under this assumption, after linearizing the active power on the tie line according to formula (13), it can be expressed as shown in formula (15):

[0079]

[0080] In the formula,

[0081]

[0082] Among them, ρ i is the active power variation of the i-th regional tie line. If the i-th tie line is connected to the sub-region under study, it is calculated according to formula (16); otherwise, it is taken as 0; Δθ i is the phase angle variation between the i-th regional tie line and the connected node within the sub-region to be studied.

[0083] The constraint conditions of the robust optimization problem are as follows:

[0084] Power balance constraint: Based on formula (5), the power balance constraint can be described as F s , as shown in the following formula.

[0085]

[0086] X = [V, θ, μ] (17)

[0087] Generator output upper and lower limit constraints:

[0088]

[0089] Among them, P i,t , and respectively represent the active power, minimum output power, and maximum output power injected by the generator at node i into the power grid; ΔP i,t represents the adjustment amount of the active power injected by the generator at node i into the power grid.

[0090] Generator output ramp rate constraints:

[0091]

[0092] Among them, is the maximum negative value of the downward adjustment of the generator output at time t compared with time t - 1. is the maximum value of the upward adjustment of the generator output at time t compared with time t - 1. And and have equal absolute values.

[0093] Renewable energy output upper and lower limit constraints:

[0094]

[0095] Among them, P' i,t , and respectively represent the active power, minimum output power, and maximum output power injected by the renewable energy at node i into the power grid; ΔP' i,t represents the variation of the active power injected by the renewable energy at node i into the power grid.

[0096] Constraints on short - term variation range of new energy:

[0097]

[0098] Among them, and respectively represent the upper and lower limits of possible changes in new energy at node i per unit time.

[0099] Line power constraint:

[0100]

[0101] Among them, f, respectively represent the active power flow of the network branch and the set of upper limits of active power flow. Each element f in the vector f ij The specific expression is shown in Equation (23), representing the active power transmitted between node i and node j.

[0102] f ij = V i V j (G ij cosθ ij + B ij sinθ ij ) (23)

[0103] When the above six constraint conditions are met, calculating the objective function can minimize the power fluctuation of the system tie - line while enhancing the security and stability of the power grid under various security constraints of the power grid.

[0104] In step S03, a relaxation evolution algorithm based on a multi - population genetic algorithm is used to solve the robust optimization model established in step S02.

[0105] First, linearize the power balance constraint and the line power constraint in the robust optimization model, then relax the objective function, improve the relaxation evolution algorithm using a multi - population genetic algorithm, and repeatedly solve the model with the improved evolution algorithm until the objective function reaches a stable optimal solution.

[0106] In this embodiment, under the condition of high - proportion new energy access to the power grid, based on the robust model and algorithm with the minimum tie - line power fluctuation, without regional information inter - communication, considering the regulation of AGC and avoiding the deviation of calculation results caused by the traditional model ignoring the influence of branch impedance on the regional tie - line, this model enhances the security and stability of the power grid with the minimum tie - line power fluctuation, and the proposed solution algorithm is simple, easy to implement and has good stability.

[0107] Embodiment 2

[0108] Embodiment 2 of the present disclosure introduces an AGC collaborative control optimization system considering tie-line power stability.

[0109] As Figure 4 shown, an AGC collaborative control optimization system considering tie-line power stability includes:

[0110] A modeling module, configured to obtain the original data of the power grid and the parameter information of the tie-line, and construct an interconnected system power flow model considering AGC power distribution; based on the constructed interconnected system power flow model, with the minimum amplitude of power fluctuation on the tie-line as the objective function, establish a multi-region stability control model based on robust optimization;

[0111] An optimization module, configured to perform optimization solution of the multi-region stability control model based on robust optimization to achieve AGC collaborative control optimization.

[0112] The detailed steps are the same as those of the AGC collaborative control optimization method considering tie-line power stability provided in Embodiment 1, and will not be elaborated here.

[0113] Embodiment 3

[0114] Embodiment 3 of the present disclosure provides a computer-readable storage medium.

[0115] A computer-readable storage medium, on which a program is stored, and when the program is executed by a processor, it implements the steps in the AGC collaborative control optimization method considering tie-line power stability as described in Embodiment 1 of the present disclosure.

[0116] The detailed steps are the same as those of the AGC collaborative control optimization method considering tie-line power stability provided in Embodiment 1, and will not be elaborated here.

[0117] Embodiment 4

[0118] Embodiment 4 of the present disclosure provides an electronic device.

[0119] An electronic device includes a memory, a processor, and a program stored on the memory and executable on the processor. When the processor executes the program, it implements the steps in the AGC collaborative control optimization method considering tie-line power stability as described in Embodiment 1 of the present disclosure.

[0120] The detailed steps are the same as those of the AGC collaborative control optimization method considering tie-line power stability provided in Embodiment 1, and will not be elaborated here.

[0121] Although the specific embodiments of the present disclosure have been described above in conjunction with the accompanying drawings, they are not limitations on the protection scope of the present disclosure. Those skilled in the art should understand that, based on the technical solutions of the present disclosure, various modifications or deformations that can be made by those skilled in the art without creative efforts are still within the protection scope of the present disclosure.

Claims

1. An AGC collaborative control optimization method considering tie-line power stability, characterized in that, it includes the following steps: Obtain the original data of the power grid and the parameter information of the tie-line, and construct an interconnected system power flow model considering AGC power distribution; Based on the constructed interconnected system power flow model, with the minimum amplitude of power fluctuation on the tie-line as the objective function, establish a multi-region stability control model based on robust optimization; Carry out the optimization solution of the multi-region stability control model based on robust optimization to realize the AGC collaborative control optimization; The objective function is: Among them, ΔP t is the change in the active power flowing on all the connecting lines between the sub-region and other sub-regions; P r is the change in the new energy power generation output within the sub-region under study; the matrix α is the participation factor matrix of all generators participating in AGC within the region; When studying a sub-region, there is no power disturbance in other systems outside the region. After linearizing the active power of the tie-line, we get: where ρ i is the active power change of the i-th regional tie line. If the i-th tie line is connected to the sub-region under study, it is calculated according to the formula; otherwise, it is taken as 0; Δθ i is the phase angle change of the nodes connected within the sub-region to be studied and the i-th regional tie line; The constraint conditions of the multi-region stability control model based on robust optimization include power balance constraints, upper and lower limits of generator output, generator output ramp constraints, upper and lower limits of new energy output, short-term change range constraints of new energy, and line power constraints; Under the condition of simultaneously satisfying all constraint conditions, calculate the objective function to minimize the amplitude of power fluctuation on the tie-line; Use a multi-population genetic algorithm relaxation evolution algorithm to solve the established multi-region stability control model based on robust optimization.

2. An AGC collaborative control optimization method considering tie-line power stability as described in claim 1, characterized in that, the parameter information of the tie-line includes at least iteration convergence conditions, iteration times, tie-line node voltage, and line resistance.

3. An AGC collaborative control optimization method considering tie-line power stability as described in claim 1, characterized in that, In the process of constructing an interconnected system power flow model considering AGC power distribution, set the active power exchange between each sub-region and the external region as a fixed value, regard the regulation effect of AGC as the collaborative action of multiple balance machines in the system to distribute the unbalanced power according to the distribution factor, then introduce the unbalanced power distribution coefficient into the conventional power flow model to obtain the power balance equation and section power flow equation, and combine the obtained power balance equation and section power flow equation to obtain an interconnected system power flow model considering AGC power distribution.

4. An AGC collaborative control optimization method considering tie-line power stability as described in claim 1, characterized in that, Linearize the power balance constraints and line power constraints in the established multi-region stability control model based on robust optimization, then relax the objective function, improve the relaxation evolution algorithm using a multi-population genetic algorithm, and repeatedly solve the established multi-region stability control model based on robust optimization using the improved evolution algorithm until the objective function reaches a stable optimal solution.

5. An AGC collaborative control optimization system considering tie-line power stability, characterized in that, it includes: A modeling module configured to obtain the original data of the power grid and the parameter information of the tie-line, and construct an interconnected system power flow model considering AGC power distribution; based on the constructed interconnected system power flow model, with the minimum amplitude of power fluctuation on the tie-line as the objective function, establish a multi-region stability control model based on robust optimization; An optimization module, configured to perform optimization and solution of a multi-region stable control model based on robust optimization, and implement AGC collaborative control optimization; The objective function is as follows: Among them, ΔP t is the change in active power flowing on all connection lines between the sub-region and other sub-regions; P r is the change in new energy power generation output within the sub-region under study; the matrix α is the participation factor matrix of all generators participating in AGC within the region; When studying a sub-region, no power disturbance occurs in other systems outside the region. After linearizing the active power of the tie line, we get: where ρ i is the active power change of the i-th regional tie line. If the i-th tie line is connected to the sub-region under study, it is calculated according to the formula; otherwise, it is taken as 0; Δθ i is the phase angle change of the nodes connected to the i-th regional tie line within the sub-region to be studied; The constraint conditions of the multi-region stable control model based on robust optimization include power balance constraints, upper and lower limits of generator output constraints, generator output ramp constraints, upper and lower limits of new energy output constraints, short-term change range constraints of new energy, and line power constraints; Under the condition of simultaneously satisfying all constraint conditions, calculate the objective function to minimize the amplitude of power fluctuation on the tie line; Use a multi-population genetic algorithm relaxation evolution algorithm to solve the established multi-region stable control model based on robust optimization.

6. A computer-readable storage medium, on which a program is stored, characterized in that, when the program is executed by a processor, it implements the steps in the AGC collaborative control optimization method considering tie line power stability as described in any one of claims 1-4.

7. An electronic device, including a memory, a processor, and a program stored on the memory and executable on the processor, characterized in that, when the processor executes the program, it implements the steps in the AGC collaborative control optimization method considering tie line power stability as described in any one of claims 1-4.

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