Power grid power flow optimization method and system considering HPFC device control mode switching

Abstract

The invention discloses a power grid power flow optimization method and system considering control mode switching of an HPFC device, and the method comprises the steps: constructing a power grid power flow optimization model employing control target values of the HPFC device corresponding to different control modes as decision variables, and solving the power grid power flow optimization model under a plurality of scene working condition combinations, and taking a control mode corresponding to the optimal solution as an optimal control mode of the HPFC device under each scene working condition combination, and taking a control target value corresponding to the optimal control mode of the HPFC device under different scene working condition combinations as a power grid power flow optimization scheme. According to the power grid power flow optimization model constructed by the method, the economical efficiency and the safety of the power grid are considered at the same time, and the influence of different control modes of the HPFC device on power flow distribution of the power grid is considered when the power flow calculation containing the HPFC device is carried out.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system control, and in particular relates to a power grid flow optimization method and system taking into account the switching of HPFC device control modes. Background Art

[0002] As a new type of FACTS device, the Hybrid Power Flow Controller (HPFC) provides a new solution to improve the flexible regulation capability of the power grid.

[0003] Most of the existing studies on power flow optimization of power grids containing HPFC devices use a constant power control mode when calculating power flow of power grids containing HPFC devices. In this control mode, the active and reactive flows of the controlled lines are set to the same active and reactive target values ​​under different operating conditions. However, under different operating conditions, the power flow distribution of the system is different. Simply controlling the line flow at the same target value may cause the HPFC device to be over-regulated under some conditions, resulting in a waste of device control capacity and affecting the safe and economic operation of the power grid. Existing power flow optimization studies that take into account the control mode of FACTS devices usually first set the FACTS device to a certain control mode, and then perform calculations under different operating conditions. In essence, the same control mode is still used under different operating conditions, ignoring the adaptability of different control modes to different operating conditions, and the control capacity of FACTS devices cannot be fully utilized. In addition, most of the existing studies do not consider the impact of large-scale new energy access scenarios and FACTS device control modes at the same time, and lack supporting optimization scheduling methods for power systems containing FACTS devices.

[0004] Therefore, how to design a power grid power flow optimization method and system taking into account the control mode switching of the HPFC device, fully tap the flexible regulation capability of the FACTS device, and optimize the power flow distribution of the power grid is an urgent problem to be solved by technical personnel in this field. Summary of the invention

[0005] In view of the deficiencies of the prior art, the present invention proposes a power grid power flow optimization method and system taking into account the switching of control modes of HPFC devices.

[0006] The present invention can be achieved through the following technical solutions:

[0007] A method for optimizing power flow in a power grid taking into account the switching of control modes of a HPFC device, the method comprising:

[0008] Obtain several scenario and operating condition combinations related to typical power grid operating scenarios and operating conditions;

[0009] A power flow optimization model is constructed with the control target values ​​of the HPFC device corresponding to different control modes as decision variables, wherein the objective function of the power flow optimization model includes minimizing the active power loss of the power grid and minimizing the line risk index, and the constraint conditions include the HPFC device operation constraints taking into account the control mode switching and the power grid operation constraints taking into account the N-1 fault; the HPFC device operation constraints include the control target constraints, the active power balance constraints, and the state variable inequality constraints;

[0010] Under each scenario operating condition combination, the power grid flow optimization model is solved, and the control mode corresponding to the optimal solution is used as the optimal control mode of the HPFC device under each scenario operating condition combination, and the control target value corresponding to the optimal control mode of the HPFC device under different scenario operating condition combinations is used as the power grid flow optimization solution.

[0011] Furthermore, the typical operation scenario of the power grid is formed by clustering the historical operation data of the power grid, and the specific method includes:

[0012] Obtain historical wind power and photovoltaic output sample data and historical load sample data, and establish a wind power output sample data matrix, a photovoltaic output sample data matrix, and a load sample data matrix respectively;

[0013] The wind power output sample data matrix, the photovoltaic output sample data matrix and the load sample data matrix are clustered respectively by using a fuzzy C-means clustering algorithm to generate a typical operation scenario; wherein the specific steps of the fuzzy C-means clustering algorithm include:

[0014] Initialize the cluster center and membership matrix, and update the cluster center and membership according to the following formula;

[0015]

[0016] In the formula, μ p is the pth cluster center; N X is the total number of samples; x q is the qth sample; u pq is the membership of the qth sample to the pth cluster; ||·|| means taking the Euclidean norm; δ is the fuzzy coefficient;

[0017] Repeat the above steps until the iteration converges. The specific criteria for iteration convergence are:

[0018]

[0019] in, is the objective function of the tth iteration; is the objective function of the t+1th iteration; ε is the convergence error; N P is the number of clusters;

[0020] According to the clustering results obtained, the comprehensive clustering effectiveness index corresponding to different cluster numbers is calculated. The cluster number with the largest comprehensive clustering effectiveness index is the optimal cluster number.

[0021] Furthermore, the calculation formula of the active power loss of the power grid is as follows:

[0022]

[0023] Among them, F1 is the comprehensive grid active power loss considering multiple scenarios; N ξ is the number of scenarios considered in the calculation; η ξ is the occurrence probability of scene ξ; f ξ1 is the system active network loss in scenario ξ; N L is the total number of lines in the power grid; is the active power flow from the head end to the end of line l in scenario ξ; is the active power flow from the end of line l to the head end in scenario ξ;

[0024] The calculation formula of the line risk index is as follows:

[0025]

[0026] Among them, F2 is a comprehensive line risk index considering multiple scenarios; A C is the set of expected N-1 fault lines; N C is the number of N-1 faulty lines; P lmax is the active power flow limit of line l; (·)(c) represents the parameter variable after N-1 fault occurs in the power grid;

[0027] The calculation formula of the control target constraint in the HPFC device operation constraint is as follows:

[0028]

[0029] The calculation formula of the active power balance constraint in the HPFC device operation constraint is as follows:

[0030]

[0031] The state variable inequality constraints in the HPFC device operation constraints include the voltage constraint of the HPFC device, the current constraint of the HPFC device and the capacity constraint of the HPFC device, and the calculation formulas are as follows:

[0032]

[0033]

[0034] Among them, P h , Qh is the active and reactive power flow on the hth control line of the HPFC device; V h ,θ h is the voltage amplitude and phase angle of the sending end of the hth control line of the HPFC device; V shu ,θ shu is the voltage amplitude and phase angle of the node on the parallel side of the HPFC device; is the current phasor flowing through HPFC; P href , Q href is the hth active and reactive power flow target value of the HPFC device under the constant power control model; V href is the hth voltage amplitude target value of the HPFC device under the voltage regulation control model; θ href is the hth voltage phase angle difference target value of the HPFC device under the phase angle regulation control model; R href , X href is the hth impedance compensation target value of the HPFC device under the impedance compensation control model;

[0035] V sh ,θ sh is the equivalent output voltage amplitude and phase angle of the HPFC parallel side; V seh ,θ seh is the equivalent output voltage amplitude and phase angle of the hth series side of UPFC in HPFC; V crh ,θ crh is the equivalent output voltage amplitude and phase angle of the hth series side of ST in HPFC;

[0036] V sehmax is the maximum equivalent output voltage amplitude of the hth series side of UPFC in HPFC; V crhmax is the maximum value of the equivalent output voltage amplitude of the hth series side of ST in HPFC; V shmax is the maximum value of the equivalent output voltage amplitude of the parallel side of HPFC; I seh ,I sehmax is the current amplitude and maximum value of the hth series side current flowing through the UPFC in the HPFC; I crh ,I crhmax is the current amplitude and maximum value of the hth series side current flowing through the ST in the HPFC; I sh ,I shmax is the parallel side current amplitude and its maximum value flowing through the HPFC; S seh , S sehmax is the hth series side capacity of UPFC in HPFC and its maximum value; S crh , S crhmax is the hth series side capacity of ST in HPFC and its maximum value; S sh , Sshmax is the parallel side capacity of HPFC and its maximum value.

[0037] The power grid operation constraints taking into account N-1 faults include equality constraints and inequality constraints;

[0038] The equality constraint is the node power balance constraint of the new energy grid, and the calculation formula is as follows:

[0039]

[0040] The inequality constraints are the line flow constraints and node voltage amplitude constraints of the new energy grid under normal operation and N-1 fault operation, and the calculation formulas are as follows:

[0041]

[0042] Among them, P ξga , Q ξga is the active and reactive output of the generator connected to node a in scenario ξ; P ξda , Q ξda is the active and reactive loads of access node a in scenario ξ; P ξta , Q ξta is the HPFC active and reactive injection power of access node a in scenario ξ. If node a is not an access node of HPFC, then P ξta =Q ξta =0; V ξa , is the voltage amplitude of nodes a and b in scenario ξ; is the voltage phase difference between nodes a and b in scenario ξ, that is G ab , B ab is the equivalent conductance and susceptance of line ab; Q gamax , Q gamin V is the upper and lower limits of the reactive power output of the generator connected to node a; max 、V min The upper and lower limits of the node voltage amplitude.

[0043] Furthermore, the specific steps of solving the power grid power flow optimization model under each scenario combination include:

[0044] Determine the installation position of the HPFC device in the power grid and the maximum value of the state variable, and use the MOMVO algorithm to iteratively solve the power flow optimization model;

[0045] Initialize each universe position in the MOMVO algorithm, where each universe is a phasor composed of the decision variables of the HPFC device;

[0046] The decision variables of the HPFC device corresponding to different universes are used to calculate the power flow of the power grid containing the HPFC device under different typical operating scenarios and operating conditions. According to the constraint satisfaction of the HPFC device under different control modes, the appropriate control mode of the HPFC device is selected, the power flow on each line is recorded, and then the active power loss and line risk index of the power grid are calculated, which is the objective function corresponding to the universe; if the HPFC device does not satisfy the constraints under all control modes, the HPFC device is taken as a constant power control mode, and a maximum value is added to the line risk index;

[0047] According to the objective function calculation results, solutions that do not dominate each other are selected as compromise solutions, and a compromise solution storage archive is established; an upper capacity limit is set in the storage archive, and when the number of compromise solutions exceeds the upper limit, solutions with objective functions similar to other compromise solutions are deleted;

[0048] If the algorithm does not reach the target number of iterations, the position of each universe is updated and the objective function is recalculated; if the algorithm reaches the target number of iterations, the compromise solution is output and stored in the archive;

[0049] The optimal solution is determined according to the fuzzy satisfaction index of each compromise solution, and the optimal control mode and corresponding control target value of the HPFC device corresponding to the optimal solution under different scenario operating conditions are obtained.

[0050] Furthermore, the specific steps of selecting a suitable control mode of the HPFC device are:

[0051] The initial control mode of the HPFC device under normal operating conditions and N-1 fault operating conditions is determined to be the constant power control mode; then the state variable inequality constraints of the HPFC device are checked, and the appropriate control mode is switched according to the constraint satisfaction. The specific method is as follows:

[0052] If the HPFC device does not meet the voltage constraint or the capacity constraint in the constant power control mode, it switches to the voltage / phase angle regulation control mode, and selects the control mode with a better line load rate, that is, the control mode corresponding to the better objective function value of the power grid power flow optimization model.

[0053] If the HPFC device does not meet the current constraint in the constant power control mode, it switches to the impedance compensation control mode;

[0054] If the HPFC device is in other situations in the constant power control mode, it will be switched to the voltage / phase angle adjustment control mode first;

[0055] If the HPFC device does not meet the current constraint after switching to the voltage / phase angle regulation control mode, it switches to the impedance compensation control mode;

[0056] If the HPFC device still does not meet the voltage constraint or capacity constraint after switching to the voltage / phase angle regulation control mode, the constant power control mode is the appropriate control mode;

[0057] If the HPFC device does not meet the voltage constraint or the capacity constraint after switching to the impedance compensation control mode, it switches to the voltage / phase angle regulation control mode;

[0058] If the HPFC device still does not meet the current constraint after switching to the impedance compensation control mode, the constant power control mode is the appropriate control mode.

[0059] Furthermore, the updating method of the cosmic position is as follows:

[0060]

[0061]

[0062] Where z is the λth parameter of the ρth universe; NI ρ is the standardized objective function of the ρth universe; r1, r2, r3, r4 are random numbers in the range [0,1]; z is the λth parameter of the σth universe generated by roulette; z′ is the λth parameter of the σth universe after update; y λ(t) is the λth parameter of the optimal universe at the tth iteration; ub λ lb λ are the upper and lower limits of the position of the λth parameter; H is the threshold; WEP, WEP max , WEP min are the wormhole existence rate and its upper and lower limits respectively; t is the current iteration number; N T is the maximum number of iterations; TDR is the travel distance rate; χ is the detection speed.

[0063] Furthermore, the calculation method of the fuzzy satisfaction index of the compromise solution is:

[0064]

[0065] Among them, τ δ is the fuzzy satisfaction index of the δth objective function of the compromise solution; f δ is the δth objective function of the compromise solution; f δmax 、f δmin are the maximum and minimum values ​​of the δth objective function in the compromise solution storage archive respectively.

[0066] A power grid power flow optimization system taking into account the switching of control modes of HPFC devices, comprising a scenario operating condition combination acquisition module, a model building module and a model solving module;

[0067] The scenario operating condition combination acquisition module is used to acquire several scenario operating condition combinations related to typical power grid operation scenarios and operation conditions;

[0068] The model building module is used to build a power flow optimization model with the control target value of the HPFC device corresponding to different control modes as the decision variable, the objective function of the power flow optimization model includes minimizing the active power loss of the power grid and minimizing the line risk index, and the constraint conditions include the HPFC device operation constraint considering the control mode switching and the power grid operation constraint considering N-1 faults; the HPFC device operation constraint includes the control target constraint, the active power balance constraint, and the state variable inequality constraint;

[0069] The model solving module is used to solve the power grid flow optimization model under each scenario operating condition combination, and use the control mode corresponding to the optimal solution as the optimal control mode of the HPFC device under each scenario operating condition combination, and use the control target value corresponding to the optimal control mode of the HPFC device under different scenario operating condition combinations as the power grid flow optimization solution.

[0070] An electronic device comprises a memory and a processor, wherein the memory stores a computer program, and the processor is used to call and run the computer program stored in the memory to execute any of the methods described above.

[0071] A computer-readable storage medium stores a computer program, wherein the computer program implements the steps of any of the above methods when executed by a processor.

[0072] Compared with the prior art, the present invention has the following beneficial effects:

[0073] The present invention proposes a power grid flow optimization method taking into account the switching of the control mode of the HPFC device. The method constructs a power grid flow optimization model with the control target value of the HPFC device corresponding to different control modes as the decision variable, and solves the power grid flow optimization model under several scene condition combinations, and takes the control mode corresponding to the optimal solution as the optimal control mode of the HPFC device under each scene condition combination, and takes the control target value corresponding to the optimal control mode of the HPFC device under different scene condition combinations as the power grid flow optimization scheme. The power grid flow optimization model constructed by the method of the present invention takes into account the economy and safety of the power grid at the same time, and takes into account the influence of different control modes of the HPFC device on the power grid flow distribution when performing the flow calculation containing the HPFC device. Under different scene condition combinations, according to the over-limit situation of the state variable of the HPFC device, the control mode of the appropriate HPFC device is switched, the optimal control mode of the HPFC device under different scene condition combinations is determined, and the system flow distribution under different scene condition combinations is optimized, so as to further exert the control potential of the HPFC device and effectively improve the economy and safety of the system operation.

[0074] The method of the present invention forms a typical operation scenario of a power grid through clustering. The method is based on the time series characteristics of renewable energy output and load, combines three currently commonly used clustering validity functions in a standardized way, obtains a clustering validity criterion based on multi-angle data characteristics, and uses fuzzy C-means clustering to extract and classify the wind power, photovoltaic output and load of the power grid. The wind power, photovoltaic output and load with similar characteristics are replaced by the cluster center of their classification, forming a typical operation scenario, effectively reducing the amount of data required for calculation, and improving the power flow calculation efficiency of the power grid considering the uncertainty of renewable energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Figure 1 A schematic diagram of a flow chart of a power grid power flow optimization method in an embodiment;

[0076] Figure 2 Schematic diagram of control mode switching of the HPFC device in the embodiment;

[0077] Figure 3 It is a schematic diagram of the calculation results of the clustering effectiveness index in the application embodiment;

[0078] Figure 4 It is a schematic diagram of line load rate under some working conditions in the application embodiment. DETAILED DESCRIPTION

[0079] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0080] Embodiment 1:

[0081] A power grid power flow optimization method considering the switching of control modes of HPFC devices is provided in the power grid, such as Figure 1 As shown, the method includes:

[0082] Obtain several combinations of scenarios and conditions related to typical grid operation scenarios and operating conditions; generally, typical operation scenarios can be formed by clustering historical grid operation data, or by screening historical grid operation data based on experience and research purposes, such as selecting scenarios with large output of renewable energy and large load as typical operation scenarios. Operating conditions generally include normal operation conditions and line N-1 fault operation conditions.

[0083] A power flow optimization model is constructed with the control target values ​​of the HPFC device corresponding to different control modes as decision variables. The objective function of the power flow optimization model includes minimizing the active power loss of the power grid and minimizing the line risk index. The constraints include the operation constraints of the HPFC device taking into account the control mode switching and the operation constraints of the power grid taking into account the N-1 fault. The operation constraints of the HPFC device include the control target constraints, the active power balance constraints, and the state variable inequality constraints.

[0084] Under each scenario condition combination, the power grid flow optimization model is solved, and the control mode corresponding to the optimal solution is used as the optimal control mode of the HPFC device under each scenario condition combination, and the control target value corresponding to the optimal control mode of the HPFC device under different scenario condition combinations is used as the power grid flow optimization solution.

[0085] Embodiment 2:

[0086] This example is further designed on the basis of the first embodiment in that the typical operation scenario of the power grid in this example is formed by clustering the historical operation data of the power grid, and the specific method includes:

[0087] Obtain historical wind power and photovoltaic output sample data and historical load sample data, and establish a wind power output sample data matrix, a photovoltaic output sample data matrix, and a load sample data matrix respectively;

[0088] The fuzzy C-means clustering algorithm is used to cluster the wind power output sample data matrix, the photovoltaic output sample data matrix and the load sample data matrix to generate typical operation scenarios. The specific steps of the fuzzy C-means clustering algorithm include:

[0089] Initialize the cluster center and membership matrix, and update the cluster center and membership according to the following formula;

[0090]

[0091] In the formula, μ p is the pth cluster center; NX is the total number of samples; x q is the qth sample; u pq is the membership of the qth sample to the pth cluster; ||·|| means taking the Euclidean norm; δ is the fuzzy coefficient;

[0092] Repeat the above steps until the iteration converges. The specific criteria for iteration convergence are:

[0093]

[0094] in, is the objective function of the tth iteration; is the objective function of the t+1th iteration; ε is the convergence error; N P is the number of clusters;

[0095] According to the clustering results obtained, the comprehensive clustering effectiveness index corresponding to different cluster numbers is calculated. The cluster number with the largest comprehensive clustering effectiveness index is the optimal cluster number.

[0096] The following example illustrates the acquisition of several scenario condition combinations related to typical operation scenarios and operation conditions of power grids. In this example, there are 4 typical operation scenarios of power grids. In order to take into account both computing efficiency and data change characteristics, this example extracts feature information from long time series data, and forms typical operation scenarios by intercepting, reducing, and merging. The 4 typical operation scenarios of power grids are specifically divided into small generation of new energy and small load, small generation of new energy and large load, large generation of new energy and small load, and large generation of new energy and large load. This example considers that when an N-1 fault occurs in a line, it is very easy to cause heavy load or overload in the nearby lines, threatening the safe operation of the power grid. There are 4 operating conditions, which are specifically divided into normal operation, N-1 fault in line 1, N-1 fault in line 2, and N-1 fault in line 3, thereby forming 4*4=16 scenario condition combinations, as shown in the following table.

[0097]

[0098]

[0099] Embodiment three:

[0100] This example is further designed on the basis of the first embodiment in that the calculation formula of the active power loss of the power grid in this example is as follows:

[0101]

[0102] Among them, F1 is the comprehensive grid active power loss considering multiple scenarios; N ξ is the number of scenarios considered in the calculation; η ξ is the occurrence probability of scene ξ; fξ1 is the system active network loss in scenario ξ; N L is the total number of lines in the power grid; is the active power flow from the head end to the end of line l in scenario ξ; is the active power flow from the end of line l to the head end in scenario ξ;

[0103] The calculation formula of line risk index is as follows:

[0104]

[0105] Among them, F2 is a comprehensive line risk index considering multiple scenarios; A C is the set of expected N-1 fault lines; N C is the number of N-1 faulty lines; P lmax is the active power flow limit of line l; (·) (c) Indicates the parameter variable after the N-1 fault occurs in the power grid;

[0106] The calculation formula of the control target constraint in the HPFC device operation constraint is as follows:

[0107]

[0108] The calculation formula of active power balance constraint in the operation constraint of HPFC device is as follows:

[0109]

[0110] The state variable inequality constraints in the HPFC device operation constraints include the voltage constraint of the HPFC device, the current constraint of the HPFC device, and the capacity constraint of the HPFC device. The calculation formulas are as follows:

[0111]

[0112] Among them, P h , Q h is the active and reactive power flow on the hth control line of the HPFC device; V h ,θ h is the voltage amplitude and phase angle of the sending end of the hth control line of the HPFC device; V shu ,θ shu is the voltage amplitude and phase angle of the node on the parallel side of the HPFC device; is the current phasor flowing through HPFC; P href , Q href is the hth active and reactive power flow target value of the HPFC device under the constant power control model; V href is the hth voltage amplitude target value of the HPFC device under the voltage regulation control model; θ hrefis the hth voltage phase angle difference target value of the HPFC device under the phase angle regulation control model; R href , X href is the hth impedance compensation target value of the HPFC device under the impedance compensation control model;

[0113] V sh ,θ sh is the equivalent output voltage amplitude and phase angle of the HPFC parallel side; V seh ,θ seh is the equivalent output voltage amplitude and phase angle of the hth series side of UPFC in HPFC; V crh ,θ crh is the equivalent output voltage amplitude and phase angle of the hth series side of ST in HPFC;

[0114] V sehmax is the maximum equivalent output voltage amplitude of the hth series side of UPFC in HPFC; V crhmax is the maximum value of the equivalent output voltage amplitude of the hth series side of ST in HPFC; V shmax is the maximum value of the equivalent output voltage amplitude of the parallel side of HPFC; I seh ,I sehmax is the current amplitude and maximum value of the hth series side current flowing through the UPFC in the HPFC; I crh ,I crhmax is the current amplitude and maximum value of the hth series side current flowing through the ST in the HPFC; I sh ,I shmax is the parallel side current amplitude and its maximum value flowing through the HPFC; S seh , S sehmax is the hth series side capacity of UPFC in HPFC and its maximum value; S crh , S crhmax is the hth series side capacity of ST in HPFC and its maximum value; S sh , S shmax is the parallel side capacity of HPFC and its maximum value.

[0115] The grid operation constraints considering N-1 faults include equality constraints and inequality constraints;

[0116] The equality constraint is the node power balance constraint of the power grid containing new energy, and the calculation formula is as follows:

[0117]

[0118] The inequality constraints are the line flow constraints and node voltage amplitude constraints of the new energy grid under normal operation and N-1 fault operation. The calculation formulas are as follows:

[0119]

[0120] Among them, P ξga , Q ξga is the active and reactive output of the generator connected to node a in scenario ξ; P ξda , Q ξda is the active and reactive loads of access node a in scenario ξ; P ξta , Q ξta is the HPFC active and reactive injection power of access node a in scenario ξ. If node a is not an access node of HPFC, then P ξta =Q ξta =0; V ξa , is the voltage amplitude of nodes a and b in scenario ξ; is the voltage phase difference between nodes a and b in scenario ξ, that is G ab , B ab is the equivalent conductance and susceptance of line ab; Q gamax , Q gamin V is the upper and lower limits of the reactive power output of the generator connected to node a; max 、V min The upper and lower limits of the node voltage amplitude.

[0121] Embodiment 4:

[0122] This example is further designed on the basis of the first embodiment in that in this example, under each scenario condition combination, the specific steps of solving the power grid power flow optimization model include:

[0123] Determine the installation location of the HPFC device in the power grid and the maximum value of the state variable, and use the MOMVO algorithm to iteratively solve the power flow optimization model;

[0124] Initialize each universe position in the MOMVO algorithm, where each universe is a phasor composed of the decision variables of the HPFC device;

[0125] The decision variables of the HPFC device corresponding to different universes are used to calculate the power flow of the power grid containing the HPFC device under different typical operating scenarios and operating conditions. According to the constraint satisfaction of the HPFC device under different control modes, the appropriate control mode of the HPFC device is selected, the power flow on each line is recorded, and then the active power loss and line risk index of the power grid are calculated, which is the objective function corresponding to the universe; if the HPFC device does not satisfy the constraints under all control modes, the HPFC device is taken as a constant power control mode, and a maximum value is added to the line risk index;

[0126] According to the objective function calculation results, solutions that do not dominate each other are selected as compromise solutions, and a compromise solution storage archive is established; an upper capacity limit is set in the storage archive, and when the number of compromise solutions exceeds the upper limit, solutions with objective functions similar to other compromise solutions are deleted;

[0127] If the algorithm does not reach the target number of iterations, the position of each universe is updated and the objective function is recalculated; if the algorithm reaches the target number of iterations, the compromise solution is output and stored in the archive;

[0128] The optimal solution is determined according to the fuzzy satisfaction index of each compromise solution, and the optimal control mode and corresponding control target value of the HPFC device corresponding to the optimal solution under different scenario operating conditions are obtained.

[0129] Embodiment five:

[0130] This example is further designed on the basis of the fourth embodiment in that, Figure 2 As shown, the specific steps for selecting a suitable control mode for the HPFC device in this example are:

[0131] The initial control mode of the HPFC device under normal operating conditions and N-1 fault operating conditions is determined to be the constant power control mode; then the state variable inequality constraints of the HPFC device are checked, and the appropriate control mode is switched according to the constraint satisfaction. The specific method is as follows:

[0132] If the HPFC device does not meet the voltage constraint or capacity constraint in the constant power control mode, it switches to the voltage / phase angle regulation control mode, and selects the control mode with a better line load rate, that is, the control mode with a better objective function value of the power grid power flow optimization model;

[0133] If the HPFC device does not meet the current constraint in the constant power control mode, it switches to the impedance compensation control mode;

[0134] If the HPFC device is in other situations in the constant power control mode, it will be switched to the voltage / phase angle adjustment control mode first;

[0135] If the HPFC device does not meet the current constraint after switching to the voltage / phase angle regulation control mode, it switches to the impedance compensation control mode;

[0136] If the HPFC device still does not meet the voltage constraint or capacity constraint after switching to the voltage / phase angle regulation control mode, the constant power control mode is the appropriate control mode;

[0137] If the HPFC device does not meet the voltage constraint or the capacity constraint after switching to the impedance compensation control mode, it switches to the voltage / phase angle regulation control mode;

[0138] If the HPFC device still does not meet the current constraint after switching to the impedance compensation control mode, the constant power control mode is the appropriate control mode.

[0139] Embodiment six:

[0140] This example is further designed on the basis of the fourth embodiment in that the method for updating the cosmic position in this example is as follows:

[0141]

[0142] Where z is the λth parameter of the ρth universe; NI ρ is the standardized objective function of the ρth universe; r1, r2, r3, r4 are random numbers in the range [0,1]; z is the λth parameter of the σth universe generated by roulette; z′ is the λth parameter of the σth universe after update; y λ(t) is the λth parameter of the optimal universe at the tth iteration; ub λ lb λ are the upper and lower limits of the position of the λth parameter; H is the threshold, usually 0.5; WEP, WEP max , WEP min They are the wormhole existence rate and its upper and lower limits, usually WEP max =1, WEP min =0.2; t is the current iteration number; N T is the maximum number of iterations; TDR is the travel distance rate; χ is the detection speed, usually χ=6.

[0143] Embodiment seven:

[0144] This example is further designed on the basis of the fourth embodiment in that the calculation method of the fuzzy satisfaction index of the compromise solution in this example is:

[0145]

[0146] Among them, τ δ is the fuzzy satisfaction index of the δth objective function of the compromise solution; f δ is the δth objective function of the compromise solution; f δmax 、f δmin are the maximum and minimum values ​​of the δth objective function in the compromise solution storage archive respectively.

[0147] Embodiment eight:

[0148] A power grid power flow optimization system taking into account the switching of control modes of HPFC devices of the present invention comprises a scenario operating condition combination acquisition module, a model building module and a model solving module;

[0149] A scenario condition combination acquisition module is used to acquire several scenario condition combinations related to typical operation scenarios and operation conditions of the power grid;

[0150] A model building module is used to build a power flow optimization model with the control target value of the HPFC device corresponding to different control modes as the decision variable. The objective function of the power flow optimization model includes minimizing the active power loss of the power grid and minimizing the line risk index. The constraints include the operation constraints of the HPFC device taking into account the control mode switching and the operation constraints of the power grid taking into account the N-1 fault. The operation constraints of the HPFC device include the control target constraint, the active power balance constraint, and the state variable inequality constraint.

[0151] The model solving module is used to solve the power grid flow optimization model under each scenario operating condition combination, and use the control mode corresponding to the optimal solution as the optimal control mode of the HPFC device under each scenario operating condition combination, and use the control target value corresponding to the optimal control mode of the HPFC device under different scenario operating condition combinations as the power grid flow optimization solution.

[0152] Embodiment nine:

[0153] An electronic device comprises a memory and a processor, wherein the memory stores a computer program, and the processor is used to call and run the computer program stored in the memory to execute the method described in any of the above embodiments.

[0154] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the method described in any of the above embodiments.

[0155] Application examples:

[0156] In this example, the method of the present invention is used to optimize the mechanical energy flow of the northwest power grid in Nantong, Jiangsu. In this example, the wind power output coefficient, photovoltaic output coefficient and load level data of the Nantong power grid in 2019 (sampling interval is 1h) are used as sample data for fuzzy C-means clustering, and the corresponding wind power output coefficient, photovoltaic output coefficient and load level clustering sets when the cluster number K=2-20 are obtained respectively, and the clustering effectiveness index results corresponding to different cluster numbers are calculated as follows: Figure 3 shown.

[0157] from Figure 3 It can be seen that when the number of clusters is 3, D comp The calculation result of is the largest, and the clustering effect is the best. The relevant data of the typical operation scenario corresponding to the cluster number of 3 is shown in Table 1.

[0158] Table 1

[0159] Scene Number Wind power output coefficient Photovoltaic output coefficient Load Level Scenario Probability 1 0.1706 0.1721 0.9463 48.52％ 2 0.5958 0.2222 0.9720 21.43％ 3 0.1678 0.5535 1.1070 30.05％

[0160] The parameters of the MOMVO algorithm set in this example are as follows: the number of universes is 50; the maximum number of iterations is 100. The relevant parameters of the HPFC device are set as follows: the upper limit of the output voltage amplitude of the ST is set to 0.5pu, the number of taps is 5, and the capacity is 60MVA; the upper limit of the output voltage amplitude of the UPFC is set to 0.0722pu, the capacity is 40MVA; the capacity baseline value of the system is set to 100MVA.

[0161] The following two optimization schemes are used to perform power flow optimization calculations.

[0162] Solution 1: When calculating the power flow of a power system containing HPFC devices, the control mode of HPFC is not considered.

[0163] Solution 2: When calculating the power flow of a power system containing an HPFC device, the influence of different HPFC control modes on the power flow distribution of the system is considered, that is, the method of the present invention.

[0164] The calculation results of the comprehensive objective function obtained by using two optimization schemes are shown in Table 2.

[0165] Table 2

[0166] Optimization plan System active network loss / MW Line load factor index Number of overloaded lines Before optimization 232.70 0.2796 2 Solution 1 224.32 0.2033 0 Solution 2 219.94 0.1769 0

[0167] As can be seen from Table 2, after installing HPFC for optimization, the system active network loss and line load rate have been significantly reduced, and the line overload phenomenon has been significantly eliminated, indicating that after the regulation of HPFC, the economy and safety of the system have been significantly improved. Compared with before optimization, the system active network loss calculated by Scheme 1 decreased by 3.60%, and the line load rate index decreased by 27.31%; the system active network loss calculated by Scheme 2 decreased by 5.48%, and the line load rate index decreased by 36.75%. Compared with Scheme 1, the optimization result of Scheme 2 has better economy and safety.

[0168] The load rate of the line near the HPFC installation location under some working conditions is as follows: Figure 4 shown.

[0169] from Figure 4 It can be seen that after the N-1 fault occurred in line 16, the remaining loop had a power flow overload phenomenon before optimization. After optimization using Scheme 1, line 16 changed from an overload state to a heavy load state, and its load rate remained at a high level. After optimization using Scheme 2, the load rate of line 16 can be further reduced, and the load rate distribution of each line is more uniform. This shows that by selecting a suitable HPFC control mode, HPFC can adapt to the control requirements of different operating conditions and give full play to the control function of HPFC.

Claims

1. A method for optimizing power flow in a power grid taking into account the switching of control modes of a HPFC device, characterized in that: The method comprises: Obtain several scenario and operating condition combinations related to typical power grid operating scenarios and operating conditions; A power flow optimization model is constructed with the control target values ​​of the HPFC device corresponding to different control modes as decision variables, wherein the objective function of the power flow optimization model includes minimizing the active power loss of the power grid and minimizing the line risk index, and the constraint conditions include the HPFC device operation constraints taking into account the control mode switching and the power grid operation constraints taking into account the N-1 fault; the HPFC device operation constraints include the control target constraints, the active power balance constraints, and the state variable inequality constraints; Under each scenario operating condition combination, the power grid flow optimization model is solved, and the control mode corresponding to the optimal solution is used as the optimal control mode of the HPFC device under each scenario operating condition combination, and the control target value corresponding to the optimal control mode of the HPFC device under different scenario operating condition combinations is used as the power grid flow optimization solution.

2. The method for optimizing power flow of a power grid taking into account the switching of control modes of a HPFC device according to claim 1, characterized in that: The typical operation scenario of the power grid is formed by clustering the historical operation data of the power grid, and the specific method includes: Obtain historical wind power and photovoltaic output sample data and historical load sample data, and establish a wind power output sample data matrix, a photovoltaic output sample data matrix, and a load sample data matrix respectively; The wind power output sample data matrix, the photovoltaic output sample data matrix and the load sample data matrix are clustered respectively by using a fuzzy C-means clustering algorithm to generate a typical operation scenario; wherein the specific steps of the fuzzy C-means clustering algorithm include: Initialize the cluster center and membership matrix, and update the cluster center and membership according to the following formula; In the formula, μ p is the pth cluster center; N X is the total number of samples; x q is the qth sample; u pq is the membership of the qth sample to the pth cluster; ||·|| means taking the Euclidean norm; δ is the fuzzy coefficient; Repeat the above steps until the iteration converges. The specific criteria for iteration convergence are: in, is the objective function of the tth iteration; is the objective function of the t+1th iteration; ε is the convergence error; N P is the number of clusters; According to the clustering results obtained, the comprehensive clustering effectiveness index corresponding to different cluster numbers is calculated. The cluster number with the largest comprehensive clustering effectiveness index is the optimal cluster number.

3. The method for optimizing power flow of a power grid taking into account the switching of control modes of a HPFC device according to claim 1, characterized in that: The calculation formula of the active power loss of the power grid is as follows: Among them, F1 is the comprehensive grid active power loss considering multiple scenarios; N ξ is the number of scenarios considered in the calculation; η ξ is the occurrence probability of scene ξ; f ξ1 is the system active network loss in scenario ξ; N L is the total number of lines in the power grid; is the active power flow from the head end to the end of line l in scenario ξ; is the active power flow from the end of line l to the head end in scenario ξ; The calculation formula of the line risk index is as follows: Among them, F2 is a comprehensive line risk index considering multiple scenarios; A C is the set of expected N-1 fault lines; N C is the number of N-1 faulty lines; P lmax is the active power flow limit of line l; (·)(c) represents the parameter variable after N-1 fault occurs in the power grid; The calculation formula of the control target constraint in the HPFC device operation constraint is as follows: The calculation formula of the active power balance constraint in the HPFC device operation constraint is as follows: The state variable inequality constraints in the HPFC device operation constraints include the voltage constraint of the HPFC device, the current constraint of the HPFC device and the capacity constraint of the HPFC device, and the calculation formulas are as follows: Among them, P h , Q h is the active and reactive power flow on the hth control line of the HPFC device; V h ,θ h is the voltage amplitude and phase angle of the sending end of the hth control line of the HPFC device; V shu ,θ shu is the voltage amplitude and phase angle of the node on the parallel side of the HPFC device; is the current phasor flowing through HPFC; P href , Q href is the hth active and reactive power flow target value of the HPFC device under the constant power control model; V href is the hth voltage amplitude target value of the HPFC device under the voltage regulation control model; θ href is the hth voltage phase angle difference target value of the HPFC device under the phase angle regulation control model; R href , X href is the hth impedance compensation target value of the HPFC device under the impedance compensation control model; V sh ,θ sh is the equivalent output voltage amplitude and phase angle of the HPFC parallel side; V seh ,θ seh is the equivalent output voltage amplitude and phase angle of the hth series side of UPFC in HPFC; V crh ,θ crh is the equivalent output voltage amplitude and phase angle of the hth series side of ST in HPFC; V sehmax is the maximum equivalent output voltage amplitude of the hth series side of UPFC in HPFC; V crhmax is the maximum value of the equivalent output voltage amplitude of the hth series side of ST in HPFC; V shmax is the maximum value of the equivalent output voltage amplitude of the parallel side of HPFC; I seh ,I sehmax is the current amplitude and maximum value of the hth series side current flowing through the UPFC in the HPFC; I crh ,I crhmax is the current amplitude and maximum value of the hth series side current flowing through the ST in the HPFC; I sh ,I shmax is the parallel side current amplitude and its maximum value flowing through the HPFC; S seh , S sehmax is the capacity of the hth series side of UPFC in HPFC and its maximum value; S crh , S crhmax is the hth series side capacity of ST in HPFC and its maximum value; S sh , S shmax is the parallel side capacity of HPFC and its maximum value; The power grid operation constraints taking into account N-1 faults include equality constraints and inequality constraints; The equality constraint is the node power balance constraint of the new energy grid, and the calculation formula is as follows: The inequality constraints are the line flow constraints and node voltage amplitude constraints of the new energy grid under normal operation and N-1 fault operation, and the calculation formulas are as follows: Among them, P ξga , Q ξga is the active and reactive output of the generator connected to node a in scenario ξ; P ξda , Q ξda is the active and reactive loads of access node a in scenario ξ; P ξta , Q ξta is the HPFC active and reactive injection power of access node a in scenario ξ. If node a is not an access node of HPFC, then P ξta =Q ξta =0; V ξa , is the voltage amplitude of nodes a and b in scenario ξ; is the voltage phase difference between nodes a and b in scenario ξ, that is G ab , B ab is the equivalent conductance and susceptance of line ab; Q gamax , Q gamin V is the upper and lower limits of the reactive power output of the generator connected to node a; max 、V min The upper and lower limits of the node voltage amplitude.

4. The method for optimizing power flow of a power grid taking into account the switching of control modes of a HPFC device according to claim 1, characterized in that: The specific steps of solving the power grid power flow optimization model under each scenario combination include: Determine the installation position of the HPFC device in the power grid and the maximum value of the state variable, and use the MOMVO algorithm to iteratively solve the power flow optimization model; Initialize each universe position in the MOMVO algorithm, where each universe is a phasor composed of the decision variables of the HPFC device; The decision variables of the HPFC device corresponding to different universes are used to calculate the power flow of the power grid containing the HPFC device under different typical operating scenarios and operating conditions. According to the constraint satisfaction of the HPFC device under different control modes, the appropriate control mode of the HPFC device is selected, the power flow on each line is recorded, and then the active power loss and line risk index of the power grid are calculated, which is the objective function corresponding to the universe; if the HPFC device does not satisfy the constraints under all control modes, the HPFC device is taken as a constant power control mode, and a maximum value is added to the line risk index; According to the objective function calculation results, solutions that do not dominate each other are selected as compromise solutions, and a compromise solution storage archive is established; an upper capacity limit is set in the storage archive, and when the number of compromise solutions exceeds the upper limit, solutions with objective functions similar to other compromise solutions are deleted; If the algorithm does not reach the target number of iterations, the position of each universe is updated and the objective function is recalculated; if the algorithm reaches the target number of iterations, the compromise solution is output and stored in the archive; The optimal solution is determined according to the fuzzy satisfaction index of each compromise solution, and the optimal control mode and corresponding control target value of the HPFC device corresponding to the optimal solution under different scenario operating conditions are obtained.

5. The method for optimizing power flow of a power grid taking into account the switching of control modes of a HPFC device according to claim 4, characterized in that: The specific steps of selecting a suitable control mode of the HPFC device are: The initial control mode of the HPFC device under normal operating conditions and N-1 fault operating conditions is determined to be the constant power control mode; then the state variable inequality constraints of the HPFC device are checked, and the appropriate control mode is switched according to the constraint satisfaction. The specific method is as follows: If the HPFC device does not meet the voltage constraint or the capacity constraint in the constant power control mode, it switches to the voltage / phase angle regulation control mode, and selects the control mode with a better line load rate, that is, the control mode corresponding to the better objective function value of the power grid power flow optimization model. If the HPFC device does not meet the current constraint in the constant power control mode, it switches to the impedance compensation control mode; If the HPFC device is in other situations in the constant power control mode, it will be switched to the voltage / phase angle adjustment control mode first; If the HPFC device does not meet the current constraint after switching to the voltage / phase angle regulation control mode, it switches to the impedance compensation control mode; If the HPFC device still does not meet the voltage constraint or capacity constraint after switching to the voltage / phase angle regulation control mode, the constant power control mode is the appropriate control mode; If the HPFC device does not meet the voltage constraint or the capacity constraint after switching to the impedance compensation control mode, it switches to the voltage / phase angle regulation control mode; If the HPFC device still does not meet the current constraint after switching to the impedance compensation control mode, the constant power control mode is the appropriate control mode.

6. The method for optimizing power flow of a power grid taking into account the switching of control modes of a HPFC device according to claim 4, characterized in that: The updating method of the cosmic position is as follows: Where z is the λth parameter of the ρth universe; NI ρ is the standardized objective function of the ρth universe; r1, r2, r3, r4 are random numbers in the range [0,1]; z is the λth parameter of the σth universe generated by roulette; z′ is the λth parameter of the σth universe after update; y λ(t) is the λth parameter of the optimal universe at the tth iteration; ub λ lb λ are the upper and lower limits of the position of the λth parameter; H is the threshold; WEP, WEP max , WEP min are the wormhole existence rate and its upper and lower limits respectively; t is the current iteration number; N T is the maximum number of iterations; TDR is the travel distance rate; χ is the detection speed.

7. The method for optimizing power flow of a power grid taking into account the switching of control modes of a HPFC device according to claim 4, characterized in that: The calculation method of the fuzzy satisfaction index of the compromise solution is: Among them, τ δ is the fuzzy satisfaction index of the δth objective function of the compromise solution; f δ is the δth objective function of the compromise solution; f δmax 、f δmin are the maximum and minimum values ​​of the δth objective function in the compromise solution storage archive, respectively.

8. A power grid flow optimization system taking into account the switching of control modes of HPFC devices, characterized in that: It includes a scenario condition combination acquisition module, a model building module and a model solving module; The scenario operating condition combination acquisition module is used to acquire several scenario operating condition combinations related to typical power grid operation scenarios and operation conditions; The model building module is used to build a power flow optimization model with the control target value of the HPFC device corresponding to different control modes as the decision variable, the objective function of the power flow optimization model includes minimizing the active power loss of the power grid and minimizing the line risk index, and the constraint conditions include the HPFC device operation constraint considering the control mode switching and the power grid operation constraint considering N-1 faults; the HPFC device operation constraint includes the control target constraint, the active power balance constraint, and the state variable inequality constraint; The model solving module is used to solve the power grid flow optimization model under each scenario operating condition combination, and use the control mode corresponding to the optimal solution as the optimal control mode of the HPFC device under each scenario operating condition combination, and use the control target value corresponding to the optimal control mode of the HPFC device under different scenario operating condition combinations as the power grid flow optimization solution.

9. An electronic device, characterized in that: The electronic device comprises a memory and a processor, wherein the memory stores a computer program, and the processor is configured to call and run the computer program stored in the memory to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method as claimed in any one of claims 1 to 7 are implemented.

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