UPFC configuration method, apparatus, device, storage medium and computer program product

Abstract

The application relates to a UPFC configuration method, device, equipment, storage medium and computer program product. The method comprises the following steps: obtaining an AC-DC optimization configuration model, the AC-DC optimization configuration model comprising a target function and a plurality of mixed integer convex programming constraint equations, decision variables of the target function comprising an access node and a UPFC capacity; inputting known parameters related to an AC-DC power transmission system into the AC-DC optimization configuration model, solving the target function based on the plurality of mixed integer convex programming constraint equations, and obtaining an access node value and a UPFC capacity value. The method can efficiently calculate the optimal solution of the access node and the capacity of the UPFC installed in the AC-DC power transmission system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power systems, and particularly relates to a UPFC configuration method and device, equipment, a storage medium and a computer program product. BACKGROUND

[0002] The UPFC (unified power flow controller) is a new generation of flexible AC power transmission device with powerful functions and superior characteristics, which can realize power flow control, AC bus voltage control, low-frequency oscillation damping, and improvement of system transmission capacity, and coordinated optimization control of multiple control targets.

[0003] In the related art, the optimization calculation method for the access node selection and capacity configuration of the UPFC installed in the pure AC power grid usually selects the access node first, and then selects the UPFC capacity at the selected access node.

[0004] However, the influence between the access node selection and the capacity configuration result is not considered in the related art, and therefore the result obtained is a non-optimal solution. In addition, the optimization configuration model of the UPFC is a mixed integer nonlinear programming model, and the existing research usually directly solves the model by using an intelligent algorithm, which has low calculation efficiency. SUMMARY

[0005] Therefore, it is necessary to provide a UPFC configuration method, device, equipment, storage medium and computer program product capable of efficiently calculating the optimal solution of the access node and capacity of the UPFC installed in the AC / DC power transmission system.

[0006] In a first aspect, the present application provides a UPFC configuration method. The method comprises:

[0007] obtaining an AC / DC optimization configuration model, the AC / DC optimization configuration model comprising a target function and a plurality of mixed integer convex programming constraint equations, decision variables of the target function comprising an access node and a UPFC capacity; inputting known parameters related to the AC / DC power transmission system into the AC / DC optimization configuration model, solving the target function based on the plurality of mixed integer convex programming constraint equations, and obtaining an access node value and a UPFC capacity value.

[0008] In one of the embodiments, the target function is:

[0009]

[0010] wherein, C ins is the annual investment cost of the UPFC, C los is the total operating loss power cost of the AC / DC power transmission system per hour, and Dy is the number of hours in a year, r is the discount rate of UPFC, y is the life of UPFC, a0 is the fixed installation cost of UPFC per access node, a1 is the unit price of UPFC per access node, N UP is the set of candidate nodes of AC / DC power transmission system to access UPFC, u i is a binary variable, S i represents the UPFC capacity of node i to access UPFC, K los is the unit loss power cost, N G is the set of generator nodes in AC / DC power transmission system, P Gi is the active power of the i-th generator, N L is the set of load nodes in AC / DC power transmission system, P Li is the active power of the i-th load node, t is 1 hour, and ∑ represents summation operation.

[0011] In one embodiment, the plurality of mixed integer convex programming constraint equations include: an AC power flow constraint equation, a DC operating characteristic constraint equation, a UPFC configuration constraint equation, and a variable constraint equation; the AC power flow constraint equation is a constraint on the operating characteristics of the AC transmission lines of the AC / DC power transmission system; the DC operating characteristic constraint equation is a constraint on the operating characteristics of the DC transmission lines of the AC / DC power transmission system; the UPFC configuration constraint equation is a constraint on the configuration capacity of the UPFC accessed by the AC / DC power transmission system; and the variable constraint equation is a constraint on the upper and lower limits of the variables of the AC / DC power transmission system.

[0012] In one embodiment, the AC power flow constraint equation includes a conventional node power balance equation, a UPFC operating characteristic equation, a UPFC candidate node power balance equation, and a power balance equation of the AC node of the DC converter station.

[0013] In one embodiment, the construction process of the AC power flow constraint equation includes:

[0014] The second-order cone relaxation method is used to convexly relax the original conventional node power balance equation, and the conventional node power balance equation is obtained, wherein the original conventional node power balance equation is a mixed integer nonlinear constraint equation, and the conventional node power balance equation is a mixed integer convex programming constraint equation; the second-order cone relaxation method is used to convexly relax the original UPFC operation characteristic equation, and the UPFC operation characteristic equation is obtained, wherein the original UPFC operation characteristic equation is a mixed integer nonlinear constraint equation, and the UPFC operation characteristic equation is a mixed integer convex programming constraint equation; the second-order cone relaxation method is used to convexly relax the original power balance equation of the AC node of the DC converter station, and the power balance equation of the AC node of the DC converter station is obtained, wherein the original power balance equation of the AC node of the DC converter station is a mixed integer nonlinear constraint equation, and the power balance equation of the AC node of the DC converter station is a mixed integer convex programming constraint equation; the two-step convexification method is used to convexly relax the original UPFC candidate node power balance equation, and the UPFC candidate node power balance equation is obtained, wherein the original UPFC candidate node power balance equation is a mixed integer nonlinear constraint equation, and the UPFC candidate node power balance equation is a mixed integer convex programming constraint equation.

[0015] In one of the embodiments, the two-step convexification method is used to convexly relax the original UPFC candidate node power balance equation, and the UPFC candidate node power balance equation is obtained, including: the second-order cone relaxation method is used to convexly relax the original UPFC candidate node power balance equation, and the Big-M method is used for linearization processing after the convex relaxation, and the UPFC candidate node power balance equation is obtained.

[0016] In one of the embodiments, the DC operation characteristic constraint equation includes a conventional DC operation characteristic constraint equation and a flexible DC operation characteristic constraint equation; the construction process of the DC operation characteristic constraint equation includes: the convex relaxation method is used to convexly relax the original conventional DC operation characteristic constraint equation, and the conventional DC operation characteristic constraint equation is obtained; the convex envelope method is used to convexly relax the original flexible DC operation characteristic constraint equation, and the flexible DC operation characteristic constraint equation is obtained.

[0017] In one of the embodiments, the construction process of the UPFC configuration constraint equation includes:

[0018] The Big-M method is used to linearize the original UPFC configuration constraint equation, and the UPFC configuration constraint equation is obtained, wherein the original UPFC configuration constraint equation is a mixed integer nonlinear constraint equation, and the UPFC configuration constraint equation is a mixed integer convex programming constraint equation.

[0019] In one of the embodiments, the variables in the variable constraint equations include AC system busbar active power, AC system busbar reactive power, bus voltage, transmission line active power flow, first UPFC equivalent voltage source voltage, and second UPFC equivalent voltage source voltage.

[0020] In a second aspect, the application further provides a UPFC configuration device. The device comprises:

[0021] a obtaining module, configured to obtain an AC / DC optimization configuration model, the AC / DC optimization configuration model comprising a target function and a plurality of mixed integer convex programming constraint equations, decision variables of the target function including access nodes and UPFC capacity;

[0022] a solving module, configured to input known parameters related to the AC / DC power transmission system into the AC / DC optimization configuration model, solve the target function based on the plurality of mixed integer convex programming constraint equations, and obtain access node values and UPFC capacity values.

[0023] In one of the embodiments, the target function is:

[0024]

[0025] wherein, C ins is the annual investment cost of the UPFC, C los is the total hourly operation network loss cost of the AC / DC power transmission system, D y is the number of hours contained in a year, r is the cost discount rate of the UPFC, y is the service life of the UPFC, a0 is the fixed installation cost of each access node of the UPFC, a1 is the unit price of each access node of the UPFC, N UP is a candidate node set of the AC / DC power transmission system to which the UPFC is to be accessed, u i is a binary variable, S i represents the UPFC capacity of the node i accessing the UPFC, K los is the unit network loss cost, N G is a generator node set in the AC / DC power transmission system, P Gi is the active power of the i-th generator, N L is a load node set in the AC / DC power transmission system, P Li is the active power of the i-th load node, and t is 1 hour, and ∑ represents summation operation.

[0026] In one of the embodiments, the plurality of mixed integer convex programming constraint equations comprises: an AC power flow constraint equation, a DC operating characteristic constraint equation, a UPFC configuration constraint equation, and a variable constraint equation; the AC power flow constraint equation is a constraint on an operating characteristic of an AC transmission line of the AC / DC power transmission system; the DC operating characteristic constraint equation is a constraint on an operating characteristic of a DC transmission line of the AC / DC power transmission system; the UPFC configuration constraint equation is a constraint on a configuration capacity of a UPFC connected to the AC / DC power transmission system; and the variable constraint equation is a constraint on upper and lower limits of a variable of the AC / DC power transmission system.

[0027] In one of the embodiments, the AC power flow constraint equation comprises a regular node power balance equation, a UPFC operating characteristic equation, a UPFC candidate node power balance equation, and a power balance equation of an AC node of a DC converter station.

[0028] In one of the embodiments, the apparatus further comprises:

[0029] The first constructing module is configured to: perform convex relaxation on an original regular node power balance equation by using a second-order cone relaxation method to obtain the regular node power balance equation, wherein the original regular node power balance equation is a mixed integer nonlinear constraint equation, and the regular node power equation is a mixed integer convex programming constraint equation; perform convex relaxation on an original UPFC operating characteristic equation by using the second-order cone relaxation method to obtain the UPFC operating characteristic equation, wherein the original UPFC operating characteristic equation is a mixed integer nonlinear constraint equation, and the UPFC operating characteristic equation is a mixed integer convex programming constraint equation; perform convex relaxation on an original power balance equation of an AC node of a DC converter station by using the second-order cone relaxation method to obtain the power balance equation of the AC node of the DC converter station, wherein the original power balance equation of the AC node of the DC converter station is a mixed integer nonlinear constraint equation, and the power balance equation of the AC node of the DC converter station is a mixed integer convex programming constraint equation; and perform convex relaxation on an original UPFC candidate node power balance equation by using a two-step convexification method to obtain the UPFC candidate node power balance equation, wherein the original UPFC candidate node power balance equation is a mixed integer nonlinear constraint equation, and the UPFC candidate node power balance equation is a mixed integer convex programming constraint equation.

[0030] In one of the embodiments, the first constructing module is specifically configured to: perform convex relaxation on the original UPFC candidate node power balance equation by using the second-order cone relaxation method, and perform linearization by using a Big-M method after the convex relaxation to obtain the UPFC candidate node power balance equation.

[0031] In one of the embodiments, the DC operation characteristic constraint equation comprises a conventional DC operation characteristic constraint equation and a flexible DC operation characteristic constraint equation; the device further comprises:

[0032] The second construction module is configured to perform convex relaxation processing on the original conventional DC operation characteristic constraint equation by using a convex hull relaxation method to obtain the conventional DC operation characteristic constraint equation, and perform convex relaxation processing on the original flexible DC operation characteristic constraint equation by using a convex envelope method to obtain the flexible DC operation characteristic constraint equation.

[0033] In one of the embodiments, the device further comprises:

[0034] The third construction module is configured to perform linearization processing on an original UPFC configuration constraint equation by using a Big-M method to obtain the UPFC configuration constraint equation, wherein the original UPFC configuration constraint equation is a mixed integer nonlinear constraint equation, and the UPFC configuration constraint equation is a mixed integer convex programming constraint equation.

[0035] In one of the embodiments, the variables in the variable constraint equation comprise AC system balance node active power, AC system balance node reactive power, bus voltage, transmission line active power flow, first UPFC equivalent voltage source voltage, and second UPFC equivalent voltage source voltage.

[0036] In a third aspect, the present application further provides a computer device, comprising a memory and a processor, the memory stores a computer program, and the processor implements the steps of the method in any one of the first aspect when executing the computer program.

[0037] In a fourth aspect, the present application further provides a computer readable storage medium, which stores a computer program, and the computer program implements the steps of the method in any one of the first aspect when executed by a processor.

[0038] In a fifth aspect, the present application further provides a computer program product, which comprises a computer program, and the computer program implements the steps of the method in any one of the first aspect when executed by a processor.

[0039] The UPFC configuration method, device, equipment, storage medium and computer program product. First, an AC-DC optimization configuration model including an objective function and a plurality of mixed integer convex programming constraint equations is obtained, decision variables of the objective function including an access node and a UPFC capacity; then known parameters related to the AC-DC power transmission system are input into the AC-DC optimization configuration model, and the objective function is solved based on the plurality of mixed integer convex programming constraint equations, to obtain the access node value and the UPFC capacity value. Since the AC-DC power transmission system takes the access node and the UPFC capacity as decision variables together for optimization calculation, the configuration result of the UPFC is an optimal solution; at the same time, since the AC-DC optimization configuration model includes a plurality of mixed integer convex programming constraint equations, solving based on the plurality of mixed integer convex programming constraint equations effectively improves the calculation efficiency, reliability and accuracy of the AC-DC optimization configuration model. BRIEF DESCRIPTION OF DRAWINGS

[0040] Figure 1 A flowchart of a UPFC configuration method in an embodiment;

[0041] Figure 2 A schematic diagram of an equivalent mathematical model of a UPFC in an embodiment;

[0042] Figure 3 A flowchart of a construction process of an AC power flow constraint equation in an embodiment;

[0043] Figure 4 A wiring schematic diagram of an improved IEEE39 node AC-DC power transmission system in an embodiment;

[0044] Figure 5 A structural block diagram of a UPFC configuration device in an embodiment;

[0045] Figure 6 An internal structure diagram of a computer device in an embodiment. DETAILED DESCRIPTION

[0046] In order to make the purpose, technical scheme and advantages of the present application clearer, the present application is further described in detail below in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0047] The UPFC (unified power flow controller) is a new generation of flexible AC power transmission device with powerful functions and superior characteristics, which can simultaneously realize power flow control, AC bus voltage control, low-frequency oscillation damping, and improvement of system transmission capacity, and coordinated optimization control of multiple control objectives. For the problem of uneven power flow distribution in AC-DC power transmission systems, the UPFC can be used to actively control the power flow distribution to solve the bottleneck problem of transmission capacity at the cross section.

[0048] In the related art, the optimization calculation method for the UPFC installation node selection and capacity configuration in the pure AC power grid usually selects the installation node first, and then selects the UPFC capacity at the selected installation node.

[0049] However, the related art does not consider the influence between the installation node selection and the capacity configuration result, so the result obtained is a non-optimal solution. Moreover, the existing optimization configuration method of the UPFC installed in the power grid is only applicable to the AC power transmission system, and there is currently no research on the optimization configuration method of the UPFC installed in the AC-DC hybrid power transmission system. The optimization configuration model of the UPFC is a mixed integer nonlinear programming model, and the existing research usually directly solves it by using an intelligent algorithm, which is easy to fall into a local optimal solution and has low calculation efficiency.

[0050] Therefore, the present application provides a UPFC configuration method, device, equipment, storage medium and computer program product, which can reliably calculate the optimal solution of the UPFC installation node and capacity in the AC-DC power transmission system.

[0051] In one embodiment, as shown in Figure 1 A UPFC configuration method is provided, and the embodiments of the present application are exemplified by the method applied to a terminal. It can be understood that the method can also be applied to a server, and can also be applied to a system including a terminal and a server, and can be realized through the interaction of the terminal and the server. The terminal can be, but is not limited to, various personal computers, notebook computers, smart phones, tablet computers, servers and the like. The server can be realized by an independent server or a server cluster composed of multiple servers. The UPFC configuration method comprises the following steps:

[0052] Step 101, obtaining an AC-DC optimization configuration model, the AC-DC optimization configuration model comprising a target function and a plurality of mixed integer convex programming constraint equations, the decision variables of the target function comprising an installation node and a UPFC capacity.

[0053] The access node and the UPFC capacity are considered to be closely related to the optimal configuration of the UPFC access node and the capacity, and are taken as decision variables of the AC / DC optimal configuration model. The AC / DC power transmission system includes a plurality of existing nodes, each of which is a sending end or a receiving end of a line in the AC / DC power transmission system. The access node refers to a position of the UPFC installed on the line in the AC / DC power transmission system. The configuration capacity of the UPFC refers to the capacity of the series and parallel transformers and the converter. The AC / DC optimal configuration model is used to calculate the position of the access node and the UPFC capacity when the UPFC is installed in the AC / DC power transmission system. The access node and the UPFC capacity obtained based on the AC / DC optimal configuration model are optimal solutions, which can effectively save economic costs and maximize the use of the UPFC. The objective function is to minimize the annual equivalent investment and operation cost of the AC / DC power transmission system.

[0054] In step 102, the known parameters related to the AC / DC power transmission system are input into the AC / DC optimal configuration model, and the objective function is solved based on the plurality of mixed integer convex programming constraint equations to obtain the access node value and the UPFC capacity value.

[0055] The known parameters are reference parameters for calculating the access node and the UPFC configuration capacity, and can include the number of UPFC configurations, the upper and lower limits of the internal parallel voltage source and the series voltage source of the UPFC, the fixed cost of the UPFC installation, the unit capacity installation cost of the UPFC, the configuration margin, the device discount rate, and the device service life, and other conventional known parameters. The AC / DC optimal configuration model can solve the objective function based on the plurality of mixed integer convex programming constraint equations to obtain the access node and the UPFC capacity, so that the UPFC can be accessed at the position of the access node to optimally control the power flow of the AC / DC power transmission system.

[0056] The UPFC configuration method described above first obtains an AC / DC optimal configuration model including an objective function and a plurality of mixed integer convex programming constraint equations. The decision variables of the objective function include the access node and the UPFC capacity. Then, the known parameters related to the AC / DC power transmission system are input into the AC / DC optimal configuration model, and the objective function is solved based on the plurality of mixed integer convex programming constraint equations to obtain the access node value and the UPFC capacity value. Since the AC / DC power transmission system takes the access node and the UPFC capacity as decision variables together for optimization calculation, the configuration result of the UPFC is an optimal solution. At the same time, since the AC / DC optimal configuration model includes a plurality of mixed integer convex programming constraint equations, the calculation efficiency, reliability, and accuracy of the AC / DC optimal configuration model are effectively improved based on the plurality of mixed integer convex programming constraint equations.

[0057] In one embodiment, the objective function is:

[0058]

[0059] where C ins is the annual investment cost of the UPFC, C los is the total hourly operating loss cost of the AC / DC transmission system, D y is the number of hours contained in a year, r is the cost discount rate of the UPFC, y is the service life of the UPFC, a0 is the fixed installation cost of the UPFC at each access node, a1 is the unit price of the UPFC at each access node, N UP is the set of candidate nodes of the AC / DC transmission system to which the UPFC is to be accessed, u i is a binary variable, S i represents the UPFC capacity of the node i to which the UPFC is accessed, K los is the unit loss cost, N G is the set of generator nodes in the AC / DC transmission system, P Gi is the active power of the i-th generator, N L is the set of load nodes in the AC / DC transmission system, P Li is the active power of the i-th load node, t is 1 hour, and ∑ represents the summation operation.

[0060] In the embodiments of the present application, the objective function of the AC / DC optimization configuration model is to minimize the annual equivalent investment operating cost of the AC / DC transmission system, and the installation cost of the UPFC includes the fixed installation cost a0 and the unit price a1 proportional to the capacity. In formula 1, f is the objective function. By solving the objective function, the calculation results of S i and u i are obtained. Wherein, i represents an existing node in the AC / DC transmission system, u i is a binary variable, if u i is equal to 1, it indicates that the node i can be installed with the UPFC, that is, the node i is an access node; if u i is equal to 0, it indicates that the node i is not installed with the UPFC. S i represents the UPFC capacity of the node i to which the UPFC is accessed.

[0061] In one embodiment, a configuration model is proposed, which can calculate the access node of the UPFC installed in the AC / DC transmission system and the UPFC capacity according to the configuration model.

[0062] Specifically, the equivalent circuit model of the UPFC access power system operation work adopts an equivalent injected power model, as shown in Figure 2 , which includes a parallel converter equivalent branch, a series converter equivalent branch, and a π-type equivalent circuit of a transmission line.

[0063] According to the equivalent circuit model of UPFC, the operating characteristic equation of UPFC can be obtained as shown in formulas 2 to 4. Formula 4 indicates that the shunt converter in UPFC provides active power for the series converter to maintain the DC bus voltage constant, so the active power of the two converters is considered to be equal after ignoring the converter loss.

[0064]

[0065]

[0066]

[0067] In the above formula, P sr +jQ sr and P rs +jQ rs are the equivalent injected power at the two ends of the device after the UPFC is connected to the AC transmission line, R E +jX E is the equivalent impedance of the shunt transformer, R B +jX B is the equivalent impedance of the series transformer, is the equivalent voltage source voltage of the shunt converter in UPFC, is the equivalent voltage source voltage of the series converter in UPFC, is the voltage of the node of the UPFC connection line, wherein the node s and the node m are the sending end node and the receiving end node of the AC transmission line respectively, and the node r is the newly added node after the UPFC is connected, is the current of the shunt branch, is the current of the series branch, j is the complex number unit, * indicates taking the conjugate of the complex number, and Re{·} indicates taking the real part of the complex number.

[0068] The AC power flow constraints in the AC / DC power transmission system include the power balance equations of the conventional nodes, the operating characteristic equations of the UPFC, the power balance equations of the UPFC candidate connection nodes, and the power balance equations of the AC nodes connected to the DC converter station. The power balance equations of the conventional nodes are as follows:

[0069]

[0070] In the above formula, n is the total number of nodes in the AC / DC power transmission system, P Gi is the active power of the i th generator, Q Gi is the reactive power of the i th generator, P Li is the active power of the i th load device, Q Li is the reactive power of the i th load device, and Σ indicates summation.i V is the voltage magnitude of node i, V j G is the voltage magnitude of node j, G ij B is the conductance between node i and node j, B ij θ is the susceptance between node i and node j, θ ij θ is the phase angle difference between node i and node j. Both node i and node j refer to the conventional nodes, which are the existing nodes in the AC / DC power transmission system that do not participate in the installation of UPFC.

[0071] The phasor equations of the UPFC operating characteristics, equations 2 to 4, are written in the form of algebraic equations as shown in equation 6:

[0072]

[0073] In equation 6, P sr P is the equivalent injected active power at the s end of the UPFC access line, P sr Q is the equivalent injected reactive power at the s end of the UPFC access line, Q rs P is the equivalent injected active power at the r end of the UPFC access line, P rs Q is the equivalent injected reactive power at the r end of the UPFC access line, Q E G is the equivalent conductance of the shunt transformer, G E B is the equivalent susceptance of the shunt transformer, B B G is the equivalent conductance of the series transformer, G B B is the equivalent susceptance of the series transformer, V s V is the voltage magnitude of node s, V r V is the voltage magnitude of node r, V E V is the equivalent voltage source voltage magnitude of the shunt converter in UPFC, V B V is the equivalent voltage source voltage magnitude of the series converter in UPFC, θ sE θ is the voltage phase angle difference between node s and the equivalent voltage source E, θ sB θ is the voltage phase angle difference between node s and the equivalent voltage source B, θ rB θ is the voltage phase angle difference between node r and the equivalent voltage source B, θ rs θ is the voltage phase angle difference between node r and node s, θ sE θ is the voltage phase angle difference between node s and the equivalent voltage source E, θ Es θ is the voltage phase angle difference between the equivalent voltage source E and node s, θ sr θ is the voltage phase angle difference between node s and node r, θ Bs θ is the voltage phase angle difference between the equivalent voltage source B and node s, θ Br θ is the voltage phase angle difference between the equivalent voltage source B and node r.

[0074] Considering UPFC accessing AC-DC power transmission system, binary variable u i is introduced in all candidate AC lines of UPFC i u i = 1 means that line i is equipped with UPFC, u m = 0 means that line i is not equipped with UPFC. The power balance equation of UPFC candidate access node is as follows:

[0075]

[0076] In formula 7, V rm is the voltage amplitude of node m, G rm is the mutual conductance between node r and node m, B rm is the mutual susceptance between node r and node m, θ rr is the voltage phase angle difference between node r and node m, G rr is the self conductance of node r, B ij is the mutual conductance between nodes i and j after UPFC is accessed, B ij is the mutual susceptance between nodes i and j after UPFC is accessed. N UP is the candidate node set. The last two formulas represent the power balance equation of the new node r after the line is equipped with UPFC.

[0077] The DC line of AC-DC power transmission system includes LCC conventional DC line and VSC flexible DC line, wherein the power balance equation of AC node connected with LCC DC converter station is as follows:

[0078]

[0079] In formula 8, N LCC is the AC node set connected with LCC DC converter station.

[0080] K pi is the pole pair number of node i connected with DC power transmission system; U di is the DC side voltage of node i connected with DC converter station; I di is the DC current of node i connected with DC converter station; is the power factor angle of node i connected with DC converter station. The positive and negative signs in formula 8 are negative for rectifier station and positive for inverter station.

[0081] The power balance equation of AC node connected with VSC DC converter station is as follows:

[0082]

[0083] In formula 9, N VSCP vi Q vi Q

[0084] For LCC DC transmission line, considering the influence of converter transformer and commutation reactance, the operating characteristic constraints are as follows:

[0085]

[0086]

[0087] U dRi = U dIi + I di R dc (Equation 12)

[0088] In Equations 10-12, K di is the transformer ratio of the DC converter station connected to node i; θ i is the converter control angle of the DC converter station connected to node i; X ci is the commutation reactance of the DC converter station connected to node i; R dc is the DC line resistance; U dRi is the rectifier side DC voltage; U dIi is the inverter side DC voltage.

[0089] In the process of solving the AC / DC optimal configuration model, the control mode of the LCC DC transmission line is first given. In combination with the actual engineering operation, the control mode of fixed DC current and rectifier station DC voltage is adopted for the LCC DC transmission line, and in combination with Equation 12, the inverter side DC voltage U dIi can be obtained. That is, after the control mode is given, the DC voltage and DC current of the LCC DC circuit can be considered as known quantities.

[0090] For VSC DC transmission line, considering the influence of VSC converter station equivalent resistance, the operating characteristic constraints are as shown in Equations 13-16:

[0091]

[0092]

[0093]

[0094]

[0095] In Equations 13-16, μ iFor the utilization of DC voltage, it is a constant value when PWM modulation strategy is determined, generally 0.866; M i For modulation ratio; V i For the AC side voltage of the DC converter station connected to node i; Y i For the equivalent admittance of the DC converter station connected to node i; δ i For the phase angle difference between the AC voltage of node i and the input voltage of the connected converter, α i For the equivalent impedance angle of the converter station; g il,d For the element in the conductance matrix of the DC network node after removing the tie node; m is the number of AC nodes connected to the VSC DC converter station, U dl For the DC voltage of the AC node l connected to the VSC DC converter station.

[0096] For the VSC DC transmission system, the control mode of rectifier station fixed DC voltage and reactive power and inverter station fixed DC current and reactive power is adopted. In combination with formula 16, the rectifier side DC current and the inverter side DC voltage can be obtained. That is, after the given control mode, the DC voltage and DC current of the VSC DC transmission system can be considered as known quantities.

[0097] The configuration capacity of UPFC refers to the capacity of series and parallel transformers and converters, wherein the capacity required by series transformer and converter is generally greater than that of parallel transformer and converter, but in order to facilitate standby, the series and parallel transformers and converters of UPFC are configured to the same capacity in practice, that is, the capacity of series transformer is taken. Therefore, only the capacity of series transformer is taken as the decision variable of UPFC configuration capacity. The configuration capacity of UPFC should be greater than the transmission power of the added AC line and leave a certain margin. Therefore, the configuration capacity S i of UPFC is:

[0098] S i ≥u i ·P ij (1+α) (formula 17)

[0099] In the above formula, P ij is the active power at the sending end node i of the candidate configuration UPFC AC line i-j, and α is the margin of UPFC configuration capacity. Among them, P ij The calculation equation is as follows:

[0100] P ij =-V i 2 G ij +V i V j (G ij cosθ ij +Bij sin θ ij ) (Equation 18)

[0101] In order to facilitate actual operation management, it is necessary to add a maximum constraint on the number of UPFCs accessing the power transmission system:

[0102]

[0103] In the above formula, N max is the maximum number of UPFCs accessing the AC-DC power transmission system.

[0104] The upper and lower limit constraints of the variables of the AC-DC power transmission system include the upper and lower limit constraints of the active power and reactive power of the generator of the balance node of the AC system, the upper and lower limit constraints of the bus voltage amplitude, the upper and lower limit constraints of the active power flow of the transmission line, and the upper and lower limit constraints of the voltage amplitudes of the two equivalent voltage sources of the UPFC series and parallel:

[0105] x min ≤ x ≤ x max (Equation 20)

[0106] In the above formula, x = (P Gn , Q Gn , V i , P ij , V E , V B ), x min and x max are the lower limit and upper limit of the variable x, P Gn is the active power of the generator of the balance node, Q Gn is the reactive power of the generator of the balance node, V i is the bus voltage amplitude, P ij is the active power flow of the transmission line, V E is the voltage amplitude of the parallel equivalent voltage source, and V B is the voltage amplitude of the series equivalent voltage source.

[0107] The configuration model is obtained according to Equations 1 to 20. The above configuration model contains a plurality of mixed integer nonlinear constraint equations, which can be solved by using the solver SBB in the commercial software GAMS, but the calculation of the solver is very dependent on the selection of initial values, and it is easy to fall into local optimization, and the solving efficiency is also relatively low. Therefore, in order to improve the reliability and efficiency of model solving, the convex relaxation method is used to optimize the above configuration model, and the AC-DC optimization configuration model in the UPFC configuration method is obtained.

[0108] In one embodiment, the multiple mixed integer convex programming constraint equations in the AC-DC optimal configuration model include: an AC power flow constraint equation, a DC operating characteristic constraint equation, a UPFC configuration constraint equation, and a variable constraint equation; the AC power flow constraint equation is a constraint on the operating characteristics of the AC transmission line of the AC-DC power transmission system; the DC operating characteristic constraint equation is a constraint on the operating characteristics of the DC transmission line of the AC-DC power transmission system; the UPFC configuration constraint equation is a constraint on the configuration capacity of the UPFC accessed by the AC-DC power transmission system; and the variable constraint equation is a constraint on the upper and lower limits of the variables of the AC-DC power transmission system.

[0109] The AC power flow constraint equation includes a conventional node power balance equation, a UPFC operating characteristic equation, a UPFC candidate node power balance equation, and a power balance equation of the AC node of the DC converter station. The DC operating characteristic constraint equation includes a conventional DC operating characteristic constraint equation and a flexible DC operating characteristic constraint equation. The variables in the variable constraint equation include the active power of the AC system balance node, the reactive power of the AC system balance node, the bus voltage, the active power flow of the transmission line, and the voltage amplitude of the two equivalent voltage sources. The variable constraint equation is formula 20.

[0110] Please refer to Figure 3 , which shows a flowchart of the construction process of an AC power flow constraint equation according to an embodiment of the present application. The construction process of the AC power flow constraint equation includes:

[0111] Step 301: using a second-order cone relaxation method to perform convex relaxation processing on the original conventional node power balance equation to obtain the conventional node power balance equation, wherein the original conventional node power balance equation is a mixed integer nonlinear constraint equation, and the conventional node power equation is a mixed integer convex programming constraint equation.

[0112] The original conventional node power balance equation is formula 5. In order to improve the calculation efficiency and the reliability and accuracy of the calculation results, the second-order cone relaxation method is used to perform convex relaxation processing on the original conventional node power balance equation to obtain the conventional node power balance equation.

[0113] Specifically, intermediate variables R ij , I ij , and U i are introduced, and the intermediate variables satisfy the constraint of formula 21, and a linear equation of the first-order Taylor series expansion of the equation constraint that the sum of the phase angle differences of the node voltages at both ends of each branch in the loop is 0, so that the original conventional node power balance equation formula 5 and the line active power flow equation formula 18 can be convexly relaxed into formula 22.

[0114]

[0115]

[0116] In the above formula, C k is the branch set included in the kth independent loop, G ii is the self conductance of node i, B ii is the self admittance of node i, P Gi is the lower limit value of P Gi , is the upper limit value of P Gi , Q Gi is the lower limit value of Q Gi , is the upper limit value of Q Gi , V i is the lower limit value of V i , is the upper limit value of V i , is the upper limit value of P ij , and ||·|| represents the two norm.

[0117] In step 302, the second-order cone relaxation method is used to perform convex relaxation processing on the original UPFC operation characteristic equation to obtain the UPFC operation characteristic equation, wherein the original UPFC operation characteristic equation is a mixed integer nonlinear constraint equation, and the UPFC operation characteristic equation is a mixed integer convex programming constraint equation.

[0118] In the formula 6, the original UPFC operation characteristic equation is obtained by using the second-order cone relaxation method to perform convex relaxation processing on the original UPFC operation characteristic equation, and the equation that the sum of the voltage phase angles of each branch in the UPFC is 0 is added, as shown in the formula 23, to obtain the UPFC operation characteristic equation, as shown in the formula 24.

[0119] θ sB + θ Br + θ rs = tan -1 (I sB / R sB ) + tan -1 (I Br / R Br ) + tan -1 (I rs / R rs ) = 0 (formula 23)

[0120]

[0121] In the formula 23 to 24, U s , R sE , I sE , R sB , I sBand other parameters are intermediate variables, that is, I sB , Bs , sB , Br , rB , Br , rB , rs , sr , rs , sr , sE , Es , sE , Es , s , r , E are intermediate variables, and satisfy equations in the form of formula 21, such as U s = V s 2 , U r and U E are the same; R sE = V s V E cos θ sE , I sE = V s V E sin θ sE , R sB = V s V B cos θ sB , I sB = V s V B sin θ sB , I Bs , I Br , I rB , R Br , R rB , R rs , R sr , I rs , I sr , R Es , I Es are the same.

[0122] In step 303, a second-order cone relaxation method is used to perform convex relaxation processing on the power balance equation of the original AC node of the DC converter station to obtain the power balance equation of the AC node of the DC converter station, wherein the original power balance equation of the AC node of the DC converter station is a mixed integer nonlinear constraint equation, and the power balance equation of the AC node of the DC converter station is a mixed integer convex programming constraint equation.

[0123] The power balance equations for the original AC node of the DC converter station refer to Equations 8 and 9. Similarly, the power balance equations for the original AC node of the DC converter station are obtained by applying a second-order cone relaxation method to perform convex relaxation.

[0124] Step 304: The original UPFC candidate node power balance equation is subjected to convex relaxation processing using a two-step convexization method to obtain the UPFC candidate node power balance equation. The original UPFC candidate node power balance equation is a mixed integer nonlinear constraint equation, and the UPFC candidate node power balance equation is a mixed integer convex programming constraint equation.

[0125] Specifically, a two-step convexity method is used to perform convex relaxation on the original UPFC candidate node power balance equation to obtain the UPFC candidate node power balance equation. This includes: performing convex relaxation on the original UPFC candidate node power balance equation using a second-order cone relaxation method, and then performing linearization using the Big-M method after convex relaxation to obtain the UPFC candidate node power balance equation.

[0126] The original UPFC candidate node power balance equation is Equation 7. Since Equation 7 is a mixed-integer nonlinear equation, a second-order cone relaxation method is first used for convex relaxation, thus transforming Equation 9 into Equation 25. Furthermore, because some equations in Equation 25 contain binary variables u... i Multiplying with continuous variables, we further linearize Equation 25 using the Big-M method. For example, we can introduce an intermediate variable ω into the first equation of Equation 25. i and φ i ,make ω i =φ i (1-u i This can be linearized into Equation 26, which is a part of the power balance equations for the UPFC candidate node. The other equations in Equation 25 are also linearized in the same way, thus transforming the nonlinear, non-convex constraint equations into convex constraint equations, resulting in the power balance equations for the UPFC candidate node.

[0127]

[0128]

[0129] In formulas 25 and 26, U j U r I rm R rmare intermediate variables, and also satisfy equations in the form of formula 21, and the definitions of the intermediate variables in formula 24 are the same, that is, as U r = V r 2 , R rm = V r V m cosθ rm , I rm = V r V m sinθ rm . R ij is the initial value of the first-order Taylor series expansion, I ij is the initial value of the first-order Taylor series expansion, and M is a positive number.

[0130] In the embodiments of the application, based on a second-order convex relaxation method, the mixed integer nonlinear constraint equation is converted into a mixed integer convex programming constraint equation, and the mixed integer nonlinear constraint equation in the configuration model is replaced with the mixed integer convex programming constraint equation, so that more efficient and reliable solving is realized, and an optimal configuration scheme of UPFC access node and UPFC capacity selection that minimizes the total investment operation cost of the AC / DC power transmission system is obtained.

[0131] In one embodiment, the construction process of the DC operation characteristic constraint equation includes: performing convex relaxation processing on an original conventional DC operation characteristic constraint equation by using a convex hull relaxation method to obtain the conventional DC operation characteristic constraint equation; and performing convex relaxation processing on an original flexible DC operation characteristic constraint equation by using a convex envelope method to obtain the flexible DC operation characteristic constraint equation.

[0132] The original conventional DC operation characteristic constraint equation is formula 10 to formula 12. There is a nonlinear term V i cosθ i in formula 12. When cosθ i is regarded as a continuous variable as a whole, the term is the multiplication of two continuous variables, and an intermediate variable w i = V i cosθ i is introduced. The convex hull relaxation method can be used to convert it into the following linear constraint:

[0133]

[0134] In formula 27, V imin is the lower limit value of V i , V imax is the upper limit value of V i , and (cosθ i )min the lower limit value of cos 0 i i the upper limit value of cos 0 max i

[0135] Since U i = V i 2 in the convexification process in formula 21, it is also necessary to add the quadratic equation of this non-convex equation. It can be converted into two convex inequality constraints as follows by using the convex hull relaxation method:

[0136]

[0137] After squaring both sides of formula 11, there is a nonlinear term An intermediate variable is introduced. It can also be converted into two convex inequality constraints by using the convex hull relaxation method, that is, formula 11 can be converted into convex constraints as follows:

[0138]

[0139] In formula 29, is the lower limit value of , is the upper limit value of .

[0140] Formulas 27 to 29 are the conventional DC operating characteristic constraint equations after convex relaxation processing.

[0141] For a VSC DC system, the original flexible DC operating characteristic constraint equation is formula 13 to formula 16. Since the phase angle difference 5 i between the AC node voltage connected to node i and the converter input voltage is a very small angle value, it can be considered that sin 5 i ≈ 5 i , cos 5 i ≈ 1, and then an intermediate variable L vi = M i V i , K vi = M i V i 5 i = L vi 5 i , O i = M i 2 is introduced, and formula 13 to formula 15 are converted into linear and convex inequality constraints by using the convex envelope method as shown in formula 30 to formula 33:

[0142] ​​​

[0143]

[0144]

[0145]

[0146] In formulas 30 to 33, M i M is the modulation ratio. imin For M i The lower limit value, M imax For M i The upper limit value, L vimin For L vi The lower limit value, L vimax For L vi The upper limit of δ imin For δ i The lower limit value, δ imax For δ i The upper limit.

[0147] In one embodiment, the process of constructing the UPFC configuration constraint equation includes: linearizing the original UPFC configuration constraint equation using the Big-M method to obtain the UPFC configuration constraint equation, wherein the original UPFC configuration constraint equation is a mixed integer nonlinear constraint equation, and the UPFC configuration constraint equation is a mixed integer convex programming constraint equation.

[0148] The original UPFC configuration constraint equation is Equation 17. The Big-M method is used to linearize the original UPFC configuration constraint equation, introducing the intermediate variable ζ. i Let ζ i =u i P ij It can be linearized into Equation 34:

[0149]

[0150] In Formula 34, M is a positive number.

[0151] In summary, by performing convex relaxation on the non-convex constraints in the above configuration model, the original mixed-integer nonlinear programming model is transformed into a mixed-integer convex programming model. The mixed-integer nonlinear constraint equations in the configuration model are then replaced with the corresponding mixed-integer convex programming constraint equations, resulting in an AC / DC optimal configuration model. This model can then be solved using the commercial software GAMS by calling the solver GUROBI, and the optimal solution for the optimal configuration model of the AC / DC transmission system with UPFC can be obtained efficiently and reliably.

[0152] In one embodiment, such as Figure 4Fig. 1 is a schematic diagram of an improved IEEE39-bus AC / DC transmission system. The line 4-14 of the IEEE39-bus system is changed to access the LCC DC line, and the lines 11-6 and 11-12 are changed to access the three-terminal VSC DC line. The AC / DC transmission system is divided into two areas, and the six tie lines between the two areas, i.e., 9-39, 4-3, 14-15, 19-16, 24-16, and 22-21, are assumed to be candidate branches for configuring UPFC. The upper limit of the number of configured UPFC is given as two, and the feasible region of the internal parameters of UPFC is defined as: the upper and lower limits of the shunt voltage source V E are 1.0 and 0; and the upper and lower limits of the series voltage source V B are 0.5 and 0. The fixed cost of installing UPFC is 100 million yuan, the installation cost of unit capacity of UPFC is 420,000 yuan / MW, the margin a of configured capacity is 10%, the discount rate of equipment is 7%, and the service life is 30 years. The access node and the UPFC capacity are calculated according to the above AC / DC optimal configuration model, and the results are shown in Table 1.

[0153]

[0154] (Table 1)

[0155] As shown in Table 1, the access nodes of UPFC calculated by the configuration model and the AC / DC optimal configuration model are the same, and both are at the node 14 in the line 14-15 in the AC / DC transmission system. The UPFC configuration capacities calculated by the two models are close to each other, and the capacity result of the AC / DC optimal configuration model is 4.3975 MW more than that of the original optimal configuration model, with a relative error of 0.96%. However, the annual equivalent investment and operation cost of the objective function is reduced by 15,408.4 yuan compared with the configuration model, with a relative error of 1.84%. It is shown that the proposed AC / DC optimal configuration model has high calculation accuracy, and can obtain a better configuration scheme than the configuration model. In terms of running time, the AC / DC optimal configuration model takes less time to solve than the configuration model, and has higher calculation efficiency. In addition, the solving speed and calculation result quality of the configuration model are highly dependent on the given initial value, and the configuration model is prone to fall into local optimum. The AC / DC optimal configuration model after convex relaxation can quickly and reliably solve the optimal solution under any given initial value, and has higher calculation reliability. Moreover, the obtained solution can save more annual equivalent investment and operation cost although a larger UPFC capacity is configured, i.e., a UPFC optimal configuration scheme with higher quality is obtained.

[0156] As shown in Table 2, compared with the optimal power flow calculation result of the AC-DC power transmission system without UPFC, the feasibility and economy of the UPFC-attached AC-DC power transmission system for actively improving power flow distribution and improving system transmission capacity are great. It can be seen that after the UPFC is attached, an equivalent voltage source with impedance is respectively connected in series and parallel with the transmission line, and by adjusting the voltage amplitude and phase angle of the equivalent voltage source, the power flow distribution of the transmission system can be actively improved. Compared with the optimal power flow calculation result without UPFC, the result of the UPFC configuration model and the result of the AC-DC optimal configuration model respectively make the transmission capacity of the line 14-15 increased by 14.2984 MW and 18.2961 MW, respectively, and make the system loss power decreased by 5.0348 MW and 7.6363 MW, respectively, and make the annual operating loss cost of the system saved by 30,070,880 yuan and 45,621,400 yuan, respectively, and make the annual equivalent total investment operating cost saved by 8,867,000 yuan and 24,275,400 yuan, respectively, which fully shows that the AC-DC power transmission system with the UPFC has high technical feasibility and good economy.

[0157]

[0158] (Table 2)

[0159] In conclusion, the UPFC configuration method proposed in the embodiments of the application adopts the AC-DC optimal configuration model to calculate the attachment node and capacity, the proposed optimal configuration method of the AC-DC power transmission system with the UPFC considers the collaborative operation of the conventional LCC DC and the multi-terminal flexible VSC DC two kinds of DC lines and the UPFC which are widely used in current power grid engineering. Meanwhile, the method considers the close correlation between the UPFC attachment node selection and capacity optimal configuration, and optimizes and calculates the attachment node selection and capacity configuration together. In the optimization model, the UPFC attachment node and configuration capacity are taken as decision variables together, which avoids the problem that the configuration scheme obtained by the method of selecting the point first and then determining the capacity cannot achieve the overall optimal solution. Further, the method is based on the convex relaxation technology, converts the mixed integer nonlinear programming model of the AC-DC power transmission system with the UPFC optimal configuration into a mixed integer convex programming model, can solve the optimal solution with higher quality, and has higher calculation efficiency.

[0160] It should be understood that although the steps in the flowcharts involved in the embodiments described above are shown in sequence according to the arrows, the steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, the execution of the steps is not strictly limited in sequence, and the steps can be executed in other orders. Moreover, at least some of the steps in the flowcharts involved in the embodiments described above can include multiple steps or multiple stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution order of the steps or stages is not necessarily sequential, but can be executed alternately or alternately with at least part of other steps or steps or stages in other steps.

[0161] Based on the same inventive concept, the embodiments of the present application also provide a UPFC configuration device for implementing the UPFC configuration method described above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme described in the above method, so the specific limitations in one or more UPFC configuration device embodiments provided below can refer to the limitations of the UPFC configuration method described above, and will not be repeated here.

[0162] In one embodiment, as shown in Figure 5 a UPFC configuration device is provided. The UPFC configuration device 500 includes an acquisition module 501 and a solving module 502, wherein:

[0163] The acquisition module 501 is configured to acquire an AC / DC optimization configuration model, the AC / DC optimization configuration model including a target function and a plurality of mixed integer convex programming constraint equations, decision variables of the target function including access nodes and UPFC capacity;

[0164] The solving module 502 is configured to input known parameters related to the AC / DC power transmission system into the AC / DC optimization configuration model, solve the target function based on the plurality of mixed integer convex programming constraint equations, and obtain access node values and UPFC capacity values.

[0165] In one embodiment, the target function is:

[0166]

[0167] wherein C ins is the annual investment cost of the UPFC, C los is the total operating loss power cost of the AC / DC power transmission system per hour, D y is the number of hours contained in a year, r is the cost discount rate of the UPFC, y is the service life of the UPFC, a0 is the fixed installation cost of each access node of the UPFC, a1 is the unit price of each access node of the UPFC, NUP u is the set of candidate nodes for UPFC candidate access in AC / DC power transmission system i S is a binary variable i K represents the UPFC capacity of node i accessing UPFC los N is the unit net loss power cost G P is the set of generator nodes in AC / DC power transmission system Gi P is the active power of the i-th generator L P is the set of load nodes in AC / DC power transmission system Li P is the active power of the i-th load node, t is 1 hour, and ∑ represents summation operation.

[0168] In one embodiment, the plurality of mixed integer convex programming constraint equations include: an AC power flow constraint equation, a DC operating characteristic constraint equation, a UPFC configuration constraint equation, and a variable constraint equation; the AC power flow constraint equation is a constraint on the operating characteristics of the AC transmission line of the AC / DC power transmission system; the DC operating characteristic constraint equation is a constraint on the operating characteristics of the DC transmission line of the AC / DC power transmission system; the UPFC configuration constraint equation is a constraint on the configuration capacity of the UPFC accessed by the AC / DC power transmission system; and the variable constraint equation is a constraint on the upper and lower limits of the variables of the AC / DC power transmission system.

[0169] In one embodiment, the AC power flow constraint equation includes a conventional node power balance equation, a UPFC operating characteristic equation, a UPFC candidate node power balance equation, and a power balance equation of the AC node of the DC converter station.

[0170] In one embodiment, the apparatus further comprises:

[0171] The first building module is configured to perform convex relaxation on the original conventional node power balance equation by using a second-order cone relaxation method, so as to obtain the conventional node power balance equation, wherein the original conventional node power balance equation is a mixed integer nonlinear constraint equation, and the conventional node power balance equation is a mixed integer convex programming constraint equation; perform convex relaxation on the original UPFC operation characteristic equation by using the second-order cone relaxation method, so as to obtain the UPFC operation characteristic equation, wherein the original UPFC operation characteristic equation is a mixed integer nonlinear constraint equation, and the UPFC operation characteristic equation is a mixed integer convex programming constraint equation; perform convex relaxation on the original power balance equation of the AC node of the DC converter station by using the second-order cone relaxation method, so as to obtain the power balance equation of the AC node of the DC converter station, wherein the original power balance equation of the AC node of the DC converter station is a mixed integer nonlinear constraint equation, and the power balance equation of the AC node of the DC converter station is a mixed integer convex programming constraint equation; and perform convex relaxation on the original UPFC candidate node power balance equation by using a two-step convexification method, so as to obtain the UPFC candidate node power balance equation, wherein the original UPFC candidate node power balance equation is a mixed integer nonlinear constraint equation, and the UPFC candidate node power balance equation is a mixed integer convex programming constraint equation.

[0172] In one embodiment, the first building module is specifically configured to perform convex relaxation on the original UPFC candidate node power balance equation by using the second-order cone relaxation method, and perform linearization on the convex relaxation by using the Big-M method, so as to obtain the UPFC candidate node power balance equation.

[0173] In one embodiment, the DC operation characteristic constraint equation includes a conventional DC operation characteristic constraint equation and a flexible DC operation characteristic constraint equation; and the device further includes:

[0174] The second building module is configured to perform convex relaxation on the original conventional DC operation characteristic constraint equation by using a convex envelope method, so as to obtain the conventional DC operation characteristic constraint equation; and perform convex relaxation on the original flexible DC operation characteristic constraint equation by using the convex envelope method, so as to obtain the flexible DC operation characteristic constraint equation.

[0175] In one embodiment, the device further includes:

[0176] The third building module is configured to perform linearization on the original UPFC configuration constraint equation by using the Big-M method, so as to obtain the UPFC configuration constraint equation, wherein the original UPFC configuration constraint equation is a mixed integer nonlinear constraint equation, and the UPFC configuration constraint equation is a mixed integer convex programming constraint equation.

[0177] In one embodiment, the variables in the variable constraint equation include AC system busbar active power, AC system busbar reactive power, bus voltage, transmission line active power flow, first UPFC equivalent voltage source voltage, and second UPFC equivalent voltage source voltage.

[0178] The modules in the UPFC device described above can be implemented in whole or in part by software, hardware, and combinations thereof. The modules described above can be embedded in or independent of a processor in a computer device in hardware form, or stored in a memory in a computer device in software form, so as to be called and executed by a processor to perform the operations corresponding to the modules.

[0179] In one embodiment, a computer device is provided, which can be a server, and an internal structure diagram thereof can be as shown in Figure 6 The computer device includes a processor, a memory, and a network interface connected through a system bus. The processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for running the operating system and the computer program in the non-volatile storage medium. The database of the computer device is configured to store UPFC configuration data. The network interface of the computer device is configured to communicate with an external terminal through a network connection. The computer program is executed by the processor to implement a UPFC configuration method.

[0180] Those skilled in the art can understand that Figure 6 The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. A specific computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0181] In one embodiment, a computer device is also provided, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps in the method embodiments described above.

[0182] In one embodiment, a computer readable storage medium is provided, which stores a computer program. The computer program is executed by a processor to implement the steps in the method embodiments described above.

[0183] In one embodiment, a computer program product is provided, which includes a computer program. The computer program is executed by a processor to implement the steps in the method embodiments described above.

[0184] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when the computer program is executed, the processes of the above-mentioned embodiments of the methods can be included. Any reference to memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (Read-Only Memory, ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive memory (ReRAM), magnetoresistive random access memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric memory (Ferroelectric Random Access Memory, FRAM), phase change memory (Phase Change Memory, PCM), graphene memory, etc. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, etc. As an illustration but not limitation, RAM can be in various forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), etc. The database involved in the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a block chain, etc., without being limited thereto. The processor involved in the embodiments provided in the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., without being limited thereto.

[0185] Any combination of the technical features of the above embodiments can be made. In order to make the description simple, all possible combinations of the technical features in the above embodiments are not described, however, as long as the combination of the technical features does not exist contradictory, it should be considered as the scope of the present application.

[0186] The above embodiments only express several implementation manners of the present application, and the description is more specific and detailed, but it should not be understood as a limitation on the scope of the patent of the present application. It should be pointed out that for ordinary skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are within the scope of protection of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. A UPFC configuration method, characterized in that, The method includes: Obtain an AC / DC optimal configuration model, which includes an objective function and multiple mixed integer convex programming constraint equations. The decision variables of the objective function include access nodes and UPFC capacity. The known parameters related to the AC / DC transmission system are input into the AC / DC optimal configuration model, and the objective function is solved based on the multiple mixed integer convex programming constraint equations to obtain the access node value and UPFC capacity value. The objective function is: Among them, C ins For the annual investment cost of UPFC, C los D represents the cost of total hourly network loss electricity in the AC / DC transmission system. y Let N be the number of hours included in a year, r be the discount rate for UPFC, y be the service life of UPFC, a0 be the fixed installation cost per UPFC access node, a1 be the unit price per UPFC access node, and N be the total cost of UPFC. UP For the set of candidate nodes for AC / DC transmission system to be connected to UPFC, u i S is a binary variable. i K represents the UPFC capacity of node i connected to the UPFC. los The cost per unit of network loss electricity, N G Let P be the set of generator nodes in an AC / DC transmission system. Gi Let N be the active power of the i-th generator. L Let P be the set of load nodes in an AC / DC transmission system. Li Let be the active power of the i-th load node, Δt be 1 hour, and ∑ represent the summation operation; The multiple mixed-integer convex programming constraint equations include: AC power flow constraint equations, DC operating characteristic constraint equations, UPFC configuration constraint equations, and variable constraint equations; the AC power flow constraint equations constrain the operating characteristics of the AC transmission lines of the AC / DC transmission system; the DC operating characteristic constraint equations constrain the operating characteristics of the DC transmission lines of the AC / DC transmission system; the UPFC configuration constraint equations constrain the configuration capacity of the UPFCs connected to the AC / DC transmission system; and the variable constraint equations constrain the upper and lower limits of the variables of the AC / DC transmission system.

2. The method according to claim 1, characterized in that, The AC power flow constraint equations include the power balance equations of conventional nodes, the operating characteristic equations of UPFC, the power balance equations of UPFC candidate nodes, and the power balance equations of AC nodes in DC converter stations.

3. The method according to claim 2, characterized in that, The process of constructing the AC power flow constraint equations includes: The original conventional node power balance equation is subjected to convex relaxation using a second-order cone relaxation method to obtain the conventional node power balance equation. The original conventional node power balance equation is a mixed integer nonlinear constraint equation, and the conventional node power balance equation is a mixed integer convex programming constraint equation. The original UPFC operating characteristic equations are subjected to convex relaxation using a second-order cone relaxation method to obtain the UPFC operating characteristic equations. The original UPFC operating characteristic equations are mixed-integer nonlinear constraint equations, and the UPFC operating characteristic equations are mixed-integer convex programming constraint equations. The power balance equations of the original DC converter station AC nodes are subjected to convex relaxation using the second-order cone relaxation method to obtain the power balance equations of the DC converter station AC nodes. The original power balance equations of the DC converter station AC nodes are mixed integer nonlinear constraint equations, and the power balance equations of the DC converter station AC nodes are mixed integer convex programming constraint equations. A two-step convexification method is used to perform convex relaxation on the original UPFC candidate node power balance equation to obtain the UPFC candidate node power balance equation. The original UPFC candidate node power balance equation is a mixed integer nonlinear constraint equation, and the UPFC candidate node power balance equation is a mixed integer convex programming constraint equation.

4. The method according to claim 3, characterized in that, The two-step convexity method is used to perform convex relaxation on the original UPFC candidate node power balance equations to obtain the UPFC candidate node power balance equations, including: The original UPFC candidate node power balance equations are subjected to convex relaxation using a second-order cone relaxation method, and then linearized using the Big-M method after convex relaxation to obtain the UPFC candidate node power balance equations.

5. The method according to claim 1, characterized in that, The DC operating characteristic constraint equations include conventional DC operating characteristic constraint equations and flexible DC operating characteristic constraint equations; the construction process of the DC operating characteristic constraint equations includes: The original conventional DC operating characteristic constraint equations are subjected to convex relaxation processing using the convex hull relaxation method to obtain the conventional DC operating characteristic constraint equations. The original flexible DC operation characteristic constraint equations are subjected to convex relaxation using the convex envoy method to obtain the flexible DC operation characteristic constraint equations.

6. The method according to claim 1, characterized in that, The process of constructing the UPFC configuration constraint equations includes: The original UPFC configuration constraint equations are linearized using the Big-M method to obtain the UPFC configuration constraint equations, wherein the original UPFC configuration constraint equations are mixed-integer nonlinear constraint equations, and the UPFC configuration constraint equations are mixed-integer convex programming constraint equations.

7. The method according to claim 1, characterized in that, The variables in the variable constraint equation include the active power of the AC system slack node, the reactive power of the AC system slack node, the bus voltage, the active power flow of the transmission line, the voltage of the first UPFC equivalent voltage source, and the voltage of the second UPFC equivalent voltage source.

8. A UPFC configuration device, characterized in that, The device includes: The acquisition module is used to acquire the AC / DC optimal configuration model, which includes an objective function and multiple mixed-integer convex programming constraint equations. The decision variables of the objective function include the access nodes and the UPFC capacity. The objective function is: Among them, C ins For the annual investment cost of UPFC, C los D represents the cost of total hourly network loss in the AC / DC transmission system. y Let N be the number of hours included in a year, r be the discount rate for UPFC, y be the service life of UPFC, a0 be the fixed installation cost per UPFC access node, a1 be the unit price per UPFC access node, and N be the total cost of UPFC. UP For the set of candidate nodes for AC / DC transmission system to be connected to UPFC, u i S is a binary variable. i K represents the UPFC capacity of node i connected to the UPFC. los The cost per unit of network loss electricity, N G Let P be the set of generator nodes in an AC / DC transmission system. Gi Let N be the active power of the i-th generator. L Let P be the set of load nodes in an AC / DC transmission system. Li Let be the active power of the i-th load node, Δt be 1 hour, and ∑ represent the summation operation; The multiple mixed-integer convex programming constraint equations include: AC power flow constraint equations, DC operating characteristic constraint equations, UPFC configuration constraint equations, and variable constraint equations; the AC power flow constraint equations constrain the operating characteristics of the AC transmission lines of the AC / DC transmission system; the DC operating characteristic constraint equations constrain the operating characteristics of the DC transmission lines of the AC / DC transmission system; the UPFC configuration constraint equations constrain the configuration capacity of the UPFCs connected to the AC / DC transmission system; and the variable constraint equations constrain the upper and lower limits of the variables of the AC / DC transmission system. The solution module is used to input known parameters related to the AC / DC transmission system into the AC / DC optimal configuration model, and solve the objective function based on the multiple mixed integer convex programming constraint equations to obtain the access node value and UPFC capacity value.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

11. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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