Research Method for the Intelligent Soft-Switching Grid-Connected Operation Model Based on the Second-Order Cone

Abstract

The invention discloses a research method for an intelligent soft-switching grid-connected operation model based on a second-order cone, comprising the following steps: S1: Establish a simplified model of the intelligent soft switch according to the intelligent soft-switch structure; S2: Establish the power constraint conditions of the intelligent soft switch according to the working mode of the intelligent soft switch in the distribution network; S3: According to the optimization solution algorithm, relax the non-convex constraint conditions into inequality constraints; S4: Establish the power constraint of the intelligent soft switch with symbolic variable parameter constraints according to the actual working conditions of the intelligent soft switch; S5: Add non-convex constraints using second-order cone constraints to tighten the power constraint of the intelligent soft switch; S6: Take the minimum distribution network loss cost as the objective function, solve with the foregoing constraints as conditions to obtain the optimization result, and set the day-ahead scheduling strategy and the intra-day operation strategy of the intelligent soft switch according to the optimization result. The invention reduces the error caused by the relaxed constraints of the intelligent soft switch, and thus obtains more accurate calculation results.

Description

Technical Field

[0001] The present invention relates to the field of power grid operation adjustment, and particularly to a research method for an intelligent soft-switch grid-connected operation model based on a second-order cone. Background Art

[0002] In the operation of a power system, an intelligent soft switch SOP has the ability to flexibly adjust power flow and voltage. The intelligent soft switch SOP is a new type of switch proposed compared with a traditional tie switch. The intelligent soft switch affects or changes the power flow distribution of the entire system by adjusting the power exchange between the two sides of the feeder. Compared with the traditional tie switch, it can exchange active power more flexibly, compensate reactive power, thereby regulating the voltage and realizing the transfer of load.

[0003] However, most of the current research on the intelligent soft switch SOP ignores the limitation of its operation loss, or only uses the method of not tightening the constraint after using the relaxation constraint. Under the current calculation of such an intelligent soft switch model, the operation of the power grid may be affected because an inaccurate power flow solution is used. Therefore, constructing an intelligent soft switch model that re-tightens after relaxation has certain practical significance for grid-connected operation. Summary of the Invention

[0004] Aiming at the problem that the unreasonable constraint conditions of the intelligent soft switch in the prior art affect the operation of the power grid, the present invention provides a research method for an intelligent soft switch grid-connected operation model based on a second-order cone, studies the operation characteristics of the intelligent soft switch in the distribution network, establishes corresponding power constraint conditions, and tightens the relaxed inequality constraints through second-order cone constraints, which can effectively reduce the error caused by the relaxed constraints of the intelligent soft switch, provides an effective guarantee for the grid-connected operation containing the intelligent soft switch, and further helps to obtain a more reasonable day-ahead scheduling strategy and an intelligent soft switch intra-day operation strategy, which is beneficial to the stable operation of the power grid.

[0005] The following are the technical solutions of the present invention.

[0006] A research method for an intelligent soft switch grid-connected operation model based on a second-order cone includes the following steps:

[0007] S1: Establish a simplified model of the intelligent soft switch according to the intelligent soft switch structure;

[0008] S2: Establish the power constraint conditions of the intelligent soft switch according to the working mode of the intelligent soft switch in the distribution network;

[0009] S3: Relax the non-convex constraint conditions into inequality constraints according to the optimization solution algorithm;

[0010] S4: Establish the power constraint of the intelligent soft switch with signed variable parameter constraints according to the actual working conditions of the intelligent soft switch;

[0011] S5: Add non-convex constraints with second-order cone constraints to tighten the power constraints of the intelligent soft switch.

[0012] S6: Take the minimum distribution network loss cost as the objective function, solve it under the above-mentioned constraints to obtain the optimization result, and set the day-ahead scheduling strategy and the in-day operation strategy of the intelligent soft switch according to the optimization result.

[0013] According to the operating characteristics of the intelligent soft switch in the distribution network, the present invention establishes corresponding power constraint conditions, which can reduce the error caused by the loose constraints of the intelligent soft switch, and thus obtain more accurate calculation results.

[0014] Preferably, the process of step S2 includes:

[0015] S21: Analyze the node injection power and the internal power loss of the intelligent soft switch SOP, and establish a power balance constraint:

[0016]

[0017] In the formula: P i sop and respectively represent the active power injected into the intelligent soft switch SOP at nodes i and j; P i loss and respectively represent the internal power losses of the intelligent soft switch SOP generated at nodes i and j;

[0018] S22: Analyze the reactive power and complex power of the intelligent soft switch SOP, and establish the following constraints:

[0019]

[0020]

[0021] In the formula: enb k,t represents the energized state of node k at time t, indicating that if the node is energized, then enb k,t = 1, if the node is not energized, then enb k,t = 0. represents the reactive power of the intelligent soft switch SOP at node k; and the minimum and maximum values of the reactive power of the intelligent soft switch SOP at node k; and respectively represent the active power and reactive power of the intelligent soft switch SOP at node k at time t; represents the complex power of the intelligent soft switch SOP at node k; S23: Analyze the internal power loss of the intelligent soft switch SOP, and establish the following constraints:

[0022]

[0023]

[0024] In the formula: and are the loss coefficients of the intelligent soft switch SOP.

[0025] Preferably, the inequality constraint in step S3 is:

[0026]

[0027] Preferably, establishing the intelligent soft switch power constraint with signed variable parameter constraints in step S4 includes:

[0028] α i +α j = 1

[0029]

[0030]

[0031] In the formula: The binary variables α i and α j are the sign indicator variables of P i sop and ; If P i sop is non - negative, then α i = 1, otherwise α i = 0; If j is non - negative, then α j = 1, otherwise α

[0032] Preferably, the process of step S5 includes:

[0033] S51: To make the relaxed constraints bounded, introduce voltage auxiliary variables and add non - convex constraints as follows:

[0034]

[0035]

[0036] In the formula: r l,t and t l,t are the voltage auxiliary variables of line l; u m,t and u n,t are the voltage auxiliary variables of nodes m and n respectively;

[0037] S52: Analyze the second-order cone constraint and expand the first two orders of the variables using the Taylor formula; generalize it to the second-order cone constraints of multiple variables, and expand the two constraints in step S51 as follows:

[0038]

[0039]

[0040] In the formula: and are respectively and The results of the previous iteration. and are respectively and The results of the previous iteration; and are respectively r l,t and t l,t The results of the previous iteration.

[0041] The substantial effects of the present invention include: establishing corresponding power constraint conditions according to the operating characteristics of intelligent soft switches in the distribution network; tightening the relaxed inequality constraints through second-order cone constraints; effectively reducing the errors caused by the relaxed constraints of intelligent soft switches, providing a powerful analysis tool for the grid-connected operation of intelligent soft switches; and obtaining more reasonable day-ahead scheduling strategies and intra-day operation strategies of intelligent soft switches, which is beneficial to the stable operation of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 is a schematic flowchart of the method according to an embodiment of the present invention;

[0043] Figure 2 is the basic structure of the B2B-VSC intelligent soft switch used in an embodiment of the present invention;

[0044] Figure 3 is the SOP simplified model diagram of the B2B-VSC intelligent soft switch used in an embodiment of the present invention;

[0045] Figure 4 is the 9-node network topology diagram used in an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0046] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in combination with the embodiments. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0047] The terms "first", "second", "third", "fourth", etc. (if any) in the description and claims of the present invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein.

[0048] It should be understood that in various embodiments of the present invention, the magnitude of the serial numbers of the various processes does not mean the sequence of execution, and the execution sequence of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.

[0049] It should be understood that in the present invention, "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0050] It should be understood that in the present invention, "B corresponding to A", "B corresponding to A relatively", "A corresponding to B relatively" or "B corresponding to A relatively" means that B is associated with A, and B can be determined according to A. Determining B according to A does not mean determining B only according to A, and B can also be determined according to A and / or other information. The matching of A and B means that the similarity between A and B is greater than or equal to a preset threshold.

[0051] The technical solutions of the present invention will be described in detail below with specific embodiments. The embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments.

[0052] Embodiment:

[0053] A research method for an intelligent soft-switch grid-connected operation model based on a second-order cone, as Figure 1 shown, includes the following steps:

[0054] S1: Establish a simplified model of the intelligent soft switch according to the intelligent soft switch structure;

[0055] S2: Establish the power constraint conditions of the intelligent soft switch according to the working mode of the intelligent soft switch in the distribution network;

[0056] S3: According to the optimization solution algorithm, relax the non-convex constraint conditions into inequality constraints;

[0057] S4: Establish the power constraint of the intelligent soft switch with signed variable parameter constraints according to the actual working conditions of the intelligent soft switch;

[0058] S5: Add non-convex constraints with second-order cone constraints to tighten the power constraints of the intelligent soft switch.

[0059] S6: Take the minimum distribution network loss cost as the objective function, solve it under the above-mentioned constraints to obtain the optimization result, and set the day-ahead scheduling strategy and the in-day operation strategy of the intelligent soft switch according to the optimization result.

[0060] According to the operating characteristics of the intelligent soft switch in the distribution network, this embodiment establishes corresponding power constraint conditions, which can reduce the errors caused by the relaxed constraints of the intelligent soft switch, and thus obtain more accurate calculation results.

[0061] Specifically, in step 1, analyze the basic structure of the B2B-VSC intelligent soft switch as shown in Figure 2 and establish a simplified model diagram of the intelligent soft switch SOP as shown in Figure 3

[0062] Figure 3 In and respectively represent the active power and reactive power injected into the intelligent soft switch SOP at nodes i and j. The topology structure of the intelligent soft switch SOP is realized by two converters in parallel with a DC capacitor.

[0063] The process of step S2 includes:

[0064] S21: Analyze the power injected at the node and the internal power loss of the intelligent soft switch SOP, and establish a power balance constraint:

[0065]

[0066] In the formula: P i sop and respectively represent the active power injected into the intelligent soft switch SOP from nodes i and j; P i loss and respectively represent the internal power losses of the intelligent soft switch SOP generated at nodes i and j;

[0067] S22: Analyze the reactive power and complex power of the intelligent soft switch SOP, and establish the following constraints:

[0068]

[0069]

[0070] In the formula: enb k,t represents the energized state of node k at time t, indicating that if the node is energized, then enb k,t ​= 1. If the node is not powered on, then enb k,t = 0. Represents the reactive power of the intelligent soft switch SOP at node k; and The minimum and maximum values of the reactive power of the intelligent soft switch SOP at node k; and Respectively represent the active power and reactive power of the intelligent soft switch SOP at node k at time t; Represents the complex power of the intelligent soft switch SOP at node k; S23: Analyze the internal power loss of the intelligent soft switch SOP and establish the following constraints:

[0071]

[0072]

[0073] In the formula: and Are the loss coefficients of the intelligent soft switch SOP.

[0074] The inequality constraint of step S3 is:

[0075]

[0076] In step S4, establish the intelligent soft switch power constraint with signed variable parameters, including:

[0077] α i +α j = 1

[0078]

[0079]

[0080] In the formula: The binary variable α i and α j Are the sign indicator variables of P i sop and ; P i sop If it is non - negative, then α i = 1, otherwise α i = 0; If it is non - negative, then α j = 1, otherwise α j = 0.

[0081] The process of step S5 includes:

[0082] S51: To make the relaxed constraints bounded, introduce voltage auxiliary variables and add non - convex constraints as follows:

[0083]

[0084]

[0085] where: r l,t and t l,t are the voltage auxiliary variables of line l; u m,t and u n,t are the voltage auxiliary variables of nodes m and n respectively;

[0086] S52: Analyze the second-order cone constraints and expand the first two orders of the variables using the Taylor formula; generalize to the second-order cone constraints of multiple variables, and expand the two constraints in step S51 as follows:

[0087]

[0088]

[0089] where: and are respectively and the results of the previous iteration. and are respectively and the results of the previous iteration; and are respectively the results of r l,t and t l,t of the previous iteration;

[0090] S53: Establish a simple network topology structure as shown in Figure 4 ; where node 1 and node 9 are generator nodes respectively, and the remaining nodes are ordinary load nodes; node 6 and node 7 are connected by the intelligent soft switch SOP;

[0091] S54: Table 1 gives the active power and reactive power flows on both sides of the intelligent soft switch SOP, and gives the internal active power loss of the intelligent soft switch SOP at nodes 6 and 7.

[0092] Power <![CDATA[P sop > <![CDATA[P loss > <![CDATA[Q sop > Node 6 0.75993 0.01000 0.65000 Node 7 -0.77993 0.00999 0.62582 Total Power -0.01999 0.01999 1.27586

[0093] Table 1 Power flow conditions on both sides of the intelligent soft switch SOP

[0094] In this embodiment, corresponding power constraint conditions are established according to the operating characteristics of intelligent soft switches in the distribution network; through second-order cone constraints, the relaxed inequality constraints are tightened; the error caused by the relaxed constraints of intelligent soft switches is effectively reduced, providing a powerful analysis tool for the grid-connected operation with intelligent soft switches; a more reasonable day-ahead scheduling strategy and the in-day operation strategy of intelligent soft switches can be obtained, which is beneficial to the stable operation of the power grid.

[0095] From the description of the above embodiments, those skilled in the art can understand that, for the convenience and conciseness of description, only the division of the above functional modules is used as an example. In actual applications, the above functions can be allocated to different functional modules according to needs, that is, the internal structure of a specific device is divided into different functional modules to complete all or part of the functions described above.

[0096] In addition, each functional unit in the embodiments of the present application can be integrated into a processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0097] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiments of the present application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product stored in a storage medium, including several instructions for causing a device (which can be a single-chip microcomputer, a chip, etc.) or a processor to execute all or part of the steps of the methods in the various embodiments of the present application. The foregoing storage medium includes: various media such as a USB flash drive, a mobile hard disk, a read only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disc that can store program codes.

[0098] The above content is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in the present application, and all of them should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. Research method for intelligent soft-switching grid-connected operation model based on second-order cone, characterized in that Including the following steps: S1: Establish a simplified model of the intelligent soft switch according to the intelligent soft switch structure; S2: Establish the power constraint conditions of the intelligent soft switch according to the working mode of the intelligent soft switch in the distribution network; S3: According to the optimization algorithm, relax the non-convex constraint conditions into inequality constraints; S4: Establish the power constraint of the intelligent soft switch with signed variable parameter constraints according to the actual working conditions of the intelligent soft switch; S5: Add non-convex constraints using second-order cone constraints to tighten the power constraint of the intelligent soft switch; S6: Take the minimum distribution network loss cost as the objective function, solve it with the above constraints as conditions to obtain the optimization result, and set the day-ahead scheduling strategy and the in-day operation strategy of the intelligent soft switch according to the optimization result; The process of step S5 includes: S51: To make the relaxed constraints bounded, introduce voltage auxiliary variables and add non-convex constraints as follows: where: r l,t and t l,t are the voltage auxiliary variables of line l; u m,t and u n,t are the voltage auxiliary variables of nodes m and n, respectively; S52: Analyze the second-order cone constraints and expand the first two orders of the variables using the Taylor formula; generalize to the second-order cone constraints of multiple variables and expand the two constraints in step S51 as follows: Where: and are respectively and the results of the previous iteration; and are respectively and the results of the previous iteration; and are respectively r l,t and t l,t the results of the previous iteration.

2. The research method of the intelligent soft-switching grid-connected operation model based on the second-order cone according to claim 1, characterized in that, The process of step S2 includes: S21: Analyze the node injection power and the internal power loss of the intelligent soft switch SOP, and establish a power balance constraint: Wherein: and respectively represent the active power injected into the intelligent soft switch SOP at nodes i and j; and respectively represent the internal power losses of the intelligent soft switch SOP generated at nodes i and j; S22: Analyze the reactive power and complex power of the intelligent soft switch SOP, and establish the following constraints: Where: enb k,t represents the energized state of node k at time t, indicating that enb k,t = 1 if the node is energized, and enb k,t = 0 if the node is not energized; represents the reactive power of the intelligent soft switch SOP at node k; and the minimum and maximum values of the reactive power of the intelligent soft switch SOP at node k; and respectively represent the active power and reactive power of the intelligent soft switch SOP at node k at time t; represents the complex power of the intelligent soft switch SOP at node k; S23: Analyze the internal power loss of the intelligent soft switch SOP, and establish the following constraints: Wherein: and are the loss coefficients of the intelligent soft switch SOP.

3. The research method of the intelligent soft-switching grid-connected operation model based on the second-order cone according to claim 2, wherein The inequality constraint of step S3 is:

4. The research method of the intelligent soft-switching grid-connected operation model based on the second-order cone according to claim 1, characterized in that, The power constraint of the intelligent soft switch with signed variable parameter constraints established in step S4 includes: α i +α j ＝1 Where: the binary variable α i and α j are and sign-indicating variables; If i is non-negative, then α i = 1; otherwise α If j is non-negative, then α j = 0.

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