Snowflake power distribution network dynamic reconstruction method, system, equipment and medium

Abstract

The invention is suitable for the technical field of power distribution network reconstruction, and discloses a snowflake power distribution network dynamic reconstruction method, system, device and medium, and the method comprises the steps: obtaining the basic data of a snowflake power distribution network in a research area; based on the basic data of the snowflake power distribution network in the research area, taking the minimum load balance degree and active power loss as targets, and constructing a load balance-oriented initial snowflake power distribution network dynamic reconstruction model; adopting a second-order cone relaxation technology and a large M method to convert the initial snowflake power distribution network dynamic reconstruction model into a target snowflake power distribution network dynamic reconstruction model of mixed integer second-order cone programming; solving the dynamic reconstruction model of the target snowflake power distribution network; and obtaining a snowflake power distribution network dynamic reconstruction strategy for load balancing based on a solving result. According to the method, the safety and economy of system operation can be considered, load transfer is carried out by comprehensively considering the load conditions of a plurality of superior substations and a plurality of feeder lines, so that the load balancing of the whole network is realized, the solving difficulty is low, and the decision-making time is short.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network reconstruction, and in particular to a method, system, equipment and medium for dynamic reconstruction of a snowflake distribution network. Background Art

[0002] With the access of high-penetration distributed power sources and the uneven distribution of regional industrial and residential spaces, there has always been a problem of heavy load and light load coexisting in urban power grids. Some lines operate at high levels for a long time during peak load periods, and the load demand and resource allocation are mismatched in multiple dimensions of time and space, which can easily lead to reduced equipment resource utilization, network congestion, and increased line losses, seriously affecting the stability and economy of distribution network operation. As one of the key technologies for distribution network operation and management, network reconstruction can improve network flow distribution and optimize system operation by dynamically adjusting the opening and closing status of the segmented contact switches in the line, which is of great significance for achieving feeder load balancing.

[0003] At present, dynamic reconstruction mainly involves the substation level or single feeder level, while the urban snowflake distribution network uses the ring network box as the networking unit to build a "3-station 6-line" or "4-station 8-line" feeder cluster, with multiple intra-station and inter-station interconnection lines, each line contains multiple segmented interconnection switches, and the topological structure presents a flexible and controllable ring network feature. When the traditional dynamic reconstruction scheme is used to reconstruct the urban snowflake distribution network for load balancing, the difficulty of solving the reconstruction scheme and the decision-making time are greatly increased. Summary of the invention

[0004] To solve the above problems, the present invention provides a dynamic reconstruction method, system, equipment and medium of a snowflake distribution network, which can take into account the safety and economy of system operation, comprehensively consider the load conditions of multiple superior substations and multiple feeders to transfer loads and thus achieve load balancing of the entire network, and can adapt to the coexistence of heavy loads and light loads in urban distribution networks brought about by the rapid development of distributed power sources. It has low solution difficulty and fast decision-making time.

[0005] To achieve the above object, the present invention adopts the following technical solution:

[0006] In a first aspect, the present invention provides a method for dynamically reconfiguring a snowflake distribution network, comprising:

[0007] Obtain basic data of the snowflake distribution network in the study area;

[0008] Based on the basic data of the snowflake distribution network in the study area, with the goal of load balancing and minimizing active power loss, a dynamic reconstruction model of the initial snowflake distribution network for load balancing is constructed;

[0009] The initial snowflake distribution network dynamic reconstruction model is transformed into the target snowflake distribution network dynamic reconstruction model of mixed integer second-order cone programming by using the second-order cone relaxation technology and the big M method.

[0010] Solve the dynamic reconstruction model of the target snowflake distribution network;

[0011] Based on the solution results, a dynamic reconstruction strategy of the snowflake distribution network for load balancing is obtained.

[0012] Furthermore, the basic data of the snowflake distribution network in the study area include: components, composition structure, equipment parameters, and economic parameters.

[0013] Furthermore, the objective function of the initial snowflake distribution network dynamic reconstruction model satisfies:

[0014] min f=ω1f1+ω2f2

[0015]

[0016] Where f1 represents the line load balance; f2 represents the total power loss; ω1 and ω2 are the load balance and power loss weight coefficients; S ij (t) is the transmission capacity of branch ij at time t; S ij，max is the maximum transmission capacity of branch ij; P ij (t) represents the active power flowing from node i to node j at time t; Q ij (t) represents the reactive power flowing from node i to node j at time t; Ω l I represents a branch set; ij (t) represents the current amplitude of line ij at time t; R ij Represents the resistance of line ij; T represents the total number of time periods for calculation.

[0017] Furthermore, the constraints of the initial snowflake distribution network dynamic reconstruction model include:

[0018] Snowflake distribution network flow constraints, node power balance constraints, feeder head end node output constraints, branch capacity constraints, node voltage constraints, network topology constraints, section interconnection switch state constraints, and energy storage operation constraints.

[0019] Furthermore, the power flow constraint expression of the snowflake distribution network satisfies:

[0020]

[0021] In the formula, Ω in (j),Ω out (j) respectively represent the set of nodes j as the terminal node and the head node in the distribution network; a ij (t) represents the opening and closing state of the switch on branch ij; P ij (t) represents the active power flowing from node i to node j at time t; Q ij(t) represents the reactive power flowing from node i to node j at time t; I ij (t) represents the current amplitude of line ij at time t; R ij Indicates the resistance of line ij; a jk (t) represents the opening and closing state of the switch on branch jk; P jk (t) represents the active power flowing from node j to node k at time t; Q jk (t) represents the reactive power flowing from node j to node k at time t; U j (t) represents the voltage amplitude at node j at time t; P j (t) represents the active injected power of node j at time t; Q j (t) represents the reactive power injected into node j at time t; X ij Indicates the reactance of line ij.

[0022] Furthermore, the node power balance constraint expression satisfies:

[0023]

[0024] Where P j (t) represents the active injected power of node j at time t; Q j (t) represents the reactive injection power of node j at time t; P DG，j (t) represents the active power output of the distributed photovoltaic power plant at time t in the ring network node; P load，j (t) represents the load active power of ring network node j at time t; Q load，j (t) represents the load reactive power of the ring network node j at time t; P ESS，dis，j (t) is the ring network node, the discharge power of the connected energy storage at time t; P ESS，ch，j (t) is the charging power of the energy storage connected to the ring network node j at time t.

[0025] Furthermore, the output constraint expression of the feeder head end node satisfies:

[0026]

[0027] Where: P i，sub (t) represents the active output of the substation; Q i，sub (t) represents the reactive power output of the substation; P i，sub，max (t) represents the upper limit of the substation active output at time t; P i，sub，min (t) represents the lower limit of the substation active output at time t; Q i，sub，max (t) represents the upper limit of the reactive power output of the substation at time t; Q i，sub，min (t) represents the lower limit of the reactive power output of the substation at time t.

[0028] Furthermore, the branch capacity constraint expression satisfies:

[0029]

[0030] Where: a ij (t) represents the opening and closing state of the switch on branch ij; S ij，max is the maximum transmission capacity of branch ij; P ij (t) represents the active power flowing from node i to node j at time t; Q ij (t) represents the reactive power flowing from node i to node , at time t.

[0031] Furthermore, the node voltage constraint expression satisfies:

[0032] U i，min ≤U i (t)≤U i，max

[0033] Where U i，max is the upper limit of the voltage at node i; U i，min is the lower limit of the node i voltage.

[0034] Furthermore, the network topology constraints satisfy:

[0035]

[0036] In the formula, Ω l Represents a branch set; a ij (t) represents the opening and closing state of the switch on branch ij; N bus Indicates the number of nodes in the network; N sub They represent the number of substations in the network respectively.

[0037] Furthermore, the state constraint expression of the sectionalized tie switch satisfies:

[0038] α ij (t)-β ij (t) = a ij (t)-a ij (t-1)

[0039] α ij (t)+β ij (t)≤1

[0040]

[0041] In the formula, α ij (t) is the closing flag of the switch on branch ij at time t; β ij(t) is the disconnection flag of the switch on branch ij at time t; It indicates the maximum number of switching actions within a certain period; T indicates the total number of time periods calculated.

[0042] Furthermore, the energy storage operation constraint expression satisfies:

[0043]

[0044]

[0045] 0≤μ dis (t)+μ ch (t)≤1

[0046]

[0047] In the formula, S ESS，i (t) represents the amount of energy stored in the ring network node i at time t; η ch is the charging efficiency of energy storage; η dis is the discharge efficiency of energy storage; P ESS，dis，i (t) is the discharge power of the energy storage connected to the ring network node i at time t; P ESS，ch，i (t) is the charging power of the energy storage connected to the ring network node i at time t; P ESS，i is the rated power of the energy storage connected to the ring network node i; E ESS，i is the rated capacity of the energy storage connected to the ring network node i; μ ch (t) represents the charging state of the energy storage at time t; μ dis (t) represents the discharge state of the stored energy at time t; Indicates the maximum value of the energy storage charge state at time t; Indicates the minimum value of the energy storage charge state at time t.

[0048] In a second aspect, the present invention further provides a snowflake distribution network dynamic reconstruction system, comprising:

[0049] The acquisition module is used to obtain the basic data of the snowflake distribution network in the study area;

[0050] The construction module is used to construct a dynamic reconstruction model of the initial snowflake distribution network for load balancing based on the basic data of the snowflake distribution network in the study area, with the load balancing degree and the minimum active power loss as the goals;

[0051] A conversion module is used to convert the initial snowflake distribution network dynamic reconstruction model into a target snowflake distribution network dynamic reconstruction model of mixed integer second-order cone programming by using second-order cone relaxation technology and large-M method;

[0052] A solution module, used to solve the dynamic reconstruction model of the target snowflake distribution network;

[0053] A module is obtained, which is used to obtain a dynamic reconstruction strategy of the snowflake distribution network for load balancing based on the solution results.

[0054] In a third aspect, the present invention further provides an electronic device, comprising: a processor and a memory;

[0055] The processor is coupled to the memory;

[0056] The processor is used to read and execute the program or instructions stored in the memory, so that the device executes the method of the first aspect.

[0057] In a fourth aspect, the present invention further provides a computer-readable storage medium storing a computer program, which implements the method of the first aspect when executed by a processor.

[0058] The technical solution provided by the present invention has at least the following technical effects or advantages:

[0059] The present invention gives full play to the advantages of the ring network structure with multiple connections between stations within the snowflake distribution network, establishes a dynamic reconstruction model of the urban snowflake distribution network for load balancing with load balancing and minimum active power loss as the comprehensive objective function, and sets constraints including flow constraints, node power balance constraints, feeder head node output constraints, branch capacity constraints, node voltage constraints, network topology constraints, segmented contact switch state constraints, energy storage operation constraints, etc. Then the model is transformed by mathematical programming, and the CPLEX mathematical optimization software is used to solve the model and output relevant results to obtain a dynamic reconstruction strategy for the snowflake distribution network for load balancing. The technical solution of the present invention can take into account the safety and economy of system operation, comprehensively consider the load conditions of multiple upper-level substations and multiple feeders to transfer loads and achieve load balancing of the entire network, and can adapt to the problem of heavy load and light load coexistence in urban distribution networks brought about by the rapid development of distributed power sources. The difficulty of solving is low and the decision-making time is fast.

[0060] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0062] Figure 1 It is a flowchart of a method for dynamic reconstruction of a snowflake distribution network in an embodiment of the present invention;

[0063] Figure 2 This is a schematic diagram of the structure of a 3-station 6-line snowflake distribution network in an embodiment of the present invention;

[0064] Figure 3 Schematic diagram of typical day photovoltaic and various types of load curves in an embodiment of the present invention;

[0065] Figure 4 Schematic diagram of the load rate of each feeder of the snowflake distribution network before and after dynamic reconstruction in an embodiment of the present invention;

[0066] Figure 5 It is a schematic diagram of active power loss of the snowflake power distribution network at various times before and after dynamic reconstruction in an embodiment of the present invention;

[0067] Figure 6 Schematic diagram of voltage fluctuations at nodes of the snowflake power distribution network before and after dynamic reconstruction in an embodiment of the present invention;

[0068] Figure 7 It is a structural schematic diagram of a dynamic reconstruction system of a snowflake distribution network in an embodiment of the present invention;

[0069] Figure 8 The present invention is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0070] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0071] Figure 1 The following is a flow chart of a method for dynamic reconstruction of a snowflake distribution network according to an embodiment of the present invention. As shown in the figure, the method includes:

[0072] S101. Obtain the basic data of the snowflake distribution network in the study area;

[0073] The basic data of the Snowflake distribution network in the study area include components, structure, line parameters, equipment parameters, etc.

[0074] S102. Based on the basic data of the snowflake distribution network in the study area, with the load balancing degree and the minimum active power loss as the goal, an initial snowflake distribution network dynamic reconstruction model for load balancing is constructed; the initial snowflake distribution network dynamic reconstruction model is a mixed integer non-convex nonlinear model;

[0075] The objective function of the initial snowflake distribution network dynamic reconstruction model satisfies:

[0076] min f=ω1f1+ω2f2 (1)

[0077]

[0078] Among them, f1 represents the line load balance, f2 represents the total power loss, ω1 and ω2 are the load balance and power loss weight coefficients, and ω1+ω2=1. The load balance index f1 specifically represents the sum of the squares of the ratio of the branch transmission complex power to the branch maximum transmission capacity, S ij (t) is the transmission capacity of branch ij at time t, S ij，max is the maximum transmission capacity of branch ij; P ij (t), Q ij (t) respectively represent the active and reactive power flowing from node i to node j at time t; Ω l I represents a branch set; ij (t) represents the current amplitude of line ij at time t; R ij Represents the resistance of line ij; T represents the total number of time periods for calculation.

[0079] The constraints of the initial snowflake distribution network dynamic reconstruction model include: snowflake distribution network flow constraints, node power balance constraints, feeder head end node output constraints, branch capacity constraints, node voltage constraints, network topology constraints, section interconnection switch state constraints, energy storage operation constraints, etc.

[0080] The power flow constraint expression of the snowflake distribution network satisfies:

[0081]

[0082] Among them, Ω in (j),Ω out (j) respectively represent the set of nodes j as the terminal node and the head node in the distribution network; a ij (t) represents the open / close state of the switch on branch ij, a ij When (t) is 1, it means that the tie switch on branch ij is in the closed state at time t, and when it is 0, it means that it is in the open state; ij (t), Q ij (t) represent the active and reactive power flowing from node i to node j at time t, respectively; I ij(t) represents the current amplitude of line ij at time t; R ij Indicates the resistance of line ij; a jk (t) represents the opening and closing state of the switch on branch jk; P jk (t) represents the active power flowing from node j to node k at time t; Q jk (t) represents the reactive power flowing from node j to node k at time t; U j (t) represents the voltage amplitude at node j at time t; P j (t), Q i (t) represent the active and reactive injected power of node j at time t respectively; X ij Indicates the reactance of line ij.

[0083] The node power balance constraint expression satisfies:

[0084]

[0085] Where P DG，j (t) represents the active power output of the distributed photovoltaic connected to the ring network node j at time t; P load，j (t), Q load，j (t) represents the load active power and reactive power of the ring network node j at time t; P ESS，dis，j (t) and P ESS，ch，j (t) is the discharge power and charging power of the energy storage connected to the ring network node j at time t.

[0086] The output constraint expression of the feeder head end node satisfies:

[0087]

[0088] Where: P i，sub (t), Q i，sub (t) represents the active and reactive output of the substation, P i，sub，max (t) and P i，sub，min (t) represent the upper and lower limits of the substation’s active output at time t; Q i，sub，max (t) and Q i，sub，min (t) represent the upper and lower limits of the reactive power output of the substation at time t.

[0089] The branch capacity constraint expression satisfies:

[0090]

[0091] The node voltage constraint expression satisfies:

[0092] U i，min ≤U i (t)≤U i，max(10)

[0093] Where U i，max and U i，min are the upper and lower limits of the node i voltage respectively.

[0094] The network topology constraints meet the following conditions:

[0095]

[0096] Where N bus and N sub Respectively represent the number of nodes and substations in the network. Ensure that the snowflake distribution network is still in open-loop operation during topology reconstruction.

[0097] The constraint expression of the sectionalized tie switch state satisfies:

[0098] α ij (t)-β ij (t) = a ij (t)-a ij (t-1) (12)

[0099] α ij (t)+β ij (t)≤1 (13)

[0100]

[0101] In the formula, α ij (t) and β ij (t) is the flag position of the switch on branch ij at time t. ij (t) = 1 means that the switch on branch ij changes from open to closed at time t. ij When (t) = 1, the tie switch on branch ij changes from closed to open at time t; Indicates the maximum number of switching actions within a certain period.

[0102] The energy storage operation constraint expression satisfies:

[0103]

[0104] 0≤μ dis (t)+μ ch (t)≤1(18)

[0105]

[0106] In the formula, S ESS，i (t) represents the amount of energy stored in the ring network node i at time t; η ch and ηdis is the charging and discharging efficiency of energy storage, which is 90% in this scheme; P ESS，i and E ESS，i is the rated power and rated capacity of the energy storage connected to the ring network node i; μ ch (t) and μ dis (t) represents the charging and discharging state of the energy storage at time t. ch (t) is 1, μ dis (t) is 0, when discharging μ dis (t) is 1, μ ch (t) is 0, and is 0 when idle; and Indicates the maximum and minimum values ​​of the energy storage charge state at time t.

[0107] S103, using the second-order cone relaxation technology and the big M method, the initial snowflake distribution network dynamic reconstruction model is transformed into the target snowflake distribution network dynamic reconstruction model of the mixed integer second-order cone programming;

[0108] The process of transforming the mixed integer non-convex nonlinear initial snowflake distribution network dynamic reconstruction model into the target snowflake distribution network dynamic reconstruction model of mixed integer second-order cone programming is as follows:

[0109] (1) By introducing the auxiliary variable i ij (t),u ij (t) Variable transformation is performed to obtain equations (20) and (21), and further relaxation is performed through the large < method to obtain equation (22);

[0110]

[0111] (2) Combining the constraints of formulas (20), (21), and (22) in (1) above, the power flow constraint condition expression (4) of the snowflake distribution network is transformed into formula (23), and formula (5) is relaxed by the large M method to obtain formula (24);

[0112]

[0113] (3) The objective function of the initial snowflake distribution network dynamic reconstruction model (3) is transformed into (25) accordingly;

[0114]

[0115] (4) The second-order cone relaxation of the snowflake distribution network power flow constraint expression (6) is performed to obtain equation (26), and then it is further converted into the standard second-order cone form (27) through further equivalence;

[0116]

[0117] (5) Introducing auxiliary variable f ij (t) Variable substitution is performed to realize the linearization of the objective function (2) of the initial snowflake distribution network dynamic reconstruction model, that is, using f ij (t) The objective function By replacing the variables, we get equation (28), and equation (2) is transformed into equation (29) accordingly.

[0118]

[0119] (6) Formula (28) is subjected to second-order cone relaxation to obtain formula (30), which is further transformed into the standard second-order cone form, as shown in formula (31):

[0120]

[0121] (7) Through the conversion processing of (1)-(6) above, the target snowflake distribution network dynamic reconstruction model of mixed integer second-order cone programming is obtained.

[0122] S104, solving a dynamic reconstruction model of a target snowflake distribution network;

[0123] Simulation analysis is carried out based on the MATLAB R2020a platform. YALMIP programming modeling is used to call CPLEX and other solvers to solve the target snowflake distribution network dynamic reconstruction model of the above-mentioned mixed integer second-order cone programming.

[0124] S105. Based on the solution results, a dynamic reconstruction strategy of the snowflake distribution network for load balancing is obtained;

[0125] Based on the results of solving the target snowflake distribution network dynamic reconstruction model of the above-mentioned mixed integer second-order cone programming, a dynamic reconstruction strategy of the snowflake distribution network for load balancing is obtained, that is, the load balancing degree and active power loss optimization results of the snowflake distribution network after dynamic reconstruction and the load rate of each feeder.

[0126] The technical solution of the present invention is further explained below in conjunction with a specific snowflake distribution network example:

[0127] Figure 2 This is a schematic diagram of the structure of a 3-station 6-line snowflake distribution network in an embodiment of the present invention;

[0128] Take a 3-station 6-wire snowflake distribution network (such as Figure 2 Take the example shown in Figure 1 for example. The specific steps are as follows:

[0129] Step 1: Basic data acquisition: Obtain basic data such as the composition, structure, equipment parameters, and economic parameters of the snow distribution network in the study area;

[0130] The Snowflake distribution network includes 3 substations, 6 feeders, 21 ring network box loads, 3 intra-station connections and 3 inter-station connections. The rated voltage of the Snowflake distribution network is 10kV, and the upper and lower limits of the voltage are set to 1.07pu and 0.93pu respectively. The parameters of distributed photovoltaic and energy storage are shown in Table 1. The typical daily curves of various types of loads are shown in Figure 3 As shown in the figure, set feeder F1 to heavy load, F2 to light load, F3 to heavy load, F4 to light load, and F5 and F6 to heavy load.

[0131] Table 1

[0132]

[0133] Step 2: Model establishment: Based on the basic data of the snowflake distribution network in the study area, an initial snowflake distribution network dynamic reconstruction model considering distributed power sources and electric energy storage for load balancing is constructed, with the load balancing degree and the minimum active power loss as the goal, and the constraints of the model are set, including the snowflake distribution network flow constraints, node power balance constraints, feeder head node output constraints, branch capacity constraints, node voltage constraints, network topology constraints, section interconnection switch state constraints, energy storage operation constraints and other constraints; the constructed initial snowflake distribution network dynamic reconstruction model is a mixed integer non-convex nonlinear model;

[0134] The expression of the objective function of the initial snowflake distribution network dynamic reconstruction model is:

[0135] min f=ω1f1+ω2f2 (32)

[0136]

[0137] Among them, f1 represents the line load balance, f2 represents the total power loss, ω1 and ω2 are the load balance and power loss weight coefficients, and ω1+ω2=1. The load balance index f1 specifically represents the sum of the squares of the ratio of the branch transmission complex power to the branch maximum transmission capacity, S ij (t) is the transmission capacity of branch ij at time t, S ij，max is the maximum transmission capacity of branch ij; P ij (t), Q ij (t) respectively represent the active and reactive power flowing from node i to node j at time t; Ω l I represents a branch set; ij (t) represents the current amplitude of line ij at time t; R ij Represents the resistance of line ij; T represents the total time period of calculation, which is 24h; the weight coefficients ω1 and ω2 are 0.6 and 0.4.

[0138] The constraints of the model are set, including the snowflake distribution network power flow constraints, node power balance constraints, feeder head end node output constraints, branch capacity constraints, node voltage constraints, network topology constraints, section tie switch state constraints, energy storage operation constraints, etc. The specific constraints are as follows:

[0139] The expression of power flow constraint condition of snowflake distribution network is:

[0140]

[0141] Among them, Ω in (j),Ω out (j) respectively represent the set of nodes j as the terminal node and the head node in the distribution network; a ij (t) represents the open / close state of the switch on branch ij, a ij When (t) is 1, it means that the tie switch on branch ij is in the closed state at time t, and when it is 0, it means that it is in the open state; jk (t) represents the opening and closing state of the switch on branch jk; P jk (t) represents the active power flowing from node j to node k at time t; Q jk (t) represents the reactive power flowing from node j to node k at time t; U j (t) represents the voltage amplitude at node j at time t; P j (t), Q j (t) represent the active and reactive injected power of node j at time t respectively; X ij Indicates the reactance of line ij.

[0142] Node power balance constraint expression:

[0143]

[0144] Where P DG，j (t) represents the active power output of the distributed photovoltaic connected to the ring network node j at time t; P load，j (t), Q load，j (t) represents the load active power and reactive power of the ring network node j at time t; P ESS，dis，j (t) and P ESS，ch，i (t) is the discharge power and charging power of the energy storage connected to the ring network node j at time t.

[0145] Feeder head end node output constraint expression:

[0146]

[0147] Where: P i，sub (t), Q i，sub (t) represents the active and reactive output of the substation, Pi，sub，max (t) and P i，sub，min (t) represent the upper and lower limits of the substation’s active output at time t; Q i，sub，max (t) and Q i，sub，min (t) represent the upper and lower limits of the reactive power output of the substation at time t.

[0148] Branch capacity constraint expression:

[0149]

[0150] Node voltage constraint expression:

[0151] U i，min ≤U i (t)≤U i，max (41)

[0152] Where U i，max and U i，min are the upper and lower limits of the node i voltage respectively.

[0153] Network topology constraint expression:

[0154]

[0155] Where N bus and N sub Respectively represent the number of nodes and substations in the network. Ensure that the snowflake distribution network is still in open-loop operation during topology reconstruction.

[0156] Sectional tie switch state constraint expression:

[0157] α ij (t)-β ij (t) = a ij (t)-a ij (t-1) (43)

[0158] α ij (t)+β ij (t)≤1 (44)

[0159]

[0160] In the formula, α ij (t) and β ij (t) is the flag position of the switch on branch ij at time t. ij (t) = 1 means that the switch on branch ij changes from open to closed at time t. ij When (t) = 1, the tie switch on branch ij changes from closed to open at time t; Indicates the maximum number of switching actions within a certain period.

[0161] Energy storage operation constraint expression:

[0162]

[0163] 0≤μ dis (t)+μ ch (t)≤1 (49)

[0164]

[0165] In the formula, S ESS，i (t) represents the amount of energy stored in the ring network node i at time t; η ch and η dis is the charging and discharging efficiency of energy storage, which is 90% in this embodiment; P ESS，i and E ESS，i is the rated power and rated capacity of the energy storage connected to the ring network node i; μ ch (t) and μ dis (t) represents the charging and discharging state of the energy storage at time t. ch (t) is 1, μ dis (t) is 0, when discharging μ dis (t) is 1, μ ch (t) is 0, and is 0 when idle; and Indicates the maximum and minimum values ​​of the energy storage charge state at time t, which are 0.9 and 0.1 respectively in this embodiment.

[0166] Step 3: Model transformation: The mixed integer non-convex nonlinear initial snowflake distribution network dynamic reconstruction model is transformed into the target snowflake distribution network dynamic reconstruction model of mixed integer second-order cone programming by using second-order cone relaxation technology, big M method and other technical means; the details are as follows:

[0167] By introducing the auxiliary variable i ij (t),u ij (t) is transformed into equations (51) and (52), and then relaxed using the big M method to obtain equation (53).

[0168]

[0169] Combining the constraints of the above formulas (51), (51), and (53), the power flow constraint formula (35) is transformed into formula (54), and formula (36) is relaxed by the large M method to obtain formula (55).

[0170]

[0171]

[0172] The objective function formula (34) is transformed into formula (56) accordingly.

[0173]

[0174] Formula (34) is subjected to second-order cone relaxation to obtain formula (57), which is then converted into a standard second-order cone form after further equivalence, as shown in formula (58):

[0175]

[0176] Similarly, introduce auxiliary variable f ij (t) Variable substitution is performed to realize the linearization of equation (33), that is, using f ij (t) The objective function Substituting variables, we get equation (59). Equation (33) is transformed into equation (60) accordingly.

[0177]

[0178] Formula (28) is subjected to second-order cone relaxation to obtain formula (61), which is further transformed into the standard second-order cone form, as shown in formula (62):

[0179]

[0180] Through the above conversion process, the target snowflake distribution network dynamic reconstruction model of mixed integer second-order cone programming is obtained.

[0181] Step 4. Model solving: For the target snowflake distribution network dynamic reconstruction model of mixed integer second-order cone programming for load balancing, simulation analysis is performed based on the MATLAB R2020a platform, and the converted model is solved by YALMIP programming and calling the CPLEX solver.

[0182] Step 5. Based on the solution results of the dynamic reconstruction model of the target snowflake distribution network, the dynamic reconstruction strategy of the snowflake distribution network for load balancing is obtained, that is, the load balancing degree and active power loss optimization results of the snowflake distribution network after dynamic reconstruction and the load rate of each feeder.

[0183] For the above-mentioned snowflake distribution network example, the following two scenarios are selected for comparative analysis: case 1 is the benchmark scenario before dynamic reconstruction; case 2 is after dynamic reconstruction (Table 2 is the dynamic reconstruction solution for case 2).

[0184] Table 2

[0185]

[0186] In case 1, reconstruction is not considered, and the switches on the interconnection lines B22, B23, B24, B35, B26, and B27 are turned on. In case 2, the state of the segmented interconnection switches is optimized through dynamic reconstruction to achieve a large-scale transfer of load in space, so that the load of the entire snowflake distribution network system is more balanced. The reconstruction scheme is shown in Table 2. For the case where feeders F1, F3, F5, and F6 are heavily loaded, and F2 and F4 are lightly loaded, the multi-directional transfer of load is carried out through the combination optimization of each branch switch to achieve load balancing of the entire snowflake distribution network.

[0187] Under the two situations before and after dynamic reconstruction, the load rate of each feeder in the snowflake distribution network is as follows: Figure 4 As shown, Figure 4 (a) indicates case1, Figure 4 (b) represents case 2. Compared with case 1, in case 2, based on the dynamic reconstruction technology, the overload situation of the line is alleviated to a certain extent, and the maximum load rate is reduced from 92.5% to 64.7%. Through the multi-station-to-station communication within the feeder cluster of the snowflake distribution network, the load is transferred from the side of the feeder with high load rate to the side of the feeder with low load rate, so as to achieve the balance of the full load of the feeder cluster.

[0188] The active power loss and node voltage of the snowflake distribution system in the two cases before and after dynamic reconstruction are as follows: Figure 5 and Figure 6 As shown. Dynamic reconstruction of the network topology can effectively reduce the system active power loss, effectively improve the system voltage level, and improve the power quality. The active power loss of the Snowflake distribution network throughout the day was reduced from 1681.54kWh to 1529.07kWh, a reduction of about 9.1%. The network reconstruction improves the topology structure and optimizes the power flow distribution in the system, which is conducive to the economic operation of the system. Taking the No. 14 ring network node as an example, after dynamic reconstruction, the node voltage increased, which alleviated the voltage offset and improved the system voltage safety margin.

[0189] The above scheme is based on six feeders of three substations or eight feeders of four substations to form multiple interconnection switch combinations such as intra-station and inter-station, which optimizes the system flow distribution, reduces system network losses, and improves the economic benefits of the snowflake distribution network. The multi-interconnection ring mesh feeder cluster structure brings flexible load transfer capabilities. The load in the heavy-load line can be transferred to the light-load line through intra-station interconnection or inter-station interconnection, thereby achieving load balancing of the entire network.

[0190] In summary, the technical solutions in the embodiments of the present invention have at least the following technical effects or advantages:

[0191] The present invention gives full play to the advantages of the ring network structure of multiple connections between stations within the snowflake distribution network, establishes a load-balanced urban snowflake distribution network dynamic reconstruction model with load balancing and minimum active power loss as comprehensive objective functions, and sets constraints including flow constraints, node power balance constraints, feeder head node output constraints, branch capacity constraints, node voltage constraints, network topology constraints, segmented contact switch state constraints, energy storage operation constraints, etc. Then the model is transformed by mathematical programming, and the CPLEX mathematical optimization software is used to solve the model and output relevant results to obtain a load-balanced snowflake distribution network dynamic reconstruction strategy. The technical solution of the present invention can take into account the safety and economy of system operation, comprehensively consider the load conditions of multiple upper-level substations and multiple feeders to transfer loads and achieve load balancing of the entire network, can adapt to the problem of heavy load and light load coexistence in urban distribution networks brought about by the rapid development of distributed power sources, and also has the advantages of low difficulty in solving and fast decision-making time.

[0192] Figure 7 This is a structural schematic diagram of a snowflake distribution network dynamic reconstruction system provided in an embodiment of the present invention. As shown in the figure, the system includes:

[0193] The acquisition module is used to obtain the basic data of the snowflake distribution network in the study area;

[0194] The construction module is used to construct a dynamic reconstruction model of the initial snowflake distribution network for load balancing based on the basic data of the snowflake distribution network in the study area, with the load balancing degree and the minimum active power loss as the goals;

[0195] A conversion module is used to convert the initial snowflake distribution network dynamic reconstruction model into a target snowflake distribution network dynamic reconstruction model of mixed integer second-order cone programming by using second-order cone relaxation technology and large-M method;

[0196] A solution module, used to solve the dynamic reconstruction model of the target snowflake distribution network;

[0197] A module is obtained, which is used to obtain a dynamic reconstruction strategy of the snowflake distribution network for load balancing based on the solution results.

[0198] It should be noted that, for the sake of convenience, Figure 7 For example, only the main modules of the snowflake power distribution network dynamic reconstruction system structure are shown. In practical applications, the system may also include modules or components not shown in the figure; the system is not limited to the above module structure, but can also be other module structures that implement the above method embodiments.

[0199] Figure 8 The electronic device provided in the embodiment of the present invention is a schematic diagram of the structure of the electronic device, as shown in the figure, the electronic device includes: a processor and a memory 2

[0200] The processor is used to read and execute programs and instructions stored in the memory, so that the electronic device executes the above method embodiment.

[0201] It should be noted that, for the sake of convenience, Figure 8 For example, only the main components of the electronic device are shown. In actual applications, the electronic device may also include components or assemblies not shown in the figure.

[0202] The present invention also provides a computer-readable storage medium storing a program or instruction. When a computer reads and executes the program or instruction, the computer executes the above method embodiment.

[0203] Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent substitutions for some of the technical features therein; and these modifications or substitutions do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for dynamic reconstruction of a snowflake distribution network, characterized in that: include: Obtain basic data of the snowflake distribution network in the study area; Based on the basic data of the snowflake distribution network in the study area, with the goal of load balancing and minimizing active power loss, a dynamic reconstruction model of the initial snowflake distribution network for load balancing is constructed; The initial snowflake distribution network dynamic reconstruction model is transformed into the target snowflake distribution network dynamic reconstruction model of mixed integer second-order cone programming by using the second-order cone relaxation technology and the big M method. Solve the dynamic reconstruction model of the target snowflake distribution network; Based on the solution results, a dynamic reconstruction strategy of the snowflake distribution network for load balancing is obtained.

2. The method for dynamic reconstruction of the snowflake distribution network according to claim 1 is characterized in that: The basic data of the snowflake distribution network in the study area include: components, composition structure, equipment parameters, and economic parameters.

3. The method for dynamic reconstruction of the snowflake distribution network according to claim 1 is characterized in that: The objective function of the initial snowflake distribution network dynamic reconstruction model satisfies: min f=ω1f1+ω2f2 Where f1 represents the line load balance; f2 represents the total power loss; ω1 and ω2 are the load balance and power loss weight coefficients; S ij (t) is the transmission capacity of branch ij at time t; S ij，max is the maximum transmission capacity of branch ij; P ij (t) represents the active power flowing from node i to node j at time t; Q ij (t) represents the reactive power flowing from node i to node t at time t; Ω l I represents a branch set; ij (t) represents the current amplitude of line ij at time t; R ij Represents the resistance of line ij; T represents the total number of time periods for calculation.

4. The method for dynamic reconstruction of a snowflake distribution network according to any one of claims 1 to 3, characterized in that: The constraints of the initial snowflake distribution network dynamic reconstruction model include: Snowflake distribution network flow constraints, node power balance constraints, feeder head end node output constraints, branch capacity constraints, node voltage constraints, network topology constraints, section interconnection switch state constraints, and energy storage operation constraints.

5. The method for dynamic reconstruction of the snowflake distribution network according to claim 4 is characterized in that: The snowflake distribution network power flow constraint expression satisfies: In the formula, Ω in (j),Ω out (j) respectively represent the set of nodes j as the terminal node and the head node in the distribution network; a ij (t) represents the opening and closing state of the switch on branch ij; P ij (t) represents the active power flowing from node i to node j at time t; Q ij (t) represents the reactive power flowing from node i to node j at time t; I ij (t) represents the current amplitude of line ij at time t; R ij Indicates the resistance of line ij; a jk (t) represents the opening and closing state of the switch on branch jk; P jk (t) represents the active power flowing from node j to node k at time t; Q jk (t) represents the reactive power flowing from node j to node k at time t; H j (t) represents the voltage amplitude at node j at time t; P j (t) represents the active injected power of node j at time t; Q j (t) represents the reactive injection power of node j at time t; X ij Indicates the reactance of line ij.

6. The method for dynamic reconstruction of the snowflake distribution network according to claim 4 is characterized in that: The node power balance constraint condition expression satisfies: Where P j (t) represents the active injected power of node j at time t; Q j (t) represents the reactive injection power of node j at time t; P DG，j (t) represents the active power output of the distributed photovoltaic power plant at time t in the ring network node; P load，j (t) represents the load active power of ring network node j at time t; Q load，j (t) represents the load reactive power of the ring network node j at time t; P ESS,dis，j (t) is the discharge power of the energy storage connected to the ring network node j at time t; P ESS，ch，j (t) is the ring network node and the charging power of the connected energy storage at time t.

7. The method for dynamic reconstruction of the snowflake distribution network according to claim 4 is characterized in that: The output constraint expression of the feeder head end node satisfies: Where: P i，sub (t) represents the active output of the substation; Q i，sub (t) represents the reactive power output of the substation; P i，sub，max (t) represents the upper limit of the substation active output at time t; P i，sub，min (t) represents the lower limit of the substation active output at time t; Q i，sub，max (t) represents the upper limit of the reactive power output of the substation at time t; Q i，sub，min (t) represents the lower limit of the reactive power output of the substation at time t.

8. The method for dynamic reconstruction of the snowflake distribution network according to claim 4 is characterized in that: The branch capacity constraint condition expression satisfies: Where: a ij (t) represents the open / close state of the switch on branch ij; S ij，max is the maximum transmission capacity of branch ij; P ij (t) represents the active power flowing from node i to node j at time t; Q ij (t) represents the reactive power flowing from node i to node j at time t.

9. The method for dynamic reconstruction of the snowflake distribution network according to claim 4 is characterized in that: The node voltage constraint condition expression satisfies: U i，min ≤U i (t)≤U i，max Where U i，max is the upper limit of the voltage at node i; U i，min is the lower limit of the node i voltage.

10. The method for dynamic reconstruction of the snowflake distribution network according to claim 4, characterized in that: The network topology constraints satisfy: In the formula, Ω l Represents a branch set; a ij (t) represents the opening and closing state of the switch on branch ij; N bus Indicates the number of nodes in the network; N sub They represent the number of substations in the network respectively.

11. The method for dynamic reconstruction of the snowflake distribution network according to claim 4, characterized in that: The segmented tie switch state constraint expression satisfies: α ij (t)-β ij (t)=a ij (t)-a ij (t-1) a ij (t)+β ij (t)≤1 In the formula, α ij (t) is the closing flag of the switch on branch ij at time t; β ij (t) is the disconnection flag of the switch on branch ij at time t; It indicates the maximum number of switching actions within a certain period; T indicates the total number of time periods calculated.

12. The method for dynamic reconstruction of the snowflake distribution network according to claim 4, characterized in that: The energy storage operation constraint expression satisfies: 0≤μ dis (t)+μ ch (t)≤1 In the formula, S ESS，i (t) represents the amount of electricity of the energy storage connected to the ring network node i at time t; η ch Charging efficiency for energy storage; η dis is the discharge efficiency of energy storage; P ESS，dis，i (t) is the discharge power of the energy storage connected to the ring network node i at time t; P ESS，ch，i (t) is the charging power of the energy storage connected to the ring network node i at time t; P ESS，i is the rated power of the energy storage connected to the ring network node i; E ESS，i is the rated capacity of the energy storage connected to the ring network node i; μ ch (t) represents the charging state of the energy storage at time t; μ dis (t) represents the discharge state of the stored energy at time t; Indicates the maximum value of the energy storage charge state at time t; Indicates the minimum value of the energy storage charge state at time t.

13. A snowflake distribution network dynamic reconstruction system, characterized in that: include: The acquisition module is used to obtain the basic data of the snowflake distribution network in the study area; The construction module is used to construct a dynamic reconstruction model of the initial snowflake distribution network for load balancing based on the basic data of the snowflake distribution network in the study area, with the load balancing degree and the minimum active power loss as the goals; The conversion module is used to convert the initial snowflake distribution network dynamic reconstruction model into the target snowflake distribution network dynamic reconstruction model of mixed integer second-order cone programming by using the second-order cone relaxation technology and the big M method. A solution module, used to solve the dynamic reconstruction model of the target snowflake distribution network; A module is obtained, which is used to obtain a dynamic reconstruction strategy of the snowflake distribution network for load balancing based on the solution results.

14. An electronic device, characterized in that: include: Processor and memory; The processor is coupled to a memory; The processor is used to read and execute the program or instruction stored in the memory, so that the device executes the method according to any one of claims 1 to 12.

15. A computer-readable storage medium, characterized in that: A computer program is stored, and when the program is executed by a processor, the method according to any one of claims 1 to 12 is implemented.

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