Power grid scheduling method and terminal based on flexible soft switch and tie switch

Abstract

The application discloses a power supply grid scheduling method and a terminal based on a flexible soft switch and a tie switch, sets the flexible soft switch and the tie switch in the power supply grid, constructs a day-ahead stage power supply grid interconnection scheduling model, constructs a day-ahead stage multi-scenario power supply grid scheduling model based on the model, combines the two models to obtain a day-ahead-day-in two-stage power supply grid scheduling model, solves the model, and obtains optimal switch operation modes of lines and power and node voltage of the lines, so that the purpose of grid optimization and grid coordination is achieved, the opening and closing states of the switches in the lines are determined through day-ahead stage optimization, and optimization control in the day-in stage under multiple scenes is implemented on the basis, the opening and closing states of the switches can take into account the economy of the power supply grid system under different scenes of the day-ahead stage and the day-in stage, and through reasonable regulation and control of the tie switch and the flexible soft switch, the power distribution lines are ensured to be always in a safe state, so that the economy and safety of the power supply grid are taken into account.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power grid scheduling, in particular to a power grid scheduling method and terminal based on flexible soft switches and tie switches. BACKGROUND

[0002] The distribution network is an important platform for carrying distributed power sources and is a key link for promoting the construction of smart grids and solving energy crises. With the increasing penetration of renewable energy in the distribution network, the influence of overvoltage and reverse power flow caused by renewable energy on the operation of the distribution network gradually appears, and the distributed power sources in the power grid are also developing from 'connecting as much as possible' to 'complementary and coordinated development of source and load'. Since the distributed renewable energy generation and load can only be adjusted in a small range, the active adjustment capability of the distribution network is restricted, and therefore new ways are needed for the adjustment of the distribution system so as to be able to carry more distributed sources and loads.

[0003] The distributed smart grid is one of the important paths for building a new type of power system, and a large number of distributed power sources will be connected to the distribution network. In the face of the wide connection of a large number of distributed power sources and multi-element flexible loads, the distribution network will become a power exchange system with bidirectional power flow. The existing distribution network designed according to the unidirectional power flow is difficult to accommodate a high proportion of distributed renewable energy, and therefore it is of great significance to develop the distributed smart grid and research the networking technology of the distribution network under the new form, so as to solve the carrying problem of the distribution network with diversified sources and loads, realize the complete consumption of sources and loads, and improve the operation stability and economy of the distribution network and the reliability of power supply to the terminal users. SUMMARY

[0004] The technical problem to be solved by the present application is to provide a power grid scheduling method and terminal based on flexible soft switches and tie switches, which can realize the consideration of the economy and safety of the power grid.

[0005] To solve the above technical problems, the technical scheme adopted by the present application is as follows:

[0006] A power grid scheduling method based on flexible soft switches and tie switches, comprising the steps of:

[0007] Dividing the distribution network to obtain a plurality of power grids, and setting flexible soft switches and tie switches in the power grids;

[0008] Establishing a first constraint condition and a first objective function of minimizing a first sum, and constructing a day-ahead stage power grid interconnection scheduling model according to the first constraint condition and the first objective function, wherein the first sum is the sum of network loss, wind curtailment and light curtailment cost;

[0009] establish a second constraint condition and a second objective function of minimizing a second sum based on the day-ahead stage power grid interconnection scheduling model, and build an intra-day stage multi-scenario power grid scheduling model according to the second constraint condition and the second objective function, wherein the second sum is the sum of network loss, wind power and photovoltaic power abandonment cost under intra-day wind power and photovoltaic power scenarios;

[0010] merge the day-ahead stage power grid interconnection scheduling model and the intra-day stage multi-scenario power grid scheduling model to obtain a day-ahead-intra-day two-stage power grid scheduling model, and solve the day-ahead-intra-day two-stage power grid scheduling model to obtain the optimal line switch operation mode and line power and node voltage.

[0011] In order to solve the above technical problems, another technical solution adopted by the present application is:

[0012] A power grid scheduling terminal based on a flexible soft switch and a tie switch, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor implements the following steps when executing the computer program:

[0013] The power distribution network is divided to obtain a plurality of power supply grids, and the flexible soft switch and the tie switch are arranged in the power supply grid.

[0014] establish a first constraint condition and a first objective function of minimizing a first sum, and build a day-ahead stage power grid interconnection scheduling model according to the first constraint condition and the first objective function, wherein the first sum is the sum of network loss, wind power and photovoltaic power abandonment cost;

[0015] establish a second constraint condition and a second objective function of minimizing a second sum based on the day-ahead stage power grid interconnection scheduling model, and build an intra-day stage multi-scenario power grid scheduling model according to the second constraint condition and the second objective function, wherein the second sum is the sum of network loss, wind power and photovoltaic power abandonment cost under intra-day wind power and photovoltaic power scenarios;

[0016] merge the day-ahead stage power grid interconnection scheduling model and the intra-day stage multi-scenario power grid scheduling model to obtain a day-ahead-intra-day two-stage power grid scheduling model, and solve the day-ahead-intra-day two-stage power grid scheduling model to obtain the optimal line switch operation mode and line power and node voltage.

[0017] The present application has the following advantages:

[0018] The flexible soft switch and the tie switch are arranged in the power grid, a first constraint condition and a first target function of minimizing a first sum are established, a day-ahead stage power grid interconnection scheduling model is constructed, a second constraint condition and a second target function of minimizing a second sum are established based on the model, and an intra-day stage multi-scenario power grid scheduling model is constructed, two models are combined to obtain a day-ahead-intra-day two-stage power grid scheduling model, and the model is solved to obtain the optimal switch operation mode of the line and the power of the line and the node voltage, so that the characteristics of the flexible soft switch and the tie switch are utilized to achieve the purpose of optimization in the grid and coordination between grids, the opening and closing state of the switch in the line is determined through the optimization in the day-ahead stage, and the optimization control in the multi-scenario in the intra-day stage is implemented on the basis, the opening and closing state of the switch can take into account the economy of the power grid system in different scenarios in the day-ahead stage and the intra-day stage, and the power distribution line is ensured to be in a safe state through reasonable control of the tie switch and the flexible soft switch, so that the economy and safety of the power grid are taken into account. BRIEF DESCRIPTION OF DRAWINGS

[0019] Figure 1 A step flow chart of a power grid scheduling method based on a flexible soft switch and a tie switch according to an embodiment of the present application;

[0020] Figure 2 A structure schematic diagram of a power grid scheduling terminal based on a flexible soft switch and a tie switch according to an embodiment of the present application;

[0021] Figure 3 A power grid schematic diagram divided in a power grid scheduling method based on a flexible soft switch and a tie switch according to an embodiment of the present application;

[0022] Figure 4 An intra-day photovoltaic scenario schematic diagram generated in a power grid scheduling method based on a flexible soft switch and a tie switch according to an embodiment of the present application;

[0023] Figure 5 An intra-day wind power scenario schematic diagram generated in a power grid scheduling method based on a flexible soft switch and a tie switch according to an embodiment of the present application. DETAILED DESCRIPTION

[0024] To make the technical content, the purposes and effects of the present application clear, the following will be described in detail in combination with the embodiments and the drawings.

[0025] Please refer to Figure 1 A power grid scheduling method based on a flexible soft switch and a tie switch, comprising the steps of:

[0026] The power distribution network is divided to obtain a plurality of power grids, and the flexible soft switch and the tie switch are arranged in the power grid;

[0027] establish a first constraint condition and a first objective function of minimizing a first sum, and build a day-ahead stage power grid interconnection scheduling model according to the first constraint condition and the first objective function, wherein the first sum is a sum of network loss, wind power curtailment and light power curtailment cost;

[0028] establish a second constraint condition and a second objective function of minimizing a second sum based on the day-ahead stage power grid interconnection scheduling model, and build an intra-day stage multi-scenario power grid scheduling model according to the second constraint condition and the second objective function, wherein the second sum is a sum of network loss, wind power curtailment and light power curtailment cost under intra-day wind power and photovoltaic scenarios;

[0029] merge the day-ahead stage power grid interconnection scheduling model and the intra-day stage multi-scenario power grid scheduling model to obtain a day-ahead-intra-day two-stage power grid scheduling model, and solve the day-ahead-intra-day two-stage power grid scheduling model to obtain optimal line switch operation modes and line power and node voltage.

[0030] From the above description, the beneficial effects of the present application are that: flexible soft switches and tie switches are arranged in the power grid, a first constraint condition and a first objective function of minimizing a first sum are established, a day-ahead stage power grid interconnection scheduling model is built, a second constraint condition and a second objective function of minimizing a second sum are established based on the model, and an intra-day stage multi-scenario power grid scheduling model is built, two models are merged to obtain a day-ahead-intra-day two-stage power grid scheduling model, and the day-ahead-intra-day two-stage power grid scheduling model is solved to obtain optimal line switch operation modes and line power and node voltage, so that the characteristics of the flexible soft switches and the tie switches are utilized to achieve the purpose of optimization within the grid and coordination between grids, the opening and closing states of the switches in the line are determined through day-ahead stage optimization, and optimization and control under intra-day stage multi-scenarios are implemented on this basis, the opening and closing states of the switches can take into account the economy of the power grid system under different scenarios of day-ahead and intra-day, and through reasonable regulation and control of the tie switches and the flexible soft switches, the distribution line is ensured to be in a safe state at all times, so that the economy and safety of the power grid are taken into account.

[0031] Further, the flexible soft switches and the tie switches arranged in the power grid include:

[0032] the flexible soft switches and the tie switches are arranged near the distributed power supply in the interior of the power grid, and the flexible soft switches are arranged at the power easy-crossing position of the line in the interior of the power grid;

[0033] the flexible soft switches are arranged between the power grids.

[0034] As can be known from the above description, the city scene contains a large number of distributed source-load accesses, the tie switch and the flexible soft switch are arranged in the power grid, so that the power grid can realize the maximum consumption of new energy within the range of meeting the line safety constraint by adjusting the flexible resource and the structure state of the grid, the power grids in different regions are connected through the flexible soft switch, the characteristics of the source-load and the flexible soft switch power transfer between different grids are fully utilized, and the power mutual aid between the grids is realized on the basis of the line safety constraint.

[0035] Further, the first objective function of establishing the first constraint condition and minimizing the first sum comprises:

[0036] The flexible soft switch operation constraint, the line power flow constraint, the node voltage constraint, the branch current constraint, the tie switch action frequency constraint, the network reconfiguration constraint, the distribution network operation radiation constraint, the line loss rate constraint and the power grid interconnection constraint are established, and the first constraint condition is generated according to the flexible soft switch operation constraint, the line power flow constraint, the node voltage constraint, the branch current constraint, the tie switch action frequency constraint, the network reconfiguration constraint, the distribution network operation radiation constraint, the line loss rate constraint and the power grid interconnection constraint.

[0037] The first objective function of minimizing the first sum is established.

[0038] As can be known from the above description, the flexible soft switch operation constraint, the line power flow constraint, the node voltage constraint, the branch current constraint, the tie switch action frequency constraint, the network reconfiguration constraint, the distribution network operation radiation constraint, the line loss rate constraint and the power grid interconnection constraint are established, so as to ensure the feasibility, safety and efficiency of the power grid optimization process.

[0039] Further, the first objective function of minimizing the first sum comprises:

[0040]

[0041] In the formula, C1 represents the sum of network loss, wind power abandonment and light power abandonment cost, T represents a scheduling period, v h represents a set of all distribution lines, represents the unit price of network loss cost, I ij,t represents the current flowing through the branch ij at time t, r ij represents the equivalent resistance of the branch ij, ΔT represents the length of each period, ψ wind represents a set of nodes in which wind turbines are accessed in the distribution system, represents the unit price of wind power abandonment cost, represents the predicted power output of the wind turbine at node i at time t, represents the actual power output of the wind turbine connected to the power grid at node i at time t, ψpv a set of nodes in which photovoltaic is accessed in the power distribution system, a unit price of abandoned light cost, a predicted power output of photovoltaic at node i at time t, an actual power output of photovoltaic connected to the power grid at node i at time t.

[0042] As described above, the first objective function of minimizing the sum of network loss, abandoned wind and abandoned light cost is established, which ensures the safe, economic and efficient operation of the power distribution network.

[0043] Further, after generating the first constraint condition according to the flexible soft switch operation constraint, the line flow constraint, the node voltage constraint, the branch current constraint, the number of tie switch actions constraint, the network reconfiguration constraint, the power distribution network operation radiation constraint, the line loss rate constraint and the power grid interconnection constraint, the method further comprises:

[0044] The non-convex constraint in the first constraint condition is determined, and second-order cone transformation is performed on the non-convex constraint to obtain the final first constraint condition.

[0045] As described above, after the first constraint condition is established, the non-convex constraint in the first constraint condition is subjected to second-order cone transformation, which can greatly improve the solution speed of the problem while considering the accuracy of the model.

[0046] Further, before the second constraint condition and the second objective function of minimizing the second sum are established based on the day-ahead stage power grid interconnection scheduling model, the method further comprises:

[0047] A plurality of wind power and photovoltaic scene samples are generated using a scene generation method;

[0048] The plurality of wind power and photovoltaic scene samples are clustered to obtain intra-day wind power and photovoltaic scenes.

[0049] As described above, the intra-day wind power and photovoltaic scenes are generated, and the intra-day stage is scheduled under these scenes, which ensures that the power distribution network can cope with various complex scenarios.

[0050] Further, the establishment of the second constraint condition and the second objective function of minimizing the second sum based on the day-ahead stage power grid interconnection scheduling model comprises:

[0051] The second objective function of minimizing the second sum is established based on the day-ahead stage power grid interconnection scheduling model;

[0052] The flexible soft-switching operation constraint, the line power flow constraint, the branch current constraint, the line loss constraint and the power supply grid interconnection constraint are established, and a second constraint condition is generated according to the flexible soft-switching operation constraint, the line power flow constraint, the branch current constraint, the line loss constraint and the power supply grid interconnection constraint.

[0053] As can be known from the above description, the second objective function of minimizing the second sum is established based on the day-ahead stage power supply grid interconnection scheduling model, and the flexible soft-switching operation constraint, the line power flow constraint, the branch current constraint, the line loss constraint and the power supply grid interconnection constraint are established, so that the optimal economic regulation and control of the distribution network under different wind power and photovoltaic scenarios in the day-ahead stage is realized.

[0054] Further, the second objective function of minimizing the second sum is established based on the day-ahead stage power supply grid interconnection scheduling model, and the flexible soft-switching operation constraint, the line power flow constraint, the branch current constraint, the line loss constraint and the power supply grid interconnection constraint are established, so that the optimal economic regulation and control of the distribution network under different wind power and photovoltaic scenarios in the day-ahead stage is realized.

[0055]

[0056] In the formula, C2 represents the sum of network loss, wind power abandonment and photovoltaic abandonment cost under the wind power and photovoltaic scenario in the day-ahead stage, p n represents the probability of the wind power and photovoltaic scenario n in the day-ahead stage, N represents the number of wind power and photovoltaic scenarios in the day-ahead stage, T represents the scheduling period, ΔT represents the length of each period, v h represents the set of distribution lines, represents the unit price of network loss cost in the day-ahead stage, I n,ij,t represents the current flowing through the branch ij at time t under the wind power and photovoltaic scenario n in the day-ahead stage, r ij represents the equivalent resistance of the branch ij, ψ wind represents the set of nodes in which wind turbines are connected in the distribution system, represents the unit price of wind power abandonment in the day-ahead stage, represents the predicted power output of the wind turbine at node i at time t under the wind power and photovoltaic scenario n in the day-ahead stage, ψ represents the actual power output of the wind turbine connected to the power grid at node i at time t under the wind power and photovoltaic scenario n in the day-ahead stage, ψ pv represents the set of nodes in which photovoltaic is connected in the distribution system, represents the unit price of photovoltaic abandonment in the day-ahead stage, represents the predicted power output of the photovoltaic at node i at time t under the wind power and photovoltaic scenario n in the day-ahead stage, ψ represents the actual power output of the photovoltaic connected to the power grid at node i at time t under the wind power and photovoltaic scenario n in the day-ahead stage.

[0057] As can be known from the above description, the second objective function of minimizing the sum of network loss, wind power abandonment and photovoltaic abandonment cost under the wind power and photovoltaic scenario in the day-ahead stage is established, so that the distribution network can also be safely, economically and efficiently operated under various complex scenarios.

[0058] Further, the combining the day-ahead stage power grid interconnection scheduling model and the day-ahead stage multi-scenario power grid scheduling model to obtain a day-ahead-day-in two-stage power grid scheduling model comprises:

[0059] The weights of the day-ahead stage power grid interconnection scheduling model and the day-ahead stage multi-scenario power grid scheduling model are determined respectively, and the day-ahead stage power grid interconnection scheduling model and the day-ahead stage multi-scenario power grid scheduling model are added according to the weights to obtain a day-ahead-day-in two-stage power grid scheduling model.

[0060] From the above description, the day-ahead stage power grid interconnection scheduling model and the day-ahead stage multi-scenario power grid scheduling model are added according to the weights, so as to determine the opening and closing states of the switches in the line through day-ahead stage optimization, and then the states of each device and line are obtained through optimization and control in the day-in stage multi-scenario, so as to achieve the consideration of the economy and safety of the power grid.

[0061] Please refer to Figure 2 Another embodiment of the present application provides a power grid scheduling terminal based on flexible soft switches and tie switches, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements each step in the above-mentioned power grid scheduling method based on flexible soft switches and tie switches when executing the computer program.

[0062] The above-mentioned power grid scheduling method and terminal based on flexible soft switches and tie switches can be applied to a distributed intelligent power grid, and the following will be described through a specific embodiment:

[0063] Please refer to Figure 1 , Figures 3-5 The embodiment one of the present application is:

[0064] A power grid scheduling method based on flexible soft switches and tie switches, comprising the steps of:

[0065] S1, dividing the distribution network to obtain a plurality of power grids, and setting flexible soft switches and tie switches in the power grids, specifically comprising S11-S13:

[0066] S11, dividing the distribution network to obtain a plurality of power grids.

[0067] By dividing the power supply area in the power distribution network into grids, more refined management of energy within each grid can be achieved. Each grid can serve as an independent management unit, facilitating monitoring and adjustment, thereby improving overall energy management efficiency. The power supply grid contains various types of distributed sources and loads, and each power supply grid achieves energy balance through reasonable regulation. With more distributed power sources connected to the power distribution network, the original one-way flow planning and design of the power distribution network cannot adapt to the high proportion of new energy networks.

[0068] As shown in Figure 3 , it is divided into 4 power supply grids, and the power supply grids are interconnected through flexible soft switches. At the same time, each power supply grid contains certain photovoltaic and wind power equipment.

[0069] In addition to containing adjustable power sources within the power supply grid, the power supply grid needs to install tie switches and flexible soft switches for power distribution network framework upgrading. Due to the high cost of flexible multi-state switches, conventional switches cannot be completely replaced in the short term, and both flexible multi-state switches and conventional switches will coexist, as described below.

[0070] S12, setting flexible soft switches and tie switches near the distributed power sources inside the power supply grid, and setting flexible soft switches at the power easy cross-line position of the line inside the power supply grid.

[0071] Setting flexible soft switches and tie switches near the distributed power sources inside the power supply grid to change the traditional radial power flow direction of the power distribution network, and setting flexible soft switches at the power easy cross-line position of the line inside the power supply grid to connect other nodes for power distribution.

[0072] S13, setting flexible soft switches between the power supply grids.

[0073] Setting flexible soft switches between the power supply grids to achieve power mutual aid between different power supply grids.

[0074] S2, establishing a first constraint condition and a first target function of minimizing a first sum, and constructing a day-ahead stage power supply grid interconnection scheduling model according to the first constraint condition and the first target function. The first sum is the sum of network loss, wind curtailment and light curtailment cost, specifically including S21-S24:

[0075] S21, establish flexible soft switch operation constraints, line power flow constraints, node voltage constraints, branch current constraints, tie switch action frequency constraints, network reconfiguration constraints, distribution network operation radiation constraints, line loss rate constraints and power supply grid interconnection constraints, and generate first constraint conditions according to the flexible soft switch operation constraints, the line power flow constraints, the node voltage constraints, the branch current constraints, the tie switch action frequency constraints, the network reconfiguration constraints, the distribution network operation radiation constraints, the line loss rate constraints and the power supply grid interconnection constraints.

[0076] The flexible soft switch operation constraint is:

[0077]

[0078] In the formula, Pi(t) represents the active power of the i-th voltage source converter at time t, Pj(t) represents the active power of the j-th voltage source converter at time t, Qi(t) represents the reactive power of the i-th voltage source converter at time t, Qj(t) represents the reactive power of the j-th voltage source converter at time t, min,i Pi,min represents the lower limit of the active output of the i-th voltage source converter, max,i Pi,max represents the upper limit of the active output of the i-th voltage source converter, min,j Qj,min represents the lower limit of the reactive output of the j-th voltage source converter, max,j Qj,max represents the upper limit of the reactive output of the j-th voltage source converter, Pi,cap represents the access capacity of the i-th voltage source converter, Pj,cap represents the access capacity of the j-th voltage source converter.

[0079] The flexible soft switch operation constraint ensures stable operation of the device, limits the active power and reactive power of the voltage source converter of the two flexible soft switches at a certain moment, and ensures within the capacity range of the flexible soft switch.

[0080] The line power flow constraint is:

[0081]

[0082] In the formula, Ψ i Ψi represents a set of branch end nodes with node i as the first end node, ik,t Pi,k(t) represents the active power of node i flowing to node k at time t, ji，t Pj,i(t) represents the active power of node j flowing to node i at time t, ji Rji represents the resistance of branch ji, ji，t Ij,i(t) represents the current amplitude of node j flowing to node i at time t, i，tQ represents the sum of net active power injected at node i during time period t. ik,t Q represents the reactive power flowing from node i to node k during time period t. ji，t Φ represents the reactive power flowing from node j to node i during time period t. i Let X represent the set of starting and ending nodes of a branch with node i as the ending node. ji Q represents the reactance of branch ji. i，t This represents the sum of net reactive power injected at node i during time period t. This represents the active power injected into node i during time period t. This represents the active power injected by the flexible soft switch at node i during time period t. This represents the active power of the load at node i during time period t. This represents the reactive power injected by the power source at node i during time period t. This represents the reactive power injected by the flexible soft switch at node i during time period t. U represents the reactive power of the load at node i during time period t. i,t U represents the voltage amplitude at node i during time period t. j,t This represents the voltage amplitude at node j during time period t. Line power flow constraints ensure the rational distribution and transmission of power in the power grid, preventing overload and voltage drop.

[0083] Because the load is sometimes variable, and the added distributed energy sources and reactive power compensation devices also have the same characteristics, the real-time operation of the system becomes more complex. Therefore, for system safety, node voltage constraints are added to ensure that the voltage of each node can operate within a reasonable and safe range. The node voltage constraints are as follows:

[0084] U min ≤U i,t ≤U max ;

[0085] In the formula, U min U represents the lower limit of the node voltage. max This indicates the upper limit of the node voltage.

[0086] The branch current constraint is:

[0087] I ij,t ≤I ij,max ;

[0088] In the formula, I ij,t I represents the current amplitude flowing from node i to node j during time period t. ij,max This represents the upper limit of the current amplitude flowing from node i to node j. By limiting the upper limit of the current amplitude in each branch through branch current constraints, it is possible to prevent excessive current from causing line overheating or equipment damage.

[0089] The tie switch action frequency constraint is:

[0090]

[0091] In the formula, α ji，t represents the state of the switch on the branch ji at time period t, α ji，t = 0 represents open, α ji，t = 1 represents closed, α ji,(t-1) represents the state of the switch on the branch ji at time period t-1, S ji，max represents the maximum action frequency of a single switch, N o represents the total number of nodes in the network, φ i represents the set of line nodes connected to node i, S max represents the maximum action frequency of all switches. The tie switch action frequency constraint helps to reduce mechanical wear and tear of equipment and prolong the service life of equipment. Frequent switch action can cause equipment failure, and by limiting the action frequency, the risk can be reduced.

[0092] The network reconfiguration constraint includes:

[0093]

[0094] In the formula, α ik represents the state of the tie switch on the branch ik at time period t, α ji represents the state of the tie switch on the branch ji at time period t, r ji represents the resistance of the branch ji, x ji represents the reactance of the branch ji;

[0095] The above power flow equation is non-convex, and the inequality constraint is introduced by using the large M method to relax it. At this time, for the open branch, its active power, reactive power and branch current are 0, while for the closed branch, the constraint does not work. Specifically:

[0096]

[0097] In the formula, M1 represents a first constant, M2 represents a second constant, M3 represents a third constant, α ij represents the state of the tie switch on the branch ij at time period t;

[0098] Therefore, the network reconfiguration constraint is converted to:

[0099]

[0100] In the formula, M4 represents a fourth constant. The network reconfiguration constraint allows the system to adjust the topology as necessary to optimize the operation of the power grid.

[0101] Since there is a network reconfiguration optimization problem in the current model, the mixed integer second-order cone programming method can accurately model the nonlinear characteristics in the distribution network, so as to obtain more accurate optimization results. The second-order cone constraint is more relaxed than the traditional linear constraint, which can greatly improve the solving speed of the problem while ensuring the accuracy of the model. In order to optimize the state of the tie switch by using the mixed integer second-order cone programming method, the switch state of the switch on the branch ji at the time period t is taken as the decision variable α ij，t , and a Boolean auxiliary variable β ij，t is added.

[0102]

[0103] In the formula, α ij，t is a Boolean variable, n represents the number of nodes, n f represents the number of first nodes, B represents the set of all nodes of the network, β ij，t represents the first Boolean auxiliary variable, if node j is the parent node of node i, then β ij，t is equal to 1, otherwise it is equal to 0, β ji,t represents the second Boolean auxiliary variable, if node i is the parent node of node j, then β ji,t is equal to 1, otherwise it is equal to 0, N(i) represents the set of nodes connected to node i, and N / N f represents all nodes except the first nodes.

[0104] The line loss rate constraint is:

[0105]

[0106] In the formula, P t loss represents the network loss of the distribution network system in the day-ahead stage, L represents the total power supply, c min represents the lower limit of the line loss rate, c max represents the upper limit of the line loss rate.

[0107] The power grid interconnection constraint is:

[0108]

[0109] In the formula, represents the active power exchanged between node i of power grid m and power grid k at time t, represents the active power exchanged between node j of power grid k and power grid m at time t, represents the reactive power exchanged between node i of power grid m and power grid k at time t, represents the reactive power exchanged between node j of power grid k and power grid m at time t, Pmini ′ represents the lower limit of the active power of the interaction between two grids, P maxi ′ represents the upper limit of the active power interaction between the two grids, Q minj ′ represents the lower limit of reactive power in the interaction between two grids, Q maxj ′ represents the upper limit of reactive power in the interaction between two grids.

[0110] like Figure 3 As shown, the power grids are interconnected in pairs. Taking the interconnection relationship between power grid 1 and power grid 2 as an example, power grid 1 can be regarded as a virtual power source of power grid 2, while power grid 2 can be regarded as a virtual load of power grid 1.

[0111] The feasibility, security, and efficiency of the power grid optimization process are ensured by constructing the first constraint condition.

[0112] S22. Determine the non-convex constraints in the first constraint condition, and perform a second-order cone transformation on the non-convex constraints to obtain the final first constraint condition.

[0113] Specifically, the non-convex constraints in the first constraint condition are:

[0114]

[0115] U min ≤U i,t ≤U max ;

[0116]

[0117] use and Replace the quadratic term (I) ji,t ) 2 , and The following form is used to perform a second-order cone transformation on these non-convex constraints:

[0118]

[0119] The above equation has been converted to convex constraints. To further... Represented as an intuitive convex constraint, it can be equivalently transformed into:

[0120]

[0121] S23. Establish the first objective function that minimizes the first sum, specifically:

[0122]

[0123] wherein C1 represents the sum of network loss, wind curtailment and light curtailment cost, T represents a scheduling period, v h represents a set of all distribution lines, represents a unit price of network loss cost, I ij,t represents a current flowing through branch ij at time t, r ij represents an equivalent resistance of branch ij, ΔT represents a time length of each period, ψ wind represents a set of nodes in which wind turbines are connected in the distribution system, represents a unit price of wind curtailment cost, represents a predicted power output of wind turbines at node i at time t, represents an actual power output of wind turbines connected to the power grid at node i at time t, ψ pv represents a set of nodes in which photovoltaics are connected in the distribution system, represents a unit price of light curtailment cost, represents a predicted power output of photovoltaics at node i at time t, represents an actual power output of photovoltaics connected to the power grid at node i at time t.

[0124] S24, constructing a day-ahead stage power grid interconnection scheduling model according to the first constraint condition and the first objective function.

[0125] S3, generating a plurality of wind power and photovoltaic scene samples using a scenario generation method.

[0126] S4, clustering the plurality of wind power and photovoltaic scene samples to obtain intra-day wind power and photovoltaic scenes, as shown in Figure 4 and Figure 5 .

[0127] S5, establishing a second constraint condition and a second objective function of minimizing a second sum based on the day-ahead stage power grid interconnection scheduling model, and constructing an intra-day stage multi-scene power grid scheduling model according to the second constraint condition and the second objective function, wherein the second sum is the sum of network loss, wind curtailment and light curtailment cost under intra-day wind power and photovoltaic scenes, and specifically comprising S51-S53:

[0128] S51, establishing a second objective function of minimizing a second sum based on the day-ahead stage power grid interconnection scheduling model, specifically:

[0129]

[0130] wherein C2 represents the sum of network loss, wind curtailment and light curtailment cost under intra-day wind power and photovoltaic scenes, p n represents a probability of intra-day wind power and photovoltaic scene n, N represents a number of intra-day wind power and photovoltaic scenes, T represents a scheduling period, and ΔT represents a time length of each period, denotes the unit price of the network loss cost in the intra-day stage, I n,ij,t denotes the current flowing through branch ij at time t under intra-day wind and PV scenario n, r ij denotes the equivalent resistance of branch ij, ψ wind denotes the set of nodes in the distribution system where wind turbines are connected, denotes the unit price of the wind curtailment cost in the intra-day stage, denotes the predicted power output of wind turbines at node i at time t under intra-day wind and PV scenario n, ψ denotes the actual power output of wind turbines connected to the grid at node i at time t under intra-day wind and PV scenario n, ψ pv denotes the set of nodes in the distribution system where PVs are connected, denotes the unit price of the PV curtailment cost in the intra-day stage, denotes the predicted power output of PVs at node i at time t under intra-day wind and PV scenario n, denotes the actual power output of PVs connected to the grid at node i at time t under intra-day wind and PV scenario n.

[0131] S52, establish flexible soft switch operation constraints, line power flow constraints, branch current constraints, line loss constraints and power supply grid interconnection constraints, and generate second constraint conditions according to the flexible soft switch operation constraints, the line power flow constraints, the branch current constraints, the line loss constraints and the power supply grid interconnection constraints.

[0132] wherein the flexible soft switch operation constraint is:

[0133]

[0134] wherein, denotes the active power of the i-th voltage source converter at time t under intra-day wind and PV scenario n, denotes the active power of the j-th voltage source converter at time t under intra-day wind and PV scenario n, Q min,i denotes the lower limit of the reactive power output of the i-th voltage source converter, Q max,i denotes the upper limit of the reactive power output of the i-th voltage source converter, denotes the reactive power of the i-th voltage source converter at time t under intra-day wind and PV scenario n, denotes the reactive power of the j-th voltage source converter at time t under intra-day wind and PV scenario n, denotes the connection capacity of the i-th voltage source converter under intra-day wind and PV scenario n, denotes the connection capacity of the j-th voltage source converter under intra-day wind and PV scenario n.

[0135] the line power flow constraint is:

[0136]

[0137] where P n,ik,t denotes the active power from node i to node k at time period t under intra-day wind and PV scenario n, P n，ji，t denotes the active power from node j to node i at time period t under intra-day wind and PV scenario n, P denotes the current amplitude from node j to node i at time period t under intra-day wind and PV scenario n after second-order cone transformation, P n，i，t denotes the sum of net active power injected at node i at time period t under intra-day wind and PV scenario n, Q n,ik,t denotes the reactive power from node i to node k at time period t under intra-day wind and PV scenario n, Q n，ji，t denotes the reactive power from node j to node i at time period t under intra-day wind and PV scenario n, Q n,i,t denotes the sum of net reactive power injected at node i at time period t under intra-day wind and PV scenario n, Q denotes the voltage amplitude at node j at time period t under intra-day wind and PV scenario n after second-order cone transformation, U denotes the voltage amplitude at node i at time period t under intra-day wind and PV scenario n after second-order cone transformation, U ij denotes the resistance of branch ij, U imin denotes the lower limit of voltage value at node i, U imax denotes the upper limit of voltage value at node i, U denotes the active power injected by SLC at node i at time period t under intra-day wind and PV scenario n, P denotes the active power injected by flexible soft switch at node i at time period t under intra-day wind and PV scenario n, P denotes the active power of load at node i at time period t under intra-day wind and PV scenario n, P denotes the reactive power injected by SLC at node i at time period t under intra-day wind and PV scenario n, Q denotes the reactive power injected by flexible soft switch at node i at time period t under intra-day wind and PV scenario n, Q denotes the reactive power of load at node i at time period t under intra-day wind and PV scenario n, Q.

[0138] The branch current constraint is:

[0139] I n,ij,t ≤ I ij,max ;

[0140] where I n,ij,t denotes the current amplitude from node i to node j at time period t under intra-day wind and PV scenario n.

[0141] The line loss constraint is:

[0142]

[0143] In the formula, Pn(t) represents the network loss of the power distribution network system under the wind power and photovoltaic scene n within the day.

[0144] The power grid interconnection constraint is:

[0145]

[0146] In the formula, α represents the allowed variation ratio of interconnected active power, β represents the allowed variation ratio of interconnected reactive power, Pn(t) represents the network loss of the power distribution network system under the wind power and photovoltaic scene n within the day. Pn(t) represents the network loss of the power distribution network system under the wind power and photovoltaic scene n within the day.

[0147] S53, constructing a multi-scenario power grid scheduling model for the intra-day stage according to the second constraint condition and the second objective function.

[0148] S6, merging the day-ahead stage power grid interconnection scheduling model and the intra-day stage multi-scenario power grid scheduling model to obtain a day-ahead-intra-day two-stage power grid scheduling model, and solving the day-ahead-intra-day two-stage power grid scheduling model to obtain the optimal line switching operation mode and line power and node voltage, specifically including S61-S62:

[0149] S61, determining the weights of the day-ahead stage power grid interconnection scheduling model and the intra-day stage multi-scenario power grid scheduling model respectively, and adding the day-ahead stage power grid interconnection scheduling model and the intra-day stage multi-scenario power grid scheduling model according to the weights to obtain a day-ahead-intra-day two-stage power grid scheduling model.

[0150] Specifically, the objective functions in the day-ahead stage power grid interconnection scheduling model and the intra-day stage multi-scenario power grid scheduling model are added according to the weights, and the constraint conditions are combined to obtain the day-ahead-intra-day two-stage power grid scheduling model.

[0151] The weights are adjusted according to the risk preference of the power grid operator. If the operator prefers to avoid large intra-day adjustments, the weight of the day-ahead stage can be increased to reduce the uncertainty and adjustment cost of the intra-day.

[0152] S62, solving the day-ahead-intra-day two-stage power grid scheduling model to obtain the optimal line switching operation mode and line power and node voltage.

[0153] Please refer to Figure 2Embodiment two of the present application is:

[0154] A power grid scheduling terminal based on flexible soft switches and tie switches, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements each step in the power grid scheduling method based on flexible soft switches and tie switches in embodiment one when executing the computer program.

[0155] In summary, the present application provides a power grid scheduling method and terminal based on flexible soft switches and tie switches, wherein flexible soft switches and tie switches are arranged in the power grid, a first constraint condition and a first objective function of minimizing a first sum are established, a day-ahead stage power grid interconnection scheduling model is constructed, a second constraint condition and a second objective function of minimizing a second sum are established based on the model, and an intra-day stage multi-scenario power grid scheduling model is constructed. The two models are combined to obtain a day-ahead-intra-day two-stage power grid scheduling model, which is solved to obtain the optimal switch operation mode of the line and the power of the line and the node voltage. In this way, the characteristics of the flexible soft switch and the tie switch are utilized to achieve the purpose of optimization within the grid and coordination between grids. The opening and closing state of the switch in the line is determined through the day-ahead stage optimization, and the optimization control in the intra-day stage under different scenarios is implemented on this basis. The opening and closing state of the switch can take into account the economy of the power grid system under different scenarios in the day-ahead and intra-day stages. At the same time, through reasonable regulation and control of the tie switch and the flexible soft switch, the distribution line is always in a safe state, thereby realizing the consideration of the economy and safety of the power grid. In addition, the urban scenario contains a large number of distributed source and load access. The tie switch and the flexible soft switch are arranged in the power grid, so that the power grid can realize the maximum consumption of new energy within the range of meeting the line safety constraint by adjusting the flexible resource and the structure state of the grid. The power grids in different regions are connected through the flexible soft switch, and the characteristics of the source and load and the flexible soft switch power transfer between different grids are fully utilized to realize power mutual aid between grids on the basis of line safety constraints.

[0156] The above only describes the embodiments of the present application, and does not limit the patent range of the present application. Any equivalent transformation or direct or indirect application in related technical fields based on the content of the present application specification and drawings is also included in the patent protection range of the present application.

Claims

1. A method for power grid scheduling based on flexible soft-switches and tie-switches, characterized in that, The method comprises the steps of: dividing a power distribution network to obtain a plurality of power supply grids, and setting flexible soft switches and tie switches in the power supply grids; establishing a first constraint condition and a first objective function of minimizing a first sum, and constructing a day-ahead stage power supply grid interconnection scheduling model according to the first constraint condition and the first objective function, wherein the first sum is a sum of network loss, wind power curtailment and photovoltaic power curtailment cost; based on the day-ahead stage power supply grid interconnection scheduling model, establishing a second constraint condition and a second objective function of minimizing a second sum, and constructing an intra-day stage multi-scenario power supply grid scheduling model according to the second constraint condition and the second objective function, wherein the second sum is a sum of network loss, wind power curtailment and photovoltaic power curtailment cost under intra-day wind power and photovoltaic scenarios; merging the day-ahead stage power supply grid interconnection scheduling model and the intra-day stage multi-scenario power supply grid scheduling model to obtain a day-ahead-intra-day two-stage power supply grid scheduling model, and solving the day-ahead-intra-day two-stage power supply grid scheduling model to obtain an optimal switch operation mode of a line and power and node voltage of the line; the establishing a first constraint condition and a first objective function of minimizing a first sum comprises: establishing flexible soft switch operation constraints, line power flow constraints, node voltage constraints, branch current constraints, tie switch action frequency constraints, network reconstruction constraints, power distribution network operation radiation constraints, line loss rate constraints and power supply grid interconnection constraints, and generating a first constraint condition according to the flexible soft switch operation constraints, the line power flow constraints, the node voltage constraints, the branch current constraints, the tie switch action frequency constraints, the network reconstruction constraints, the power distribution network operation radiation constraints, the line loss rate constraints and the power supply grid interconnection constraints; establishing a first objective function of minimizing a first sum; the establishing a first objective function of minimizing a first sum comprises: ； wherein C1 represents the sum of network loss, curtailment of wind and curtailment of light, T represents a scheduling period, v h represents a set of all distribution lines, represents a unit price of network loss cost, I ij,t represents a current flowing through branch ij at time t, r ij represents an equivalent resistance of branch ij, ΔT represents a length of each period, ψ wind represents a set of nodes in which wind turbines are connected in the distribution system, represents a unit price of curtailment of wind, represents a predicted power output of wind turbines at node i at time t, represents an actual power output of wind turbines connected to the grid at node i at time t, ψ pv represents a set of nodes in which photovoltaics are connected in the distribution system, represents a unit price of curtailment of light, represents a predicted power output of photovoltaics at node i at time t, represents an actual power output of photovoltaics connected to the grid at node i at time t; the establishing a second constraint condition and a second objective function of minimizing a second sum based on the day-ahead stage power supply grid interconnection scheduling model comprises: establishing a second objective function of minimizing a second sum based on the day-ahead stage power supply grid interconnection scheduling model; establishing flexible soft switch operation constraints, line power flow constraints, branch current constraints, line loss constraints and power supply grid interconnection constraints, and generating a second constraint condition according to the flexible soft switch operation constraints, the line power flow constraints, the branch current constraints, the line loss constraints and the power supply grid interconnection constraints; the establishing a second objective function of minimizing a second sum based on the day-ahead stage power supply grid interconnection scheduling model comprises: ； where C2 represents the sum of network loss, curtailment of wind power and curtailment of photovoltaic power under the wind power and photovoltaic power scenario n, p n represents the probability of the wind power and photovoltaic power scenario n, N represents the number of wind power and photovoltaic power scenarios, T represents the scheduling period, ΔT represents the length of each period, v h represents the set of distribution lines, represents the unit price of network loss cost in the daily stage, represents the current flowing through branch ij at time t under the wind power and photovoltaic power scenario n, r ij represents the equivalent resistance of branch ij, ψ wind represents the set of nodes in the distribution system where wind turbines are connected, represents the unit price of wind power curtailment cost in the daily stage, represents the predicted power output of wind turbines at node i at time t under the wind power and photovoltaic power scenario n, ψ represents the actual power output of wind turbines connected to the grid at node i at time t under the wind power and photovoltaic power scenario n, ψ pv represents the set of nodes in the distribution system where photovoltaic power is connected, represents the unit price of photovoltaic power curtailment cost in the daily stage, represents the predicted power output of photovoltaic power at node i at time t under the wind power and photovoltaic power scenario n, ψ represents the actual power output of photovoltaic power connected to the grid at node i at time t under the wind power and photovoltaic power scenario n.

2. The power grid scheduling method based on flexible soft-switch and tie-switch according to claim 1, characterized in that, the setting flexible soft switches and tie switches in the power supply grids comprises: setting flexible soft switches and tie switches near a distributed power supply in an internal line of the power supply grid, and setting flexible soft switches at a power easy-crossing position of the internal line of the power supply grid; setting flexible soft switches between the power supply grids.

3. The method of claim 1, wherein, The first constraint condition is generated according to the flexible soft switch operation constraint, the line flow constraint, the node voltage constraint, the branch current constraint, the tie switch action frequency constraint, the network reconfiguration constraint, the power distribution network operation radiation constraint, the line loss rate constraint and the power grid interconnection constraint. Non-convex constraints in the first constraint condition are determined, and second-order cone transformation is performed on the non-convex constraints to obtain a final first constraint condition.

4. The method of claim 1, wherein, Before the second constraint condition and the second objective function of minimizing the second sum are established based on the day-ahead stage power grid interconnection scheduling model, the following further includes: A plurality of wind power and photovoltaic scene samples are generated using a scene generation method; The plurality of wind power and photovoltaic scene samples are clustered to obtain intra-day wind power and photovoltaic scenes.

5. The method of claim 1, wherein, The day-ahead-intra-day two-stage power grid scheduling model is obtained by combining the day-ahead stage power grid interconnection scheduling model and the intra-day stage multi-scenario power grid scheduling model. Weights of the day-ahead stage power grid interconnection scheduling model and the intra-day stage multi-scenario power grid scheduling model are respectively determined, and the day-ahead stage power grid interconnection scheduling model and the intra-day stage multi-scenario power grid scheduling model are added according to the weights to obtain the day-ahead-intra-day two-stage power grid scheduling model.

6. A power grid dispatching terminal based on flexible soft switch and tie switch, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that, The processor executes the computer program to realize each step in the power grid scheduling method based on flexible soft switches and tie switches according to any one of claims 1 to 5.

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