Distribution network optimization operation control method and device considering discrete control

Abstract

The invention relates to the technical field of power system operation, in particular to a power distribution network optimization operation control method and device considering discrete control. The method comprises the following steps: constructing an active and reactive controllable resource adjustment characteristic constraint model of the power distribution network based on at least one of an on-load voltage regulating transformer operation constraint model, a parallel capacitor operation constraint model, an SVC operation constraint model, an energy storage operation constraint model and an electric vehicle participation load demand response operation constraint model; constructing an initial discrete control-considered power distribution network optimization operation model of the power distribution network based on the active and reactive controllable resource adjustment characteristic constraint model; and based on the target function and the integer constraint variable of the mixed integer linear programming problem in the distribution network optimization operation model initially considering the discrete control, determining the distribution network optimization operation model finally considering the discrete control of the distribution network. Therefore, the problems that the global optimality of the integer solution cannot be ensured, the calculation complexity is high and the like in related technologies are solved.

Description

Technical Field

[0001] The present application relates to the technical field of power system operation, and in particular to a distribution network optimization operation control method and device considering discrete control. Background Art

[0002] With the rapid development of distributed energy and electric vehicles, the distribution network, as the main support for the access of multiple sources and loads such as distributed energy and electric vehicle charging piles, is also facing huge challenges in economical, safe and stable operation. The random access behavior, inverter grid-connected characteristics and fluctuating operation characteristics of distributed renewable energy have not only changed the original trend of the distribution network, but also often caused local overvoltage and local line overload in the distribution network and aggravated the imbalance of three phases, and also increased the difficulty of voltage control and power grid loss reduction, and put forward higher requirements for the optimization and regulation capabilities of the distribution network.

[0003] In the related technologies, the optimization methods for the safe operation of distribution networks considering discrete control mainly include four categories: heuristic algorithms, artificial intelligence algorithms, branch and bound methods, and relaxation methods. Among them, heuristic algorithms are usually more efficient than blind search methods and can obtain an approximate optimal solution in a relatively short time; artificial intelligence algorithms are less dependent on models and may be applied to certain heuristic algorithms, with strong global search capabilities and strong adaptability; the relaxation method relaxes integer variables into continuous variables, uses classical optimization algorithms to implement solutions, and finally normalizes the obtained solutions through certain means; the branch and bound method divides the feasible domain containing integer variables into small areas (branching process), obtains the bounds of the objective function on the corresponding branches according to certain rules (bounding process), and has a very high solution efficiency.

[0004] However, in the related technology, the global optimality of the integer solution cannot be guaranteed, the computational complexity is high, and improvement is urgently needed. Summary of the invention

[0005] The present application provides a distribution network optimization operation control method and device considering discrete control, so as to solve the problems in the related technology that the global optimality of integer solutions cannot be guaranteed and the calculation complexity is high.

[0006] The first aspect of the present application provides a distribution network optimization operation control method considering discrete control, which is applied to the model construction stage, wherein the method includes the following steps: based on at least one of the on-load tap-changing transformer operation constraint model, the shunt capacitor operation constraint model, the SVC (Static Var Compensator) operation constraint model, the energy storage operation constraint model and the electric vehicle participation load demand response operation constraint model, construct an active and reactive controllable resource regulation characteristic constraint model of the distribution network; based on the active and reactive controllable resource regulation characteristic constraint model, construct an initial distribution network optimization operation model considering discrete control of the distribution network; based on the objective function and integer constraint variables of the mixed integer linear programming problem in the initial distribution network optimization operation model considering discrete control, determine the final distribution network optimization operation model considering discrete control of the distribution network.

[0007] Optionally, in one embodiment of the present application, the active and reactive controllable resource regulation characteristic constraint model of the distribution network is constructed based on at least one of the on-load tap-changing transformer operation constraint model, the shunt capacitor operation constraint model, the SVC operation constraint model, the energy storage operation constraint model and the electric vehicle participation load demand response operation constraint model, including: constructing the on-load tap-changing transformer operation constraint model based on at least one of the transformer power regulation constraint, the transformer voltage regulation constraint and the transformer gear regulation constraint; constructing the shunt capacitor operation constraint model based on the connected shunt capacitor reactive compensation power constraint and the shunt capacitor switching group number constraint; constructing the SVC operation constraint model based on the connected SVC reactive compensation power constraint; constructing the energy storage operation constraint model based on at least one of the energy storage charging and discharging total power constraint, the energy storage charging and discharging power constraint and the energy storage charge state constraint; constructing the electric vehicle participation load demand response operation constraint model based on the electric vehicle charging load power; constructing the active and reactive controllable resource regulation characteristic constraint model based on the on-load tap-changing transformer operation constraint model, the shunt capacitor operation constraint model, the SVC operation constraint model, the energy storage operation constraint model and the electric vehicle participation load demand response operation constraint model.

[0008] Optionally, in one embodiment of the present application, the construction of the distribution network optimization operation model for initial consideration of discrete control of the distribution network based on the active and reactive controllable resource regulation characteristic constraint model includes: based on the active and reactive controllable resource regulation characteristic constraint model, obtaining the dispatching cost and voltage deviation of the distribution network, and constructing the objective function of the distribution network optimization operation model for initial consideration of discrete control based on the dispatching cost and the voltage deviation; based on the active and reactive controllable resource regulation characteristic constraint model, obtaining the active power and reactive power of the distribution network, and constructing the power balance constraint of the distribution network optimization operation model for initial consideration of discrete control based on the active power and the reactive power; based on the active and reactive controllable resource regulation characteristic constraint model, obtaining the active power constraint and voltage amplitude constraint of the distribution network, and generating the safety operation constraint of the distribution network optimization operation model for initial consideration of discrete control based on the active power constraint and the voltage amplitude constraint.

[0009] Optionally, in one embodiment of the present application, the objective function and integer constraint variables of the mixed integer linear programming problem in the distribution network optimization operation model considering discrete control are used to determine the final distribution network optimization operation model considering discrete control of the distribution network, including: calculating the integer solution of the mixed integer linear programming problem based on the objective function and the integer constraint variables; judging whether the integer solution satisfies the integer constraint of the integer constraint variable; if the integer solution satisfies the integer constraint, determining the optimal solution of the mixed integer linear programming problem based on the integer solution; otherwise, determining the upper and lower bounds of the mixed integer linear programming problem based on the integer solution, and judging whether the upper and lower bounds are equal; if the upper bound is equal to the lower bound, determining the optimal solution of the mixed integer linear programming problem based on the upper bound, the lower bound and the integer solution; otherwise, segmenting the mixed integer linear programming problem based on the upper and lower bounds, so as to obtain the optimal solution of the mixed integer linear programming problem using the segmented mixed integer linear programming problem. The final distribution network optimization operation model considering discrete control is determined based on the optimal solution.

[0010] Optionally, in one embodiment of the present application, the expressions of the objective function and integer constraint variables of the mixed integer linear programming problem may be, but are not limited to,:

[0011]

[0012] in, And N I ∈N:={1,2,K,n}.

[0013] A second aspect of the present application provides a distribution network optimization operation control method considering discrete control, which is applied to the model application stage, wherein the method includes the following steps: obtaining operation constraint information of the distribution network to be optimized; inputting the operation constraint information into a pre-constructed distribution network optimization operation model that ultimately considers discrete control, so as to obtain actual integer operation constraints during operation in the distribution network to be optimized, and using the actual integer operation constraints to control the operation of the distribution network to be optimized, wherein the pre-constructed distribution network optimization operation model that ultimately considers discrete control is obtained from an active and reactive controllable resource regulation characteristic constraint model.

[0014] The third aspect of the present application provides a distribution network optimization operation control device considering discrete control, which is applied to the model construction stage, wherein the device includes: a first construction module, which is used to construct an active and reactive controllable resource regulation characteristic constraint model of the distribution network based on at least one of the on-load tap-changing transformer operation constraint model, the shunt capacitor operation constraint model, the SVC operation constraint model, the energy storage operation constraint model and the electric vehicle participation load demand response operation constraint model; a second construction module, which is used to construct the initial distribution network optimization operation model considering discrete control of the distribution network based on the active and reactive controllable resource regulation characteristic constraint model; a third construction module, which is used to determine the final distribution network optimization operation model considering discrete control of the distribution network based on the objective function and integer constraint variables of the mixed integer linear programming problem in the initial distribution network optimization operation model considering discrete control.

[0015] Optionally, in one embodiment of the present application, the first construction module includes: a first construction unit, which is used to construct an on-load tap-changing transformer operation constraint model based on at least one of a transformer power regulation constraint, a transformer voltage regulation constraint and a transformer gear regulation constraint; a second construction unit, which is used to construct a parallel capacitor operation constraint model based on the reactive compensation power constraint of the connected parallel capacitor and the constraint on the number of parallel capacitor switching groups; a third construction unit, which is used to construct an SVC operation constraint model based on the reactive compensation power constraint of the connected SVC; a fourth construction unit, which is used to construct an energy storage operation constraint model based on at least one of the total energy storage charging and discharging power constraint, the energy storage charging and discharging power constraint and the energy storage state of charge constraint; a fifth construction unit, which is used to construct an electric vehicle participating in a load demand response operation constraint model based on the electric vehicle charging load power; and a sixth construction unit, which is used to construct the active and reactive controllable resource regulation characteristic constraint model based on the on-load tap-changing transformer operation constraint model, the parallel capacitor operation constraint model, the SVC operation constraint model, the energy storage operation constraint model and the electric vehicle participating in a load demand response operation constraint model.

[0016] Optionally, in one embodiment of the present application, the second construction module includes: a first construction unit, which obtains the dispatching cost and voltage deviation of the distribution network based on the active and reactive controllable resource regulation characteristic constraint model, and constructs the objective function of the distribution network optimization operation model initially considering discrete control based on the dispatching cost and the voltage deviation; a second construction unit, which is used to obtain the active power and reactive power of the distribution network based on the active and reactive controllable resource regulation characteristic constraint model, and construct the power balance constraint of the distribution network optimization operation model initially considering discrete control based on the active power and the reactive power; a third construction unit, which is used to obtain the active power constraint and voltage amplitude constraint of the distribution network based on the active and reactive controllable resource regulation characteristic constraint model, and generate the safety operation constraint of the distribution network optimization operation model initially considering discrete control based on the active power constraint and the voltage amplitude constraint.

[0017] Optionally, in one embodiment of the present application, the third building module includes: a calculation unit, which is used to calculate the integer solution of the mixed integer linear programming problem based on the objective function and the integer constraint variable; a first judgment unit, which is used to judge whether the integer solution satisfies the integer constraint of the integer constraint variable; a first determination unit, which is used to determine the optimal solution of the mixed integer linear programming problem based on the integer solution when the integer solution satisfies the integer constraint; a second judgment unit, which is used to determine the upper and lower bounds of the mixed integer linear programming problem based on the integer solution when the integer solution does not satisfy the integer constraint, and to judge whether the upper and lower bounds are equal; a second determination unit, which is used to determine the optimal solution of the mixed integer linear programming problem based on the upper bound, the lower bound and the integer solution when the upper bound is equal to the lower bound; a segmentation unit, which is used to segment the mixed integer linear programming problem based on the upper and lower bounds when the upper bound is not equal to the lower bound, so as to obtain the optimal solution of the mixed integer linear programming problem using the segmented mixed integer linear programming problem. A third determination unit is used to determine the distribution network optimization operation model that finally considers discrete control based on the optimal solution.

[0018] Optionally, in one embodiment of the present application, the expressions of the objective function and integer constraint variables of the mixed integer linear programming problem may be, but are not limited to,:

[0019]

[0020] in, And N I ∈N:={1,2,K,n}.

[0021] In a fourth aspect, an embodiment of the present application provides a distribution network optimization operation control device considering discrete control, which is applied to a model application stage, wherein the device comprises: an acquisition module for acquiring operation constraint information of the distribution network to be optimized; a control module for inputting the operation constraint information into a pre-constructed distribution network optimization operation model that ultimately considers discrete control, so as to obtain actual integer operation constraints during operation in the distribution network to be optimized, and use the actual integer operation constraints to control the operation of the distribution network to be optimized, wherein the pre-constructed distribution network optimization operation model that ultimately considers discrete control is obtained from an active and reactive controllable resource regulation characteristic constraint model.

[0022] The fifth aspect of the present application provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the distribution network optimization operation control method considering discrete control as described in the above embodiments.

[0023] The sixth aspect of the present application provides a computer-readable storage medium, which stores a computer program. When the program is executed by a processor, it implements the above-mentioned distribution network optimization operation control method considering discrete control.

[0024] The seventh aspect of the present application provides a computer program product, including a computer program, which, when executed, implements the above-mentioned distribution network optimization operation control method considering discrete control.

[0025] The embodiment of the present application can construct the active and reactive controllable resource regulation characteristic constraint model of the distribution network based on the on-load tap-changing transformer operation constraint model, the shunt capacitor operation constraint model, the operation constraint model, the energy storage operation constraint model and the electric vehicle participation load demand response operation constraint model, and determine the final distribution network optimization operation model considering discrete control based on the objective function and integer constraint variables of the mixed integer linear programming problem in the distribution network optimization operation model based on the initial consideration of discrete control, and construct the distribution network optimization operation model considering discrete control based on the characteristic constraints of various equipment. Considering that the optimization operation model is a mixed integer linear programming problem, a branch cutting algorithm is proposed, that is, a cutting plane constraint is inserted on the basis of branch and bound, and the linear relaxation constraint is tightened so that the integer optimal solution can be obtained faster. Thus, the problems in the related art that the global optimality of the integer solution cannot be guaranteed and the computational complexity is high are solved.

[0026] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0028] Figure 1 A flow chart of a distribution network optimization operation control method considering discrete control provided according to an embodiment of the present application;

[0029] Figure 2 A flow chart of solving a mixed integer linear programming problem using a branch and cut algorithm according to an embodiment of the present application;

[0030] Figure 3 A flow chart of solving a distribution network optimization operation model that ultimately considers discrete control using a branch cutting algorithm according to an embodiment of the present application;

[0031] Figure 4 A flowchart of the working principle of a distribution network optimization operation control method considering discrete control provided according to an embodiment of the present application;

[0032] Figure 5 A block diagram of a distribution network optimization operation control device considering discrete control according to an embodiment of the present application;

[0033] Figure 6 A flow chart of a distribution network optimization operation control method considering discrete control provided according to another embodiment of the present application;

[0034] Figure 7 A block diagram of a distribution network optimization operation control device considering discrete control according to another embodiment of the present application;

[0035] Figure 8 It is a schematic diagram of the structure of an electronic device provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0036] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0037] The following is a description of the distribution network optimization operation control method and device considering discrete control of the embodiment of the present application with reference to the accompanying drawings. In view of the problem that the global optimality of the integer solution cannot be guaranteed and the computational complexity is high mentioned in the above background technology, the present application provides a distribution network optimization operation control method considering discrete control. In this method, the active and reactive controllable resource regulation characteristic constraint model of the distribution network can be constructed based on the on-load tap-changing transformer operation constraint model, the shunt capacitor operation constraint model, the operation constraint model, the energy storage operation constraint model and the electric vehicle participation load demand response operation constraint model, and the distribution network is based on the initial consideration of the discrete control distribution network optimization operation model. The objective function and integer constraint variables of the mixed integer linear programming problem, the distribution network optimization operation model of the final consideration of discrete control of the distribution network is determined, and the distribution network optimization operation model of the final consideration of discrete control is constructed based on the characteristic constraints of various equipment. Considering that the optimization operation model is a mixed integer linear programming problem, a branch cutting algorithm is proposed, that is, a cutting plane constraint is inserted on the basis of branch and bound, and the linear relaxation constraint is tightened, so that it can obtain the integer optimal solution faster. Thus, the problems that the global optimality of the integer solution cannot be guaranteed and the computational complexity is high in the related technology are solved.

[0038] Specifically, Figure 1 The present invention is a flowchart of a distribution network optimization operation control method considering discrete control according to an embodiment of the present application.

[0039] like Figure 1 As shown, the distribution network optimization operation control method considering discrete control is applied in the model building stage, wherein the method includes the following steps:

[0040] In step S101, an active and reactive controllable resource regulation characteristic constraint model of the distribution network is constructed based on at least one of an on-load tap-changing transformer operation constraint model, a shunt capacitor operation constraint model, an SVC operation constraint model, an energy storage operation constraint model, and an electric vehicle participating in load demand response operation constraint model.

[0041] As a possible implementation method, the active and reactive controllable resource regulation characteristic constraint model constructed in the embodiment of the present application may include but is not limited to: on-load tap-changing transformer operation constraint model, shunt capacitor operation constraint model, SVC operation constraint model, energy storage operation constraint model and electric vehicle participation load demand response operation constraint model, etc., which can be specifically set by technical personnel in this field according to actual conditions, and this application does not impose specific restrictions.

[0042] Optionally, in one embodiment of the present application, based on at least one of an on-load tap-changing transformer operation constraint model, a shunt capacitor operation constraint model, an SVC operation constraint model, an energy storage operation constraint model, and an electric vehicle participating in a load demand response operation constraint model, an active and reactive controllable resource regulation characteristic constraint model of the distribution network is constructed, including: constructing an on-load tap-changing transformer operation constraint model based on at least one of a transformer power regulation constraint, a transformer voltage regulation constraint, and a transformer gear regulation constraint; constructing a shunt capacitor operation constraint model based on a connected shunt capacitor reactive compensation power constraint and a shunt capacitor switching group number constraint; constructing an SVC operation constraint model based on a connected SVC reactive compensation power constraint; constructing an energy storage operation constraint model based on at least one of an energy storage charging and discharging total power constraint, an energy storage charging and discharging power constraint, and an energy storage charge state constraint; constructing an electric vehicle participating in a load demand response operation constraint model based on the electric vehicle charging load power; constructing an active and reactive controllable resource regulation characteristic constraint model based on an on-load tap-changing transformer operation constraint model, a shunt capacitor operation constraint model, an SVC operation constraint model, an energy storage operation constraint model, and an electric vehicle participating in a load demand response operation constraint model.

[0043] In some embodiments, the embodiments of the present application can construct an on-load tap-changing transformer operation constraint model, which can be but is not limited to being composed of transformer power regulation constraints, transformer voltage regulation constraints, and transformer gear regulation constraints, etc., and the present application does not make specific restrictions.

[0044] Further, in the embodiment of the present application, the expression of the transformer power regulation constraint can be, but is not limited to, expressed as:

[0045]

[0046] Among them, N OLTC represents the set of nodes connected to the on-load tap-changing transformer; Φ = {a, b, c} represents the set of phases; T represents the set of time periods within the scheduling cycle; and They represent the power injected from the substation into the distribution network node m at time t. Phase active and reactive power; and They are respectively represented by the substation outlet that can provide node m Phase active and reactive power upper and lower limits.

[0047] The expression of transformer voltage regulation constraint can be, but is not limited to, expressed as:

[0048]

[0049] in, Indicates on-load tap-changing transformer Phase primary voltage reference value, the size is 1p.u.; represents the node m at time t The square of the voltage ratio between the secondary side and the primary side of the phase on-load tap-changing transformer; They represent the on-load tap-changing transformer connected to node m. The square of the lower and upper limits of the adjustable phase voltage; g indicates the gear position; G OLTC Indicates the gear set of the on-load tap-changing transformer; It represents the square difference between the voltage ratio of gear g-1 and the voltage ratio of gear g; represents the node m at time t 0-1 identification variable corresponding to the adjustment increment of adjacent gears; They represent the difference between node m at time t and time t-1. 0-1 indicator variable that increases or decreases; represents the load-changing transformer at node m The maximum range of phase position change.

[0050] The expression of transformer gear adjustment constraint can be, but is not limited to, expressed as:

[0051]

[0052] in, It is expressed as the maximum allowable number of three-phase gear adjustments of the on-load tap-changing transformer at node m within the dispatching period.

[0053] In some embodiments, the embodiments of the present application can construct a parallel capacitor operation constraint model, which can be but is not limited to being composed of reactive compensation power constraints of connected parallel capacitors and constraints on the number of parallel capacitor switching groups, etc., and the present application does not make specific restrictions.

[0054] Further, in the embodiment of the present application, the expression of the reactive compensation power constraint of the connected parallel capacitor can be, but is not limited to, expressed as:

[0055]

[0056] Among them, N CB represents the set of nodes connecting the parallel capacitors, G CB represents the set of the number of parallel capacitor switching groups, represents the node m at time t Phase reactive compensation power, Represents the node m Reactive compensation power of single-phase parallel capacitors, Represents the node m Number of switching groups of phase parallel capacitors, Indicates the upper limit of the number of switching groups. A 0-1 identification variable corresponding to the number of switching groups.

[0057] The expression for the number of parallel capacitor switching groups can be, but is not limited to, expressed as:

[0058]

[0059] in, They represent the difference between node m at time t and time t-1. A 0-1 indicator variable indicating an increase or decrease in the number of interlocking groups; It represents the upper limit of the number of three-phase switching times of the shunt capacitor connected to node m within the scheduling period.

[0060] In some embodiments, the embodiments of the present application may construct an SVC operation constraint model, which may be composed of, but not limited to, access SVC reactive compensation power constraints, etc., and the present application does not impose any specific limitations.

[0061] Further, in the embodiment of the present application, the expression of the access SVC reactive compensation power constraint may be, but is not limited to, expressed as:

[0062]

[0063] Among them, N SVC Represents the set of nodes connected to the SVC; Represents the SVC at node m at time t Phase reactive compensation power; They represent the SVC at node m respectively. Phase reactive power compensation power upper and lower limits.

[0064] In some embodiments, the embodiments of the present application can construct an energy storage operation constraint model, which is composed of energy storage charging and discharging total power constraints, energy storage charging and discharging power constraints, and energy storage charge state constraints, etc., and the present application does not make specific limitations.

[0065] Further, in the embodiment of the present application, the expression of the total power constraint of energy storage charging and discharging can be, but is not limited to, expressed as:

[0066]

[0067] in, Respectively represent the discharge (charging) power of each phase a, b, c of the energy storage device at the tth moment; N ESS Represents a collection of energy storage nodes.

[0068] The expression of energy storage charging and discharging power constraint can be, but is not limited to, expressed as:

[0069]

[0070] in, and Respectively represent the upper and lower limits of charging power; Respectively represent the upper and lower limits of the discharge power; and is the charge and discharge state of the energy storage, which is a 0-1 variable, indicating that the energy storage device can only be in one state at time t, and cannot be in two states at the same time; Indicates the maximum number of charge and discharge state transitions of the energy storage.

[0071] The expression of energy storage state of charge constraint can be, but is not limited to, expressed as:

[0072]

[0073] in, and They represent the total three-phase discharge and charging power of the energy storage connected to node E at time t respectively; represents the amount of electricity stored in the energy storage device connected to node E at time t; η E,ch and η E,dis They represent the charging and discharging efficiency coefficients of energy storage respectively; and They represent the upper and lower limits of the amount of electricity that the energy storage can store at the tth moment; Δt represents the time interval; the second constraint is to prevent the energy storage from being overcharged / over-discharged and prolong its life.

[0074] In some embodiments, the embodiments of the present application may construct an electric vehicle participating in load demand response operation constraint model based on the electric vehicle charging load power, wherein the expression of the model may be, but is not limited to, expressed as:

[0075]

[0076] Among them, N EV represents the set of nodes containing electric vehicles; and They represent the node m at time t. Phase electric vehicle charging load power and its transferable amount; Represents the node m The upper limit of the proportion of electric vehicle load that can be transferred.

[0077] Furthermore, the embodiments of the present application can utilize the on-load tap-changing transformer operation constraint model, the shunt capacitor operation constraint model, the SVC operation constraint model, the energy storage operation constraint model, and the electric vehicle load demand response operation constraint model to construct an active and reactive controllable resource regulation characteristic constraint model.

[0078] In step S102, an initial distribution network optimization operation model considering discrete control of the distribution network is constructed based on the active and reactive controllable resource regulation characteristic constraint model.

[0079] In the actual implementation process, the embodiment of the present application can establish an optimized operation model of the distribution network that initially considers discrete control based on the active and reactive controllable resource regulation characteristic constraint model.

[0080] Optionally, in one embodiment of the present application, a distribution network optimization operation model that initially considers discrete control of the distribution network is constructed based on the active and reactive controllable resource regulation characteristic constraint model, including: based on the active and reactive controllable resource regulation characteristic constraint model, obtaining the dispatching cost and voltage deviation of the distribution network, and constructing the objective function of the distribution network optimization operation model that initially considers discrete control based on the dispatching cost and voltage deviation; based on the active and reactive controllable resource regulation characteristic constraint model, obtaining the active power and reactive power of the distribution network, and constructing the power balance constraint of the distribution network optimization operation model that initially considers discrete control based on the active power and reactive power; based on the active and reactive controllable resource regulation characteristic constraint model, obtaining the active power constraint and voltage amplitude constraint of the distribution network, and generating the safety operation constraint of the distribution network optimization operation model that initially considers discrete control based on the active power constraint and the voltage amplitude constraint.

[0081] It will be understood by those skilled in the art that the distribution network optimization operation model that initially considers discrete control is established based on the active and reactive controllable resource regulation characteristic constraint model in the embodiment of the present application, and may include but is not limited to: objective function, power balance constraints, safe operation constraints, etc., and the present application does not impose any specific restrictions.

[0082] In some embodiments, the embodiments of the present application may establish an objective function with the goal of minimizing the scheduling cost and the voltage deviation, wherein the expression of the objective function may be, but is not limited to, expressed as:

[0083]

[0084] in, represents the cost of optimizing the dispatch of active and reactive resources in the distribution network; α represents the weight coefficient of the cost; β represents the weight coefficient of the voltage deviation; They represent the voltage amplitude of node i on the tie line at time t respectively; N represents the set of all nodes in the distribution network.

[0085] In some embodiments, the embodiments of the present application may establish a power balance constraint with active power and reactive power as constraints, wherein the expression of the power balance constraint may be, but is not limited to, expressed as:

[0086]

[0087] in, They represent the balancing nodes of the distribution network connected at time t. Phase active and reactive power; represents the sum of active power flowing into node h connected to node i at time t; represents the sum of active power flowing into node i connected to node h at time t; represents the sum of reactive powers flowing into node h connected to node i at time t; represents the sum of reactive power flowing into node i connected to node h at time t; ∑g i,sh and ∑b i,sh They represent the sum of the conductance and the admittance to ground at busbar i respectively; represents the square of the voltage amplitude at node i at time t; They represent the distribution network node i at time t. Phase active and reactive load power.

[0088] In some embodiments, the embodiments of the present application may establish a safe operation constraint based on the active power constraint and the voltage amplitude constraint, wherein the expression of the safe operation constraint may be, but is not limited to, expressed as:

[0089]

[0090] in, and Respectively represent the busbar i Minimum and maximum phase voltage amplitudes; and Respectively represent the line ij The maximum and minimum values ​​of active power that a phase can transmit.

[0091] In step S103, based on the objective function and integer constraint variables of the mixed integer linear programming problem in the initial distribution network optimal operation model considering discrete control, the final distribution network optimal operation model considering discrete control of the distribution network is determined.

[0092] During the actual implementation process, the embodiments of the present application can define the standard form of the mixed integer linear programming problem based on the initial distribution network optimization operation model considering discrete control, and based on the standard form of the mixed integer linear programming problem, determine the objective function and integer constraint variables of the mixed integer linear programming problem, and then use the branch and cut algorithm to solve it, and finally obtain the distribution network optimization operation model considering discrete control.

[0093] In the embodiment of the present application, the flow chart of using the branch and cut algorithm to solve the mixed integer linear programming problem is as follows: Figure 2 As shown, it mainly includes the following steps:

[0094] Step S201: constructing an initial distribution network optimization operation model that takes discrete control into consideration.

[0095] Step S202: Relax the integer constraints of the mixed integer linear programming problem, solve the mixed integer linear programming problem, and obtain a corresponding solution.

[0096] Step S203: Determine whether the solution to the mixed integer linear programming problem satisfies the integer constraint. If not, execute step S204; otherwise, execute step S207.

[0097] Step S204: Using the feasible solution to provide an upper bound of the optimal solution.

[0098] Step S205: Determine whether the upper bound is equal to the lower bound. If not, execute step S206; otherwise, execute step S207.

[0099] Step S206: Generate a cutting plane.

[0100] Step S207: Obtain the optimal solution to the mixed integer linear programming problem.

[0101] Optionally, in one embodiment of the present application, based on the objective function and integer constraint variables of the mixed integer linear programming problem in the distribution network optimization operation model that initially considers discrete control, the final distribution network optimization operation model that considers discrete control of the distribution network is determined, including: calculating the integer solution of the mixed integer linear programming problem based on the objective function and the integer constraint variables; judging whether the integer solution satisfies the integer constraint of the integer constraint variable; if the integer solution satisfies the integer constraint, determining the optimal solution of the mixed integer linear programming problem based on the integer solution; otherwise, determining the upper and lower bounds of the mixed integer linear programming problem based on the integer solution, and judging whether the upper and lower bounds are equal; if the upper bound is equal to the lower bound, determining the optimal solution of the mixed integer linear programming problem based on the upper bound, the lower bound and the integer solution; otherwise, based on the upper and lower bounds, the mixed integer linear programming problem is segmented to obtain the optimal solution of the mixed integer linear programming problem using the segmented mixed integer linear programming problem; and determining the final distribution network optimization operation model that considers discrete control based on the optimal solution. Wherein, the expressions of the objective function and integer constraint variables of the mixed integer linear programming problem can be, but are not limited to, as follows:

[0102]

[0103] in, And N I ∈N:={1,2,K,n}.

[0104] In some embodiments, the embodiments of the present application can use a branch and cut algorithm to solve the mixed integer linear programming problem in the distribution network optimization operation model that initially considers discrete control based on the distribution network optimization operation model that initially considers discrete control, and then obtain the final distribution network optimization operation model that considers discrete control. The main contents are as follows: Figure 3 As shown, the following steps are included:

[0105] Step S301: Based on the distribution network optimization operation model initially considering discrete control, a standard form of a mixed integer linear programming problem is obtained.

[0106] Among them, the embodiment of the present application can obtain the objective function and integer constraint variables of the mixed integer linear programming problem, and its expression can be but not limited to:

[0107]

[0108] in, And N I ∈N:={1,2,K,n}.

[0109] Step S302: Relax the integer constraints and solve the mixed integer linear programming problem.

[0110] It can be understood that in order to simplify the mixed integer linear programming problem, the embodiment of the present application temporarily relaxes the integer constraints in the mixed integer programming problem, thereby converting it into a standard linear programming problem, and then obtaining an easy-to-solve relaxed problem, and providing an upper bound of the mixed integer programming problem based on the solution of the linear programming problem.

[0111] Further, the embodiment of the present application performs linear programming problem solving and integer constraint checking: a linear programming problem after relaxation is solved by a linear programming solution algorithm. If the optimal solution obtained by solving the linear programming problem just satisfies all integer constraints of the mixed integer programming problem, then the optimal solution of the mixed integer programming problem is found. However, in most cases, the optimal solution obtained by solving the linear programming problem may not satisfy all integer constraints of the mixed integer programming problem, and step S303 needs to be executed at this time.

[0112] Step S303: generating cutting planes and updating linear programming problems.

[0113] It can be understood that, in order to further narrow the search range, the embodiment of the present application needs to find a cutting plane that can cut the relaxed linear programming problem. If such a cutting plane can be found, it is added to the mixed integer programming problem, and the process returns to step S302 to solve the cut linear programming problem again.

[0114] Step S304: branch operation.

[0115] It can be understood that if the embodiment of the present application still does not obtain a solution that satisfies the integer constraint after the above steps, then a branch operation is required to select one of the variables that do not satisfy the integer constraint and create two branch nodes, corresponding to the upper and lower bounds of the integer value of the variable.

[0116] Step S305: Node selection.

[0117] Among them, in the embodiment of the present application, in the search tree, a target branch node can be selected to perform the next step of solving.

[0118] Step S306: Re-optimization and relaxation of the linear programming problem.

[0119] It can be understood that the embodiment of the present application relaxes and solves the linear programming problem again for the selected target branch node. Similar to step S302, the relaxed node integer problem is a linear programming problem, and the relaxed linear programming problem is solved using a linear programming solution algorithm.

[0120] Step S307: Node pruning and boundary update.

[0121] Among them, the embodiments of the present application can prune nodes according to the solution results of the linear programming problem, and the main contents may be: if the relaxed linear programming problem has no solution, prune the nodes; if the optimal solution of the relaxed linear programming problem exceeds the upper bound of the current optimal solution, prune the nodes; if the optimal solution of the relaxed linear programming problem does not exceed the upper bound of the current optimal solution, and the current solution is a feasible solution to the integer problem, update the current upper bound and the current optimal solution.

[0122] Step S308: Further branching and search tree expansion.

[0123] It can be understood that if the embodiment of the present application cannot trim the node, it is necessary to further perform a branch operation, create a new node and add it to the search tree. This step is repeated until the optimal solution that satisfies the integer constraint is found or it is determined that no better solution can be found.

[0124] The distribution network optimization operation control method considering discrete control proposed in the embodiment of the present application is introduced in detail below in conjunction with a specific embodiment.

[0125] in, Figure 4 The present invention is a flowchart of the working principle of a distribution network optimization operation control method considering discrete control provided according to an embodiment of the present application.

[0126] Step S401: constructing a controllable active and reactive resource regulation characteristic constraint model for a distribution network.

[0127] Among them, the embodiment of the present application can construct a distribution network active and reactive controllable resource regulation characteristic constraint model based on the on-load tap-changing transformer operation constraint model, the shunt capacitor operation constraint model, the static VAR compensator SVC operation constraint model, the energy storage operation constraint model and the electric vehicle participation load demand response operation constraint model.

[0128] Step S402: constructing an initial distribution network optimization operation model that considers discrete control of the distribution network.

[0129] Among them, the embodiment of the present application can establish a distribution network optimization operation model that initially considers discrete control based on the active and reactive controllable resource regulation characteristic constraint model.

[0130] Step S403: using a branch and cut algorithm to solve the mixed integer linear programming problem in the distribution network optimization operation model that initially considers discrete control.

[0131] Among them, the embodiments of the present application can be combined with Figure 2 , using the branch and cut algorithm to solve mixed integer linear programming problems.

[0132] Step S404: using a branch and cut algorithm to solve the distribution network optimization operation model that finally considers discrete control.

[0133] Among them, the embodiments of the present application can be combined with Figure 2 and Figure 3 , the branch and cut algorithm is used to solve the optimal operation model of the distribution network that finally considers discrete control.

[0134] According to the distribution network optimization operation control method considering discrete control proposed in the embodiment of the present application, the active and reactive controllable resource regulation characteristic constraint model of the distribution network can be constructed based on the on-load tap-changing transformer operation constraint model, the shunt capacitor operation constraint model, the operation constraint model, the energy storage operation constraint model and the electric vehicle participation load demand response operation constraint model, and the distribution network is finally determined based on the objective function and integer constraint variables of the mixed integer linear programming problem in the distribution network optimization operation model based on the initial consideration of discrete control. The distribution network optimization operation model considering discrete control is finally constructed based on the characteristic constraints of various equipment. Considering that the optimization operation model is a mixed integer linear programming problem, a branch cutting algorithm is proposed, that is, a cutting plane constraint is inserted on the basis of branch and bound, and the linear relaxation constraint is tightened so that the integer optimal solution can be obtained faster. Thus, the problems in the related art that the global optimality of the integer solution cannot be guaranteed and the computational complexity is high are solved.

[0135] Next, a distribution network optimization operation control device considering discrete control proposed in accordance with an embodiment of the present application will be described with reference to the accompanying drawings.

[0136] Figure 5 A block diagram of a distribution network optimization operation control device considering discrete control provided according to an embodiment of the present application.

[0137] like Figure 5 As shown, the distribution network optimization operation control device 50 considering discrete control is applied to the model building stage, wherein the device 50 includes: a first building module 501, a second building module 502 and a third building module 503.

[0138] Among them, the first construction module 501 is used to construct an active and reactive controllable resource regulation characteristic constraint model of the distribution network based on at least one of the on-load tap-changing transformer operation constraint model, the shunt capacitor operation constraint model, the SVC operation constraint model, the energy storage operation constraint model and the electric vehicle participation load demand response operation constraint model.

[0139] The second building module 502 is used to build an initial distribution network optimization operation model that considers discrete control of the distribution network based on the active and reactive controllable resource regulation characteristic constraint model.

[0140] The third building module 503 is used to determine the final distribution network optimization operation model considering discrete control of the distribution network based on the objective function and integer constraint variables of the mixed integer linear programming problem in the initial distribution network optimization operation model considering discrete control.

[0141] Optionally, in one embodiment of the present application, the first building block 501 includes: a first building unit, a second building unit, a third building unit, a fourth building unit, a fifth building unit and a sixth building unit.

[0142] The first construction unit is used to construct an on-load tap-changing transformer operation constraint model based on at least one of a transformer power regulation constraint, a transformer voltage regulation constraint and a transformer gear regulation constraint.

[0143] The second construction unit is used to construct a parallel capacitor operation constraint model based on the reactive compensation power constraint of the connected parallel capacitor and the switching group number constraint of the parallel capacitor.

[0144] The third construction unit is used to construct an SVC operation constraint model based on the access SVC reactive compensation power constraint.

[0145] The fourth constructing unit is used to construct an energy storage operation constraint model based on at least one of the energy storage charging and discharging total power constraint, the energy storage charging and discharging power constraint, and the energy storage charge state constraint.

[0146] The fifth construction unit is used to construct an electric vehicle participating in load demand response operation constraint model based on the electric vehicle charging load power.

[0147] The sixth construction unit is used to construct an active and reactive controllable resource regulation characteristic constraint model based on the on-load tap-changing transformer operation constraint model, the shunt capacitor operation constraint model, the SVC operation constraint model, the energy storage operation constraint model and the electric vehicle participation load demand response operation constraint model.

[0148] Optionally, in one embodiment of the present application, the second building block 502 includes: a first building unit, a second building unit and a third building unit.

[0149] Among them, the first construction unit obtains the dispatching cost and voltage deviation of the distribution network based on the active and reactive controllable resource regulation characteristic constraint model, and constructs the objective function of the distribution network optimization operation model that initially considers discrete control based on the dispatching cost and voltage deviation.

[0150] The second construction unit is used to obtain the active power and reactive power of the distribution network based on the active and reactive controllable resource regulation characteristic constraint model, and construct the power balance constraint of the distribution network optimization operation model that initially considers discrete control based on the active power and reactive power.

[0151] The third construction unit is used to obtain the active power constraint and voltage amplitude constraint of the distribution network based on the active and reactive controllable resource regulation characteristic constraint model, and generate the safe operation constraint of the distribution network optimization operation model that initially considers discrete control based on the active power constraint and the voltage amplitude constraint.

[0152] Optionally, in one embodiment of the present application, the third construction module 503 includes: a calculation unit, a first judgment unit, a first determination unit, a second judgment unit, a second determination unit, a segmentation unit and a third determination unit.

[0153] The computing unit is used to calculate the integer solution of the mixed integer linear programming problem based on the objective function and the integer constraint variables.

[0154] The first judging unit is used to judge whether the integer solution satisfies the integer constraint of the integer constraint variable.

[0155] The first determining unit is used to determine the optimal solution of the mixed integer linear programming problem based on the integer solution when the integer solution satisfies the integer constraint.

[0156] The second judgment unit is used to determine the upper bound and the lower bound of the mixed integer linear programming problem based on the integer solution when the integer solution does not satisfy the integer constraint, and to judge whether the upper bound and the lower bound are equal.

[0157] The second determining unit is used to determine the optimal solution of the mixed integer linear programming problem based on the upper bound, the lower bound and the integer solution when the upper bound is equal to the lower bound.

[0158] The segmentation unit is used for segmenting the mixed integer linear programming problem based on the upper bound and the lower bound when the upper bound is not equal to the lower bound, so as to obtain the optimal solution of the mixed integer linear programming problem by using the segmented mixed integer linear programming problem.

[0159] The third determining unit is used to determine the distribution network optimization operation model that finally considers discrete control based on the optimal solution.

[0160] Optionally, in one embodiment of the present application, the expressions of the objective function and integer constraint variables of the mixed integer linear programming problem may be, but are not limited to,:

[0161]

[0162] in, And N I ∈N:={1,2,K,n}.

[0163] It should be noted that the aforementioned explanation of the embodiment of the distribution network optimization operation control method considering discrete control is also applicable to the distribution network optimization operation control device considering discrete control of this embodiment, and will not be repeated here.

[0164] According to the distribution network optimization operation control device considering discrete control proposed in the embodiment of the present application, the active and reactive controllable resource regulation characteristic constraint model of the distribution network can be constructed based on the on-load tap-changing transformer operation constraint model, the shunt capacitor operation constraint model, the operation constraint model, the energy storage operation constraint model and the electric vehicle participation load demand response operation constraint model, and the distribution network is finally determined based on the objective function and integer constraint variables of the mixed integer linear programming problem in the distribution network optimization operation model based on the initial consideration of discrete control. The distribution network optimization operation model considering discrete control is finally constructed based on the characteristic constraints of various equipment. Considering that the optimization operation model is a mixed integer linear programming problem, a branch cutting algorithm is proposed, that is, a cutting plane constraint is inserted on the basis of branch and bound, and the linear relaxation constraint is tightened so that the integer optimal solution can be obtained faster. Thus, the problems in the related art that the global optimality of the integer solution cannot be guaranteed and the computational complexity is high are solved.

[0165] The above embodiment describes the model building stage, and the following describes an embodiment of the model application stage.

[0166] Figure 6 The present invention is a flowchart of a distribution network optimization operation control method considering discrete control according to another embodiment of the present application.

[0167] like Figure 6 As shown, the distribution network optimization operation control method considering discrete control is applied in the model application stage, wherein the method includes the following steps:

[0168] In step S601, operation constraint information of the distribution network to be optimized is obtained.

[0169] In step S602, the operation constraint information is input into a pre-constructed distribution network optimization operation model that ultimately considers discrete control to obtain the actual integer operation constraints during operation in the distribution network to be optimized, and the actual integer operation constraints are used to control the operation of the distribution network to be optimized, wherein the pre-constructed distribution network optimization operation model that ultimately considers discrete control is obtained from the active and reactive controllable resource regulation characteristic constraint model.

[0170] As a possible implementation method, the embodiment of the present application can obtain the operating constraint information of the distribution network to be optimized, select different branch cutting algorithms to analyze the solution efficiency of the distribution network optimization operation model pre-constructed by the cutting optimization and ultimately considering discrete control, and then obtain the actual integer operating constraints during operation in the distribution network to be optimized, so as to control the operation of the distribution network to be optimized.

[0171] Among them, in an embodiment of the present application, the integer constraint variables contained in the pre-built distribution network optimization operation model that ultimately considers discrete control are shown in Table 1, wherein Table 1 is a basic information table of integer constraint variables for a mixed integer linear programming problem provided according to an embodiment of the present application.

[0172] Table 1

[0173] Decision variables Continuous variables Integer variables 0-1 variables Linear Constraints 19830 19683 147 103 26819

[0174] Furthermore, the embodiment of the present application can select different branch cutting algorithms to analyze the solution efficiency of the distribution network optimization operation model that is pre-constructed by cutting optimization and ultimately considers discrete control. Under the framework of branch cutting, different cutting plane generation methods are used to solve the problem respectively (where the selected test CPU is XX). The results are shown in Table 2, where Table 2 is an information table of the solution results of the mixed integer linear programming problem provided according to an embodiment of the present application.

[0175] Table 2

[0176] Cutting Plane Method Number of branch nodes Iteration number for linear problems Solution time MIR 54 4256 76.760s GMI 41 4678 100.498s Flow Cover Inequality 66 5266 89.630s Zero-half cut plane 81 10133 107.899s Splitting Cut Plane 31 7231 66.153s

[0177] In the distribution network to be optimized, there are a large number of integer variables. As can be seen from Table 2, the GMI (Gomory Mixed Integer) and MIR (Mixed Integer Round) methods that are good at handling such variables can improve the solution speed. However, at the same time, since there are a large number of continuous variables (three-phase voltage assignments and phase angles) in the three-phase power flow equations of large-scale systems, the split cutting plane cutting that can handle continuous variable constraints can solve such problems more efficiently.

[0178] According to the distribution network optimization operation control method considering discrete control proposed in the embodiment of the present application, the operation constraint information of the distribution network to be optimized can be input into the pre-built distribution network optimization operation model considering discrete control, and the actual integer operation constraints during operation in the distribution network to be optimized can be obtained to control the operation of the distribution network to be optimized. The distribution network optimization operation model considering discrete control can be constructed based on the characteristic constraints of various equipment. Considering that the optimization operation model is a mixed integer linear programming problem, a branch and cut algorithm is proposed, that is, cutting plane constraints are inserted on the basis of branch and bound, and linear relaxation constraints are tightened so that the integer optimal solution can be obtained faster. Thus, the problems in the related art that the global optimality of the integer solution cannot be guaranteed and the computational complexity is high are solved.

[0179] Next, a distribution network optimization operation control device considering discrete control proposed in accordance with an embodiment of the present application will be described with reference to the accompanying drawings.

[0180] Figure 7 It is a block diagram of a distribution network optimization operation control device considering discrete control according to another embodiment of the present application.

[0181] like Figure 7 As shown, the distribution network optimization operation control device 70 considering discrete control is applied in the model application stage, wherein the device 70 includes: an acquisition module 701 and a control module 702 .

[0182] The acquisition module 701 is used to acquire the operation constraint information of the distribution network to be optimized.

[0183] The control module 702 is used to input the operation constraint information into the pre-built distribution network optimization operation model that ultimately considers discrete control, so as to obtain the actual integer operation constraints during operation in the distribution network to be optimized, and use the actual integer operation constraints to control the operation of the distribution network to be optimized, wherein the pre-built distribution network optimization operation model that ultimately considers discrete control is obtained by the active and reactive controllable resource regulation characteristic constraint model.

[0184] It should be noted that the aforementioned explanation of the embodiment of the distribution network optimization operation control method considering discrete control is also applicable to the distribution network optimization operation control device considering discrete control of this embodiment, and will not be repeated here.

[0185] According to the distribution network optimization operation control device considering discrete control proposed in the embodiment of the present application, the operation constraint information of the distribution network to be optimized can be input into the pre-built distribution network optimization operation model that finally considers discrete control, and the actual integer operation constraint during operation in the distribution network to be optimized is obtained to control the operation of the distribution network to be optimized. The distribution network optimization operation model that finally considers discrete control is constructed based on the characteristic constraints of various equipments. Considering that the optimization operation model is a mixed integer linear programming problem, a branch cutting algorithm is proposed, that is, cutting plane constraints are inserted on the basis of branch and bound, and linear relaxation constraints are tightened so that the integer optimal solution can be obtained faster. Thus, the problems in the related art that the global optimality of the integer solution cannot be guaranteed and the computational complexity is high are solved.

[0186] Figure 8 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of the present application. The electronic device may include:

[0187] A memory 801 , a processor 802 , and a computer program stored in the memory 801 and executable on the processor 802 .

[0188] When the processor 802 executes the program, the distribution network optimization operation control method considering discrete control provided in the above embodiment is implemented.

[0189] Furthermore, the electronic device further comprises:

[0190] The communication interface 803 is used for communication between the memory 801 and the processor 802 .

[0191] The memory 801 is used to store computer programs that can be executed on the processor 802 .

[0192] The memory 801 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0193] If the memory 801, the processor 802 and the communication interface 803 are implemented independently, the communication interface 803, the memory 801 and the processor 802 can be connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 8 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0194] Optionally, in a specific implementation, if the memory 801, the processor 802 and the communication interface 803 are integrated on a chip, the memory 801, the processor 802 and the communication interface 803 can communicate with each other through an internal interface.

[0195] The processor 802 may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0196] An embodiment of the present application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned distribution network optimization operation control method considering discrete control.

[0197] An embodiment of the present application also provides a computer program product, including a computer program, which, when executed, implements the above-mentioned distribution network optimization operation control method considering discrete control.

[0198] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0199] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0200] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0201] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or N wirings (electronic devices), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways as necessary and then storing it in a computer memory.

[0202] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above embodiment, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented by hardware, as in another embodiment, it can be implemented by any one or a combination of multiple of the following technologies known in the art: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0203] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0204] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0205] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A distribution network optimization operation control method considering discrete control, characterized in that: Applied to the model building stage, wherein the method comprises the following steps: Based on at least one of the on-load tap-changing transformer operation constraint model, the shunt capacitor operation constraint model, the static VAR compensator SVC operation constraint model, the energy storage operation constraint model, and the electric vehicle load demand response operation constraint model, a distribution network active and reactive controllable resource regulation characteristic constraint model is constructed; Based on the active and reactive controllable resource regulation characteristic constraint model, an initial distribution network optimization operation model considering discrete control of the distribution network is constructed; Based on the objective function and integer constraint variables of the mixed integer linear programming problem in the initial distribution network optimization operation model considering discrete control, the final distribution network optimization operation model considering discrete control of the distribution network is determined.

2. The method according to claim 1, characterized in that The active and reactive controllable resource regulation characteristic constraint model of the distribution network is constructed based on at least one of the on-load tap-changing transformer operation constraint model, the shunt capacitor operation constraint model, the SVC operation constraint model, the energy storage operation constraint model, and the electric vehicle load demand response operation constraint model, including: Building an on-load tap-changing transformer operation constraint model based on at least one of a transformer power regulation constraint, a transformer voltage regulation constraint, and a transformer gear regulation constraint; The parallel capacitor operation constraint model is constructed based on the reactive compensation power constraint of the connected parallel capacitor and the number constraint of the parallel capacitor switching groups; Construct the SVC operation constraint model based on the access SVC reactive compensation power constraint; Building an energy storage operation constraint model based on at least one of an energy storage charging and discharging total power constraint, an energy storage charging and discharging power constraint, and an energy storage charge state constraint; Based on the charging load power of electric vehicles, an operation constraint model for electric vehicles to participate in load demand response is constructed; Based on the on-load tap-changing transformer operation constraint model, the shunt capacitor operation constraint model, the SVC operation constraint model, the energy storage operation constraint model and the electric vehicle participating in load demand response operation constraint model, the active and reactive controllable resource regulation characteristic constraint model is constructed.

3. The method according to claim 1, characterized in that The distribution network optimization operation model based on the active and reactive controllable resource regulation characteristic constraint model for constructing the distribution network initially considering discrete control includes: Based on the active and reactive controllable resource regulation characteristic constraint model, the dispatching cost and voltage deviation of the distribution network are obtained, and based on the dispatching cost and the voltage deviation, the objective function of the distribution network optimization operation model initially considering discrete control is constructed; Based on the active and reactive controllable resource regulation characteristic constraint model, the active power and reactive power of the distribution network are obtained, and based on the active power and reactive power, a power balance constraint of the distribution network optimization operation model initially considering discrete control is constructed; Based on the active and reactive controllable resource regulation characteristic constraint model, the active power constraint and the voltage amplitude constraint of the distribution network are obtained, and based on the active power constraint and the voltage amplitude constraint, the safe operation constraint of the distribution network optimization operation model that initially considers discrete control is generated.

4. The method according to claim 1, characterized in that: The method of determining the final distribution network optimization operation model considering discrete control of the distribution network based on the objective function and integer constraint variables of the mixed integer linear programming problem in the initial distribution network optimization operation model considering discrete control comprises: Calculating an integer solution to the mixed integer linear programming problem based on the objective function and the integer constraint variables; Determining whether the integer solution satisfies the integer constraint of the integer constraint variable; If the integer solution satisfies the integer constraint, determining an optimal solution to the mixed integer linear programming problem based on the integer solution; Otherwise, determining an upper bound and a lower bound of the mixed integer linear programming problem based on the integer solution, and determining whether the upper bound and the lower bound are equal; If the upper bound is equal to the lower bound, determining an optimal solution to the mixed integer linear programming problem based on the upper bound, the lower bound and the integer solution; Otherwise, the mixed integer linear programming problem is segmented based on the upper bound and the lower bound, so as to obtain an optimal solution to the mixed integer linear programming problem by using the segmented mixed integer linear programming problem. The distribution network optimization operation model that finally considers discrete control is determined based on the optimal solution.

5. The method according to claim 1, characterized in that The expressions of the objective function and integer constraint variables of the mixed integer linear programming problem are: in, And N I ∈N:={1,2,K,n}.

6. A distribution network optimization operation control method considering discrete control, characterized in that: Applied to the model application stage, wherein the method comprises the following steps: Obtaining the operation constraint information of the distribution network to be optimized; The operation constraint information is input into a pre-constructed distribution network optimization operation model that ultimately considers discrete control to obtain actual integer operation constraints during operation in the distribution network to be optimized, and the operation of the distribution network to be optimized is controlled using the actual integer operation constraints, wherein the pre-constructed distribution network optimization operation model that ultimately considers discrete control is obtained from an active and reactive controllable resource regulation characteristic constraint model.

7. A distribution network optimization operation control device considering discrete control, characterized in that: Applied to the model building stage, wherein the device comprises: The first construction module is used to construct an active and reactive controllable resource regulation characteristic constraint model of the distribution network based on at least one of an on-load tap-changing transformer operation constraint model, a shunt capacitor operation constraint model, an SVC operation constraint model, an energy storage operation constraint model, and an electric vehicle participating in a load demand response operation constraint model; A second construction module is used to construct an initial distribution network optimization operation model of the distribution network considering discrete control based on the active and reactive controllable resource regulation characteristic constraint model; The third building block is used to determine the final distribution network optimization operation model considering discrete control of the distribution network based on the objective function and integer constraint variables of the mixed integer linear programming problem in the initial distribution network optimization operation model considering discrete control.

8. A distribution network optimization operation control device considering discrete control, characterized in that: Applied to the model application stage, wherein the device comprises: An acquisition module, used to obtain the operation constraint information of the distribution network to be optimized; A control module is used to input the operation constraint information into a pre-built distribution network optimization operation model that ultimately considers discrete control, so as to obtain the actual integer operation constraints during operation in the distribution network to be optimized, and use the actual integer operation constraints to control the operation of the distribution network to be optimized, wherein the pre-built distribution network optimization operation model that ultimately considers discrete control is obtained from an active and reactive controllable resource regulation characteristic constraint model.

9. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the distribution network optimization operation control method considering discrete control as described in any one of claims 1 to 5 or claim 6.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the distribution network optimization operation control method considering discrete control as described in any one of claims 1 to 5 or claim 6.

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