Intelligent soft switching planning method and device for active distribution network based on incremental linearization

Abstract

The present invention relates to the field of smart grid technology, and discloses an active distribution network smart soft switch planning method and device based on incremental linearization. The present invention constructs a smart soft switch planning objective function that takes into account the smart soft switch investment cost and the annual cost of the distribution network loss; based on the objective function, a distribution network smart soft switch planning model that takes into account multi-stage constraints is constructed; the multi-stage constraints include power balance constraints, branch current constraints, node voltage constraints, smart soft switch location and capacity constraints, smart soft switch operation constraints, distributed power output constraints, and line capacity constraints; the distribution network smart soft switch planning model is linearized into a corresponding MILP model using an incremental linearization method, and the corresponding MILP model is solved to obtain a distribution network smart soft switch planning scheme. The present invention can effectively improve the rationality of smart soft switch planning and improve the efficiency of solving the planning model while meeting the solution accuracy requirements.

Description

Technical Field

[0001] The present invention relates to the field of smart grid technology, and in particular to an incremental linearization-based intelligent soft switch planning method and device for an active distribution network. Background Art

[0002] With the increasing penetration of distributed power sources, energy storage systems, and new flexible loads, power distribution systems are evolving from traditional radial networks to active distribution networks. The development of active distribution networks and the widespread application of distributed generation technologies are driving changes in power distribution and utilization models and operational management mechanisms.

[0003] In active distribution networks, traditional tie switches struggle to meet the high-precision, real-time operation optimization requirements of distribution networks when renewable energy and loads fluctuate frequently. Intelligent soft open points (SOPs) are a new type of intelligent distribution device that has emerged in this context to replace traditional tie switches. Compared to on-off switching, SOP power control is safer and more reliable, and can even achieve real-time optimization, effectively addressing the randomness and fluctuations brought about by distributed power sources and loads. Currently, SOP planning methods primarily consider the benefits of SOP from the perspective of improving economic operation. Research on optimizing distribution network voltage distribution and reducing network active power loss by considering SOP switches mainly focuses on the optimization of distribution network operation. Few consider the benefits of SOP during the planning phase, resulting in a certain degree of irrationality. Furthermore, currently used SOP planning strategy solution methods mainly rely on intelligent algorithms (such as heuristic algorithms), second-order cone optimization algorithms, or hybrid algorithms combining the two. However, intelligent algorithms often obtain local optimal solutions when solving planning optimization strategies, while second-order cone optimization algorithms struggle to ensure that the required accuracy is met while improving solution efficiency when solving large-scale planning optimization problems.

[0004] The prior art provides a comprehensive planning method and system for intelligent energy storage soft switches in distribution networks (patent application number 202010564757.9). This system uses a hybrid algorithm combining second-order cone programming and simulated annealing to solve the coordinated planning model for intelligent energy storage soft switches to obtain the optimal intelligent energy storage soft switch planning scheme. The prior art also provides a planning method for intelligent soft switches in active distribution networks that considers the characteristics of distributed power sources (patent application number 201510924782.2). This method establishes a mathematical model for the intelligent soft switch planning problem in active distribution networks based on given distribution system parameters and generated planning scenarios. The system also performs cone model transformation on the nonlinear constraints in the mathematical model of the intelligent soft switch planning problem in active distribution networks using the standard form of cone programming.

[0005] Although the above two schemes have improved the efficiency of model solving to a certain extent, the execution of the simulated annealing algorithm requires setting basic parameters (such as the number of iterations) according to the actual situation of the distribution network, and it is necessary to continuously analyze the fitness of the generated scheme until the optimal solution is reached. The process is relatively complicated and the accuracy is affected by the set basic parameters.

[0006] Therefore, it is necessary to provide a new intelligent soft switch planning scheme for active distribution networks that can improve the rationality of intelligent soft switch planning and improve the efficiency of planning model solution while meeting the solution accuracy requirements. Summary of the Invention

[0007] The present invention provides an intelligent soft switch planning method and device for an active distribution network based on incremental linearization. The method uses an incremental linearization method to transform the original model into a large-scale mixed-integer linear optimization problem, so that the optimal solution with given error accuracy requirements can be obtained using mature mathematical optimization methods. This solves the technical problems of how to improve the rationality of intelligent soft switch planning and how to improve the efficiency of solving the planning model while meeting the solution accuracy requirements.

[0008] A first aspect of the present invention provides an active distribution network intelligent soft switch planning method based on incremental linearization, comprising:

[0009] Construct an intelligent soft switch planning objective function that considers the investment cost of the intelligent soft switch and the annual cost of the distribution network loss;

[0010] Based on the intelligent soft switch planning objective function, a distribution network intelligent soft switch planning model is constructed that considers multi-stage constraints; the multi-stage constraints include power balance constraints, branch current constraints, node voltage constraints, intelligent soft switch location and capacity constraints, intelligent soft switch operation constraints, distributed generation output constraints, and line capacity constraints;

[0011] The distribution network intelligent soft switch planning model is linearized into a corresponding MILP (mixed integer linear programming) model by using an incremental linearization method, and the corresponding MILP model is solved to obtain a distribution network intelligent soft switch planning scheme.

[0012] According to one implementation of the first aspect of the present invention, constructing a smart soft switch planning objective function that considers the smart soft switch investment cost and the annual cost of distribution network losses includes:

[0013] The objective function of intelligent soft switch planning is to minimize the sum of the investment cost of the intelligent soft switch and the annual cost of the distribution network loss.

[0014] According to an achievable manner of the first aspect of the present invention, minimizing the sum of the investment cost of the smart soft switch and the annual cost of the distribution network loss as the smart soft switch planning objective function includes:

[0015] The expression of the objective function of the intelligent soft switch planning is set as:

[0016] min C=C loss +C SOP

[0017]

[0018]

[0019] Where C loss is the annual cost of distribution network loss, C SOP is the investment cost of intelligent soft switch, d SOP is the investment discount rate of the smart soft switch, y SOP is the investment life of the smart soft switch, η is the operation and maintenance cost coefficient of the smart soft switch, Ω SOP It is a node set used to install intelligent soft switches, k SOP is the investment cost of the unit capacity intelligent soft switch, The total capacity of the intelligent soft switch installed for node i, N sc is the number of scenario days into which the load time series characteristics are divided according to the season and the time period in a typical day, δ sc is the number of days in a year occupied by the sc scenario day, λ t Indicates the real-time electricity price, It represents the network loss at time t on the sc-th scenario day, and Δt is the preset time interval.

[0020] According to an achievable manner of the first aspect of the present invention, constructing a distribution network intelligent soft switch planning model considering multi-stage constraints according to the intelligent soft switch planning objective function includes:

[0021] Set the power balance constraint to:

[0022]

[0023]

[0024]

[0025]

[0026]

[0027] Where, P ji,sc,t is the active power flowing from node j to node i at time t on the sc scenario day, Q ji,sc,t is the reactive power flowing from node j to node i at time t on the sc scenario day, Pi,sc,t is the active power injected into node i at time t on the sc-th scenario day, Q i,sc,t is the reactive power injected into node i at time t on the sc-th scenario day, P ik,sc,t is the active power flowing from node i to node k at time t on the sc scenario day, Q ik,sc,t is the reactive power flowing from node i to node k at time t on the sc scenario day, Ω br is the set of branches in the system, U i,sc,t is the voltage amplitude of node i at time t on the sc scenario day, U j,sc,t is the voltage amplitude of node j at time t on the sc scenario day, P ij,sc,t is the active power flowing from node i to node j at time t on the sc scenario day, Q ij,sc,t is the reactive power flowing from node i to node j at time t on the sc scenario day, x ij is the reactance of branch ij, r ij is the resistance of branch ij, is the active power consumed by the load of node i at the tth time on the scth scenario day, is the active power generated by DG at node i at time t on the sc scenario day, is the reactive power consumed by the load at node i at time t on the sc-th scenario day, is the reactive power generated by DG at node i at time t on the sc scenario day, is the active power emitted by SOP at node i at time t on the sc-th scenario day, is the reactive power generated by SOP at node i at time t on the sc scenario day, Ω b is the set of all nodes in the system, N sc It is the number of scenario days into which the load time series characteristics are divided according to the season and the time period of a typical day. T is the total power balance time under one scenario day.

[0028] Set the line capacity constraint to:

[0029]

[0030] Where, P ij,sc,t is the active power flowing from node i to node j at time t on the sc scenario day, Q ij,sc,t is the reactive power flowing from node i to node j at time t on the sc scenario day, S ij,max The capacity of branch ij;

[0031] Set the node voltage constraint to:

[0032]

[0033] Where U i,sc,t is the voltage amplitude of node i at time t on the sc scenario day, is the lower limit of the voltage at node i, is the voltage upper limit of node i;

[0034] And / or, setting the intelligent soft switch operation constraints to:

[0035]

[0036]

[0037]

[0038] Where, P i SOP is the active power injected into node i by SOP, is the active power injected into node j by SOP, is the reactive power injected into node i by SOP, is the reactive power injected into node j by SOP, The total capacity of the installed SOP for node j.

[0039] According to an achievable manner of the first aspect of the present invention, constructing a distribution network intelligent soft switch planning model considering multi-stage constraints according to the intelligent soft switch planning objective function includes:

[0040] Set the position and capacity constraints of the intelligent soft switch to:

[0041]

[0042] Where, The total capacity of SOP installed for node i, S module is the capacity of the SOP, is a non-negative integer, The number of SOP installations per unit capacity.

[0043] According to an achievable manner of the first aspect of the present invention, constructing a distribution network intelligent soft switch planning model considering multi-stage constraints according to the intelligent soft switch planning objective function includes:

[0044] Set the branch current constraint to:

[0045]

[0046] Where, I l,sc,t is the current of branch l at time t on the sc scenario day, It is the upper limit of the current allowed to pass through branch 1.

[0047] According to an achievable manner of the first aspect of the present invention, constructing a distribution network intelligent soft switch planning model considering multi-stage constraints according to the intelligent soft switch planning objective function includes:

[0048] The output constraint of the distributed power generation is set as:

[0049]

[0050] Where, is the active power generated by DG at node i at time t on the sc scenario day, is the lower limit of DG output, This is the upper limit of DG output.

[0051] According to an achievable manner of the first aspect of the present invention, linearizing the distribution network intelligent soft switch planning model into a corresponding MILP model using an incremental linearization method includes:

[0052] The objective function of the intelligent soft switching planning is linearly represented, including:

[0053] Assuming the voltage is 1 p.u., the annual cost of the distribution network loss in the objective function of the intelligent soft switch planning is simplified to:

[0054]

[0055] Where C loss (P ij,sc,t ,Q ij,sc,t ) represents the simplified variable P ij,sc,t ,Q ij,sc,t The annual cost of distribution network loss, P ij,sc,t is the active power flowing from node i to node j at time t on the sc scenario day, Q ij,sc,t is the reactive power flowing from node i to node j at time t on the sc scenario day, T is the total power balance time in a scenario day, Ω br is the set of branches in the system, r ij is the resistance of branch ij;

[0056] Determine the number of linearization segments, in P ij,sc,t and Q ij,sc,t The discrete points required for piecewise linearization are calculated according to the following formula within the range of , with a total of NPL-1 for each item:

[0057]

[0058]

[0059] Where, is the kth discrete point of the active power flowing from node i to node j at the tth time on the scth scenario day after linearization, NPL-1 is the number of linearization segments, P ij,sc,t is the lower limit of the active power flowing from node i to node j at time t on the sc scenario day, is the upper limit of the active power flowing from node i to node j at time t on the sc-th scenario day, is the kth discrete point of the reactive power flowing from node i to node j at time t on the scth scenario day after linearization, Q ij,sc,t is the lower limit of the reactive power flowing from node i to node j at time t on the sc scenario day, is the upper limit of the reactive power flowing from node i to node j at the tth time on the scth scenario day, and k is the position order of the discrete point in the power segment interval;

[0060] The new variables are introduced to linearize the simplified annual cost item of distribution network loss according to the following formula:

[0061]

[0062] δ k+1 ≤η k ,η k ≤δ k ,k=1,2,...,NPL-2

[0063] 0≤δ k+1 ≤1,k=1,2,...,NPL-1

[0064] Where, Represents the linearized variables The annual cost of distribution network loss, Represents the linearized variables The annual cost of distribution network loss, Represents the linearized variables The annual cost of distribution network loss, is the first discrete point of the active power flowing from node i to node j at time t on the sc-th scenario day after linearization, is the k+1th discrete point of the active power flowing from node i to node j at time t on the scth scenario day after linearization, is the first discrete point of the reactive power flowing from node i to node j at time t on the sc-th scenario day after linearization, is the k+1th discrete point of the reactive power flowing from node i to node j at time t on the scth scenario day after linearization, δ k The value range is 0~1, k represents the position on the kth linearized segment interval, δ k+1 represents the position on the k+1th linear segment interval, η k is a binary variable.

[0065] According to an achievable manner of the first aspect of the present invention, linearizing the distribution network intelligent soft switch planning model into a corresponding MILP model using an incremental linearization method includes:

[0066] The multi-stage constraints are linearized, including:

[0067] Define a new variable V i,sc,t To replace U i,sc,t The quadratic term in the power balance constraint Convert to V i,sc,t -V j,sc,t =2(r ij P ij,sc,t +x ij Q ij,sc,t ), and converting the node voltage constraint into

[0068] The inscribed polygon approximation method is used to represent the circular constraint, and the intelligent soft switch operation constraint is Convert to The intelligent soft switch operation constraints Convert to And transform the line capacity constraint into γ C0 P ij,sc,t +γ C1 Q ij,sc,t +γ C2 S ij，max ≤0,C=1,2,…,Ψ, where γ C0 , γ C1 and γ C2 is the polygonal approximation coefficient, Ψ represents the number of sides of the polygonal approximation method;

[0069] Introducing non-negative auxiliary variables and in represents the forward active power flowing from node i to node j at time t on the sc-th scenario day, represents the reverse active power flowing from node i to node j at time t on the sc scenario day, represents the forward reactive power flowing from node i to node j at time t on the sc scenario day, represents the reverse reactive power flowing from node i to node j at time t on the sc scenario day, and P ij,sc,t and Q ij,sc,t The equivalent replacement is:

[0070]

[0071] A second aspect of the present invention provides an intelligent soft switch planning device for an active distribution network based on incremental linearization, characterized by comprising:

[0072] The first building block is used to construct an intelligent soft switch planning objective function that considers the investment cost of the intelligent soft switch and the annual cost of the distribution network loss;

[0073] A second construction module is configured to construct a distribution network intelligent soft switch planning model that considers multi-stage constraints based on the intelligent soft switch planning objective function; the multi-stage constraints include power balance constraints, branch current constraints, node voltage constraints, intelligent soft switch location and capacity constraints, intelligent soft switch operation constraints, distributed power generation output constraints, and line capacity constraints;

[0074] The solution module is used to linearize the distribution network intelligent soft switch planning model into a corresponding MILP model by using an incremental linearization device, and solve the corresponding MILP model to obtain a distribution network intelligent soft switch planning scheme.

[0075] According to an implementation of the second aspect of the present invention, the first building block includes:

[0076] The objective function construction unit is used to minimize the sum of the investment cost of the intelligent soft switch and the annual cost of the distribution network loss as the objective function of the intelligent soft switch planning.

[0077] According to an achievable manner of the second aspect of the present invention, the objective function construction unit includes:

[0078] The setting subunit is used to set the expression of the intelligent soft switch planning objective function as follows:

[0079] min C=C loss +C SOP

[0080]

[0081]

[0082] Where C loss is the annual cost of distribution network loss, C SOPis the investment cost of intelligent soft switch, d SOP is the investment discount rate of the smart soft switch, y SOP is the investment life of the smart soft switch, η is the operation and maintenance cost coefficient of the smart soft switch, Ω SOP It is a node set used to install intelligent soft switches, k SOP is the investment cost of the unit capacity intelligent soft switch, The total capacity of the intelligent soft switch installed for node i, N sc is the number of scenario days into which the load time series characteristics are divided according to the season and the time period in a typical day, δ sc is the number of days in a year occupied by the sc scenario day, λ t Indicates the real-time electricity price, It represents the network loss at time t on the sc-th scenario day, and Δt is the preset time interval.

[0083] According to an implementation of the second aspect of the present invention, the second building block includes:

[0084] The first constraint setting unit is configured to set the power balance constraint to:

[0085]

[0086]

[0087]

[0088]

[0089]

[0090] Where, P ji,sc,t is the active power flowing from node j to node i at time t on the sc scenario day, Q ji,sc,t is the reactive power flowing from node j to node i at time t on the sc scenario day, P i,sc,t is the active power injected into node i at time t on the sc-th scenario day, Q i,sc,t is the reactive power injected into node i at time t on the sc-th scenario day, P ik,sc,t is the active power flowing from node i to node k at time t on the sc scenario day, Q ik,sc,t is the reactive power flowing from node i to node k at time t on the sc scenario day, Ω br is the set of branches in the system, U i,sc,t is the voltage amplitude of node i at time t on the sc scenario day, U j,sc,t is the voltage amplitude of node j at time t on the sc scenario day, P ij,sc,tis the active power flowing from node i to node j at time t on the sc scenario day, Q ij,sc,t is the reactive power flowing from node i to node j at time t on the sc scenario day, x ij is the reactance of branch ij, r ij is the resistance of branch ij, is the active power consumed by the load of node i at the tth time on the scth scenario day, is the active power generated by DG at node i at time t on the sc scenario day, is the reactive power consumed by the load at node i at time t on the sc-th scenario day, is the reactive power generated by DG at node i at time t on the sc scenario day, is the active power emitted by SOP at node i at time t on the sc-th scenario day, is the reactive power generated by SOP at node i at time t on the sc scenario day, Ω b is the set of all nodes in the system, N sc It is the number of scenario days into which the load time series characteristics are divided according to the season and the time period of a typical day. T is the total power balance time under one scenario day.

[0091] The second constraint setting unit is configured to set the line capacity constraint to:

[0092]

[0093] Where, P ij,sc,t is the active power flowing from node i to node j at time t on the sc scenario day, Q ij,sc,t is the reactive power flowing from node i to node j at time t on the sc scenario day, S ij,max The capacity of branch ij;

[0094] The third constraint setting unit is configured to set the node voltage constraint to:

[0095]

[0096] Where U i,sc,t is the voltage amplitude of node i at time t on the sc scenario day, is the lower limit of the voltage at node i, is the voltage upper limit of node i;

[0097] And / or, a fourth constraint setting unit, configured to set the intelligent soft switch operation constraint to:

[0098]

[0099]

[0100]

[0101] Where, P i SOP is the active power injected into node i by SOP, is the active power injected into node j by SOP, is the reactive power injected into node i by SOP, is the reactive power injected into node j by SOP, The total capacity of the installed SOP for node j.

[0102] According to an implementation of the second aspect of the present invention, the second building block includes:

[0103] The fifth constraint setting unit is used to set the position and capacity constraints of the intelligent soft switch to:

[0104]

[0105] Where, The total capacity of SOP installed for node i, S module is the capacity of the SOP, is a non-negative integer, The number of SOP installations per unit capacity.

[0106] According to an implementation of the second aspect of the present invention, the second building block includes:

[0107] A sixth constraint setting unit is configured to set the branch current constraint to:

[0108]

[0109] Where, I l,sc,t is the current of branch l at time t on the sc scenario day, It is the upper limit of the current allowed to pass through branch 1.

[0110] According to an implementation of the second aspect of the present invention, the second building block includes:

[0111] The seventh constraint setting unit is configured to set the output constraint of the distributed power supply to:

[0112]

[0113] Where, is the active power generated by DG at node i at time t on the sc scenario day, is the lower limit of DG output, This is the upper limit of DG output.

[0114] According to an achievable manner of the second aspect of the present invention, the solution module includes:

[0115] A first linearization unit, configured to linearize the intelligent soft switching planning objective function, includes:

[0116] Assuming the voltage is 1 p.u., the annual cost of the distribution network loss in the objective function of the intelligent soft switch planning is simplified to:

[0117]

[0118] Where C loss (P ij,sc,t ,Q ij,sc,t ) represents the variable P ij,sc,t ,Q ij,sc,t The annual cost of distribution network loss, P ij,sc,t is the active power flowing from node i to node j at time t on the sc scenario day, Q ij,sc,t is the reactive power flowing from node i to node j at time t on the sc scenario day, T is the total power balance time in a scenario day, Ω br is the set of branches in the system, r ij is the resistance of branch ij;

[0119] Determine the number of linearization segments, in P ij,sc,t and Q ij,sc,t The discrete points required for piecewise linearization are calculated according to the following formula within the range of , with a total of NPL-1 for each item:

[0120]

[0121]

[0122] Where, is the kth discrete point of the active power flowing from node i to node j at the tth time on the scth scenario day after linearization, NPL-1 is the number of linearization segments, P ij,sc,t is the lower limit of the active power flowing from node i to node j at time t on the sc scenario day, is the upper limit of the active power flowing from node i to node j at time t on the sc-th scenario day, is the kth discrete point of the reactive power flowing from node i to node j at time t on the scth scenario day after linearization, Q ij,sc,t is the lower limit of the reactive power flowing from node i to node j at time t on the sc scenario day, is the upper limit of the reactive power flowing from node i to node j at the tth time on the scth scenario day, and k is the position order of the discrete point in the power segment interval;

[0123] The new variables are introduced to linearize the simplified annual cost item of distribution network loss according to the following formula:

[0124]

[0125] δ k+1 ≤η k ,η k ≤δ k ,k=1,2,...,NPL-2

[0126] 0≤δ k+1 ≤1,k=1,2,...,NPL-1

[0127] Where, Represents the linearized variables The annual cost of distribution network loss, Represents the linearized variables The annual cost of distribution network loss, Represents the linearized variables The annual cost of distribution network loss, is the first discrete point of the active power flowing from node i to node j at time t on the sc-th scenario day after linearization, is the k+1th discrete point of the active power flowing from node i to node j at time t on the scth scenario day after linearization, is the first discrete point of the reactive power flowing from node i to node j at time t on the sc-th scenario day after linearization, is the k+1th discrete point of the reactive power flowing from node i to node j at time t on the scth scenario day after linearization, δ k The value range is 0~1, k represents the position on the kth linearized segment interval, δ k+1 represents the position on the k+1th linear segment interval, η k is a binary variable.

[0128] According to an achievable manner of the second aspect of the present invention, the solution module further includes:

[0129] A second linearization unit, configured to linearize the multi-stage constraints, includes:

[0130] Define a new variable V i,sc,t To replace U i,sc,tThe quadratic term in the power balance constraint Convert to V i,sc,t -V j,sc,t =2(r ij P ij,sc,t +x ij Q ij,sc,t ), and converting the node voltage constraint into

[0131] The inscribed polygon approximation method is used to represent the circular constraint, and the intelligent soft switch operation constraint is Convert to The intelligent soft switch operation constraints Convert to And transform the line capacity constraint into γ C0 P ij,sc,t +γ C1 Q ij,sc,t +γ C2 S ij，max ≤0,C=1,2,…,Ψ, where γ C0 , γ C1 and γ C2 is the polygonal approximation coefficient, Ψ represents the number of sides of the polygonal approximation method;

[0132] Introducing non-negative auxiliary variables and in represents the forward active power flowing from node i to node j at time t on the sc-th scenario day, represents the reverse active power flowing from node i to node j at time t on the sc scenario day, represents the forward reactive power flowing from node i to node j at time t on the sc scenario day, represents the reverse reactive power flowing from node i to node j at time t on the sc scenario day, and P ij,sc,t and Q ij,sc,t The equivalent replacement is:

[0133]

[0134] A third aspect of the present invention provides an intelligent soft switch planning device for an active distribution network based on incremental linearization, comprising:

[0135] A memory for storing instructions; wherein the instructions are used to implement the active distribution network intelligent soft switch planning method based on incremental linearization as described in any one of the above implementation methods;

[0136] A processor is configured to execute instructions in the memory.

[0137] A fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the active distribution network intelligent soft switch planning method based on incremental linearization as described in any of the above implementation methods.

[0138] It can be seen from the above technical solutions that the present invention has the following advantages:

[0139] The present invention constructs an intelligent soft switch planning objective function that considers the investment cost of intelligent soft switches and the annual cost of distribution network network losses; based on the objective function, a distribution network intelligent soft switch planning model that considers multi-stage constraints is constructed; the multi-stage constraints include power balance constraints, branch current constraints, node voltage constraints, intelligent soft switch location and capacity constraints, intelligent soft switch operation constraints, distributed power output constraints, and line capacity constraints; the distribution network intelligent soft switch planning model is linearized into a corresponding MILP model using an incremental linearization method, and the corresponding MILP model is solved to obtain a distribution network intelligent soft switch planning scheme; the present invention combines incremental linearization technology with a planning optimization model to achieve more reasonable distribution network intelligent soft switch planning. Compared with traditional interpolation linearization methods, the incremental linearization method has greatly improved both mean square error and interpolation error, can improve solution accuracy, meet solution accuracy requirements, and has higher solution efficiency than existing technologies. BRIEF DESCRIPTION OF THE DRAWINGS

[0140] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0141] Figure 1 A flowchart of an active distribution network intelligent soft switch planning method based on incremental linearization is provided as an optional embodiment of the present invention;

[0142] Figure 2 A structural connection block diagram of an active distribution network intelligent soft switch planning device based on incremental linearization is provided as an optional embodiment of the present invention.

[0143] Reference numerals:

[0144] 1-first building block; 2-second building block; 3-solution block. DETAILED DESCRIPTION

[0145] The embodiments of the present invention provide an intelligent soft switch planning method and apparatus for an active distribution network based on incremental linearization, which are used to solve the technical problems of how to improve the rationality of intelligent soft switch planning and improve the efficiency of planning model solution while meeting the solution accuracy requirements.

[0146] In order to make the purpose, features, and advantages of the present invention more obvious and easy to understand, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described below are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0147] The present invention provides an intelligent soft switch planning method for an active distribution network based on incremental linearization.

[0148] See also Figure 1 , Figure 1 A flowchart of an active distribution network intelligent soft switch planning method based on incremental linearization provided by an embodiment of the present invention is shown.

[0149] An embodiment of the present invention provides an incremental linearization-based intelligent soft switch planning method for an active distribution network, comprising steps S1-S3.

[0150] Step S1: constructing an intelligent soft switch planning objective function that takes into account the investment cost of the intelligent soft switch and the annual cost of the distribution network loss.

[0151] In one achievable manner, minimizing the sum of the investment cost of the smart soft switch and the annual cost of the distribution network loss is used as the objective function of the smart soft switch planning.

[0152] As a specific implementation, the expression of the intelligent soft switch planning objective function is set as:

[0153] min C=C loss +C SOP

[0154]

[0155]

[0156] Where C loss is the annual cost of distribution network loss, C SOP is the investment cost of intelligent soft switch, d SOP is the investment discount rate of the smart soft switch, y SOP is the investment life of the smart soft switch, η is the operation and maintenance cost coefficient of the smart soft switch, Ω SOPIt is a node set used to install intelligent soft switches, k SOP is the investment cost of the unit capacity intelligent soft switch, The total capacity of the intelligent soft switch installed for node i, N sc is the number of scenario days into which the load time series characteristics are divided according to the season and the time period in a typical day, δ sc is the number of days in a year occupied by the sc scenario day, λ t Indicates the real-time electricity price, It represents the network loss at time t on the sc-th scenario day, and Δt is the preset time interval.

[0157] For example, when setting N sc The load time series characteristics can be divided into four typical day scenarios according to the season and the time period of a typical day, representing the four seasons of spring, summer, autumn and winter, namely N sc =4.

[0158] In another achievable manner, the objective function for planning the smart soft switch is to minimize the weighted sum of the investment cost of the smart soft switch and the annual cost of the distribution network loss.

[0159] The weighted coefficients of the smart soft switch investment cost and the annual cost of the distribution network loss can be set according to the actual situation of the distribution network. This embodiment does not limit this.

[0160] Step S2: constructing a distribution network intelligent soft switch planning model that considers multi-stage constraints based on the intelligent soft switch planning objective function; the multi-stage constraints include power balance constraints, branch current constraints, node voltage constraints, intelligent soft switch location and capacity constraints, intelligent soft switch operation constraints, distributed power output constraints, and line capacity constraints.

[0161] In one possible implementation, the power balance constraint may be set as:

[0162]

[0163]

[0164]

[0165]

[0166]

[0167] Where, P ji,sc,t is the active power flowing from node j to node i at time t on the sc scenario day, Q ji,sc,t is the reactive power flowing from node j to node i at time t on the sc scenario day, P i,sc,tis the active power injected into node i at time t on the sc-th scenario day, Q i,sc,t is the reactive power injected into node i at time t on the sc-th scenario day, P ik,sc,t is the active power flowing from node i to node k at time t on the sc scenario day, Q ik,sc,t is the reactive power flowing from node i to node k at time t on the sc scenario day, Ω br is the set of branches in the system, U i,sc,t is the voltage amplitude of node i at time t on the sc scenario day, U j,sc,t is the voltage amplitude of node j at time t on the sc scenario day, P ij,sc,t is the active power flowing from node i to node j at time t on the sc scenario day, Q ij,sc,t is the reactive power flowing from node i to node j at time t on the sc scenario day, x ij is the reactance of branch ij, r ij is the resistance of branch ij, is the active power consumed by the load of node i at the tth time on the scth scenario day, is the active power generated by DG at node i at time t on the sc scenario day, is the reactive power consumed by the load at node i at time t on the sc-th scenario day, is the reactive power generated by DG at node i at time t on the sc scenario day, is the active power emitted by SOP at node i at time t on the sc-th scenario day, is the reactive power generated by SOP at node i at time t on the sc scenario day, Ω b is the set of all nodes in the system, N sc It is the number of scenario days into which the load timing characteristics are divided according to the season and the time period of a typical day, and T is the total power balance time in a scenario day.

[0168] In one achievable manner, the line capacity constraint may be set as:

[0169]

[0170] Where, P ij,sc,t is the active power flowing from node i to node j at time t on the sc scenario day, Q ij,sc,t is the reactive power flowing from node i to node j at time t on the sc scenario day, S ij,max The capacity of branch ij.

[0171] In one achievable manner, the node voltage constraint may be set as:

[0172]

[0173] Where U i,sc,t is the voltage amplitude of node i at time t on the sc scenario day, is the lower limit of the voltage at node i, is the upper voltage limit of node i.

[0174] In one possible implementation, the intelligent soft switch operation constraint may be set as:

[0175]

[0176]

[0177]

[0178] Where, P i SOP is the active power injected into node i by SOP, is the active power injected into node j by SOP, is the reactive power injected into node i by SOP, is the reactive power injected into node j by SOP, The total capacity of the installed SOP for node j.

[0179] In one achievable manner, the intelligent soft switch position and capacity constraints may be set as follows:

[0180]

[0181] Where, The total capacity of SOP installed for node i, S module is the capacity of the SOP, is a non-negative integer, The number of SOP installations per unit capacity.

[0182] In one achievable manner, the branch current constraint may be set as:

[0183]

[0184] Where, I l,sc,t is the current of branch l at time t on the sc scenario day, It is the upper limit of the current allowed to pass through branch 1.

[0185] In one achievable manner, the distributed power supply output constraint may be set as:

[0186]

[0187] Where, is the active power generated by DG at node i at time t on the sc scenario day, is the lower limit of DG output, This is the upper limit of DG output.

[0188] Step S3: linearize the distribution network intelligent soft switch planning model into a corresponding MILP model using an incremental linearization method, and solve the corresponding MILP model to obtain a distribution network intelligent soft switch planning scheme.

[0189] In this embodiment, the distribution network intelligent soft switch planning model is linearized into a corresponding MILP model using an incremental linearization method, and the distribution network intelligent soft switch planning scheme can be obtained by using an existing solver. Compared with the existing technology, there is no iterative calculation process, the method is simple and convenient, and can effectively improve the solution efficiency while meeting the solution accuracy requirements.

[0190] In one achievable manner, linearizing the distribution network intelligent soft switch planning model into a corresponding MILP model using an incremental linearization method includes:

[0191] The objective function of the intelligent soft switching planning is linearly represented, including:

[0192] Assuming the voltage is 1 p.u., the annual cost of the distribution network loss in the objective function of the intelligent soft switch planning is simplified to:

[0193]

[0194] Where C loss (P ij,sc,t ,Q ij,sc,t ) represents the simplified variable P ij,sc,t ,Q ij,sc,t The annual cost of distribution network loss, P ij,sc,t is the active power flowing from node i to node j at time t on the sc scenario day, Q ij,sc,t is the reactive power flowing from node i to node j at time t on the sc scenario day, T is the total power balance time in a scenario day, Ω br is the set of branches in the system, r ij is the resistance of branch ij;

[0195] Determine the number of linearization segments, in P ij,sc,t and Q ij,sc,t The discrete points required for piecewise linearization are calculated according to the following formula within the range of , with a total of NPL-1 for each item:

[0196]

[0197]

[0198] Where, is the kth discrete point of the active power flowing from node i to node j at the tth time on the scth scenario day after linearization, NPL-1 is the number of linearization segments, P ij,sc,t is the lower limit of the active power flowing from node i to node j at time t on the sc scenario day, is the upper limit of the active power flowing from node i to node j at time t on the sc-th scenario day, is the kth discrete point of the reactive power flowing from node i to node j at time t on the scth scenario day after linearization, Q ij,sc,t is the lower limit of the reactive power flowing from node i to node j at time t on the sc scenario day, is the upper limit of the reactive power flowing from node i to node j at the tth time on the scth scenario day, and k is the position order of the discrete point in the power segment interval;

[0199] The new variables are introduced to linearize the simplified annual cost item of distribution network loss according to the following formula:

[0200]

[0201] δ k+1 ≤η k ,η k ≤δ k ,k=1,2,...,NPL-2

[0202] 0≤δ k+1 ≤1,k=1,2,...,NPL-1

[0203] Where, Represents the linearized variables The annual cost of distribution network loss, Represents the linearized variables The annual cost of distribution network loss, Represents the linearized variables The annual cost of distribution network loss, is the first discrete point of the active power flowing from node i to node j at time t on the sc-th scenario day after linearization, is the k+1th discrete point of the active power flowing from node i to node j at time t on the scth scenario day after linearization, is the first discrete point of the reactive power flowing from node i to node j at time t on the sc-th scenario day after linearization, is the k+1th discrete point of the reactive power flowing from node i to node j at time t on the scth scenario day after linearization, δ k The value range is 0~1, k represents the position on the kth linearized segment interval, δ k+1 represents the position on the k+1th linear segment interval, η k is a binary variable.

[0204] In this embodiment, setting δ k+1 ≤η k ,η k ≤δ k ,k=1,2,...,NPL-2, which can ensure that during piecewise linearization, the entire segmented interval is filled continuously from left to right without jumping.

[0205] In the intelligent soft switch planning objective function of the above embodiment, the network loss term is a nonlinear term, which makes the model difficult to solve. Considering that the voltage of most nodes in the distribution network fluctuates around 1 p.u., in this embodiment, the voltage is assumed to be 1 p.u. in the network loss term to simplify the calculation, which can optimize the model solution efficiency while ensuring the accuracy of the model. and It is still a nonlinear term. In this embodiment, it is further linearized using an incremental linearization method.

[0206] It should be noted that during the linearization process, the number of linearization segments needs to be reasonably determined based on the scale and characteristics of the model being solved, balancing linearization accuracy and computational complexity. To ensure the effective implementation of this method, the number of linearization segments can be pre-set based on the actual situation of the distribution network. This allows the specific value of this parameter to be directly obtained when the relevant equipment executes this method.

[0207] In one possible implementation, linearizing the distribution network intelligent soft switch planning model into a corresponding MILP model using an incremental linearization method further includes:

[0208] The multi-stage constraints are linearized, including:

[0209] Define a new variable V i,sc,t To replace U i,sc,t The quadratic term in the power balance constraint Convert to V i,sc,t -V j,sc,t =2(r ij P ij,sc,t +x ij Q ij,sc,t ), and converting the node voltage constraint into

[0210] The inscribed polygon approximation method is used to represent the circular constraint, and the intelligent soft switch operation constraint is Convert to The intelligent soft switch operation constraints Convert to And transform the line capacity constraint into γ C0 P ij,sc,t +γ C1 Q ij,sc,t +γ C2 S ij，max ≤0,C=1,2,…,Ψ, where γ C0 , γ C1 and γ C2 is the polygonal approximation coefficient, Ψ represents the number of sides of the polygonal approximation method;

[0211] Introducing non-negative auxiliary variables and in represents the forward active power flowing from node i to node j at time t on the sc-th scenario day, represents the reverse active power flowing from node i to node j at time t on the sc scenario day, represents the forward reactive power flowing from node i to node j at time t on the sc scenario day, represents the reverse reactive power flowing from node i to node j at time t on the sc scenario day, and P ij,sc,t and Q ij,sc,t The equivalent replacement is:

[0212]

[0213] In this embodiment, a non-negative auxiliary variable is introduced and P ij,sc,t and Q ij,sc,t Perform equivalent replacement to satisfy the incremental linearization method needs to meet P ij,sc,t and Q ij,sc,t The requirement is non-negative.

[0214] In a specific embodiment, to meet the accuracy requirement of the constraint, the circular constraint can be represented by an inscribed dodecagon method, that is, the part of the intelligent soft switch operation constraint related to the quadratic nonlinear term is converted into:

[0215]

[0216]

[0217] And transform the line capacity constraint into:

[0218] γ C0 P ij,sc,t +γ C1 Q ij,sc,t +γ C2 S ij，max ≤0,C=1,2,…,12.

[0219] In this embodiment, for the quadratic nonlinear term in the intelligent soft switch operation constraint and the line capacity constraint, an inscribed polygon approximation method is used to represent the circular constraint, thereby achieving linearization of the constraint.

[0220] In the above embodiment of the present invention, through the above-mentioned multiple linearization means, the distribution network intelligent soft switch planning model can be converted from a MINLP (mixed integer nonlinear programming) problem to a MILP planning model, and then the corresponding commercial solver can be called to efficiently solve the planning scheme, which can improve the solution efficiency while ensuring a certain solution accuracy.

[0221] The present invention also provides an active distribution network intelligent soft switch planning device based on incremental linearization, which can be used to execute the active distribution network intelligent soft switch planning method based on incremental linearization described in any of the above embodiments of the present invention.

[0222] See also Figure 2 , Figure 2 A structural connection block diagram of an active distribution network intelligent soft switch planning device based on incremental linearization provided by an embodiment of the present invention is shown.

[0223] An embodiment of the present invention provides an intelligent soft switch planning device for an active distribution network based on incremental linearization, comprising:

[0224] The first building block 1 is used to construct an intelligent soft switch planning objective function that takes into account the investment cost of the intelligent soft switch and the annual cost of the distribution network loss;

[0225] A second construction module 2 is configured to construct a distribution network intelligent soft switch planning model that considers multi-stage constraints based on the intelligent soft switch planning objective function; the multi-stage constraints include power balance constraints, branch current constraints, node voltage constraints, intelligent soft switch location and capacity constraints, intelligent soft switch operation constraints, distributed power generation output constraints, and line capacity constraints;

[0226] The solution module 3 is used to linearize the distribution network intelligent soft switch planning model into a corresponding MILP model by using an incremental linearization device, and solve the corresponding MILP model to obtain a distribution network intelligent soft switch planning scheme.

[0227] In one possible implementation, the first building block 1 includes:

[0228] The objective function construction unit is used to minimize the sum of the investment cost of the intelligent soft switch and the annual cost of the distribution network loss as the objective function of the intelligent soft switch planning.

[0229] In one possible implementation, the objective function construction unit includes:

[0230] The setting subunit is used to set the expression of the intelligent soft switch planning objective function as follows:

[0231] min C=C loss +C SOP

[0232]

[0233]

[0234] Where C loss is the annual cost of distribution network loss, C SOP is the investment cost of intelligent soft switch, d SOP is the investment discount rate of the smart soft switch, y SOP is the investment life of the smart soft switch, η is the operation and maintenance cost coefficient of the smart soft switch, Ω SOP It is a node set used to install intelligent soft switches, k SOP is the investment cost of the unit capacity intelligent soft switch, The total capacity of the intelligent soft switch installed for node i, N sc is the number of scenario days into which the load time series characteristics are divided according to the season and the time period in a typical day, δ sc is the number of days in a year occupied by the sc scenario day, λ t Indicates the real-time electricity price, It represents the network loss at time t on the sc-th scenario day, and Δt is the preset time interval.

[0235] In one possible implementation, the second building module 2 includes:

[0236] The first constraint setting unit is configured to set the power balance constraint to:

[0237]

[0238]

[0239]

[0240]

[0241]

[0242] Where, P ji,sc,t is the active power flowing from node j to node i at time t on the sc scenario day, Q ji,sc,t is the reactive power flowing from node j to node i at time t on the sc scenario day, P i,sc,t is the active power injected into node i at time t on the sc-th scenario day, Q i,sc,t is the reactive power injected into node i at time t on the sc-th scenario day, P ik,sc,t is the active power flowing from node i to node k at time t on the sc scenario day, Q ik,sc,t is the reactive power flowing from node i to node k at time t on the sc scenario day, Ω br is the set of branches in the system, U i,sc,t is the voltage amplitude of node i at time t on the sc scenario day, U j,sc,t is the voltage amplitude of node j at time t on the sc scenario day, P ij,sc,t is the active power flowing from node i to node j at time t on the sc scenario day, Q ij,sc,t is the reactive power flowing from node i to node j at time t on the sc scenario day, x ij is the reactance of branch ij, r ij is the resistance of branch ij, is the active power consumed by the load of node i at the tth time on the scth scenario day, is the active power generated by DG at node i at time t on the sc scenario day, is the reactive power consumed by the load at node i at time t on the sc-th scenario day, is the reactive power generated by DG at node i at time t on the sc scenario day, is the active power emitted by SOP at node i at time t on the sc-th scenario day, is the reactive power generated by SOP at node i at time t on the sc scenario day, Ω b is the set of all nodes in the system, N sc It is the number of scenario days into which the load time series characteristics are divided according to the season and the time period of a typical day. T is the total power balance time under one scenario day.

[0243] The second constraint setting unit is configured to set the line capacity constraint to:

[0244]

[0245] Where, P ij,sc,t is the active power flowing from node i to node j at time t on the sc scenario day, Q ij,sc,tis the reactive power flowing from node i to node j at time t on the sc scenario day, S ij,max The capacity of branch ij;

[0246] The third constraint setting unit is configured to set the node voltage constraint to:

[0247]

[0248] Where U i,sc,t is the voltage amplitude of node i at time t on the sc scenario day, is the lower limit of the voltage at node i, is the voltage upper limit of node i;

[0249] And / or, a fourth constraint setting unit, configured to set the intelligent soft switch operation constraint to:

[0250]

[0251]

[0252]

[0253] Where, P i SOP is the active power injected into node i by SOP, is the active power injected into node j by SOP, is the reactive power injected into node i by SOP, is the reactive power injected into node j by SOP, The total capacity of the installed SOP for node j.

[0254] In one possible implementation, the second building module 2 includes:

[0255] The fifth constraint setting unit is used to set the position and capacity constraints of the intelligent soft switch to:

[0256]

[0257] Where, The total capacity of SOP installed for node i, S module is the capacity of the SOP, is a non-negative integer, The number of SOP installations per unit capacity.

[0258] In one possible implementation, the second building module 2 includes:

[0259] A sixth constraint setting unit is configured to set the branch current constraint to:

[0260]

[0261] Where, I l,sc,t is the current of branch l at time t on the sc scenario day, It is the upper limit of the current allowed to pass through branch 1.

[0262] In one possible implementation, the second building module 2 includes:

[0263] The seventh constraint setting unit is configured to set the output constraint of the distributed power supply to:

[0264]

[0265] Where, is the active power generated by DG at node i at time t on the sc scenario day, is the lower limit of DG output, This is the upper limit of DG output.

[0266] In one possible implementation, the solution module 3 includes:

[0267] A first linearization unit, configured to linearize the intelligent soft switching planning objective function, includes:

[0268] Assuming the voltage is 1 p.u., the annual cost of the distribution network loss in the objective function of the intelligent soft switch planning is simplified to:

[0269]

[0270] Where C loss (P ij,sc,t ,Q ij,sc,t ) represents the simplified variable P ij,sc,t ,Q ij,sc,t The annual cost of distribution network loss, P ij,sc,t is the active power flowing from node i to node j at time t on the sc scenario day, Q ij,sc,t is the reactive power flowing from node i to node j at time t on the sc scenario day, T is the total power balance time in a scenario day, Ω br is the set of branches in the system, r ij is the resistance of branch ij;

[0271] Determine the number of linearization segments, in P ij,sc,t and Q ij,sc,t The discrete points required for piecewise linearization are calculated according to the following formula within the range of , with a total of NPL-1 for each item:

[0272]

[0273]

[0274] Where, is the kth discrete point of the active power flowing from node i to node j at the tth time on the scth scenario day after linearization, NPL-1 is the number of linearization segments, P ij,sc,t is the lower limit of the active power flowing from node i to node j at time t on the sc scenario day, is the upper limit of the active power flowing from node i to node j at time t on the sc-th scenario day, is the kth discrete point of the reactive power flowing from node i to node j at time t on the scth scenario day after linearization, Q ij,sc,t is the lower limit of the reactive power flowing from node i to node j at time t on the sc scenario day, is the upper limit of the reactive power flowing from node i to node j at the tth time on the scth scenario day, and k is the position order of the discrete point in the power segment interval;

[0275] The new variables are introduced to linearize the simplified annual cost item of distribution network loss according to the following formula:

[0276]

[0277] δ k+1 ≤η k ,η k ≤δ k ,k=1,2,...,NPL-2

[0278] 0≤δ k+1 ≤1,k=1,2,...,NPL-1

[0279] Where, Represents the linearized variables The annual cost of distribution network loss, Represents the linearized variables The annual cost of distribution network loss, Represents the linearized variables The annual cost of distribution network loss, is the first discrete point of the active power flowing from node i to node j at time t on the sc-th scenario day after linearization, is the k+1th discrete point of the active power flowing from node i to node j at time t on the scth scenario day after linearization, is the first discrete point of the reactive power flowing from node i to node j at time t on the sc-th scenario day after linearization, is the k+1th discrete point of the reactive power flowing from node i to node j at time t on the scth scenario day after linearization, δ k The value range is 0~1, k represents the position on the kth linearized segment interval, δ k+1 represents the position on the k+1th linear segment interval, η k is a binary variable.

[0280] In one possible implementation, the solution module 3 further includes:

[0281] A second linearization unit, configured to linearize the multi-stage constraints, includes:

[0282] Define a new variable V i,sc,t To replace U i,sc,t The quadratic term in the power balance constraint Convert to V i,sc,t -V j,sc,t =2(r ij P ij,sc,t +x ij Q ij,sc,t ), and converting the node voltage constraint into

[0283] The inscribed polygon approximation method is used to represent the circular constraint, and the intelligent soft switch operation constraint is Convert to The intelligent soft switch operation constraints Convert to And transform the line capacity constraint into γ C0 P ij,sc,t +γ C1 Q ij,sc,t +γ C2 S ij，max ≤0,C=1,2,…,Ψ, where γ C0 , γ C1 and γ C2 is the polygonal approximation coefficient, Ψ represents the number of sides of the polygonal approximation method;

[0284] Introducing non-negative auxiliary variables and in represents the forward active power flowing from node i to node j at time t on the sc-th scenario day, represents the reverse active power flowing from node i to node j at time t on the sc scenario day, represents the forward reactive power flowing from node i to node j at time t on the sc scenario day, represents the reverse reactive power flowing from node i to node j at time t on the sc scenario day, and P ij,sc,t and Q ij,sc,t The equivalent replacement is:

[0285]

[0286] The present invention also provides an active distribution network intelligent soft switch planning device based on incremental linearization, comprising:

[0287] A memory for storing instructions; wherein the instructions are used to implement the active distribution network intelligent soft switch planning method based on incremental linearization as described in any one of the above embodiments;

[0288] A processor is configured to execute instructions in the memory.

[0289] The present invention also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for planning intelligent soft switches for an active distribution network based on incremental linearization as described in any one of the above embodiments is implemented.

[0290] The above-mentioned embodiment of the present invention introduces the driving force of power system evolution into the objective function of intelligent soft switch planning of active distribution network. The objective function takes into account the investment cost of intelligent soft switches and the annual cost of distribution network network loss, and considers system flow constraints, intelligent soft switch operation constraints, and intelligent soft switch location and capacity constraints. A two-stage planning optimization model for distribution network intelligent soft switches based on incremental linearization is established, which improves the rationality of intelligent soft switch planning. Compared with the traditional interpolation linearization method, the incremental linearization method has greatly improved the mean square error and interpolation error. The mathematical essence of the linearized planning model is a mixed integer linearization planning problem, which can be directly solved using relevant commercial solvers, while meeting the accuracy requirements and improving the overall efficiency of model solving.

[0291] By utilizing the methods and devices of the above-mentioned embodiments of the present invention, the construction and transformation of the "source-grid-load-storage" distribution network based on timing characteristics and full-process adaptability can fully consider factors such as new load demand, regional interconnection demand, and distributed power supply penetration distribution in different future planning stages. It has strong adaptability and can provide effective theoretical and practical references for adaptive planning analysis and economic scheduling of distribution networks.

[0292] Those skilled in the art can clearly understand that, for the convenience and conciseness of description, the specific working processes of the devices and modules described above can refer to the corresponding processes in the aforementioned method embodiments, and the specific beneficial effects of the devices and modules described above can refer to the corresponding beneficial effects in the aforementioned method embodiments, which will not be repeated here.

[0293] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the modules is merely a logical function division. In actual implementation, there may be other division methods, such as multiple modules or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0294] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules may be selected to achieve the purpose of the present embodiment according to actual needs.

[0295] In addition, the functional modules in various embodiments of the present invention may be integrated into a single processing module, or each module may exist physically separately, or two or more modules may be integrated into a single module. The aforementioned integrated modules may be implemented in the form of hardware or software functional modules.

[0296] If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0297] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions described in the above embodiments can still be modified, or some of the technical features thereof can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for intelligent soft switching planning of active distribution network based on incremental linearization, characterized in that: include: Construct an intelligent soft switch planning objective function that considers the investment cost of the intelligent soft switch and the annual cost of the distribution network loss; Based on the intelligent soft switch planning objective function, a distribution network intelligent soft switch planning model is constructed that considers multi-stage constraints; the multi-stage constraints include power balance constraints, branch current constraints, node voltage constraints, intelligent soft switch location and capacity constraints, intelligent soft switch operation constraints, distributed generation output constraints, and line capacity constraints; The distribution network intelligent soft switch planning model is linearized into a corresponding MILP model by using an incremental linearization method, and the corresponding MILP model is solved to obtain a distribution network intelligent soft switch planning scheme; The objective function of constructing the smart soft switch planning considering the smart soft switch investment cost and the annual cost of the distribution network loss includes: The objective function of smart soft switch planning is to minimize the sum of the investment cost of the smart soft switch and the annual cost of the distribution network loss; The method of constructing a distribution network intelligent soft switch planning model considering multi-stage constraints based on the intelligent soft switch planning objective function includes: Set the power balance constraint to: ； Where, For the Scene Day Currently, nodes Flow Node The active power, For the Scene Day Currently, nodes Flow Node The reactive power, For the Scene Day Current injection node The active power, For the Scene Day Current injection node The reactive power, For the Scene Day Currently, nodes Flow Node The active power, For the Scene Day Currently, nodes Flow Node The reactive power, is the set of branches in the system, For the Scene Day Current Node The voltage amplitude, For the Scene Day Current Node The voltage amplitude, For the Scene Day Currently, nodes Flow Node The active power, For the Scene Day Currently, nodes Flow Node The reactive power, For branch The reactance, For branch The resistance, For the Scene Day Current Node Active power consumed by the load, For the Scene Day Current Node The active power generated by DG, For the Scene Day Current Node Reactive power consumed by the load, For the Scene Day Current Node The reactive power generated by DG, For the Scene Day Current Node Depend on The active power generated, For the Scene Day Current Node Depend on The reactive power generated, is the set of all nodes in the system. It is the number of scenario days into which the load time series characteristics are divided according to the season and the time period in a typical day. is the total power balance time for a scenario day; Set the line capacity constraint to: ； Where, For the Scene Day Currently, nodes Flow Node The active power, For the Scene Day Currently, nodes Flow Node The reactive power, branch road capacity; Set the node voltage constraint to: ； Where, For the Scene Day Current Node The voltage amplitude, For nodes The lower voltage limit, For nodes The upper voltage limit; And / or, setting the intelligent soft switch operation constraints to: ； Where, Is the SOP injection node The active power, Is the SOP injection node The active power, Is the SOP injection node The reactive power, Is the SOP injection node The reactive power, For nodes The total capacity of the installed SOP.

2. The method for planning intelligent soft switches for active distribution networks based on incremental linearization according to claim 1, characterized in that: The objective function for planning the smart soft switch is to minimize the sum of the investment cost of the smart soft switch and the annual cost of the distribution network loss, including: The expression of the objective function of the intelligent soft switch planning is set as: ； Where, is the annual cost of distribution network loss, The investment cost of intelligent soft switch is is the investment discount rate of the smart soft switch, is the investment period of the intelligent soft switch, is the operation and maintenance cost coefficient of the intelligent soft switch, It is a node collection used to install intelligent soft switches. is the investment cost of the unit capacity intelligent soft switch, For nodes The total capacity of the installed intelligent soft switch, It is the number of scenario days into which the load time series characteristics are divided according to the season and the time period in a typical day. For the The number of days in a year that the scene day occupies, Indicates the real-time electricity price, Indicates the Scene Day The current network loss, The preset time interval.

3. The method for planning intelligent soft switches for active distribution networks based on incremental linearization according to claim 1, characterized in that: The method of constructing a distribution network intelligent soft switch planning model considering multi-stage constraints based on the intelligent soft switch planning objective function includes: Set the position and capacity constraints of the intelligent soft switch to: ； Where, For nodes Total capacity of installed SOP, is the capacity of the SOP, is a non-negative integer, The number of SOP installations per unit capacity.

4. The method for planning intelligent soft switches for active distribution networks based on incremental linearization according to claim 1, characterized in that: The method of constructing a distribution network intelligent soft switch planning model considering multi-stage constraints based on the intelligent soft switch planning objective function includes: Set the branch current constraint to: ； Where, For the Scene Day Branch Road The current, It is a branch road The upper limit of the current allowed to pass.

5. The method for planning intelligent soft switches for active distribution networks based on incremental linearization according to claim 1, characterized in that: The method of constructing a distribution network intelligent soft switch planning model considering multi-stage constraints based on the intelligent soft switch planning objective function includes: The output constraint of the distributed power generation is set as: ； Where, For the Scene Day Current Node The active power generated by DG, is the lower limit of DG output, This is the upper limit of DG output.

6. The method for planning intelligent soft switches for active distribution networks based on incremental linearization according to claim 1, characterized in that: The method of linearizing the distribution network intelligent soft switch planning model into a corresponding MILP model using an incremental linearization method includes: The objective function of the intelligent soft switching planning is linearly represented, including: Assuming the voltage is 1 p.u., the annual cost of the distribution network loss in the objective function of the intelligent soft switch planning is simplified to: ； Where, Indicates about variables The annual cost of distribution network loss, For the Scene Day Currently, nodes Flow Node The active power, For the Scene Day Currently, nodes Flow Node The reactive power, is the total power balance time in a scenario, is the set of branches in the system, For branch resistance; Determine the number of linearization segments, and The discrete points required for piecewise linearization are calculated according to the following formula within the range of , with a total of NPL-1 for each item: ； Where, After linearization Scene Day Currently, nodes Flow Node The active power of discrete points, is the number of linearization segments, For the Scene Day Currently, nodes Flow Node The lower limit of active power, For the Scene Day Currently, nodes Flow Node The upper limit of active power, After linearization Scene Day Currently, nodes Flow Node The reactive power of discrete points, For the Scene Day Currently, nodes Flow Node The lower limit of reactive power, For the Scene Day Currently, nodes Flow Node The upper limit of reactive power, is the position order of the discrete points in the power segment interval; The new variables are introduced to linearize the simplified annual cost item of distribution network loss according to the following formula: ； Where, Represents the linearized variables The annual cost of distribution network loss, Represents the linearized variables The annual cost of distribution network loss, Represents the linearized variables The annual cost of distribution network loss, After linearization Scene Day Currently, nodes Flow Node The first discrete point of active power, After linearization Scene Day Currently, nodes Flow Node The active power of discrete points, After linearization Scene Day Currently, nodes Flow Node The first discrete point of reactive power, After linearization Scene Day Currently, nodes Flow Node The reactive power of discrete points, The value range is 0~1, represents the position on the kth linearized segment interval, represents the position on the k+1th linearized segment interval, is a binary variable.

7. The method for planning intelligent soft switches for active distribution networks based on incremental linearization according to claim 6, characterized in that: The method of linearizing the distribution network intelligent soft switch planning model into a corresponding MILP model using an incremental linearization method includes: The multi-stage constraints are linearized, including: Defining new variables To replace The quadratic term in the power balance constraint Convert to , and transforming the node voltage constraint into ; The inscribed polygon approximation method is used to represent the circular constraint, and the intelligent soft switch operation constraint is Convert to , the intelligent soft switch operation constraints Convert to , and transforming the line capacity constraint into ,in 、 and is the approximate coefficient within the polygon, Indicates the number of sides of the polygonal approximation; Introducing non-negative auxiliary variables 、 、 and ,in Indicates the Scene Day Currently, nodes Flow Node The forward active power, Indicates the Scene Day Currently, nodes Flow Node The reverse active power, Indicates the Scene Day Currently, nodes Flow Node The forward reactive power, Indicates the Scene Day Currently, nodes Flow Node The reverse reactive power will and The equivalent replacement is: 。 8. An intelligent soft switch planning device for active distribution network based on incremental linearization, characterized in that: include: The first building block is used to construct an intelligent soft switch planning objective function that considers the investment cost of the intelligent soft switch and the annual cost of the distribution network loss; A second construction module is configured to construct a distribution network intelligent soft switch planning model that considers multi-stage constraints based on the intelligent soft switch planning objective function; the multi-stage constraints include power balance constraints, branch current constraints, node voltage constraints, intelligent soft switch location and capacity constraints, intelligent soft switch operation constraints, distributed power generation output constraints, and line capacity constraints; A solution module, configured to linearize the distribution network intelligent soft switch planning model into a corresponding MILP model using an incremental linearization device, and solve the corresponding MILP model to obtain a distribution network intelligent soft switch planning solution; The first building block includes: An objective function construction unit, configured to minimize the sum of the investment cost of the intelligent soft switch and the annual cost of the distribution network loss as the objective function for planning the intelligent soft switch; The second building block includes: The first constraint setting unit is configured to set the power balance constraint to: ； Where, For the Scene Day Currently, nodes Flow Node The active power, For the Scene Day Currently, nodes Flow Node The reactive power, For the Scene Day Current injection node The active power, For the Scene Day Current injection node The reactive power, For the Scene Day Currently, nodes Flow Node The active power, For the Scene Day Currently, nodes Flow Node The reactive power, is the set of branches in the system, For the Scene Day Current Node The voltage amplitude, For the Scene Day Current Node The voltage amplitude, For the Scene Day Currently, nodes Flow Node The active power, For the Scene Day Currently, nodes Flow Node The reactive power, For branch The reactance, For branch The resistance, For the Scene Day Current Node Active power consumed by the load, For the Scene Day Current Node The active power generated by DG, For the Scene Day Current Node Reactive power consumed by the load, For the Scene Day Current Node The reactive power generated by DG, For the Scene Day Current Node Depend on The active power generated, For the Scene Day Current Node Depend on The reactive power generated, is the set of all nodes in the system. It is the number of scenario days into which the load time series characteristics are divided according to the season and the time period in a typical day. is the total power balance time for a scenario day; The second constraint setting unit is configured to set the line capacity constraint to: ； Where, For the Scene Day Currently, nodes Flow Node The active power, For the Scene Day Currently, nodes Flow Node The reactive power, branch road capacity; The third constraint setting unit is configured to set the node voltage constraint to: ； Where, For the Scene Day Current Node The voltage amplitude, For nodes The lower voltage limit, For nodes The upper voltage limit; And / or, a fourth constraint setting unit, configured to set the intelligent soft switch operation constraint to: ； Where, Is the SOP injection node The active power, Is the SOP injection node The active power, Is the SOP injection node The reactive power, Is the SOP injection node The reactive power, For nodes The total capacity of the installed SOP.

9. An intelligent soft switch planning device for active distribution network based on incremental linearization, characterized in that: include: A memory for storing instructions; wherein the instructions are used to implement the active distribution network intelligent soft switch planning method based on incremental linearization according to any one of claims 1 to 7; A processor is configured to execute instructions in the memory.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for planning intelligent soft switches for an active distribution network based on incremental linearization is implemented.

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