Method and device for evaluating and optimizing flexible operation domain of flexible power distribution network

Abstract

The invention relates to a flexible operation domain evaluation and optimization method and device for a flexible power distribution network, and the method comprises the steps: carrying out the normalization quantification of a multi-dimensional complex safety margin index through analyzing a power flow linear sensitivity model, and comprehensively considering the upper and lower limit constraints of voltage, the power constraint of a line and the maximum load rate constraint of a transformer; quantitatively evaluating the flexible operation domain of the flexible interconnection power distribution network, and proposing a double-layer control and optimization model based on flexible operation domain evaluation; according to the method, the effect of mutual power complementation of the flexible interconnection device among different transformer areas when the system flexibility is insufficient is effectively exerted, the optimization efficiency can be greatly improved through linear solution on the premise of ensuring that the calculation error is small, and the system flexibility operation domain is improved.

Description

Technical Field

[0001] The present invention relates to the field of optimized operation and control of distribution networks, and in particular to a method and device for evaluating and optimizing the flexibility operation domain of a flexible distribution network. Background Art

[0002] In recent years, distributed generation (DG) based on photovoltaic (PV) is gradually replacing traditional energy generation. However, distributed renewable energy generation is easily affected by natural conditions such as weather, and the power demand is becoming increasingly complex, which makes the source and load side of the system highly uncertain in time and space, and easily causes a series of problems such as large fluctuations in the power of the distribution network feeder and system voltage exceeding the limit.

[0003] The traditional distribution network has poor rapid regulation and control, and it is difficult to flexibly respond to the challenges brought by the uncertainty of the source and load side, which threatens the stability of the system and seriously lacks flexibility. However, with the continuous improvement of power electronics technology, the flexible interconnected distribution network based on intelligent soft open point (SOP) has been vigorously developed, and the flexible consumption of new energy between different systems is realized through precise control of system flow and power mutual assistance between systems.

[0004] The high penetration rate of new energy access and load uncertainty first cause voltage fluctuations in the power grid, and even cause voltage over-limit problems. In order to ensure the stability of the system voltage, global optimization is often performed through centralized voltage control. Since centralized control is highly dependent on the communication architecture and is computationally difficult, it is usually performed over a long time scale or in upper-level optimization. At the same time, sensitivity analysis is a method for calculating the impact of node injection power changes on system power flow. A large number of studies have applied sensitivity analysis to the optimal operation and control of distribution networks.

[0005] At present, there is no unified view on the understanding of flexibility. The existing flexibility assessment and optimization improvement methods have certain limitations in analyzing the impact of source and load uncertainty. On the basis of considering safety, most of them only consider the power operation domain of the root node, and do not calculate and characterize the margin of all nodes or multiple key nodes. In addition, the results of multi-dimensional constraint evaluation are difficult to normalize and compare due to unit constraints. Therefore, it is difficult to quickly evaluate the flexibility of the system under multi-dimensional safety constraints. Summary of the invention

[0006] The present invention normalizes and quantifies the multi-dimensional complex safety margin indicators by analyzing the offset range of the system allowable load and the output of distributed power sources DG, intuitively reflects the operating points of the system that meet all safety boundaries with a certain flexibility, and establishes a double-layer improvement optimization model for the flexibility operating domain. When the flexibility is insufficient, it can maximize the flexibility operating domain as the goal, improve the system flexibility, and solve the defects mentioned in the above background technology.

[0007] The technical solution adopted by the present invention is as follows:

[0008] The flexible distribution network flexibility operation domain evaluation and optimization method includes the following steps:

[0009] Obtain the topological structure, line parameters and load parameters of the flexible interconnected distribution network;

[0010] A linear power flow sensitivity model is constructed and used to describe the impact of source-load uncertainty on node voltage and line power in a flexible interconnected distribution network.

[0011] Based on the constructed linear power flow sensitivity model and the obtained flexible interconnected distribution network, a flexible interconnected distribution network flexibility operation domain evaluation model considering source and load uncertainty and a flexible interconnected distribution network two-layer optimization operation and control model are constructed;

[0012] The flexible interconnected distribution network is evaluated and its flexibility is optimized by using the constructed flexibility operation domain evaluation model of the flexible interconnected distribution network and the two-layer optimization operation and control model of the flexible interconnected distribution network. In the two-layer optimization operation and control model of the flexible interconnected distribution network, the upper-layer optimization takes the minimum sum of the absolute values ​​of the voltage deviations of all nodes in the distribution network from the rated values ​​as the optimization objective function, and the lower-layer optimization takes the maximization of the flexibility operation domain as the objective function for optimization.

[0013] Furthermore, the method for evaluating and optimizing the flexibility of the flexible interconnected distribution network by using the constructed flexible interconnected distribution network flexibility operation domain evaluation model and the flexible interconnected distribution network double-layer optimization operation and control model is as follows:

[0014] In the time period t1, the upper centralized voltage control of the double-layer optimization operation and control model of the distribution network is carried out with the goal of minimizing the sum of the absolute values ​​of all nodes in the distribution network deviating from the rated values.

[0015] The flexibility of the distribution network is evaluated on the time scale of time period t2 using the distribution network flexibility operation domain evaluation model. If the result of the distribution network flexibility operation domain is less than the flexible operation boundary value, the lower-layer flexibility operation domain of the distribution network two-layer optimization operation and control model is optimized with the goal of maximizing the flexibility operation domain. If the result of the distribution network flexibility operation domain is greater than the flexible operation boundary value, the flexibility of the distribution network is evaluated on the next time scale of time period t2. This is repeated until the flexibility evaluation of the distribution network in time period t1 is completed.

[0016] Furthermore, the flexibility operation domain is defined as: the flexibility of the flexible interconnected distribution network is the system's ability to carry or respond to the randomness of renewable energy output and the high spatial and temporal uncertainty caused by load fluctuations, while the flexibility operation domain directly quantifies the system's allowable deviation range of distributed power source DG output and load while meeting the safety range boundary conditions.

[0017] Furthermore, when constructing a flexible interconnected distribution network flexibility operation domain evaluation model taking into account source-load uncertainty, the actual power values ​​of the load and distributed generation DG output at any time are made to follow the normal distribution description, and the flexibility operation domain expression under different constraints is described by analyzing the uncertainty of the allowable variation range of node power and the linear power flow sensitivity model, and the maximum deviation range per unit value α of the load and distributed generation DG output that the distribution network can allow in time period t is obtained. t,max :

[0018] α t,max =min(α i,t,Ulow ,α i,t,Uupp ,α l,t,line ,α r,t,tra )

[0019] Among them, α i,t,Ulow is the maximum deviation range of load and DG output that the distribution network can tolerate in time period t, considering the voltage lower limit constraint of node i; α i,t,Uupp is the maximum deviation range of load and DG output that the distribution network can tolerate in time period t, considering the voltage upper limit constraint of node i; α l,t,line is the upward and downward deviation range of load and distributed generation DG output that the distribution network can tolerate in time period t, considering the transmission power constraint of the lth line; α r,t,tra is the maximum deviation range of the load and DG output that the distribution network can tolerate, considering the rth transformer load factor constraint in time period t.

[0020] Furthermore, when constructing a flexible interconnected distribution network flexibility operation domain evaluation model taking into account source and load uncertainty, the uncertainty of the allowable variation range of node power includes the uncertainty variation of active power and reactive power. Assume that the reactive power and active power in the node injection power variation are constant power factor. If the active power and reactive power changes are ΔP and ΔQ respectively, the relationship σ between the active power and reactive power changes can be calculated as:

[0021]

[0022] Furthermore, in the upper-level control of the two-layer optimization operation and control model of the flexible interconnected distribution network, the output power of the on-load tap-changing transformer, capacitor group gear, static VAR compensator and intelligent soft switch is globally controlled with time period t1 as the time scale, and the control result is passed as the initial value of the lower-level optimization within the time period t1; in the lower-level optimization, the prediction error of the variable source load of the distribution network and the difference in the impact of the node injection power change on different constraints are considered, and the flexibility operation domain under different safety boundaries is quantitatively evaluated with time period t2 as the time scale. The key indicators involved in each safety boundary are normalized and measured. When the calculated flexibility operation domain is insufficient, the operating power of the intelligent soft switch SOP is quickly adjusted, and the flexibility operation domain is expanded in the upper-level control gap to reduce the risk of exceeding the operation constraint limit, so as to better adapt to the changes and fluctuations of the source load.

[0023] Furthermore, in the upper-level control of the two-layer optimization operation and control model of the flexible interconnected distribution network, the goal is to minimize the voltage deviation of all nodes in the system, and the upper-level optimization objective function is:

[0024]

[0025] in:

[0026]

[0027] Wherein, y is the number of on-load tap-changing transformers OLTC; w is the number of capacitor banks; z is the number of static VAR compensators SVC; h is the number of voltage source converters VSC, N is the total number of system nodes; n is the node position of the voltage source converter VSC numbered x in the system; γ is the node position of the capacitor bank CBs numbered λ in the system; η is the node position of the static VAR compensator SVC numbered μ in the system; W n,i,t and R n,i,t are the sensitivity coefficients of the voltage at node i at time period t to the change of active and reactive power of the voltage source converter VSC at node n; ΔP x,t represents the change in the active power command value of the voltage source converter VSC numbered x at time period t, ΔQx,t represents the change in reactive power command value of the voltage source converter VSC numbered x at time period t; H o,i,t represents the adjacency coefficient between the on-load tap changer OLTC numbered o and node i in time period t, which is 1 if connected, otherwise 0; δ o,t,Oin , δ o,t,Ode δ are 0-1 variables representing the upward and downward adjustment of the OLTC gear of the on-load tap-changing transformer numbered o in time period t; λ,t,CBin , δ λ,t,CBde 0-1 variables representing the switching status of capacitor bank CBs numbered λ in time period t; ΔU O ΔQ is the voltage regulation step of the on-load tap changer OLTC; CB The reactive power change of capacitor bank CBs; ΔQ μ,t,SVC represents the change in the reactive power command value of the static VAR compensator SVC numbered μ at time period t; U0 is the rated value of the system voltage; R η,i,t are the sensitivity coefficients of the voltage at node i to the reactive power change of the static VAR compensator SVC at node η during time period t; R γ,i,t are the sensitivity coefficients of the voltage at node i to the reactive power change of the capacitor bank CBs located at node γ during period t.

[0028] In the lower-level control of the two-level optimization operation and control model of the flexible interconnected distribution network, the goal is to maximize the flexibility operation domain, and the lower-level optimization objective function is:

[0029] Objective function:

[0030] maxf2=α t,max t∈T s

[0031] Where, T s A collection of time periods that require optimization to improve the flexibility of the operating domain.

[0032] Furthermore, when solving the constructed two-layer optimization operation and control model of the flexible interconnected distribution network, the absolute value function linearization and regular polygon linearization are used to perform linear fitting on the model, and the upper and lower layer optimizations are solved by mixed integer linear programming and linear programming methods respectively.

[0033] Furthermore, in the process of constructing the linear power flow sensitivity model, the linear power flow equation and Jacobian matrix are used to analyze the impact of node injection power changes on node voltage and line power;

[0034] Among them, according to the linear power flow equation and Jacobian matrix derivation, the node voltage sensitivity equation and line current carrying capacity sensitivity equation are obtained respectively:

[0035]

[0036] ΔP f =TΔP i (3)

[0037] ΔQ f =YΔQ i (4)

[0038] Where ΔP i , ΔQ i , Δθ and ΔV represent the node active power injection change vector, node reactive power injection change vector, node voltage phase angle change vector and node voltage amplitude change vector respectively; J RPV , J RQV , J RPθ and J RQθ All are non-singular matrices; and ΔV versus ΔP i and ΔQ i The (n-1)×(n-1) sensitivity coefficient matrix of and are Δθ versus ΔP i and ΔQ i The (n-1)×(n-1) sensitivity coefficient matrix of the node active power; T represents the sensitivity matrix of the line active power to the node active power change; Y represents the sensitivity matrix of the line reactive power to the node reactive power change; ΔP f , ΔQ f Respectively represent the changes in active and reactive transmission power of the line.

[0039] Flexible distribution network flexibility operation domain evaluation and optimization device, including:

[0040] A parameter acquisition module is used to obtain the topological structure, line parameters and load parameters of the flexible interconnected distribution network;

[0041] Linear power flow sensitivity model building module, used to build a linear power flow sensitivity model, using the sensitivity model to describe the impact of source-load uncertainty on node voltage and line power in a flexible interconnected distribution network;

[0042] The module for constructing a flexible operation domain evaluation model and a two-layer optimization operation and control model is used to construct a flexible operation domain evaluation model and a two-layer optimization operation and control model of a flexible interconnected distribution network taking into account source-load uncertainty based on the constructed linear power flow sensitivity model and the obtained flexible interconnected distribution network;

[0043] The flexible interconnected distribution network evaluation and flexibility optimization module is used to evaluate and optimize the flexibility of the flexible interconnected distribution network by using the constructed flexible interconnected distribution network flexibility operation domain evaluation model and the flexible interconnected distribution network two-layer optimization operation and control model; wherein, in the said flexible interconnected distribution network two-layer optimization operation and control model, the upper layer optimization takes the minimum sum of the absolute values ​​of the voltage deviations of all nodes in the distribution network from the rated value as the optimization objective function, and the lower layer optimization takes the maximization of the flexibility operation domain as the objective function for optimization.

[0044] Compared with the prior art, the flexible distribution network flexibility operation domain evaluation and optimization method taking into account source-load uncertainty provided by the present invention has the following beneficial effects:

[0045] 1. The present invention relates to a method for evaluating and optimizing the flexibility operation domain of a flexible interconnected distribution network taking into account uncertainty of sources and loads. By analyzing a linear sensitivity model of power flow, the voltage upper and lower limit constraints, line power constraints and transformer maximum load rate constraints are comprehensively considered, and multi-dimensional complex safety margin indicators are normalized and quantified, and the flexibility operation domain of the flexible interconnected distribution network is quantitatively evaluated. A double-layer control and optimization model based on the evaluation of the flexibility operation domain is proposed. The present invention effectively brings into play the role of power mutual assistance between different substations of the flexible interconnected device when the system flexibility is insufficient, and can greatly improve the optimization efficiency and the system flexibility operation domain through linear solution under the premise of ensuring that the calculation error is small;

[0046] 2. The present invention describes the flexible operation under different constraints by analyzing the uncertainty of the allowable variation range of node power, which can be used to compare the evaluation results under different operation constraints and better reflect the overall operation status of the system. At the same time, the proposed two-layer optimization method can ensure that the optimization efficiency is greatly improved under the premise of small calculation error. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 is a schematic diagram of the structure of a flexible interconnection device;

[0048] Figure 2 A flowchart of a method for evaluating and optimizing the flexible operation domain of a flexible distribution network taking into account source-load uncertainty according to an embodiment of the present invention;

[0049] Figure 3 It is a schematic diagram of normal distribution of node power;

[0050] Figure 4 This is a schematic diagram of the linearization of regular polygon approximation fitting;

[0051] Figure 5 The structural diagram of the three-terminal power supply flexible interconnected distribution network;

[0052] Figure 6(a) shows the data center load curve;

[0053] Figure 6(b) shows the commercial load curve;

[0054] Figure 6(c) shows the resident load curve;

[0055] Figure 6(d) shows the industrial center load curve;

[0056] Figure 6(e) is the load curve of the charging station;

[0057] Figure 6(f) shows the photovoltaic curve;

[0058] Figure 7(a) shows the comparison of the operation domain assessment during the period with greater power over-limit risk;

[0059] Figure 7(b) shows the comparison of the operation domain assessment during the period with a greater voltage over-limit risk;

[0060] Figure 8 The maximum and minimum voltage changes before and after centralized voltage control;

[0061] Fig. 9 It is the operating domain of system flexibility under different constraints;

[0062] Figure 10(a) shows the flexibility operation domain before optimization under different constraints during the 11:00 period;

[0063] Figure 10(b) shows the optimized flexibility operation domain under different constraints during the 11:00 period;

[0064] Figure 11(a) is a radar chart of the flexibility operation domain margin of each AC subsystem under different constraints before optimization during the 11:00 period;

[0065] Figure 11(b) is a radar chart of the flexibility operation domain margin after optimization of each AC subsystem under different constraints during the 11:00 period;

[0066] Figure 12(a) shows the operating domain indicators before and after optimization in the 70-77 period;

[0067] Figure 12(b) shows the optimized regulation of VSC1 during the period 70-77;

[0068] Figure 12(c) shows the optimized regulation of VSC2 during the period 70-77;

[0069] Figure 12(d) shows the optimized regulation of VSC3 during the period 70-77. DETAILED DESCRIPTION

[0070] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0071] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present application belongs.

[0072] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0073] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0074] Embodiment 1

[0075] This embodiment introduces a method for evaluating and optimizing the flexibility operation domain of a flexible distribution network taking into account source and load uncertainties.

[0076] The flexible distribution network flexibility operation domain evaluation and optimization method of this embodiment includes:

[0077] Obtain the topological structure, line parameters and load parameters of the flexible interconnected distribution network, initialize network parameters, and determine the initial values ​​of each device;

[0078] Construct a linear power flow sensitivity model based on the power flow calculation equation;

[0079] According to the constructed linear power flow sensitivity model and the obtained flexible interconnected distribution network, a flexible interconnected distribution network flexibility operation domain evaluation model taking into account source and load uncertainty is constructed;

[0080] According to the flexible interconnected distribution network flexibility operation domain evaluation model, a two-layer optimization operation and control model of the flexible interconnected distribution network is constructed;

[0081] Solve the constructed flexible interconnected distribution network flexibility operation domain evaluation and optimization model to improve the flexibility of the flexible interconnected distribution network.

[0082] This embodiment relates to a flexible distribution network configuration using SOP as a flexible interconnection device; the internal structure of SOP is as follows Figure 1As shown in the figure, the internal structure of SOP is composed of back-to-back voltage source converters (VSCs), which can improve the power flow distribution in the grid by adjusting the active power and reactive power between different feeders, improve the voltage control effect, and thus enhance the flexibility of the system operation domain. In terms of control mode, taking two VSCs as an example, when the AC side of VSC1 adopts active power control and reactive power control, the DC side of VSC2 adopts constant voltage control. The active power of VSC2 on the AC side is obtained from the power balance of SOP and is a non-controlled variable. When the influence of SOP loss is ignored, the active transmission power of VSC2 is the same as that of VSC1, and the reactive power of VSC2 on the AC side is still a controllable variable.

[0083] This embodiment adopts the sensitivity concept to describe the impact of source-load uncertainty on node voltage, line power, etc. in the flexible interconnected distribution network. By using sensitivity to describe the safety boundary, the flexibility operation domain is obtained and the constraints in the optimization improvement are realized. The sensitivity equation is derived from the linear power flow equation and the Jacobian matrix, and the derivation formula is:

[0084]

[0085] Where V and θ are the node voltage amplitude and phase angle vector respectively; P i and Q i are the active power and reactive power injected into the grid by node i; the block sub-matrices are and They represent the sensitivity of node voltage phase angle to active injection power, node voltage amplitude to active injection power, node voltage phase angle to reactive injection power and node voltage amplitude to reactive injection power respectively; ΔP i , ΔQ i , Δθ and ΔV represent the node active power injection change vector, node reactive power injection change vector, node voltage phase angle change vector and node voltage amplitude change vector respectively; P ij is the line l with the first node i and the last node j ij Active power; Q ij is the line l with the first node i and the last node j ij Reactive power; ΔP ij and ΔQ ij Respectively represent line l ij Active and reactive power change vector; block sub-matrix and They respectively represent the sensitivity of line active power to node voltage phase angle, line active power to node voltage amplitude, line reactive power to node voltage phase angle and line reactive power to node voltage amplitude.

[0086] From equations (1) and (2), the node voltage sensitivity equation and line current carrying capacity sensitivity equation are obtained as follows:

[0087]

[0088]

[0089] in:

[0090]

[0091] In the formula, J RPV , J RQV , J RPθ and J RQθ are all non-singular matrices; let ΔV versus ΔP i and ΔQ i The (n-1)×(n-1) sensitivity coefficient matrix of and are Δθ versus ΔP i and ΔQ i The (n-1)×(n-1) sensitivity coefficient matrix, when the network topology does not change and Also approximately remains unchanged; T and B represent the sensitivity matrix of line active power to node active and reactive power changes; Z and Y represent the sensitivity matrix of line reactive power to node active and reactive power changes; ΔP f , ΔQ f Respectively represent the changes in active and reactive transmission power of the line.

[0092] Among them, considering the coupling effect of active power and reactive power, when considering the impact of node injection power changes (active power changes, reactive power changes) on line power, combined with the simplified method in power flow calculation, the change of node active (reactive) injection power has little impact on line reactive (active) power; for this reason, equations (5) and (6) can be simplified.

[0093] Based on the sensitivity equation, the flexible operation domain of this embodiment can be quantified by analyzing the deviation range of the system allowable load and DG output, assuming that the actual power values ​​of the load and DG output at any moment follow the normal distribution description. Figure 3 As shown in the figure, the probability of a node power randomly increasing or decreasing active power or reactive power within a certain period of time t is equal, where the increase or decrease of active power is represented by α t P j,t ; Increase or decrease reactive power is expressed as β t Q j,t ;P j,t and Q j,tThey represent the active and reactive power injected into node j at time t. t can be used as a probability positive real number to quantify the range of random fluctuations of load and DG active power output; similarly, β t Quantify the range of random fluctuation deviation of load and DG reactive power output, α t , β t The larger the value, the greater the range of random fluctuations of the system allowable load and DG power output, that is, the greater the system flexibility. Therefore, if the system performs power flow calculation in the initial situation and uses the linear power flow model, it can be inferred that when the active power and reactive power of node j increase or decrease by α t P j,t , β t Q j,t When the voltage lower limit U of node i in time period t is i,t,low and upper limit U i,t,upp , the maximum transmission power S of line l l,t,linmax And the expression of the maximum load rate Lr of transformer r can be used to obtain the flexible operation domain expression under various safety constraints:

[0094]

[0095] Where m is the number of load nodes; W j,i,t and R j,i,t are the sensitivity coefficients of the voltage at node i to the changes in active and reactive power at node j during time period t; T j,l,t and T j,r,t are the sensitivity coefficients of the transmission active power of line l and transformer r to the change of active power of node j in time period t; Y j,l,t and Y j,r,t are the sensitivity coefficients of the transmission reactive power of line l and transformer r to the reactive power change of node j in time period t. The sensitivity of transformer r is numerically equal to the power sensitivity of the node power change to the power of the line connected to the transformer; U i,t,s0 represents the initial voltage of node i at time period t; P l,t,s0,line and Q l,t,s0,line It is expressed as the initial active and reactive power of line l at time period t; P r,t,s0,tra and Q r,t,s0,tra It is expressed as the initial active and reactive power of transformer r at time period t, S r,tramax is the maximum value of the transformer r power capacity.

[0096] It should be noted that Figure 3In the above equation, the active power offset range of the load and DG output is Ω1, and the reactive power offset range of the load and DG output is Ω2. Therefore, the allowable active power offset range of the load and DG output is Ω3. j,t is the apparent power within a certain period of time t.

[0097] Since the system power is coupled by active and reactive power, and the reactive and active power change according to a fixed power factor when the source and load nodes inject power changes, a constant power factor is set. is 0.95. If the active power and reactive power changes are ΔP and ΔQ respectively, the relationship σ between the active power and reactive power changes that the system can carry can be calculated as:

[0098]

[0099] That is, ΔP·σ=ΔQ. Substituting the obtained relationship into the calculation of system carrying capacity, we can get: t P j,t σ=β t Q j,t :

[0100]

[0101] Therefore, by substituting formula (16) into formula (11)-(14), we can obtain:

[0102]

[0103]

[0104] Therefore, the flexibility operation domains of the flexible interconnected distribution network under various constraints are:

[0105] In time period t, considering the voltage lower limit constraint of node i, the maximum deviation range of load and DG output that the system can tolerate is α i,t,Ulow for:

[0106]

[0107] Where U i,min is the minimum voltage allowed at node i in the system.

[0108] In time period t, considering the voltage upper limit constraint of node i, the maximum deviation range of load and DG output that the system can tolerate is α i,t,Uupp for:

[0109]

[0110] Where U i,max is the maximum voltage allowed at node i in the system.

[0111] In time period t, considering the transmission power constraint of the lth line, calculate the upward and downward deviation range α of the system's allowable load and DG output l,t,line :

[0112]

[0113] In the formula, S l,t,s0,line It is expressed as the initial apparent power of line l at time period t.

[0114] In time period t, considering the rth transformer load factor constraint, the maximum deviation range of load and DG output that the system can tolerate is α r,t,tra for:

[0115]

[0116] In the formula, S r,t,s0,tra Expressed as the initial apparent power of transformer r at time period t.

[0117] From formulas (21)-(24), it can be seen that under different constraints, the maximum deviation range of load and DG output that the system can tolerate is different. Taking into account all key safety constraints, the maximum deviation range of load and DG output that the system can tolerate in time period t is α t,max for:

[0118] α t,max =min(α i,t,Ulow ,α i,t,Uupp ,α l,t,line ,α r,t,tra ) (25)

[0119] When there is uncertainty in the source load in the system, the voltage fluctuation in the power grid is first caused, and even the voltage over-limit problem is caused. For this reason, this embodiment adopts double-layer optimization for regulation. In the upper optimization (long time scale stage): according to the prediction results of the variable source load of the distribution network, the on-load tap change transformer (OLTC), capacitor bank (CBs) gear, static var compensator (SVC) and SOP output power are globally controlled on an hourly time scale, and the control result per hour is used as the initial value of the lower optimization within the hour for transmission. In order to deal with the voltage over-limit problem caused by source load uncertainty, a centralized voltage control model is established with the goal of minimizing the sum of the absolute values ​​of all node voltage deviations from the rated value. Through the coordinated optimization of traditional reactive voltage regulation equipment and SOP, voltage control and stability within the overall range of the system are achieved. In the lower-level optimization (short-time scale stage): Consider the difference in the impact of the prediction error of the variable source load of the distribution network and the change of the node injection power on different constraints, quantitatively evaluate the flexibility operation domain under different safety boundaries at a time scale of every 15 minutes, and normalize the key indicators involved in each safety boundary. When the calculated flexibility operation domain is insufficient, the operating power of the SOP is quickly adjusted to expand the flexibility operation domain in the upper-level control gap, reduce the risk of exceeding the operating constraint limit, and better adapt to the changes and fluctuations of the source load.

[0120] As an example, in this embodiment, the number of OLTCs in the distribution network is y; the number of capacitor banks is w; the number of SVCs is z; the number of VSCs is h, and a number set of 1, ..., x, ..., h, x∈h is set for the VSCs, and the number of the constant voltage control VSC is set to 1. Each VSC selects master-slave control in the DC side control mode, one of the VSCs is constant voltage control, the other VSCs are constant power control, and the AC side selects constant reactive power control; the minimum sum of the absolute values ​​of the voltage deviations of all nodes in the system from the rated value is taken as the optimization objective function:

[0121]

[0122] Where U i,t is the voltage of node i at time t in the system, and U0 is the rated value of the system voltage.

[0123] Voltage sensitivity is used to represent the impact of power output changes of each regulating device on node voltage, then:

[0124]

[0125] in:

[0126]

[0127] Where N is the total number of nodes in the system; n is the node position of the VSC numbered x in the system; γ is the node position of the CBs numbered λ in the system; η is the node position of the SVC numbered μ in the system; W n,i,t and R n,i,t are the sensitivity coefficients of the voltage at node i to the active and reactive power changes of the VSC at node n during time period t; ΔP x,t Indicates the change in the active power command value of the VSC numbered x at time period t, ΔQ x,t represents the change in reactive power command value of the VSC numbered x at time period t; H o,i,t Indicates the adjacency coefficient between OLTC numbered o and node i in time period t. If they are connected, it is 1, otherwise it is 0; δ o,t,Oin , δ o,t,Ode δ represents a 0-1 variable indicating the upward or downward adjustment of the OLTC gear numbered o in period t; λ,t,CBin , δ λ,t,CBde The 0-1 variable represents the switching state of the CBs numbered λ in time period t; ΔU O is the OLTC voltage regulation step; ΔQ CB is the reactive power change of CBs switching; ΔQ μ,t,SVC represents the change in the reactive power command value of the static VAR compensator SVC numbered μ at time period t; R η,i,t are the sensitivity coefficients of the voltage at node i to the reactive power change of the static VAR compensator SVC at node η during time period t; R γ,i,t are the sensitivity coefficients of the voltage at node i to the reactive power change of the capacitor bank CBs located at node γ during period t.

[0128] The constraints must not only meet the safety constraints in each boundary scenario, such as node voltage constraints and line power constraints, but also meet the capacity and operating status constraints of various control equipment;

[0129] Voltage Constraints:

[0130]

[0131] Transmission capacity constraints of the line:

[0132]

[0133] in:

[0134]

[0135]

[0136] Where, Tn,l,t Y is the sensitivity coefficient of the active power of line l to the change of the active power of the VSC located at node n during period t; n,l,t It represents the sensitivity coefficient of the line reactive power to the change of the reactive power of the VSC located at the node n during the period t.

[0137] VSC transmission capacity constraints:

[0138] P x,t,s0,VSC +ΔP x,t ≤P x,VSCmax (34)

[0139] P x,t,s0,VSC +ΔP x,t ≥P x,VSCmin (35)

[0140] Q x,t,s0,VSC +ΔQ x,t ≤Q x,VSCmax (36)

[0141] Q x,t,s0,VSC +ΔQ x,t ≥Q x,VSCmin (37)

[0142] Where P x,t,s0,VSC and Q x,t,s0,VSC are the initial active power and reactive power of the VSC numbered x at time period t; P x,vscmax With P x,vscmin are the maximum and minimum values ​​of the active power transmission allowed by the VSC numbered x at time period t; Q x,vscmax With Q x,vscmin are the maximum and minimum values ​​of the reactive power allowed to be transmitted by the VSC numbered x at time period t, respectively.

[0143] At the same time, the apparent power of each VSC must be within its capacity limit:

[0144]

[0145] In the formula, S x,VSC The maximum capacity of the VSC numbered x.

[0146] Transmission capacity constraints of SOP:

[0147]

[0148] OLTC operation constraints:

[0149]

[0150] Where D o,t,OD is the OLTC gear numbered o in period t; Omax N is the maximum gear of OLTC; Omax is the upper limit of the number of actions of OLTC in the entire scheduling cycle; T=24 is the total number of time.

[0151] CBs operating constraints:

[0152]

[0153]

[0154] Where D λ,t,CB is the number of switching groups of CBs numbered λ in period t; D CBmax is the maximum number of switching groups of CBs; N CBmax It is the upper limit of the number of actions of CBs in the whole scheduling cycle.

[0155] SVC operation constraints:

[0156] Q μ,SVCmin ≤Q μ,t,s0,SVC +ΔQ μ,t,SVC ≤Q μ,SVCmax (44)

[0157] In the formula, Q μ,SVCmax and Q μ,SVCmin is the maximum and minimum reactive power that the SVC numbered μ can provide; Q μ,t,s0,SVC is the initial power of the SVC numbered μ in period t.

[0158] In this embodiment, after the centralized voltage control ends at the beginning of the hour, the system evaluates the flexibility operation domain every fifteen minutes. When the evaluation result is less than the flexibility operation boundary, the lower-level flexibility improvement optimization is performed. In this embodiment, the lower-level optimization objective function can be expressed as:

[0159] maxf2=α t,max t∈T s (45)

[0160] Among them, T s A collection of time periods that require optimization to improve the flexibility of the operating domain.

[0161] The constraints for optimizing the flexible operation domain of the flexible distribution network include node voltage constraints, VSC transmission capacity constraints, line power constraints, transformer load factor constraints, and SOP transmission capacity constraints. The VSC transmission capacity constraints and SOP transmission capacity constraints are consistent with the constraints in centralized voltage control. The node voltage constraints (46)-(47), line power constraints (48)-(49), and transformer load factor constraints (50) are:

[0162]

[0163] in:

[0164]

[0165] The double-layer flexibility improvement optimization method adopted in this embodiment is a nonlinear model. In order to solve the problems of slow nonlinear solution speed and high difficulty in model solution, this embodiment uses absolute value linearization and regular polygon linearization to transform the model. After absolute value linearization, the objective function and supplementary constraints of the upper centralized voltage control can be expressed as:

[0166]

[0167] Where M is a sufficiently large positive number and ε is a binary 0-1 variable such that V i,t The value of is equivalent to the original absolute value function.

[0168] According to the above optimization model, there are nonlinear calculations and constraints, and they are all circular domain calculations or constraints. For the circular feasible domain, a regular polygon approximation consisting of multiple square constraints can be used to convert the circular constraint into a linear constraint. The schematic diagram of the linearization method is shown in Figure 4 As shown. k d k and c k is the corresponding coefficient of the polygonal approximate fitting straight line, and the specific values ​​are shown in Table 1.

[0169] Table 1 Approximate fitting coefficients of regular dodecagon

[0170] <![CDATA[a k ]]> <![CDATA[d k ]]> <![CDATA[c k ]]> 0.2679 1 1 1 1 1.366 1 0.2679 1 1 -0.2679 1 1 -1 1.366 0.2679 -1 1 -0.2679 -1 1 -1 -1 1.366 -1 -0.2679 1 -1 0.2679 1 -1 1 1.366 -0.2679 1 1

[0171] It can be seen from equations (23) and (24) that when evaluating the flexible operating domain, the calculation of the maximum operating domain of the line power and the maximum operating domain of the transformer requires solving high-order nonlinear equations. Direct solution will result in slow solution speed and difficulty in solving. If equations (19) and (20) are linearized by polygon fitting, we can obtain:

[0172]

[0173] In time period t, the system considers the line power constraint and transformer load factor constraint respectively, and the maximum deviation range a of the allowable load and DG output is l,t,line and a r,t,tra for:

[0174]

[0175] In terms of constraint conditions, the nonlinear constraints, i.e., equations (31), (38), (48) and (50), are also processed by polygonal approximation fitting, and the linear constraint group can be expressed as:

[0176] a k ΔP l +d k ΔQ l ≤c k S l,linemax -a k P l,t,s0,line -d k Q l,t,s0,line (57)

[0177] a k ΔP x,t +d k ΔQ x,t ≤c k S x,VSC -a k P x,t,s0,VSC -d k Q x,t,s0,VSC (58)

[0178]

[0179] The following is an example analysis. Figure 5 Taking the three sections of flexible interconnected distribution network shown in the figure as an example, the accuracy of the proposed flexibility operation domain evaluation method and the effectiveness and rapidity of the two-layer optimization model for flexibility improvement are verified. In this embodiment, the reference voltage and reference capacity of the system are set to 10kV and 10MVA respectively. The capacity of each VSC in the SOP is 1MVA. Constant power factor of photovoltaic in AC system The voltage is 0.95, and the voltage range is set to 0.95pu-1.05pu. OLTC has 11 gears, each with a step of 0.01pu, and the sixth gear is rated at 1.0pu; CBs have three groups at each location, each with 200kVar, and the maximum number of operations is 6 times; the upper and lower capacity limits of SVC are 500kVar.

[0180] In the two-layer centralized control and optimization of the flexible interconnected distribution network for improving the flexible operation domain, the centralized voltage control takes one hour as an optimization period, and the evaluation and optimization of the flexible operation domain takes one 15-minute optimization period. Combined with the predicted change trend of variable load and photovoltaic output, the output of a certain period is expressed by the per-unit value of load and photovoltaic power compared with the rated value. The change curve of the whole period is as follows: Figure 6(a) to 6(f) shown.

[0181] According to the above analysis, the flexible interconnected distribution network can meet the scenario of any DG output or load fluctuation within the safety constraint range, and the maximum allowable uncertainty change of the system is the boundary of the safety margin range. Therefore, the range of the flexibility operation domain can be determined by using the extreme scenario. The binary method continuously constructs two or more scenarios with different DG output and load fluctuation degrees, and iterates the selection of scenarios by judging whether the scenarios meet the safety operation constraints until the range scale reaches the predetermined accuracy requirements. The maximum change scenario obtained is the range size of the flexibility operation domain, which verifies the rationality and effectiveness of the flexibility operation domain evaluation method proposed in this embodiment. The evaluation of the flexibility operation domain proposed in this embodiment is calculated by sensitivity, and the evaluation error comes from the sensitivity error. The smaller the system flexibility, the greater the error proportion. The accuracy of the method proposed in this embodiment is verified by analyzing the period with the greatest risk of system over-limit. Table 2 shows the comparison between the maximum value of the change range obtained by the binary method under the safety operation constraint and the index required for the evaluation of the system flexibility operation domain proposed in this embodiment when the system voltage over-limit risk is the largest (18:15) and the power over-limit risk is the largest (11:00) after considering the uncertainty of source and load. It can be seen from Table 2 that, based on the results obtained by the binary search method, when the voltage over-limit risk is the greatest, the relative errors before and after optimization in the evaluation method proposed in this embodiment are 2.534% and 1.150% respectively; when the line power over-limit risk is the greatest, the relative errors before and after optimization are 4.700% and 0.624% respectively. The errors are within an acceptable range, which shows the rationality and effectiveness of the flexible operation domain evaluation system and linearization solution proposed in this embodiment. And Figure 7(a) to 7(b) The evaluation is compared when the power over-limit risk is greater and when the voltage over-limit risk is greater. It can be seen that the method proposed in this paper has the same evaluation trend as the scenario dichotomy method, and the absolute error near the flexible operation boundary is less than 1%.

[0182] Table 2 Flexibility operation domain evaluation analysis

[0183]

[0184] Use normal distribution to describe the actual values ​​of load and PV power; Figure 8 The maximum and minimum voltage changes before and after centralized voltage control. Fig. 9 for Figure 8 In the system state after centralized control shown in the figure, the maximum deviation range of the system allowable load and photovoltaic output under the constraints of the highest node voltage constraint, the lowest node voltage constraint, the line power constraint and the maximum load rate of the transformer are considered respectively in the period of 8:45-13:00; it can be seen that the maximum deviation range of the photovoltaic output and load allowed by the system under different constraint conditions is different.

[0185] The flexibility operation domain is optimized for the period when the maximum deviation range of the system allowable load and DG output is less than 20%. Figures 10(a) to 10(b) The radar chart of the flexibility operation domain index of the system under different constraints before and after optimization in the 11:00 period is shown, and the flexibility operation domain in the final state is represented by a red solid circle. It can be seen that through the optimization of the flexibility operation domain, the system flexibility is increased from 12.03% to 40.33%, which effectively reduces the risk of system over-limit.

[0186] Figures 11(a) to 11(b) The figure shows the flexibility operation domain margin of each AC subsystem under different constraints during the 11:00 period. It can be seen that the high photovoltaic output during this period leads to a greater risk of voltage exceeding the upper limit. Among them, the voltage of AC system 1AC1 is the largest. The upper layer of voltage optimization is to meet the goal of minimum voltage deviation. In addition to using traditional reactive voltage regulation methods, the power in AC1 is transmitted to other subsystems through SOP, and each VSC in SOP absorbs reactive power to reduce voltage. After the upper layer optimization is completed, the voltage is effectively controlled, but at the same time, the return flow of AC2 is aggravated. The reactive control of VSC increases the reactive flow in each line, resulting in an increase in the risk of line power exceeding the limit in AC2, and the flexibility operation domain cannot meet the flexible operation boundary. The lower layer optimization is to increase the overall flexibility margin of the system. The power in AC2 is transferred through SOP to weaken the return flow, and at the same time, each VSC is made to output reactive power to reduce the reactive power in the line, thereby improving the flexibility operation domain and achieving a balance between the line power constraint of each subsystem and the voltage upper limit constraint margin.

[0187] Figure 12(a) shows the maximum deviation range of the system allowable load and DG output before and after optimization during the period of 70-77, and Figure 12(b)-(d) shows the output power changes of each VSC in the corresponding period before and after optimization. At 17:15-19:00, the photovoltaic output is almost 0 and the load increases. The voltage lower limit risk in AC3 is the greatest. The upper optimization takes the minimum voltage deviation as the goal and considers the overall voltage state of the system, but there may still be a situation where the voltage of a certain node exceeds the limit. The lower optimization is to balance the voltage of each system node, by reducing the active power transmission of VSC1 or VSC2 to increase the power transmission of VSC3, thereby increasing the system voltage and enhancing the system's ability to withstand voltage exceeding the upper limit. However, the optimization of AC3 voltage will consume the flexibility margin of other AC subsystems for compensation. At the same time, due to the SOP and the capacity constraints of each VSC, after each VSC performs active coordination, the reactive power of the VSC follows the change, which will have a certain suppression on the regulation effect, but the resistance in the distribution network is large, and the impact of reactive regulation is relatively small.

[0188] To test the effectiveness of the proposed optimization model, when the load and photovoltaic power are described by normal distribution, the predicted power at 11:00 is set as the mean, and the different ranges of power absolute value are used as the standard deviation. The Monte Carlo simulation method is used to randomly generate 1000 scenarios, and the effectiveness of the proposed optimization model is judged by calculating the degree of scenario over-limit in different ranges before and after optimization. Table 3 shows the degree of over-limit of 1000 scenarios before and after optimization in different ranges. It can be seen from Table 3 that the optimized system can effectively reduce the number of over-limit scenarios, and the proposed optimization method has certain effectiveness.

[0189] Table 3 Comparison of the effectiveness of the lower-level optimization model

[0190]

[0191] The voltage flexibility evaluation index and capacity flexibility margin index are selected for comparison with the flexibility operation domain evaluation method proposed in this embodiment, and the over-limit ratio of the scenario in the Monte Carlo simulation method is used to verify the accuracy of the evaluation, where the standard deviation of the node power is set to 30% of the predicted data. The flexibility margin values ​​under different evaluation methods are shown in Table 4.

[0192] Table 4 Comparative analysis of flexibility operation domain evaluation

[0193]

[0194] It can be seen from Table 4 that the voltage flexibility evaluation index and the capacity flexibility margin index are both evaluated according to the physical meaning of the index. Before optimization, different evaluation methods can effectively express the changes in system flexibility under various constraints, but after optimization, the voltage lower limit flexibility evaluation index is smaller than the transformer capacity flexibility margin index, which is contrary to the result obtained by the Monte Carlo simulation method. The evaluation method proposed in this embodiment can effectively and accurately present the flexibility under various constraints. Therefore, the voltage flexibility evaluation index and the capacity flexibility margin index are difficult to compare across dimensions due to different measurement units. In the proposed flexibility operation domain evaluation method, the flexibility operation under different constraints is described by analyzing the uncertainty of the allowable variation range of node power. The proposed method has the same measurement unit and can be used to compare the evaluation results under different operation constraints, which can better reflect the overall operation status of the system.

[0195] In order to verify the effectiveness and rapidity of the flexibility operation domain improvement optimization method proposed in this embodiment, it is planned to compare with the genetic algorithm, and use the over-limit ratio of the scene in the Monte Carlo simulation method to reflect the degree of optimization, where the standard deviation of the node power is set to 15% of the predicted data. The comparative analysis of the optimization effect of the flexibility operation domain is shown in Table 5. It can be seen that the method proposed in this embodiment has a certain error compared with the genetic algorithm due to linearization, and the error is relatively small, all around 1%. From the analysis of the results of the Monte Carlo simulation method, the results of the two methods are slightly different, and there are cases where the method proposed in this embodiment is better. Therefore, compared with the genetic algorithm, the method of this embodiment can ensure that the calculation efficiency is increased by 100 times under the premise of small calculation errors, which greatly improves the optimization efficiency.

[0196] Table 5 Comparative analysis of optimization effects of flexible operation domain

[0197]

[0198] Embodiment 2

[0199] This embodiment provides a flexible distribution network flexibility operation domain evaluation and optimization device, including:

[0200] A parameter acquisition module is used to obtain the topological structure, line parameters and load parameters of the flexible interconnected distribution network;

[0201] Linear power flow sensitivity model building module, used to build a linear power flow sensitivity model, using the sensitivity model to describe the impact of source-load uncertainty on node voltage and line power in a flexible interconnected distribution network;

[0202] The flexibility operation domain evaluation model and the two-layer optimization operation and control model construction module are used to construct the flexibility operation domain evaluation model of the flexible interconnected distribution network taking into account the uncertainty of source and load and the two-layer optimization operation and control model of the flexible interconnected distribution network according to the constructed linear power flow sensitivity model and the obtained flexible interconnected distribution network;

[0203] The flexible interconnected distribution network evaluation and flexibility optimization module is used to evaluate and optimize the flexibility of the flexible interconnected distribution network by using the constructed flexible interconnected distribution network flexibility operation domain evaluation model and the flexible interconnected distribution network two-layer optimization operation and control model; wherein, in the said flexible interconnected distribution network two-layer optimization operation and control model, the upper layer optimization takes the minimum sum of the absolute values ​​of the voltage deviations of all nodes in the distribution network from the rated value as the optimization objective function, and the lower layer optimization takes the maximization of the flexibility operation domain as the objective function for optimization.

[0204] A computing device is also provided, comprising:

[0205] one or more processing units;

[0206] A storage unit for storing one or more programs,

[0207] Among them, when the one or more programs are executed by the one or more processing units, the one or more processing units execute the flexible distribution network flexibility operation domain evaluation and optimization method in this embodiment; it should be noted that the computing device may include but is not limited to a processing unit and a storage unit; those skilled in the art can understand that the computing device includes a processing unit and a storage unit and does not constitute a limitation on the computing device, and may include more components, or a combination of certain components, or different components. For example, the computing device may also include input and output devices, network access devices, buses, etc.

[0208] A computer-readable storage medium having a non-volatile program code executable by a processor, wherein the computer program, when executed by the processor, implements the steps of the flexible distribution network flexibility operation domain evaluation and optimization method in this embodiment; it should be noted that the readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination thereof; the program contained on the readable medium may be transmitted using any appropriate medium, including, but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination thereof. For example, the program code for performing the operation of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and also conventional procedural programming languages ​​such as C language or similar programming languages. The program code may be executed entirely on a user computing device, partially on a user device, as an independent software package, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0209] The above is only a preferred embodiment of the present embodiment and is not intended to limit the present embodiment. For those skilled in the art, the present embodiment may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present embodiment shall be included in the protection scope of the present embodiment.

Claims

1. A method for evaluating and optimizing the flexibility operation domain of a flexible distribution network, characterized in that: The steps include: Obtain the topological structure, line parameters and load parameters of the flexible interconnected distribution network; A linear power flow sensitivity model is constructed and used to describe the impact of source-load uncertainty on node voltage and line power in a flexible interconnected distribution network. Based on the constructed linear power flow sensitivity model and the obtained flexible interconnected distribution network, a flexible interconnected distribution network flexibility operation domain evaluation model taking into account source and load uncertainty and a flexible interconnected distribution network two-layer optimization operation and control model are constructed; The flexible interconnected distribution network is evaluated and its flexibility is optimized by using the constructed flexibility operation domain evaluation model of the flexible interconnected distribution network and the two-layer optimization operation and control model of the flexible interconnected distribution network. In the two-layer optimization operation and control model of the flexible interconnected distribution network, the upper-layer optimization takes the minimum sum of the absolute values ​​of the voltage deviations of all nodes in the distribution network from the rated values ​​as the optimization objective function, and the lower-layer optimization takes the maximization of the flexibility operation domain as the objective function for optimization.

2. The method for evaluating and optimizing the flexible distribution network flexibility operation domain according to claim 1 is characterized by: The method for evaluating and optimizing the flexibility of the flexible interconnected distribution network by using the constructed flexible interconnected distribution network flexibility operation domain evaluation model and the flexible interconnected distribution network double-layer optimization operation and control model is as follows: In the time period t1, the upper centralized voltage control of the double-layer optimization operation and control model of the distribution network is carried out with the goal of minimizing the sum of the absolute values ​​of all nodes in the distribution network deviating from the rated values. The flexibility of the distribution network is evaluated on the time scale of time period t2 using the distribution network flexibility operation domain evaluation model. If the result of the distribution network flexibility operation domain is less than the flexible operation boundary value, the lower-layer flexibility operation domain of the distribution network two-layer optimization operation and control model is optimized with the goal of maximizing the flexibility operation domain. If the result of the distribution network flexibility operation domain is greater than the flexible operation boundary value, the flexibility of the distribution network is evaluated on the next time scale of time period t2. This is repeated until the flexibility evaluation of the distribution network in time period t1 is completed.

3. The method for evaluating and optimizing the flexible distribution network operation domain according to claim 2 is characterized in that: The flexibility operation domain is defined as follows: the flexibility of the flexible interconnected distribution network is the system's ability to carry or respond to the randomness of renewable energy output and the high spatial and temporal uncertainty caused by load fluctuations, while the flexibility operation domain directly quantifies the system's allowable deviation range of distributed generation DG output and load while meeting the safety range boundary conditions.

4. The method for evaluating and optimizing the flexible distribution network flexibility operation domain according to claim 2 is characterized in that: When constructing a flexible interconnected distribution network flexibility operation domain evaluation model taking into account source-load uncertainty, the actual power values ​​of the load and distributed generation DG output at any time are made to follow the normal distribution description, and the flexibility operation domain expression under different constraints is described by analyzing the uncertainty of the allowable variation range of node power and the linear power flow sensitivity model, and the maximum deviation range per unit value α of the load and distributed generation DG output that the distribution network can allow in time period t is obtained. t,max : a t,max =min(a i,t,Ulow ,a i,t,Uupp ,a l,t,line ,a r,t,tra ) Among them, α i,t,Ulow is the maximum deviation range of load and DG output that the distribution network can tolerate in time period t, considering the voltage lower limit constraint of node i; α i,t,Uupp is the maximum deviation range of load and DG output that the distribution network can tolerate in time period t, considering the voltage upper limit constraint of node i; α l,t,line is the upward and downward deviation range of load and distributed generation DG output that the distribution network can tolerate in time period t, considering the transmission power constraint of the lth line; α r,t,tra is the maximum deviation range of the load and DG output that the distribution network can tolerate, considering the rth transformer load factor constraint in time period t.

5. The method for evaluating and optimizing the flexible operation domain of a flexible distribution network according to claim 4 is characterized in that: When constructing a flexible interconnected distribution network flexibility operation domain evaluation model taking into account source and load uncertainty, the uncertainty of the allowable variation range of node power includes the uncertainty variation of active power and reactive power. Assume that the reactive power and active power in the node injection power variation are constant power factors. If the active power and reactive power changes are ΔP and ΔQ respectively, the relationship σ between the active power and reactive power changes can be calculated as:

6. The method for evaluating and optimizing the flexible operation domain of a flexible distribution network according to claim 4 is characterized in that: In the upper-level control of the two-layer optimization operation and control model of the flexible interconnected distribution network, the output power of the on-load tap-changing transformer, capacitor group gear, static VAR compensator and intelligent soft switch is globally controlled with time period t1 as the time scale, and the control result is passed as the initial value of the lower-level optimization within the time period t1; in the lower-level optimization, the prediction error of the variable source load of the distribution network and the difference in the impact of the node injection power change on different constraints are considered, and the flexibility operation domain under different safety boundaries is quantitatively evaluated with time period t2 as the time scale. The key indicators involved in each safety boundary are normalized and measured. When the calculated flexibility operation domain is insufficient, the operating power of the intelligent soft switch SOP is quickly adjusted, and the flexibility operation domain is expanded in the upper-level control gap to reduce the risk of exceeding the operation constraint limit, so as to better adapt to the changes and fluctuations of the source load.

7. The method for evaluating and optimizing the flexible distribution network operation domain according to claim 6 is characterized by: In the upper-level control of the two-layer optimization operation and control model of the flexible interconnected distribution network, the goal is to minimize the voltage deviation of all nodes in the system, and the upper-level optimization objective function is: in: Wherein, y is the number of on-load tap-changing transformers OLTC; w is the number of capacitor banks; z is the number of static VAR compensators SVC; h is the number of voltage source converters VSC, N is the total number of system nodes; n is the node position of the voltage source converter VSC numbered x in the system; γ is the node position of the capacitor bank CBs numbered λ in the system; η is the node position of the static VAR compensator SVC numbered μ in the system; W n,i,t and R n,i,t are the sensitivity coefficients of the voltage at node i at time period t to the change of active and reactive power of the voltage source converter VSC at node n; ΔP x,t represents the change in the active power command value of the voltage source converter VSC numbered x at time period t, ΔQ x,t represents the change in reactive power command value of the voltage source converter VSC numbered x at time period t; H o,i,t represents the adjacency coefficient between the on-load tap changer OLTC numbered o and node i in time period t, which is 1 if connected, otherwise 0; δ o,t,Oin ,δ o,t,Ode δ are 0-1 variables representing the upward and downward adjustment of the OLTC gear of the on-load tap-changing transformer numbered o in time period t; λ,t,CBin ,δ λ,t,CBde 0-1 variables representing the switching status of capacitor bank CBs numbered λ in time period t; ΔU O ΔQ is the voltage regulation step of the on-load tap changer OLTC; CB The reactive power change of capacitor bank CBs; ΔQ μ,t,SVC represents the change in the reactive power command value of the static VAR compensator SVC numbered μ at time period t; U0 is the rated value of the system voltage; R η,i,t are the sensitivity coefficients of the voltage at node i to the reactive power change of the static VAR compensator SVC at node η during time period t; R γ,i,t are the sensitivity coefficients of the voltage at node i to the reactive power change of the capacitor bank CBs located at node γ during period t. In the lower-level control of the two-level optimization operation and control model of the flexible interconnected distribution network, the goal is to maximize the flexibility operation domain, and the lower-level optimization objective function is: Objective function: maxf2=α t,max t∈T s Where, T s A collection of time periods that require optimization to improve the flexibility of the operating domain.

8. The method for evaluating and optimizing the flexible distribution network flexibility operation domain according to claim 1 is characterized by: When solving the constructed two-layer optimization operation and control model of the flexible interconnected distribution network, the absolute value function linearization and regular polygon linearization are used to perform linear fitting on the model, and the upper and lower layer optimizations are solved by mixed integer linear programming and linear programming methods respectively.

9. The method for evaluating and optimizing the flexible distribution network operation domain according to claim 1 is characterized by: In the process of constructing the linear power flow sensitivity model, the linear power flow equation and Jacobian matrix are used to analyze the impact of node injection power changes on node voltage and line power; Among them, according to the linear power flow equation and Jacobian matrix derivation, the node voltage sensitivity equation and line current carrying capacity sensitivity equation are obtained respectively: ΔP f =TΔP i (3) ΔQ f =YΔQ i (4) Where ΔP i , ΔQ i , Δθ and ΔV represent the node active power injection change vector, node reactive power injection change vector, node voltage phase angle change vector and node voltage amplitude change vector respectively; J RPV , J RQV , J RPθ and J RQθ All are non-singular matrices; and ΔV versus ΔP i and ΔQ i The (n-1)×(n-1) sensitivity coefficient matrix of and are Δθ versus ΔP i and ΔQ i The (n-1)×(n-1) sensitivity coefficient matrix of the node active power; T represents the sensitivity matrix of the line active power to the node active power change; Y represents the sensitivity matrix of the line reactive power to the node reactive power change; ΔP f , ΔQ f Respectively represent the changes in active and reactive transmission power of the line.

10. Flexible distribution network flexibility operation domain evaluation and optimization device, characterized in that: include: A parameter acquisition module is used to obtain the topological structure, line parameters and load parameters of the flexible interconnected distribution network; Linear power flow sensitivity model building module, used to build a linear power flow sensitivity model, using the sensitivity model to describe the impact of source-load uncertainty on node voltage and line power in a flexible interconnected distribution network; The flexibility operation domain evaluation model and the two-layer optimization operation and control model construction module are used to construct the flexibility operation domain evaluation model of the flexible interconnected distribution network taking into account the uncertainty of source and load and the two-layer optimization operation and control model of the flexible interconnected distribution network according to the constructed linear power flow sensitivity model and the obtained flexible interconnected distribution network; The flexible interconnected distribution network evaluation and flexibility optimization module is used to evaluate and optimize the flexibility of the flexible interconnected distribution network by using the constructed flexible interconnected distribution network flexibility operation domain evaluation model and the flexible interconnected distribution network two-layer optimization operation and control model; wherein, in the said flexible interconnected distribution network two-layer optimization operation and control model, the upper layer optimization takes the minimum sum of the absolute values ​​of the voltage deviations of all nodes in the distribution network from the rated value as the optimization objective function, and the lower layer optimization takes the maximization of the flexibility operation domain as the objective function for optimization.

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