Active and reactive combined continuous time flexibility evaluation method and system for flexible distribution network

Abstract

The present invention proposes a method and system for evaluating the continuous-time flexibility of active and reactive power in a flexible distribution network, and relates to the technical field of power systems. The method includes modeling a flexible distribution network; relaxing the nonlinear terms in the flexible distribution network model, modeling the uncertainty of distributed photovoltaic power generation equipment and loads, and defining indicators that can maximize the apparent power in a certain period and maximize the PQ output in the same period, thereby obtaining an evaluation model for the continuous-time flexibility of active and reactive power in a flexible distribution network, and using chance constraints to handle uncertainty; converting a deterministic evaluation model for the continuous-time flexibility of active and reactive power in a flexible distribution network into a plurality of discrete sub-problems, solving all sub-problems, and obtaining the continuous-time flexibility of active and reactive power at any time. The present invention introduces a new concept of continuous-time TDI flexibility evaluation for three-phase unbalanced FDN.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system operation and control, and in particular to a method and system for evaluating the continuous-time flexibility of active and reactive power combined in a flexible distribution network. Background Art

[0002] With the widespread application of advanced power electronic devices, modern distribution networks are gradually distinguished from traditional distribution networks and become more and more controllable and flexible. Modern distribution networks are called flexible distribution networks (FDNs). Due to the application of power electronics technology, flexible distribution networks have many interesting new features, two of which are particularly relevant to improving flexibility: (i) it becomes feasible for distributed generators (DGs) to generate continuous active power and static var compensators (SVCs) to generate continuous reactive power; (ii) flexible network devices, such as soft open points (SOPs), become one of the options for adjusting network power flow. Therefore, flexible distribution networks can be regarded as one of the effective means to provide active and reactive power support to the upper transmission networks (TNs), which do need such active and reactive power resources. The provision of active and / or reactive power from FDN to TN occurs at the transmission-distribution-interface (TDI) and can be regarded as a flexibility resource for transmission system operators (TSOs). Therefore, a reliable assessment of TDI flexibility is needed.

[0003] Prior art studies have investigated the TDI flexibility assessment problem for traditional distribution networks, but have not fully considered flexibility devices. These studies mainly include the following aspects. First, how to solve the uncertainty problem of DG and / or load power; second, how to solve the nonlinear problems of power flow equations and other operating constraints; and finally, how to accurately model or relax the discrete optimization variables related to tap positions, capacitor bank switching, and energy storage device operation.

[0004] In addition, and more importantly, most existing studies use discrete-time models to evaluate TDI flexibility. The discrete-time model here refers to an evaluation model with a time granularity set to 5 minutes, 15 minutes, or 1 hour, which is consistent with the power system dispatch cycle. The discrete-time model assumes that the node power injection is constant for each dispatch cycle. Due to the significant randomness and volatility of renewable energy generation, the related node power injection will experience considerable real-time fluctuations within a dispatch cycle, and the existing discrete-time TN dispatch models with a granularity of 5 minutes or longer cannot accurately capture these fluctuations. Therefore, the dispatch decision of the TSO will be affected, affecting subsequent dispatch.

[0005] The limitations of the above-mentioned discrete-time models have aroused people's interest in the research of continuous-time models. In order to make reasonable dispatch decisions, TSOs prefer to understand both the real-time changing node power injection and the real-time system flexibility, the latter of which is TDI flexibility. However, the existing discrete-time TDI flexibility evaluation model may not be suitable for this scenario, so the continuous-time alternative model becomes indispensable. In addition, the continuous-time TDI flexibility evaluation model is also conducive to ensuring the operation of FDN, especially when the DG penetration rate is high, because this mode can reflect real-time security while the FDN provides support for the upper-level TN. However, most continuous-time studies in the field of power system operation are related to economic dispatch and unit combination problems, and most of them are related to TN. There are few literatures that study the reliable evaluation of continuous-time TDI flexibility of FDN. Summary of the invention

[0006] In order to overcome the deficiencies of the above-mentioned prior art, the present invention provides a method and system for evaluating the active and reactive combined continuous-time flexibility of a flexible distribution network. Firstly, a new FDN continuous-time TDI flexibility evaluation mathematical model is established, and an indicator is defined that can maximize the apparent power in a certain period and maximize the PQ output at the same time in the selected period. Secondly, chance constraints are used to deal with uncertainty, thereby ensuring the safety of FDN when providing flexibility. Finally, in order to effectively solve the established model, an algorithm for visually decoupling PQ flexibility is provided, which makes it easier for TSO to utilize this flexibility.

[0007] To achieve the above objectives, one or more embodiments of the present invention provide the following technical solutions:

[0008] A first aspect of the present invention provides a method for evaluating the continuous-time flexibility of active and reactive power combined in a flexible distribution network.

[0009] The method for evaluating the continuous time flexibility of active and reactive power combined in flexible distribution network includes the following steps:

[0010] Modeling of flexible distribution networks;

[0011] The nonlinear terms in the flexible distribution network model are relaxed, the uncertainty of distributed photovoltaic power generation equipment and loads is modeled, and the indicators that can maximize the apparent power in a certain period and the PQ output in the same period are defined to obtain the active and reactive joint continuous time flexibility evaluation model of the flexible distribution network;

[0012] Chance constraints are used to deal with the uncertainty in the flexible distribution network active and reactive joint continuous time flexibility evaluation model, and a deterministic flexible distribution network active and reactive joint continuous time flexibility evaluation model is obtained.

[0013] The deterministic flexible distribution network active and reactive combined continuous-time flexibility evaluation model is transformed into multiple discretized sub-problems. All the sub-problems are solved, and finally the active and reactive combined continuous-time flexibility at any time is obtained.

[0014] A second aspect of the present invention provides a flexible distribution network active and reactive power combined continuous-time flexibility assessment system.

[0015] Flexible distribution network active and reactive power combined continuous time flexibility assessment system, including:

[0016] The flexible distribution network model building module is configured to: model the flexible distribution network;

[0017] The flexibility assessment model building module is configured to: relax the nonlinear terms in the flexible distribution network model, model the uncertainty of distributed photovoltaic power generation equipment and loads, and define indicators that can maximize the apparent power in a certain period and maximize the PQ output in the same period, so as to obtain the active and reactive joint continuous time flexibility assessment model of the flexible distribution network;

[0018] The uncertainty processing module is configured to: use chance constraints to process the uncertainty in the flexible distribution network active and reactive joint continuous time flexibility evaluation model to obtain a deterministic flexible distribution network active and reactive joint continuous time flexibility evaluation model;

[0019] The solution module is configured to: transform the deterministic flexible distribution network active and reactive combined continuous-time flexibility evaluation model into multiple discrete sub-problems, solve all the sub-problems, and finally obtain the active and reactive combined continuous-time flexibility at any time.

[0020] One or more of the above technical solutions have the following beneficial effects:

[0021] The present invention provides a method and system for evaluating the continuous time flexibility of active and reactive power in a flexible distribution network, introduces a new concept of continuous time TDI flexibility evaluation for three-phase unbalanced FDN, and establishes its mathematical model. The model can not only accurately capture the real-time situation of FDN operation, but also obtain reliable and maximum TDI flexibility evaluation by fully coordinating the multi-source flexibility from DG, ESS and SVC, and utilizing SOP.

[0022] In addition, the present invention also copes with uncertainty by adopting chance constraints, thereby ensuring the security of FDN while providing flexibility.

[0023] Finally, the continuous-time TDI flexibility calculation model and visualization decoupling method proposed in this paper can ensure that TSO can fully utilize the potential of FDN without affecting the safety of FDN, thereby providing reliable support for the upper power grid.

[0024] Advantages of additional aspects of the present invention will be given in part in the following description, and in part will become obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0026] Figure 1 This is a flow chart of the method of the first embodiment.

[0027] Figure 2 A diagram showing the selection of the initial point for visualizing the decoupled PQ flexibility algorithm. DETAILED DESCRIPTION

[0028] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. 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 invention belongs.

[0029] It should be noted that the terms used herein are for describing specific embodiments only and are not intended to be limiting of exemplary embodiments according to the present invention.

[0030] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0031] Terminology explanation:

[0032] FDN: Flexible distribution network;

[0033] DG: distributed generator;

[0034] SVC: static VAR compensator;

[0035] SOP: soft switching;

[0036] TN: upper transmission network;

[0037] TDI: Transmission and Distribution Indicator;

[0038] TSO: Transmission System Operator;

[0039] OLTC: On-load voltage-changing transformer.

[0040] Embodiment 1

[0041] The purpose of this embodiment is to propose a continuous-time evaluation and decoupling visualization method for the active-reactive joint flexibility of a random flexible distribution network. This method first establishes a new mathematical model for the continuous-time TDI flexibility evaluation of FDN, and adopts chance constraints to deal with uncertainty, thereby ensuring the safety of FDN when providing flexibility. Secondly, an indicator is defined that can maximize the apparent power in a certain period and maximize the PQ output at the same time in the selected period. Finally, in order to effectively solve the established model, an effective solution method is designed, and an algorithm for visual decoupling PQ flexibility is provided, making it easier for TSOs to utilize this flexibility.

[0042] like Figure 1 As shown, the flexible distribution network active and reactive power combined continuous time flexibility assessment method may include the following steps:

[0043] Modeling of flexible distribution networks;

[0044] The nonlinear terms in the flexible distribution network model are relaxed, the uncertainty of distributed photovoltaic power generation equipment and loads is modeled, and the indicators that can maximize the apparent power in a certain period and the PQ output in the same period are defined to obtain the active and reactive joint continuous time flexibility evaluation model of the flexible distribution network;

[0045] Chance constraints are used to deal with the uncertainty in the flexible distribution network active and reactive joint continuous time flexibility evaluation model, and a deterministic flexible distribution network active and reactive joint continuous time flexibility evaluation model is obtained.

[0046] The deterministic flexible distribution network active and reactive combined continuous-time flexibility evaluation model is transformed into multiple discretized sub-problems. All the sub-problems are solved, and finally the active and reactive combined continuous-time flexibility at any time is obtained.

[0047] More specifically, this may include:

[0048] Step 1: Model the problem.

[0049] For a three-phase unbalanced radial FDN with N+1 nodes and N branches, let N = {0, 1, ..., N} be the node set, where node 0 represents TDI, N PV ,N Load are the node sets connected to the distributed photovoltaic device and the load respectively; let L = {1,...,N} be the branch set. Φ = {a,b,c} represents each phase.

[0050] (1) Modeling of distributed photovoltaic power generation equipment.

[0051] Assume that the distributed photovoltaic unit considered in the present invention is equipped with a smart inverter connected to the grid, which can Regulate the real-time reactive power output of the photovoltaic unit The details are as follows:

[0052]

[0053] Furthermore, (1) can be obtained by introducing the SOS-2 variable Linearization is (2):

[0054]

[0055] in, V1, V2 are the relevant parameters of the smart inverter. is the time set within the study range. is the auxiliary variable introduced; is the square of the voltage amplitude at node i;

[0056] After considering the operating constraints of distributed photovoltaic units, the distributed photovoltaic power generation equipment at node i can finally be modeled as:

[0057]

[0058] in[·] L Represents the linearization of the quadratic constraint using inner polygon approximation. is the real-time active power output of the photovoltaic unit, and Indicates the maximum allowable apparent power and maximum active power output of the photovoltaic unit.

[0059] out. It is the collection of active and reactive power for the safe operation of distributed photovoltaic power generation equipment.

[0060] (2) Model the load.

[0061] Assume that the load at node i is star-connected and has a fixed power factor but and Satisfies the following relationship:

[0062]

[0063] in, and They are the reactive output and active output of the load respectively.

[0064] (3) Model the SOP.

[0065] The operating constraints of SOP include power balance constraints, reactive power limitations, and capacity limitations. For the SOP connecting node i and node j, the following modeling can be performed:

[0066]

[0067] in Represent the active and reactive power output by SOP at node i and node j respectively, and Indicates the minimum and maximum values ​​of the SOP output power factor, and They represent the maximum allowable apparent power and minimum and maximum active output of SOP respectively. It is the active and reactive power vector set of SOP when SOP is running safely.

[0068] (4) Modeling energy storage.

[0069] Energy storage constraints generally include capacity constraints, energy storage charge and discharge constraints and complementary relaxation constraints. In the present invention, the energy storage charge and discharge constraints and complementary relaxation constraints are processed, and finally the energy storage at node i is modeled as follows:

[0070]

[0071] in is a parameter indicating the discharge of energy storage, is the amount of energy stored at time t0, P C,i and P D,i are the charging and discharging power of energy storage, η C and η D Represents the charging and discharging efficiency of energy storage, is the maximum capacity of energy storage. For safe operation of energy storage A collection of .

[0072] (5) Model SVC.

[0073] Research shows that as the system voltage decreases, the ability of SVC to output inductive reactive power will also decrease. Therefore, for the reactive power output of SVC at node i, The modeling is as follows:

[0074]

[0075] where k SVC It represents the slope of SVC current changing with voltage, represents the square of the voltage amplitude at node i, U ref It is the reference voltage when the SVC output current is 0. A collection of reactive outputs for SVC safe operation.

[0076] (6) Model the capacitor bank.

[0077] Generally, direct modeling of the capacitor bank will introduce bilinear terms, which makes it more difficult to solve the model. Therefore, the present invention uses McCormick relaxation to process the bilinear terms and obtain the output of the capacitor bank at node i: as follows:

[0078]

[0079] Where k represents the number of gears of the capacitor bank, q Ck Indicates the capacitance value of each gear of the capacitor bank, is the auxiliary variable introduced due to relaxation, is the square of the voltage amplitude at node i, and are the maximum and minimum values ​​allowed for the node voltage, respectively. It is the reactive power output when the capacitor bank is operating safely.

[0080] (7) Model the three-phase unbalanced distribution network.

[0081] For convenience, we will Divided into three sets, Indicates a branch containing OLTC. Indicates the branch containing the voltage regulator, Indicates a general branch:

[0082]

[0083] in is the net active and reactive power at node i, and Represents the parent node and child node of node i, and is the active and reactive power flowing through the branch connecting node i and node j, v i (t) is the three-phase voltage vector of node i, i ij (t) is the three-phase current vector of node i, U OLTC,j (t) is the vector of the three-phase node voltage of node j connected to OLTC and the dot product of itself, S ij (t) is the three-phase power matrix of the branch connecting node i and node j, and its diagonal elements are Z ij is the three-phase impedance matrix of the branch, L ij is the matrix of the three-phase current vector of the branch multiplied by its own conjugate transpose, a OLTC,k is the transformation ratio of the kth gear of OLTC,

[0084] is the auxiliary vector introduced to handle the bilinear term, 1∈R 3×1 is a vector with elements equal to 1, U max =[1.05,1.05,1.05] T ,U min =[0.95,0.95,0.95] T ,τ min / τ max It is the minimum / maximum setting of the voltage regulator.

[0085] It can be found that the nonlinearity in (9) is caused by (10). By relaxing (10), it is transformed into (11):

[0086]

[0087] Among them U i,m,n (t),l ij,m,n (t),S ij,m,n (t) are matrices U i (t), L ij (t), S ij The element at row m and column n of (t).

[0088] Modeling uncertainties in distributed PV and loads.

[0089] Assuming that the uncertainties of distributed photovoltaics and loads obey Gaussian distribution and are independent and identically distributed, the uncertainty modeling is as follows:

[0090]

[0091] in, and represents the uncertain part, N(0,σ 2) means the mean is 0 and the variance is σ 2 Gaussian distribution. Represents the node set connected to distributed photovoltaics, Represents the set of nodes connected to the load. Therefore, there is the following relationship:

[0092]

[0093] Define the index M(t) that maximizes the apparent power in a certain period:

[0094]

[0095] Where θ is the power factor angle, is the amplitude of the apparent power output on TDI; is the amplitude of the active power output on TDI; is the amplitude of reactive power output on TDI; t1 and t2 are the start and end times of the study period. We can show that by decoupling the power factor angle and Trends over time.

[0096] The variables in the previous model are divided into three categories:

[0097] Uncertain variables

[0098] Independent Variable

[0099] and the dependent variable

[0100] Therefore, the FDN continuous time TDI flexibility evaluation model is as follows:

[0101]

[0102] Among them A P0 ,A Q0 ,A,B,F,C,D,G,H are the coefficient matrices of the equations and inequalities obtained by reorganizing (9)-(12), and c,d,h,w are the coefficient vectors. It should be noted that the model assumes that there is no higher-order uncertainty in the second-order cone constraints. Go to step 2.

[0103] Step 2: Model (14) is an infinite-dimensional non-convex model with uncertainty and cannot be solved. Step 2 deals with model uncertainty.

[0104] Chance constraints are used to handle uncertainty in the model, assuming that the probability of constraint violation is α:

[0105] (1) For equality constraints, such as Ax(t)+By(t)+Fu=c, since the mean of the uncertain parameters is 0, the equality constraint becomes Ax(t)+By(t)=c. The same is true for other equality constraints containing uncertainty.

[0106] (2) For inequality constraints, such as Cx(t)+Dy(t)+Gu≤d, the chance constraint form is It can be transformed into the deterministic inequality constraint in (15):

[0107] Cx(t)+Dy(t)≤d-Ψ -1 (1-α)·σ·ξ(15)

[0108] Among them Ψ -1 (·) is the inverse cumulative distribution function of the Gaussian distribution, and σ and ξ are defined as follows:

[0109]

[0110] where ξ i is the i-th element of ξ, and the same is true for the other inequality constraints containing uncertainty;

[0111] (3) For uncertain second-order cone constraints, such as ‖Hy(t)‖2≤h T y(t)+w T u, written as a chance constraint It can be transformed into the deterministic second-order cone constraint in (17):

[0112] ‖Hy(t)‖2≤h T y(t)-Ψ -1 (1-α)·σ·||w||1(17)

[0113] The same goes for the other second-order cone constraints containing uncertainties.

[0114] After processing, the deterministic model can be obtained as shown in (18):

[0115]

[0116]

[0117] Go to step 3.

[0118] Step 3: Step 3 designs a suitable solution algorithm for model (18).

[0119] For the non-convex model caused by parameter θ, we discretize θ and discretize the continuous θ into a set Θ = {θ k}, for example, it can make θ k is an equally divided point on the circle, for each θ kSolve the corresponding problems respectively.

[0120] To deal with the continuous time problem of the model, the fourth-order Bernstein polynomial is used to transform the continuous time model. Assume that the time period of the study is [t1, t2], which is divided into m time periods, and the starting time of each time period is t m =t1+(m-1)T. Subscripts B0, B1, B2 and B3 represent the four control coefficients of the physical quantity in one cycle, and subscript m represents the time period of the control coefficient. Bernstein polynomials are very common for the transformation of equations, inequalities and integral forms, which will not be introduced here. Only the transformation of second-order cone constraints is given separately.

[0121] For the second-order cone constraint a 2 +b 2 ≤cd, which can be transformed into the following form through the Bernstein polynomial:

[0122]

[0123] where a Bj ,b Bj ,c Bj ,d Bj is the jth Bernstein polynomial coefficient of abcd.

[0124] After transformation, (18) is transformed into m sub-problems, each of which is in the form of (19):

[0125]

[0126] The optimization variable x in the model B,m =[x B0,m ;x B1,m ;x B2,m ;x B3,m ], the model parameters in (19) are obtained by replacing the parameters of Bernstein polynomials in (18) one by one, H1 and H2 are the coefficient matrices after the second-order cone constraint transformation, C B,m is the constant matrix after the second-order cone constraint transformation, y B,m =[y B0,m ;y B1,m ;y B2,m ;y B3,m ].

[0127] At this point the subproblem is transformed into a mixed integer second-order cone optimization and can be solved. After solving it, go to step 4.

[0128] Step 4: At a specific θ kUnder this condition, all subproblems m are solved. After the solution is obtained, the specific θ can be obtained through the inverse transformation of Bernstein polynomials. k Next Combined with θ k and θ k +π, we can get a θ k - Slice (TDI flexibility is cut along the time axis), with upper and lower flexibility boundaries of θ k and θ k +π for According to (20), we can get

[0129]

[0130] All theta k -slices and their affine combinations form a tubular region, which we consider to be the continuous-time TDI flexibility at a specific time obtained by the present invention. By selecting a specific t, the PQ-TDI flexibility at any time can also be obtained.

[0131] In fact, under certain generation and load conditions, the FDN may not be able to achieve any power factor output on the TDI by adjusting its own generation and flexibility equipment. This may cause some θ corresponding to equation (19) to be unsolvable. Therefore, in general, the TDI flexibility range at a specific moment is not necessarily convex and may have gaps. Go to step 5.

[0132] Step 5: Note that in real scheduling operations, TSOs may also pay attention to the adjustable range of PQ decoupling of FDN. A diagram that can intuitively display the range of such decoupled PQ flexibility will help TSOs make appropriate scheduling decisions. Therefore, here we provide an algorithm for visualizing the decoupled PQ flexibility, making it easier for TSOs to utilize this flexibility.

[0133] The core idea of ​​the algorithm is:

[0134] At a specific moment, we first select a suitable initial point. Regarding the selection of the initial point, we consider the following three situations: Figure 2 As shown:

[0135] (a) TDI flexibility exists at any θ, initial point:

[0136]

[0137] (b) There exists θ such that TDI flexibility does not exist, and the largest region satisfies maxθ k -minθ k ≤π, then:

[0138]

[0139] (c) There exists θ such that TDI flexibility does not exist, and the largest region satisfies maxθ k -minθ k >π, then:

[0140]

[0141] After selecting the initial point, the decoupling flexibility area is expanded according to certain rules until there is no possibility of further expansion in any direction.

[0142] The specific implementation process of the algorithm is as follows. After selecting a suitable initial point, update the expansion amount P1, P2, Q1, Q2 in the four directions; calculate whether the vertex of the domain after expansion is within the boundary Ω. If so, continue to expand and perform new calculations. If not, return the vertex to the last position and check whether the step size is less than the given value. If not, reduce the step size to one tenth and continue to expand. If so, the direction is fixed and no further expansion is continued; if all directions are no longer expanded, the domain is fixed and the results are output.

[0143] The method of this embodiment introduces a new concept of continuous-time TDI flexibility evaluation for three-phase unbalanced FDN and establishes its mathematical model. This model can not only accurately capture the real-time situation of FDN operation, but also obtain reliable and maximum TDI flexibility evaluation by fully coordinating the multi-source flexibility from DG, ESS and SVC, and utilizing SOP. In addition, uncertainty is dealt with by adopting chance constraints to ensure the safety of FDN when providing flexibility. The continuous-time TDI flexibility calculation model and visualization method proposed in the present invention can ensure that TSO can fully utilize the potential of FDN without affecting the safety of FDN, thereby providing reliable support for the upper power grid.

[0144] Embodiment 2

[0145] This embodiment discloses a flexible distribution network active and reactive power combined continuous-time flexibility assessment system.

[0146] Flexible distribution network active and reactive power combined continuous time flexibility assessment system, including:

[0147] The flexible distribution network model building module is configured to: model the flexible distribution network;

[0148] The flexibility assessment model building module is configured to: relax the nonlinear terms in the flexible distribution network model, model the uncertainty of distributed photovoltaic power generation equipment and loads, and define indicators that can maximize the apparent power in a certain period and maximize the PQ output in the same period, so as to obtain the active and reactive joint continuous time flexibility assessment model of the flexible distribution network;

[0149] The uncertainty processing module is configured to: use chance constraints to process the uncertainty in the flexible distribution network active and reactive joint continuous time flexibility evaluation model to obtain a deterministic flexible distribution network active and reactive joint continuous time flexibility evaluation model;

[0150] The solution module is configured to: transform the deterministic flexible distribution network active and reactive combined continuous-time flexibility evaluation model into multiple discrete sub-problems, solve all the sub-problems, and finally obtain the active and reactive combined continuous-time flexibility at any time.

[0151] Those skilled in the art should understand that the modules or steps of the present invention described above can be implemented by a general-purpose computer device, and optionally, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device. The present invention is not limited to any specific combination of hardware and software.

[0152] Although the above describes the specific implementation mode of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without creative work are still within the scope of protection of the present invention.

Claims

1. A method for evaluating the continuous-time flexibility of active and reactive power combined in a flexible distribution network, characterized in that: The following steps are involved: Modeling of flexible distribution networks; The nonlinear terms in the flexible distribution network model are relaxed, the uncertainty of distributed photovoltaic power generation equipment and loads is modeled, and the indicators that can maximize the apparent power in a certain period and the PQ output in the same period are defined to obtain the active and reactive joint continuous time flexibility evaluation model of the flexible distribution network; Chance constraints are used to deal with the uncertainty in the flexible distribution network active and reactive joint continuous time flexibility evaluation model, and a deterministic flexible distribution network active and reactive joint continuous time flexibility evaluation model is obtained. The deterministic flexible distribution network active and reactive combined continuous-time flexibility evaluation model is transformed into multiple discrete sub-problems, all sub-problems are solved, and finally the active and reactive combined continuous-time flexibility at any time is obtained; The variable types are divided into uncertain variables u, independent variables x(t) and dependent variables y(t), and the active and reactive joint continuous-time flexibility evaluation model of the flexible distribution network is obtained: Among them A P0 ,A Q0 ,A,B,F,C,D,G,H are coefficient matrices, c,d,h,w are coefficient vectors; They are the auxiliary variables introduced by linearizing the square of the node voltage amplitude of the smart inverter and its reactive output, relaxing the bilinear term constraint of the capacitor bank, and relaxing the bilinear term constraint of the OLTC; Φ={a,b,c} represents each phase; SOS-2 is a variable; θ is the power factor angle; is the amplitude of the apparent power output on TDI.

2. The method for evaluating the active and reactive power combined continuous time flexibility of a flexible distribution network according to claim 1, characterized in that: Distributed photovoltaic power generation equipment, loads, soft switches, energy storage, static VAR compensators and capacitor banks in the flexible distribution network are considered and modeled separately to achieve modeling of the flexible distribution network.

3. The method for evaluating the active and reactive power combined continuous time flexibility of a flexible distribution network according to claim 2, characterized in that: Distributed photovoltaic power generation equipment is modeled by introducing SOS-2 variables and linearizing the quadratic constraints using inner polygon approximation. The soft switch is modeled by considering the power balance constraint, reactive power limitation and capacity limitation of the soft switch; Modeling energy storage by considering energy storage charging and discharging constraints and complementary relaxation constraints; The capacitor bank is modeled using McCormick relaxation to handle the bilinear terms introduced by capacitor bank modeling.

4. The method for evaluating the active and reactive power combined continuous time flexibility of a flexible distribution network according to claim 1, characterized in that: The index that can maximize the apparent power in a certain period and the PQ output in the same period is defined as M(t): Where θ is the power factor angle; is the amplitude of the apparent power output on TDI; is the amplitude of the active power output on TDI; is the amplitude of reactive power output on TDI; t1 and t2 are the start and end times of the study period.

5. The method for evaluating the active and reactive power combined continuous time flexibility of a flexible distribution network according to claim 1, characterized in that: Chance constraints are used to deal with the uncertainty in the continuous-time flexibility assessment model of active and reactive power combined with flexible distribution network, specifically: Set the uncertain parameters in the equality constraints containing uncertain parameters to 0; Convert inequality constraints with uncertain parameters into deterministic inequality constraints; The second-order cone constraints with uncertain parameters are transformed into deterministic second-order cone constraints.

6. The method for evaluating the active and reactive power combined continuous time flexibility of a flexible distribution network according to claim 1, characterized in that: The fourth-order Bernstein polynomial is used to transform the deterministic flexible distribution network active and reactive joint continuous-time flexibility assessment model into multiple discrete sub-problems.

7. The method for evaluating the active and reactive power combined continuous time flexibility of a flexible distribution network according to claim 1, characterized in that: Solve the sub-problems, including: Discretize the continuous θ into a set Θ = {θ k }, at a specific θ k Next, solve all sub-problems; After the solution is obtained, the specific θ is obtained by inverse transformation of Bernstein polynomial k Next Combined with θ k and θ k +π, we get a θ k -slice; for According to the following formula, we can get Through all the θ k -slices and their affine combinations to obtain the continuous-time flexibility of active and reactive joints within a specific time.

8. The method for evaluating the active and reactive power combined continuous time flexibility of a flexible distribution network according to claim 7, characterized in that: For the case where the active and reactive combined continuous time flexibility has been obtained, the decoupled PQ flexibility is solved by visual decoupling PQ flexibility: At a specific moment, choose a suitable initial point; After selecting the initial point, the decoupling flexibility area is expanded according to certain rules until there is no possibility of further expansion in any direction; Among them, the selection of the initial point needs to consider that the active and reactive joint continuous time flexibility exists in any θ, there is θ such that the active and reactive joint continuous time flexibility does not exist, and the largest area satisfies maxθ k -minθ k ≤π, and there exists θ such that the active and reactive joint continuous time flexibility does not exist, and the largest area satisfies maxθ k -minθ k >π three cases.

9. Flexible distribution network active and reactive power combined continuous time flexibility evaluation system, characterized by: include: The flexible distribution network model building module is configured to: model the flexible distribution network; The flexibility assessment model building module is configured to: relax the nonlinear terms in the flexible distribution network model, model the uncertainty of distributed photovoltaic power generation equipment and loads, and define indicators that can maximize the apparent power in a certain period and maximize the PQ output in the same period, so as to obtain the active and reactive joint continuous time flexibility assessment model of the flexible distribution network; The variable types are divided into uncertain variables u, independent variables x(t) and dependent variables y(t), and the active and reactive joint continuous-time flexibility evaluation model of the flexible distribution network is obtained: Among them A P0 ,A Q0 ,A,B,F,C,D,G,H are coefficient matrices, c,d,h,w are coefficient vectors; They are the auxiliary variables introduced by linearizing the square of the node voltage amplitude of the smart inverter and its reactive output, relaxing the bilinear term constraint of the capacitor bank, and relaxing the bilinear term constraint of the OLTC; φ={a,b,c} represents each phase; SOS-2 is a variable; θ is the power factor angle; is the amplitude of the apparent power output on TDI; The uncertainty processing module is configured to: use chance constraints to process the uncertainty in the flexible distribution network active and reactive joint continuous time flexibility evaluation model to obtain a deterministic flexible distribution network active and reactive joint continuous time flexibility evaluation model; The solution module is configured to: transform the deterministic flexible distribution network active and reactive combined continuous-time flexibility evaluation model into multiple discrete sub-problems, solve all the sub-problems, and finally obtain the active and reactive combined continuous-time flexibility at any time.

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