CN111509784B - Uncertainty-considered virtual power plant robust output feasible region identification method and device - Google Patents

Abstract

The invention discloses a method and device for identifying a feasible region of a robust output of a virtual power plant considering uncertainty, wherein the method includes: equalizing an active distribution network with distributed generation and flexible loads as an active power and a flexible load. A virtual power plant with adjustable reactive power output; construct a safe operation feasible region of the virtual power plant considering the uncertainty of renewable energy output and power load; project the safe operation feasible region of the virtual power plant into the robust output feasible region of the virtual power plant through constraint aggregation; The vertices of the feasible region of the robust output of the virtual power plant are identified by vertex enumeration and column constraint generation algorithm, and then the linear inequality set describing the feasible region is obtained. The method realizes that uncertainty factors are embedded in the identification of the output feasible region of the virtual power plant, which can ensure the output feasible region where the virtual power plant can operate safely under any uncertain disturbance, which is conducive to promoting the efficient use of distributed generation resources. , Conducive to ensuring the safe and reliable operation of the power system with distributed power sources.

Description

Method and device for identification of feasible region of robust output of virtual power plant considering uncertainty

technical field

The invention relates to the technical field of power system dispatching operation, in particular to a method and device for identifying a feasible region of a robust output of a virtual power plant that takes into account uncertainty.

Background technique

With the continuous development of the economy, the power load of cities, towns and industrial parks has grown rapidly. Distributed photovoltaics, wind power, energy storage, small cogeneration units, biomass power generation and other distributed power sources are connected to the distribution network in large numbers due to their high efficiency, economy and environmental protection. The continuous access of distributed power sources can not only supply local loads, but also send power through the distribution network when new energy power generation increases and local loads are low, providing support for loads in other areas of the transmission network. At the same time, distributed power cluster control can provide more adjustment means for grid scheduling operation. Therefore, the distribution network with a large number of distributed power sources is gradually developing into a virtual power plant with two-way energy exchange with the power grid.

The virtual power plant aggregates the massive distributed resources in the distribution network to participate in the optimal dispatching operation of the transmission network, and can provide active and reactive power adjustment flexibility for the operation of the transmission network. Its typical operation mode is that the transmission network dispatching agency optimizes and decides the active and reactive power output of the virtual power plant, and the virtual power plant controls the internal distributed power generation to execute the dispatching result according to the dispatching result of the transmission network. The key for virtual power plants to participate in the optimal dispatching operation of transmission grid is to calculate the output feasible region of virtual power plants, that is, the active and reactive output ranges of virtual power plants that do not violate the safe operation constraints of distribution systems and distributed power sources. However, the output of intermittent distributed power sources such as distributed photovoltaics and distributed wind power has significant uncertainty and volatility, and the active and reactive loads in the distribution network also have uncertainties. If the output feasible region of the virtual power plant is only calculated based on the point prediction results, the dispatching result may not be executed for the virtual power plant due to the fluctuation of the output of the distributed power supply and the error of the load forecast, which will cause the power shortage of the distribution network, the over-limit of the distribution network voltage, Safety issues such as power flow exceeding the limit of the distribution network.

The existing literature and technology are still unable to consider the uncertainty of distributed power output and power load in the virtual power plant output feasible domain calculation, and cannot ensure that the virtual power plant can operate safely and reliably under any uncertainty fluctuations.

SUMMARY OF THE INVENTION

The present invention aims to solve one of the technical problems in the related art at least to a certain extent.

Therefore, an object of the present invention is to propose a method for identifying the feasible region of the robust output of a virtual power plant that takes into account the uncertainty. The output feasible region in which virtual power plants can operate safely under any uncertain disturbance is conducive to promoting the efficient use of distributed generation resources and ensuring the safe and reliable operation of power systems with distributed generation.

Another object of the present invention is to provide a feasible region identification device for a robust output of a virtual power plant that takes into account uncertainty.

In order to achieve the above object, an embodiment of the present invention proposes a method for identifying a feasible region of a robust output of a virtual power plant that takes into account uncertainty, including the following steps: acquiring operating parameters of the internal power distribution system of the virtual power plant; according to the operating parameters Construct a feasible domain model for safe operation of virtual power plants that takes into account uncertainty, and obtain a safe operation constraint coefficient matrix; build a virtual power plant operation uncertainty set according to the operating parameters, and operate the virtual power plant according to the safe operation constraint coefficient matrix and the virtual power plant. The robust output feasible region model of the virtual power plant is constructed based on the uncertain set; the feasible region of the virtual power plant robust output is identified by vertex enumeration, and the vertex identification problem of the feasible region is solved by the column constraint generation method.

The method for identifying the feasible region of the robust output of the virtual power plant considering the uncertainty in the embodiment of the present invention considers uncertain factors in the calculation of the feasible region of the virtual power plant output through the method of uncertainty set and robust optimization. The feasible region of robust output of virtual power plant is identified, and the range of active and reactive power output that virtual power plant can operate safely under any uncertain disturbance is explicitly described, which can ensure that any dispatching result in the feasible region is under the disturbance of any uncertain variable. It does not violate the safe operation constraints inside the virtual power plant, which is beneficial to improve the safety and economy of the power system with distributed power generation.

In addition, the method for identifying the feasible region of the robust output of the virtual power plant according to the above embodiments of the present invention may also have the following additional technical features:

Further, in an embodiment of the present invention, the operating parameters include one or more of distribution network parameters, distributed power generation operating parameters and predicted parameters.

Further, in an embodiment of the present invention, the virtual power plant operation uncertainty set is:

分别是第k个不确定变量的置信区间的上界和下界；

是不确定变量置信区间的中点，即

Δw是不确定变量相对置信区间中点的偏移量，Δw_k是Δw的第k个元素；N_w是不确定变量的数量；Γ是不确定预算，取值为0～N_w的正整数；Γ限制了不确定集中最多可同时偏离预测区间中心的不确定变量的数量，进而控制了不确定集的大小。Among them, w is the uncertain variable of virtual power plant operation, including active power load, reactive power load, intermittent distributed resource active power output,

are the upper and lower bounds of the confidence interval for the k-th uncertain variable, respectively;

is the midpoint of the confidence interval for the uncertain variable, i.e.

Δw is the offset of the uncertain variable relative to the midpoint of the confidence interval, Δw _k is the kth element of Δw; N _w is the number of uncertain variables; Γ is the uncertainty budget, a positive integer from 0 to N _w ;Γ limits the number of uncertain variables in the uncertainty set that can deviate from the center of the prediction interval at the same time, thereby controlling the size of the uncertainty set.

Further, in an embodiment of the present invention, identifying the feasible region of the robust output of the virtual power plant through vertex enumeration includes: searching for the vertices of the feasible region of the robust output of the virtual power plant along different directions, and using the searched An approximate polygon is constructed from the vertices of the approximate polygon; the vertex search direction is updated by the normal vector of each boundary of the approximate polygon, and the remaining vertices of the virtual power plant robust output feasible region are searched; the above process is repeated to obtain the virtual power plant robust output feasible region by calculation.

Further, in an embodiment of the present invention, by solving the two-stage tunable robust optimization problem, the initial vertex set and initial approximate polygon of the robust output feasible region of the virtual power plant are calculated:

Among them, F(x,w)={(y,s)|By-s≤d-Ax-Cw,s≥0}, s is a slack variable, 1 represents a column vector with all 1 elements; M ₀ is a A positive real number far greater than 1, its typical value can be 1000 times of the transformer capacity of the distribution gateway; η _m is the objective function coefficient of the optimization problem, representing the search direction; η _m takes the values (1, 1), ( 1,-1), (-1,1), (-1,-1); the optimal solution of the corresponding optimization problem is denoted as v _m , which is the m-th initial vertex; the set of feasible region vertices is initialized as V ₀ ={v ₁ , v ₂ , v ₃ , v ₄ }; denote the set of equations of each boundary of the polygon formed by the initial vertices as H ^T ; calculate the center of the polygon formed by the points in V ₀ , namely v ₀ =(v ₁ +v ₂ +v ₃ +v ₄ )/4.

In order to achieve the above object, another embodiment of the present invention provides a feasible region identification device for a robust output of a virtual power plant that takes into account uncertainty, including: an acquisition module for acquiring operating parameters of the internal power distribution system of the virtual power plant; a building module for constructing a feasible domain model for safe operation of the virtual power plant considering the uncertainty according to the operating parameters, and obtaining a safe operation constraint coefficient matrix; a second building module for constructing a virtual power plant operation uncertainty matrix according to the operating parameters determining the set, and constructing a feasible region model for the robust output of the virtual power plant according to the safe operation constraint coefficient matrix and the virtual power plant operation uncertainty set; the identification module is used to identify the feasible region of the virtual power plant robust output through vertex enumeration, and The feasible region vertex identification problem is solved by the column constraint generation method.

The device for identifying the feasible region of the robust output of the virtual power plant according to the embodiment of the present invention considers the uncertain factors in the calculation of the feasible region of the output of the virtual power plant through the method of uncertainty set and robust optimization. The feasible region of robust output of virtual power plant is identified, and the range of active and reactive power output that virtual power plant can operate safely under any uncertain disturbance is explicitly described, which can ensure that any dispatching result in the feasible region is under the disturbance of any uncertain variable. It does not violate the safe operation constraints inside the virtual power plant, which is beneficial to improve the safety and economy of the power system with distributed power generation.

In addition, the device for identifying the feasible region of the robust output of the virtual power plant according to the above embodiments of the present invention may also have the following additional technical features:

Further, in an embodiment of the present invention, the operating parameters include one or more of distribution network parameters, distributed power generation operating parameters and predicted parameters.

Further, in an embodiment of the present invention, the virtual power plant operation uncertainty set is:

分别是第k个不确定变量的置信区间的上界和下界；

是不确定变量置信区间的中点，即

Δw是不确定变量相对置信区间中点的偏移量，Δw_k是Δw的第k个元素；N_w是不确定变量的数量；Γ是不确定预算，取值为0～N_w的正整数；Γ限制了不确定集中最多可同时偏离预测区间中心的不确定变量的数量，进而控制了不确定集的大小。Among them, w is the uncertain variable of virtual power plant operation, including active power load, reactive power load, intermittent distributed resource active power output,

are the upper and lower bounds of the confidence interval for the k-th uncertain variable, respectively;

is the midpoint of the confidence interval for the uncertain variable, i.e.

Δw is the offset of the uncertain variable relative to the midpoint of the confidence interval, Δw _k is the kth element of Δw; N _w is the number of uncertain variables; Γ is the uncertainty budget, a positive integer from 0 to N _w ;Γ limits the number of uncertain variables in the uncertainty set that can deviate from the center of the prediction interval at the same time, thereby controlling the size of the uncertainty set.

Further, in an embodiment of the present invention, the identification module is further configured to search for the vertices of the feasible region of the robust output of the virtual power plant along different directions, and use the vertices obtained by the search to construct an approximate polygon; The normal vector of each boundary of the polygon updates the vertex search direction, searches for the remaining vertices of the virtual power plant robust output feasible region, repeats the above process, and calculates the virtual power plant robust output feasible region.

Further, in an embodiment of the present invention, by solving the two-stage tunable robust optimization problem, the initial vertex set and initial approximate polygon of the robust output feasible region of the virtual power plant are calculated:

Among them, F(x,w)={(y,s)|By-s≤d-Ax-Cw,s≥0}, s is a slack variable, 1 represents a column vector with all 1 elements; M ₀ is a A positive real number far greater than 1, its typical value can be 1000 times of the transformer capacity of the distribution gateway; η _m is the objective function coefficient of the optimization problem, representing the search direction; η _m takes the values (1, 1), ( 1,-1), (-1,1), (-1,-1); the optimal solution of the corresponding optimization problem is denoted as v _m , which is the m-th initial vertex; the set of feasible region vertices is initialized as V ₀ ={v ₁ , v ₂ , v ₃ , v ₄ }; denote the set of equations of each boundary of the polygon formed by the initial vertices as H ^T ; calculate the center of the polygon formed by the points in V ₀ , namely v ₀ =(v ₁ +v ₂ +v ₃ +v ₄ )/4.

Additional aspects and advantages of the present invention will be set forth, in part, from the following description, and in part will be apparent from the following description, or may be learned by practice of the invention.

Description of drawings

The above and/or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of embodiments taken in conjunction with the accompanying drawings, wherein:

FIG. 1 is a flowchart of a method for identifying a feasible region for a robust output of a virtual power plant according to an embodiment of the present invention;

2 is a flowchart of a method for identifying a feasible region of a virtual power plant robust output that takes into account uncertainty according to an embodiment of the present invention;

FIG. 3 is a schematic structural diagram of an apparatus for identifying a feasible region for a robust output of a virtual power plant according to an embodiment of the present invention.

Detailed ways

The following describes in detail the embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals refer to the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary, and are intended to explain the present invention and should not be construed as limiting the present invention.

The purpose of the present invention is to fill the technical gap that the uncertainty of distributed power generation output and power load cannot be considered in the calculation of the output feasible region of the virtual power plant, and proposes a method for identifying the feasible region of the virtual power plant robust output considering the uncertainty and the device, constructing the feasible region of the robust output of the virtual power plant considering the uncertainty through the method of uncertainty set and robust optimization, and identifying the feasible region of the robust output of the virtual power plant through the vertex enumeration algorithm and the column constraint generation algorithm, and outputting the virtual power plant robust output feasible region. The explicit inequality constraints of the active and reactive power output of the power plant can ensure that any dispatching results in the feasible region will not violate the safe operation constraints inside the virtual power plant under the disturbance of any uncertain variables, and give full play to the economic and environmental advantages of distributed generation. At the same time, it ensures the safe and reliable operation of the power system.

The method and device for identifying the feasible region of a robust output of a virtual power plant according to the embodiments of the present invention will be described below with reference to the accompanying drawings. Robust output feasible region identification method for power plants.

FIG. 1 is a flowchart of a method for identifying a feasible region for a robust output of a virtual power plant in consideration of uncertainty according to an embodiment of the present invention.

As shown in Figure 1, the method for identifying the feasible region of the robust output of the virtual power plant considering the uncertainty includes the following steps:

In step S101, the operation parameters of the internal power distribution system of the virtual power plant are obtained.

Wherein, in an embodiment of the present invention, the active distribution network containing distributed generation and flexible loads is equivalent to a virtual power plant with adjustable active and reactive power output; operating parameters include distribution network parameters, distribution One or more of the power generation operating parameters and forecast parameters.

In step S102, a feasible region model for safe operation of the virtual power plant is constructed according to the operating parameters, and a safe operation constraint coefficient matrix is obtained.

It can be understood that the embodiment of the present invention constructs a safe operation feasible region of the virtual power plant that takes into account the uncertainty of renewable energy output and power load according to distribution network parameters and distributed power generation operation parameters.

In step S103, the virtual power plant operation uncertainty set is constructed according to the operation parameters, and the virtual power plant robust output feasible domain model is constructed according to the safe operation constraint coefficient matrix and the virtual power plant operation uncertainty set.

It can be understood that the embodiment of the present invention constructs the virtual power plant operation uncertainty set according to the predicted parameters, and projects the virtual power plant safe operation feasible region into the virtual power plant robust output feasible region through constraint aggregation.

In step S104, the feasible region of the robust output of the virtual power plant is identified through vertex enumeration, and the feasible region vertex identification problem is solved through the column constraint generation method.

It can be understood that the vertices of the feasible region of the robust output of the virtual power plant are identified by the vertex enumeration and column constraint generation algorithm, and then the linear inequality set describing the feasible region is obtained.

To sum up, the method of the embodiment of the present invention realizes that uncertainty factors are embedded in the identification of the output feasible region of the virtual power plant, which can ensure the output feasible region in which the virtual power plant can operate safely under any uncertain disturbance, which is conducive to promoting The efficient use of distributed power generation resources is conducive to ensuring the safe and reliable operation of the power system with distributed power generation.

In the following, the method for identifying the feasible region of the robust output of the virtual power plant considering the uncertainty will be described in detail with reference to Fig. 2. The details are as follows:

1) Obtain the operating parameters of the internal power distribution system of the virtual power plant, including:

1-1) A virtual power plant refers to the equivalent of an active distribution network with distributed power sources and a distribution energy management system as a power plant. Active and reactive power at the parallel interface of the electrical system;

1-2) Obtain the basic operating parameters of the internal power distribution system of the virtual power plant, including:

1-2-1) Distribution network network parameters: distribution network connection topology, conductance of distribution lines and transformers, susceptance parameters, transmission capacity limits of distribution lines, upper and lower limits of node voltages allowed for safe operation;

1-2-2) Distributed power generation operating parameters: installed capacity, minimum active power output, and maximum power factor angle of each distributed power source within the distribution network;

1-2-3) Prediction parameters: Confidence interval for active load forecast, Confidence interval for reactive load forecast, Confidence interval for active output forecast of intermittent distributed generation

2) Build a feasible domain model for safe operation of virtual power plants that takes into account uncertainty, including:

2-1) According to the basic parameters of power distribution system operation in 1-2-1) and 1-2-2), write the constraints for safe operation of virtual power plants. The detailed expressions are as follows:

分别为配电线路的有功和无功潮流，b_ij、g_ij分别为配电线路串联电纳、串联电导，u_i、u_j分别为节点i和节点j电压幅值的平方，θ_i、θ_j分别为节点i和节点j电压的相角，

是配电网节点编号集合，

表示与节点i通过配电线路直接相连的节点编号的集合；where Equation 1 and Equation 2 are the active and reactive power flows of the distribution line ij based on the reduced-order power flow equation, respectively,

are the active and reactive power flows of the distribution line, respectively, b _ij and g _ij are the series susceptance and series conductance of the distribution line, respectively, u _i and u _j are the squares of the voltage amplitudes at node i and node j, respectively, θ _i , θ _j is the phase angle of node i and node j voltage, respectively,

is the set of distribution network node numbers,

Represents the set of node numbers directly connected to node i through the distribution line;

分别是节点i处分布式电源的有功出力和无功出力；

分别是节点i的有功负荷和无功负荷；P₀、Q₀分别表示虚拟电厂的有功出力和无功出力；Equation 3 and Equation 4 are the active and reactive power balance equations of each node of the distribution network, respectively; where

are the active power output and reactive power output of the distributed power source at node i, respectively;

are the active load and reactive load of node i, respectively; P ₀ and Q ₀ represent the active and reactive power output of the virtual power plant, respectively;

是该配电线路的传输容量极限，N_s是对传输容量约束进行线性化分段表示的分段数量；Equation 5 expresses the transmission capacity constraint of distribution line ij, where

is the transmission capacity limit of the distribution line, and _Ns is the number of segments that linearize the segmental representation of the transmission capacity constraint;

是该配电变压器的容量上限；Equation 6 represents the capacity constraint of the distribution transformer at the distribution gateway port, where

is the upper capacity limit of the distribution transformer;

v _i分别为节点i允许的电压幅值上限和下限；Equation 7 represents the upper and lower limit constraints of the voltage of each node in the distribution network, where

v _i are the upper and lower limits of the voltage amplitude allowed by node i, respectively;

是节点i的分布式电源的有功出力上下限；

是分布式电源i的最大功率因数角；

是分布式电源i的容量上限；Equation 8, Equation 9 and Equation 10 describe the upper and lower limit constraints, power factor constraints and capacity constraints of the distributed power generation i respectively; where

is the upper and lower limit of the active power output of the distributed power source of node i;

is the maximum power factor angle of the distributed power source i;

is the upper limit of the capacity of the distributed power source i;

为元素的列向量，即：

定义运行决策变量y为以u_i、θ_i、

为元素的列向量，即：

进而，可将约束(1)～(10)整理为如下形式：2-2) Define the coordination variable x as a column vector with P ₀ and Q ₀ as elements, that is, x=(P ₀ , Q ₀ ); define the uncertain variable w as

is a column vector of elements, that is:

Define the running decision variable y as u _i , θ _i ,

is a column vector of elements, that is:

Furthermore, constraints (1) to (10) can be organized into the following forms:

Ax+By+Cw≤d (11)

where A, B, and C are coefficient matrices, and d is a constant vector;

2-3) Construct the feasible region of safe operation of virtual power plant, denoted as Φ, which is the value set of variables (x, w, y) under the constraints of safe operation of virtual power plant, which can be expressed as:

Φ={(x,w,y)|Ax+By+Cw≤d} (12)

3) According to the prediction parameters of 1-2-3), construct the uncertainty set of virtual power plant operation, denoted as W; W is described by a set of linear inequalities about w, and the expression is as follows:

分别是第k个不确定变量的置信区间的上界和下界；

是不确定变量置信区间的中点，即

Δw是不确定变量相对置信区间中点的偏移量，Δw_k是Δw的第k个元素；N_w是不确定变量的数量；Γ是不确定预算，取值为0～N_w的正整数；Γ限制了不确定集中最多可同时偏离预测区间中心的不确定变量的数量，进而控制了不确定集的大小；in,

are the upper and lower bounds of the confidence interval for the k-th uncertain variable, respectively;

is the midpoint of the confidence interval for the uncertain variable, i.e.

Δw is the offset of the uncertain variable relative to the midpoint of the confidence interval, Δw _k is the kth element of Δw; N _w is the number of uncertain variables; Γ is the uncertainty budget, a positive integer from 0 to N _w ;Γ limits the number of uncertain variables in the uncertainty set that can deviate from the center of the prediction interval at the same time, thereby controlling the size of the uncertainty set;

4) Build a feasible domain model for robust output of virtual power plants, including:

4-1) The so-called robust output feasible region of the virtual power plant is a set of feasible values for the coordination variable x, satisfying any variable in the uncertain set W, which makes the safe operation feasible region Φ of the virtual power plant non-empty;

4-2) The mathematical representation of the feasible region of the robust output of the virtual power plant is as follows:

The virtual power plant robust output feasible region Ω defined according to formula 15 is a bounded region in two-dimensional space, and the virtual power plant output (P ₀ , Q ₀ ) corresponding to the point in the feasible region is disturbed by any uncertain variable can be executed in the power distribution system inside the virtual power plant without violating the safe operation constraints of the power distribution system;

5) Identify the feasible region of the robust output of the virtual power plant through vertex enumeration: this method searches for the vertices of the feasible region Ω of the robust output of the virtual power plant along different directions, and uses the searched vertices to construct an approximate polygon. The normal vector of , updates the search direction, and in this cycle, the feasible region of the robust output of the virtual power plant is calculated. The specific flow of the algorithm is described as follows:

5-1) By solving the following two-stage tunable robust optimization problem, calculate the initial vertex set and initial approximate polygon of the robust output feasible region of the virtual power plant:

where F(x,w)={(y,s)|By-s≤d-Ax-Cw,s≥0}, s is a slack variable, 1 represents a column vector with all 1 elements; M ₀ is a large A positive real number less than 1, its typical value can be 1000 times of the transformer capacity of the distribution gateway port, and its specific value does not affect the calculation results of the model; η _m is the objective function coefficient of the optimization problem, representing the search direction; for η _m Take the values (1,1), (1,-1), (-1,1), (-1,-1) in turn; solve problem (16) through the algorithm in 6); the optimal solution of the corresponding optimization problem Denote it as v _m , which is the mth initial vertex; initialize the set of feasible region vertices as V ₀ ={v ₁ ,v ₂ ,v ₃ ,v ₄ }; denote the set of equations of each boundary of the polygon formed by the initial vertices is H ^T ; calculate the center of the polygon formed by the points in V ₀ , namely v ₀ =(v ₁ +v ₂ +v ₃ +v ₄ )/4;

5-2) Initialize the newly added boundary set H ^N to be an empty set, that is

，该边界两端的顶点分别为v_m1、v_m2；通过6)中的算法求解问题(16)，将最优解记为v_m，最优值记为

5-3) Vertex search: search for the vertex of Ω by translating the boundary in ^HT along the direction away from _v0 ; for the mth boundary in ^HT , the equation of this boundary is

, the vertices at both ends of the boundary are respectively v _m1 and v _m2 ; solve problem (16) through the algorithm in 6), denote the optimal solution as v _m , and denote the optimal value as

5-4) Update vertex set: add the vertex to the feasible domain vertex set V;

5-5) Determine whether it is necessary to increase the boundary to be translated, as follows:

5-5-1) Calculate the improvement of vertex search in step 5-3) according to the following formula:

5-5-2) If Δg _m > 0, calculate the equations h _m1 and h _m2 of the straight line determined by v _m and v _m1 and v _m2 respectively, add them to the newly added boundary set H ^N , and go back to step 5-3) , use the m+1th boundary in H ^T for vertex search;

5-5-3) If Δg _m =0, go back to step 5-3), and use the m+1th boundary in ^HT to perform vertex search;

5-6) Judging whether the algorithm is terminated: After performing vertex search on all boundary equations in H ^T , judge whether the algorithm is terminated by judging whether H ^N is an empty set, namely:

，将待搜索边界集H^T更新为H^N，即H^T←H^N，并回到步骤5-3)对更新后的H^T中的边界进行顶点搜索；5-6-1) If

, update the boundary set to be searched H ^T to H ^N , that is, H ^T ← H ^N , and go back to step 5-3) to perform vertex search on the boundary in the updated H ^T ;

，算法终止；顶点集V中的顶点确定的多边形即为虚拟电厂鲁棒出力可行域的计算结果，该多边形的各边界不等式即为表征虚拟电厂鲁棒出力可行域的线性不等式约束；5-6-2) If

, the algorithm is terminated; the polygon determined by the vertices in the vertex set V is the calculation result of the feasible region of the robust output of the virtual power plant, and the boundary inequalities of the polygon are the linear inequality constraints that characterize the feasible region of the robust output of the virtual power plant;

6) Solve the feasible region vertex identification problem (16) by the column constraint generation method, which specifically includes:

，初始化极端场景集

，初始化迭代计数变量k＝06-1) Initialize extreme scenarios

, initialize the extreme scene set

, initialize the iteration count variable k=0

6-2) Solve the main robust optimization problem:

The problem is a linear programming problem, which can be solved by commercial solvers (such as CPLEX, GUROBI); after optimization, the optimal solution of problem (18) is

，求解鲁棒优化子问题：6-3) Given

, solve the robust optimization subproblem:

In order to facilitate the solution, the robust optimization subproblem can be further transformed into the following mixed integer linear programming problem through the duality principle:

，最优值为

where μ is the dual variable of (11); z ₊ , z _- are 0-1 variables that characterize the value of the uncertain variable, when z _{+ i} =1, z _-i =0, the uncertain variable w _i reaches the upper part of the prediction interval When z _{+ i} = 0, z _-i = 1, the uncertain variable w _i reaches the lower bound of the prediction interval; τ ₊ , τ _- are auxiliary decision variables; M is a large enough positive real number, and its value should not be less than The 1-norm of C, that is, M≥||C|| ₁ ; use commercial solvers (such as CPLEX, GUROBI) to solve the mixed integer linear programming problem, and the optimal solution is

, the optimal value is

：更新计数变量，k＝k+1；将

加入极端场景集合

，即

回到6-2)，继续执行算法；6-4) If

: update the count variable, k=k+1;

Join the extreme scene collection

,Right now

Go back to 6-2), continue to execute the algorithm;

，算法终止，此时的

即为问题(16)的解。6-5) If

, the algorithm terminates, at this time the

That is the solution of problem (16).

According to the method for identifying the feasible region of the robust output of the virtual power plant that takes into account the uncertainty proposed in the embodiment of the present invention, the uncertain factors are considered in the calculation of the feasible region of the output of the virtual power plant through the method of uncertainty set and robust optimization. Constructing and identifying the feasible region of the robust output of the virtual power plant, which explicitly describes the range of active and reactive power output that the virtual power plant can operate safely under any uncertain disturbance, which can ensure that any dispatching result in the feasible region is in any uncertain variable. It does not violate the safe operation constraints inside the virtual power plant under the disturbance of the distributed power generation, which is beneficial to improve the safety and economy of the power system with distributed power generation.

Next, a feasible region identification device for a robust output of a virtual power plant according to an embodiment of the present invention is described with reference to the accompanying drawings.

FIG. 3 is a schematic structural diagram of an apparatus for identifying a feasible region for a robust output of a virtual power plant according to an embodiment of the present invention.

As shown in FIG. 3 , the device 10 for identifying a feasible region for robust output of a virtual power plant considering uncertainty includes: an acquisition module 100 , a first construction module 200 , a second construction module 300 and an identification module 400 .

Wherein, the acquisition module 100 is used to acquire the operation parameters of the internal power distribution system of the virtual power plant; the first construction module 200 is used to construct a feasible domain model for the safe operation of the virtual power plant considering the uncertainty according to the operation parameters, and obtain a safe operation constraint coefficient matrix; The second construction module 300 is used to construct the virtual power plant operation uncertainty set according to the operating parameters, and to construct the virtual power plant robust output feasible domain model according to the safety operation constraint coefficient matrix and the virtual power plant operation uncertainty set; the identification module 400 is used to enumerate through the vertices The robust output feasible region of the virtual power plant is identified, and the feasible region vertex identification problem is solved by the column constraint generation method. The device 10 of the embodiment of the present invention realizes that uncertainty factors are embedded in the identification of the output feasible region of the virtual power plant, which can ensure the output feasible region where the virtual power plant can operate safely under any uncertain disturbance, which is conducive to promoting distributed power generation. The efficient use of power generation resources is conducive to ensuring the safe and reliable operation of power systems including distributed power sources.

Further, in one embodiment of the present invention, the operating parameters include one or more of distribution network parameters, distributed power generation operating parameters, and predicted parameters.

Further, in an embodiment of the present invention, the virtual power plant operation uncertainty set is:

分别是第k个不确定变量的置信区间的上界和下界；

是不确定变量置信区间的中点，即

Δw是不确定变量相对置信区间中点的偏移量，Δw_k是Δw的第k个元素；N_w是不确定变量的数量；Γ是不确定预算，取值为0～N_w的正整数；Γ限制了不确定集中最多可同时偏离预测区间中心的不确定变量的数量，进而控制了不确定集的大小。Among them, w is the uncertain variable of virtual power plant operation, including active power load, reactive power load, intermittent distributed resource active power output,

are the upper and lower bounds of the confidence interval for the k-th uncertain variable, respectively;

is the midpoint of the confidence interval for the uncertain variable, i.e.

Δw is the offset of the uncertain variable relative to the midpoint of the confidence interval, Δw _k is the kth element of Δw; N _w is the number of uncertain variables; Γ is the uncertainty budget, a positive integer from 0 to N _w ;Γ limits the number of uncertain variables in the uncertainty set that can deviate from the center of the prediction interval at the same time, thereby controlling the size of the uncertainty set.

Further, in an embodiment of the present invention, the identification module 400 is further configured to search for the vertices of the feasible region of the robust output of the virtual power plant along different directions, and use the searched vertices to construct an approximate polygon; The normal vector of , updates the vertex search direction, searches the remaining vertices of the virtual power plant robust output feasible region, repeats the above process, and calculates the virtual power plant robust output feasible region.

Further, in an embodiment of the present invention, by solving the two-stage tunable robust optimization problem, the initial vertex set and initial approximate polygon of the robust output feasible region of the virtual power plant are calculated:

Among them, F(x,w)={(y,s)|By-s≤d-Ax-Cw,s≥0}, s is a slack variable, 1 represents a column vector with all 1 elements; M ₀ is a A positive real number far greater than 1, its typical value can be 1000 times of the transformer capacity of the distribution gateway; η _m is the objective function coefficient of the optimization problem, representing the search direction; η _m takes the values (1, 1), ( 1,-1), (-1,1), (-1,-1); the optimal solution of the corresponding optimization problem is denoted as v _m , which is the m-th initial vertex; the set of feasible region vertices is initialized as V ₀ ={v ₁ , v ₂ , v ₃ , v ₄ }; denote the set of equations of each boundary of the polygon formed by the initial vertices as H ^T ; calculate the center of the polygon formed by the points in V ₀ , namely v ₀ =(v ₁ +v ₂ +v ₃ +v ₄ )/4.

It should be noted that the foregoing explanations of the embodiment of the method for identifying the feasible region of the robust output of the virtual power plant considering the uncertainty are also applicable to the device for identifying the feasible region of the robust output of the virtual power plant considering the uncertainty, It will not be repeated here.

According to the device for identifying the feasible region of the robust output of the virtual power plant that takes into account the uncertainty proposed in the embodiment of the present invention, the uncertain factors are considered in the calculation of the feasible region of the output of the virtual power plant through the method of uncertainty set and robust optimization. Constructing and identifying the feasible region of the robust output of the virtual power plant, which explicitly describes the range of active and reactive power output that the virtual power plant can operate safely under any uncertain disturbance, which can ensure that any dispatching result in the feasible region is in any uncertain variable. It does not violate the safe operation constraints inside the virtual power plant under the disturbance of the distributed power generation, which is beneficial to improve the safety and economy of the power system with distributed power generation.

In addition, the terms "first" and "second" are only used for descriptive purposes, and should not be construed as indicating or implying relative importance or implying the number of indicated technical features. Thus, a feature delimited with "first", "second" may expressly or implicitly include at least one of that feature. In the description of the present invention, "plurality" means at least two, such as two, three, etc., unless otherwise expressly and specifically defined.

In the description of this specification, description with reference to the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples", etc., mean specific features described in connection with the embodiment or example , structure, material or feature is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, those skilled in the art may combine and combine the different embodiments or examples described in this specification, as well as the features of the different embodiments or examples, without conflicting each other.

Although the embodiments of the present invention have been shown and described above, it should be understood that the above-mentioned embodiments are exemplary and should not be construed as limiting the present invention. Embodiments are subject to variations, modifications, substitutions and variations.

Claims (6)

1. A virtual power plant robust output feasible region identification method considering uncertainty is characterized by comprising the following steps:

acquiring operation parameters of an internal power distribution system of a virtual power plant;

according to the operation parameters, a virtual power plant safe operation feasible region model considering uncertainty is constructed, and a safe operation constraint coefficient matrix is obtained, wherein the construction of the virtual power plant safe operation feasible region model considering uncertainty specifically comprises the following steps: writing a safety operation constraint condition of a virtual power plant according to an operation basic parameter list of a power distribution system, wherein the expression is as follows:

wherein formula 1 and formula 2 are the active and reactive power flows of the distribution line ij based on the reduced order power flow equation,

respectively the active and reactive power flow of the distribution line, b_ij、g_ijRespectively series susceptance, series conductance, u, of the distribution line_i、u_jThe square of the voltage amplitude of node i and node j, θ_i、θ_jThe phase angles of the voltages at node i and node j respectively,

is a collection of numbers of nodes of the power distribution network,

representing a set of node numbers directly connected with the node i through the distribution line;

formula 3 and formula 4 are the active and reactive balance equations of each node of the distribution network respectively; in the formula P_i ^g、Q_i ^gRespectively the active output and the reactive output of the distributed power supply at the node i;

respectively the active load and the reactive load of the node i; p₀、Q₀Respectively representing active output and reactive output of the virtual power plant;

equation 5 represents the transmission capacity constraint of the distribution line ij, where

Is the transmission capacity limit, N, of the distribution line_sIs the number of segments that represent the transmission capacity constraint in a linearized segment;

equation 6 represents the distribution transformer capacity constraint at the distribution grid gateway, where

Is the upper limit of the capacity of the distribution transformer;

equation 7 represents the upper and lower voltage limits of each node of the distribution network, where

v _iRespectively an upper limit and a lower limit of the voltage amplitude allowed by the node i;

the active output upper and lower limit constraints, the power factor constraints and the capacity constraints of the distributed power supply i are respectively described in a formula 8, a formula 9 and a formula 10; in the formula

P _i ^gThe active output upper and lower limits of the distributed power supply of the node i;

is the maximum power factor angle of the distributed power source i;

is the upper limit of the capacity of the distributed power source i;

defining a coordination variable x as P₀、Q₀Is a column vector of elements, i.e. x ═ P₀,Q₀) (ii) a Defining an uncertain variable w as

Is a column vector of elements, i.e.:

defining the operation decision variable y as u_i、θ_i、P_i ^g、

Is a column vector of elements, i.e.:

the constraints (1) to (10) are arranged in the following form:

Ax+By+Cw≤d (11)

wherein A, B, C is a coefficient matrix and d is a constant vector;

constructing a virtual power plant safe operation feasible region, marking as phi, which is a value set of variables (x, w, y) under the virtual power plant safe operation constraint and is expressed as:

Φ＝{(x,w,y)|Ax+By+Cw≤d} (12)；

establishing a virtual power plant operation uncertain set according to the operation parameters, and establishing a virtual power plant robust output feasible region model according to the safe operation constraint coefficient matrix and the virtual power plant operation uncertain set, wherein the virtual power plant operation uncertain set is as follows:

wherein w is an uncertain variable of virtual power plant operation, comprising active power load, reactive power load, intermittent distributed resource active output,

upper and lower bounds, respectively, of a confidence interval for the kth uncertain variable;

is the midpoint of the confidence interval of the uncertain variable, i.e.

Δ w is the offset of the uncertain variable from the midpoint of the confidence interval, Δ w_kIs the kth element of Δ w; n is a radical of_wIs the number of uncertain variables; gamma is the uncertain budget and takes the value of 0-N_wA positive integer of (d); the gamma limits the number of uncertain variables which can deviate from the center of the prediction interval at most in the uncertain set, and further controls the size of the uncertain set; the method for constructing the robust output feasible region model of the virtual power plant specifically comprises the following steps: the robust output feasible region of the virtual power plant is a feasible value set of a coordinated variable x, and the condition that any variable in an uncertain set W is not null is met, so that the feasible region phi of the safe operation of the virtual power plant is not null; the robust output feasible region mathematical representation of the virtual power plant is as follows:

the robust output feasible region omega of the virtual power plant defined according to the formula is a bounded region in a two-dimensional spacePoint-corresponding virtual plant output (P) within the feasible region₀,Q₀) The method can be executed in a power distribution system in a virtual power plant under the disturbance of any uncertain variable without violating the safe operation constraint of the power distribution system; and

identifying the feasible region of virtual power plant robust output through vertex enumeration, and solving the problem of identifying the feasible region of vertex through a column constraint generation method, wherein identifying the feasible region of virtual power plant robust output through vertex enumeration comprises: searching vertexes of a feasible robust output domain of the virtual power plant along different directions, and constructing an approximate polygon by using the searched vertexes; updating the vertex searching direction through normal vectors of all boundaries of the approximate polygon, and searching other vertexes of a robust output feasible region of the virtual power plant; and repeating the process to calculate the feasible region of the robust output of the virtual power plant.

2. The method of claim 1, wherein the operating parameters comprise distribution network parameters, distributed generation operating parameters, and predictive parameters.

3. The method of claim 1, wherein the initial vertex set and the initial approximation polygon of the robust output feasible region of the virtual power plant are calculated by solving a two-stage adjustable robust optimization problem:

wherein, F (x, w) { (y, s) | By-s ≦ d-Ax-Cw, s ≧ 0}, s is a relaxation variable, and 1 represents a column vector with all elements being 1; m₀The real number is a positive real number larger than 1, and the typical value of the real number is 1000 times of the capacity of a gateway transformer of a power distribution network; eta_mIs an optimization problem objective function coefficient, representing the search direction; to eta_mSequentially taking values of (1,1), (1, -1), (-1, -1); the corresponding optimal solution of the optimization problem is recorded as v_mNamely the mth initial vertex; initializing a set of feasible domain vertices to V₀＝{v₁,v₂,v₃,v₄}; let the set of equations for each boundary of the polygon formed by the initial vertices be denoted as H^T(ii) a Calculating V₀The center of the polygon formed by the points in (1), i.e. v₀＝(v₁+v₂+v₃+v₄)/4。

4. A virtual power plant robust output feasible region identification device considering uncertainty is characterized by comprising the following components:

the acquisition module is used for acquiring the operation parameters of the internal power distribution system of the virtual power plant;

the first construction module is used for constructing a virtual power plant safe operation feasible region model considering uncertainty according to the operation parameters to obtain a safe operation constraint coefficient matrix, wherein the construction of the virtual power plant safe operation feasible region model considering uncertainty specifically comprises the following steps: writing a safety operation constraint condition of a virtual power plant according to an operation basic parameter list of a power distribution system, wherein the expression is as follows:

wherein formula 1 and formula 2 are the active and reactive power flows of the distribution line ij based on the reduced order power flow equation,

respectively the active and reactive power flow of the distribution line, b_ij、g_ijRespectively series susceptance, series conductance, u, of the distribution line_i、u_jThe square of the voltage amplitude of node i and node j, θ_i、θ_jThe phase angles of the voltages at node i and node j respectively,

is a collection of numbers of nodes of the power distribution network,

the representation is directly connected with the node i through a distribution lineA set of node numbers of;

formula 3 and formula 4 are the active and reactive balance equations of each node of the distribution network respectively; in the formula P_i ^g、

Respectively the active output and the reactive output of the distributed power supply at the node i;

respectively the active load and the reactive load of the node i; p₀、Q₀Respectively representing active output and reactive output of the virtual power plant;

equation 5 represents the transmission capacity constraint of the distribution line ij, where

Is the transmission capacity limit, N, of the distribution line_sIs the number of segments that represent the transmission capacity constraint in a linearized segment;

equation 6 represents the distribution transformer capacity constraint at the distribution grid gateway, where

Is the upper limit of the capacity of the distribution transformer;

equation 7 represents the upper and lower voltage limits of each node of the distribution network, where

v_iRespectively an upper limit and a lower limit of the voltage amplitude allowed by the node i;

the active output upper and lower limit constraints, the power factor constraints and the capacity constraints of the distributed power supply i are respectively described in a formula 8, a formula 9 and a formula 10; in the formula

P_i ^gThe active output upper and lower limits of the distributed power supply of the node i;

is the maximum power factor angle of the distributed power source i;

is the upper limit of the capacity of the distributed power source i;

defining a coordination variable x as P₀、Q₀Is a column vector of elements, i.e. x ═ P₀,Q₀) (ii) a Defining an uncertain variable w as

Is a column vector of elements, i.e.:

defining the operation decision variable y as u_i、θ_i、P_i ^g、

Is a column vector of elements, i.e.:

the constraints (1) to (10) are arranged in the following form:

Ax+By+Cw≤d (11)

wherein A, B, C is a coefficient matrix and d is a constant vector;

constructing a virtual power plant safe operation feasible region, marking as phi, which is a value set of variables (x, w, y) under the virtual power plant safe operation constraint and is expressed as:

Φ＝{(x,w,y)|Ax+By+Cw≤d} (12)；

the second construction module is used for constructing a virtual power plant operation uncertain set according to the operation parameters, and constructing a virtual power plant robust output feasible region model according to the safe operation constraint coefficient matrix and the virtual power plant operation uncertain set, wherein the virtual power plant operation uncertain set is as follows:

wherein w is an uncertain variable of virtual power plant operation, comprising active power load, reactive power load, intermittent distributed resource active output,

upper and lower bounds, respectively, of a confidence interval for the kth uncertain variable;

is the midpoint of the confidence interval of the uncertain variable, i.e.

Δ w is the offset of the uncertain variable from the midpoint of the confidence interval, Δ w_kIs the kth element of Δ w; n is a radical of_wIs the number of uncertain variables; gamma is the uncertain budget and takes the value of 0-N_wA positive integer of (d); the gamma limits the number of uncertain variables which can deviate from the center of the prediction interval at most in the uncertain set, and further controls the size of the uncertain set; the method for constructing the robust output feasible region model of the virtual power plant specifically comprises the following steps: the robust output feasible region of the virtual power plant is a feasible value set of a coordinated variable x, and the condition that any variable in an uncertain set W is not null is met, so that the feasible region phi of the safe operation of the virtual power plant is not null; the robust output feasible region mathematical representation of the virtual power plant is as follows:

the robust output feasible region omega of the virtual power plant defined according to the above formula is a bounded region in two dimensions, and the corresponding virtual power plant output (P) within the feasible region₀,Q₀) The method can be executed in a power distribution system in a virtual power plant under the disturbance of any uncertain variable without violating the safe operation constraint of the power distribution system; and

the identification module is used for identifying the robust output feasible region of the virtual power plant through vertex enumeration and solving the identification problem of the vertexes of the feasible region through a column constraint generation method, wherein the identification module is further used for searching the vertexes of the robust output feasible region of the virtual power plant along different directions and constructing an approximate polygon by using the searched vertexes; and updating the vertex searching direction through the normal vector of each boundary of the approximate polygon, searching the other vertexes of the feasible robust output domain of the virtual power plant, repeating the process, and calculating to obtain the feasible robust output domain of the virtual power plant.

5. The apparatus of claim 4, wherein the operating parameters comprise distribution network parameters, distributed generation operating parameters, and predictive parameters.

6. The apparatus of claim 4, wherein the initial vertex set and the initial approximate polygon of the robust output feasible region of the virtual power plant are calculated by solving a two-stage adjustable robust optimization problem:

wherein, F (x, w) { (y, s) | By-s ≦ d-Ax-Cw, s ≧ 0}, s is a relaxation variable, and 1 represents a column vector with all elements being 1; m₀The real number is a positive real number larger than 1, and the typical value of the real number is 1000 times of the capacity of a gateway transformer of a power distribution network; eta_mIs an optimization problem objective function coefficient, representing the search direction; to eta_mSequentially taking values of (1,1), (1, -1), (-1, -1); the corresponding optimal solution of the optimization problem is recorded as v_mNamely the mth initial vertex; initializing a set of feasible domain vertices to V₀＝{v₁,v₂,v₃,v₄}; let the set of equations for each boundary of the polygon formed by the initial vertices be denoted as H^T(ii) a Calculating V₀The center of the polygon formed by the points in (1), i.e. v₀＝(v₁+v₂+v₃+v₄)/4。

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