Bottom-up virtual power plant resource configuration-decomposition method and device

Abstract

The invention relates to a bottom-up virtual power plant resource configuration-decomposition method and device.The method comprises the steps that firstly, technical characteristics of distributed resources configured by a virtual power plant are constructed into a unified constraint form, and a polyhedral model of port power is constructed through a Gaussian elimination method; secondly, a bilevel programming model based on feasible region affine transformation is constructed, the upper layer meets the power grid adjustment requirement and meanwhile minimizes the resource allocation number, the lower layer carries out scaling and translation transformation of a single distributed resource feasible region, and a single distributed resource feasible region is obtained based on a preset Taylor expansion relaxation strategy, a KKT condition and a convex envelope relaxation strategy; and the model is reconstructed into a linear programming model, so that a decomposition configuration decomposition scheme is obtained while the solving speed is increased, and the flexibility requirement of the power grid is met. Therefore, the problems that in the prior art, the utilization rate of the resource adjusting capacity is low, the collaboration problem of different time sequences is neglected, and consequently the instruction cannot be effectively executed are solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power system resource configuration, and particularly relates to a bottom-up virtual power plant resource configuration-decomposition method and device. BACKGROUND

[0002] With the accelerated construction of new power systems, flexible distributed resources (DER) appear at the user side at the end of the distribution network, and the rapid growth of DER brings opportunities and challenges to the power system. On the one hand, the proposal of virtual power plant (VPP) technology can widely integrate small DERs in the distribution network, thereby effectively supporting grid regulation requirements and improving the flexibility of the power system. On the other hand, due to the time coupling characteristics of storage resources, it is extremely complex for VPP to perform optimal power decomposition when executing grid dispatching instructions. Current engineering practice usually adopts a simplified top-down decomposition method, that is, the dispatching instruction is decomposed according to the capacity proportion of each resource. Since this method is simple to calculate and easy to implement, the top-down decomposition method is initially used for large-scale electric vehicles and energy storage, and then widely used for VPP decomposition with different resource types.

[0003] However, the above method pre-allocates each time sequence dispatching instruction to all resources, ignores the coordination problem of different time sequences, and causes the instruction to be unable to be effectively executed. In addition, each decomposition is an approximate estimation of the feasible region, and if multiple regulation services are involved, the VPP needs to perform multiple decompositions, so that the capacity quantization error is accumulated, thereby causing inefficient use of resource regulation capacity.

[0004] In summary, the utilization rate of the resource regulation capacity of the prior art is low, and the coordination problem of different time sequences is ignored, which causes the instruction to be unable to be effectively executed, and needs to be solved urgently. SUMMARY

[0005] The present application provides a bottom-up virtual power plant resource configuration-decomposition method and device to solve the problems of low utilization rate of resource regulation capacity of the prior art, and ignoring the coordination problem of different time sequences, which causes the instruction to be unable to be effectively executed.

[0006] The first aspect embodiment of the application provides a bottom-up virtual power plant resource configuration-decomposition method, comprising the following steps: constructing an operation model of a target virtual power plant configured distributed resource into a unified constraint form, and based on the unified constraint form, and using a preset Gaussian elimination strategy to establish a polyhedral model of a port power of the target virtual power plant, and constructing a virtual power plant decomposition problem corresponding to the polyhedral model; based on a preset feasible region affine transformation strategy, constructing an upper-layer virtual power plant decomposition model and a lower-layer feasible region approximation model corresponding to the virtual power plant decomposition problem, to construct a double-layer programming model through the upper-layer virtual power plant decomposition model and the lower-layer feasible region approximation model; based on a preset Taylor expansion relaxation strategy, KKT condition and convex envelope relaxation strategy, reconstructing the double-layer programming model into a linear programming model, to obtain a target configuration decomposition scheme corresponding to the target virtual power plant according to the linear programming model.

[0007] Optionally, in an embodiment of the application, the operation model of the target virtual power plant configured distributed resource is constructed into a unified constraint form, and based on the unified constraint form, and using a preset Gaussian elimination strategy to establish a polyhedral model of a port power of the target virtual power plant, and constructing a virtual power plant decomposition problem corresponding to the polyhedral model, comprising: normalizing modeling of a single distributed resource, to be expressed in a unified constraint form; converting the unified constraint form into a closed polyhedron composed of port input and output powers through the Gaussian elimination strategy; calculating a parameter average value of the target virtual power plant configured distributed resource, and constructing a basic isomorphic polyhedron corresponding to the closed polyhedron according to the parameter average value; approximating a feasible region of each single distributed resource by affine transformation of the basic isomorphic polyhedron, and algebraically summing to obtain an aggregated feasible region corresponding to the target virtual power plant, and inversely solving the aggregated feasible region to construct a virtual power plant decomposition problem corresponding to the polyhedral model.

[0008] Optionally, in an embodiment of the application, the upper-layer virtual power plant decomposition model and the lower-layer feasible region approximation model corresponding to the virtual power plant decomposition problem are constructed based on a preset feasible region affine transformation strategy, comprising: determining a power grid regulation demand corresponding to the target virtual power plant, and based on a resource configuration scheme used by the target virtual power plant decomposition under the condition of meeting the power grid regulation demand, constructing a corresponding objective function, to establish the upper-layer virtual power plant decomposition model according to the power grid regulation demand and the objective function; based on the feasible region affine transformation strategy, constructing the lower-layer feasible region approximation model.

[0009] Optionally, in an embodiment of the present application, the reconstructing the bi-level programming model into a linear programming model based on the preset Taylor expansion relaxation strategy, KKT condition and convex envelope relaxation strategy to obtain the target configuration decomposition scheme corresponding to the target virtual power plant according to the linear programming model comprises: expressing the polynomial term in the bi-level programming model as a bilinear term by using a preset auxiliary variable, and linearizing the bilinear term by the convex envelope relaxation strategy to obtain a linear programming model corresponding to the bi-level programming model; performing linearization relaxation operation on the high-dimensional fractional constraint term of the upper-layer virtual power plant decomposition model corresponding to the linear programming model by the Taylor expansion relaxation strategy to obtain an upper-layer linearization relaxation model; converting the lower-layer feasible region approximation model into a constraint equation based on the KKT condition, embedding the constraint equation into the upper-layer linearization relaxation model, and solving the upper-layer linearization relaxation model to obtain the target configuration decomposition scheme.

[0010] The second aspect embodiment of the present application provides a bottom-up virtual power plant resource configuration-decomposition device, comprising: a first modeling module configured to construct an operation model of a distributed resource configured by a target virtual power plant into a unified constraint form, and based on the unified constraint form, and using a preset Gaussian elimination strategy to establish a polyhedral model of port power of the target virtual power plant, and to construct a virtual power plant decomposition problem corresponding to the polyhedral model; a second modeling module configured to construct an upper-layer virtual power plant decomposition model and a lower-layer feasible region approximation model corresponding to the virtual power plant decomposition problem based on a preset feasible region affine transformation strategy, to construct a bi-level programming model through the upper-layer virtual power plant decomposition model and the lower-layer feasible region approximation model; and a configuration decomposition module configured to reconstruct the bi-level programming model into a linear programming model based on a preset Taylor expansion relaxation strategy, KKT condition and convex envelope relaxation strategy, to obtain a target configuration decomposition scheme corresponding to the target virtual power plant according to the linear programming model.

[0011] Optionally, in an embodiment of the present application, the first modeling module comprises: a normalization modeling unit configured to perform normalization modeling on a single distributed resource to express it into a unified constraint form; a transformation unit configured to transform the unified constraint form into a closed polyhedron composed of port input and output power through the Gaussian elimination strategy; a calculation unit configured to calculate a parameter average value of the distributed resource configured by the target virtual power plant, and to construct a basic isomorphic polyhedron corresponding to the closed polyhedron according to the parameter average value; and a summation unit configured to approximate a feasible region of each single distributed resource by affine transformation on the basic isomorphic polyhedron, and to obtain an aggregate feasible region corresponding to the target virtual power plant by algebraic summation, and to inversely solve the aggregate feasible region to construct a virtual power plant decomposition problem corresponding to the polyhedral model.

[0012] Optionally, in an embodiment of the present application, the second modeling module comprises: a determining unit configured to determine a grid regulation requirement corresponding to the target virtual power plant, and construct a corresponding target function based on a resource configuration scheme used by the target virtual power plant to meet the grid regulation requirement, so as to establish the upper-layer virtual power plant decomposition model according to the grid regulation requirement and the target function; and a constructing unit configured to construct the lower-layer feasible region approximation model based on the affine transformation strategy of the feasible region.

[0013] Optionally, in an embodiment of the present application, the configuration decomposition module comprises: a linearization unit configured to express a polynomial term in the bi-level programming model as a bilinear term by using a preset auxiliary variable, and linearize the bilinear term by the convex envelope relaxation strategy to obtain a linear programming model corresponding to the bi-level programming model; a relaxation unit configured to linearize and relax a high-dimensional fractional constraint term of an upper-layer virtual power plant decomposition model corresponding to the linear programming model by the Taylor expansion relaxation strategy to obtain an upper-layer linearized relaxation model; and a solving unit configured to convert the lower-layer feasible region approximation model into a constraint equation based on the KKT condition, embed the constraint equation into the upper-layer linearized relaxation model, and solve the upper-layer linearized relaxation model to obtain the target configuration decomposition scheme.

[0014] An embodiment of the third aspect of the present application provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the bottom-up virtual power plant resource configuration-decomposition method as described in the above embodiments.

[0015] An embodiment of the fourth aspect of the present application provides a computer readable storage medium, which stores a computer program, and the program is executed by a processor to implement the bottom-up virtual power plant resource configuration-decomposition method as described above.

[0016] An embodiment of the fifth aspect of the present application provides a computer program product, comprising a computer program, and the computer program is executed to implement the bottom-up virtual power plant resource configuration-decomposition method as described above.

[0017] Therefore, the embodiments of the present application have the following beneficial effects:

[0018] The embodiment of the present application can construct the operation model of the distributed resource configured by the target virtual power plant into a unified constraint form, and based on the unified constraint form, and adopt a preset Gaussian elimination strategy to establish a polyhedral model of the port power of the target virtual power plant, and construct a virtual power plant decomposition problem corresponding to the polyhedral model; based on a preset feasible region affine transformation strategy, construct an upper virtual power plant decomposition model and a lower feasible region approximation model corresponding to the virtual power plant decomposition problem, to construct a bi-level programming model through the upper virtual power plant decomposition model and the lower feasible region approximation model; based on a preset Taylor expansion relaxation strategy, KKT condition and convex envelope relaxation strategy, reconstruct the bi-level programming model into a linear programming model, to obtain a target configuration decomposition scheme corresponding to the target virtual power plant according to the linear programming model, so as to meet the flexibility requirement of the power grid. Thus, the problems of the prior art, such as low utilization rate of resource regulation capacity and easy loss of opportunity cost, are solved.

[0019] Additional aspects and advantages of the present application will be in part apparent and in part pointed out hereinafter. BRIEF DESCRIPTION OF DRAWINGS

[0020] The above and / or additional aspects and advantages of the present application will become apparent and be readily appreciated from the following description, taken in conjunction with the accompanying drawings, in which:

[0021] Figure 1 A flow chart of a bottom-up virtual power plant resource configuration-decomposition method according to an embodiment of the present application;

[0022] Figure 2 An example diagram of a bottom-up virtual power plant resource configuration-decomposition apparatus according to an embodiment of the present application;

[0023] Figure 3 A structural schematic diagram of an electronic device according to an embodiment of the present application.

[0024] Wherein, 10-bottom-up virtual power plant resource configuration-decomposition apparatus; 100-first modeling module, 200-second modeling module, 300-configuration decomposition module; 301-memory, 302-processor, 303-communication interface. DETAILED DESCRIPTION

[0025] The embodiments of the present application are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar notations represent the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the accompanying drawings are exemplary, and are intended to explain the present application, and cannot be understood as a limitation of the present application.

[0026] A bottom-up virtual power plant resource configuration-decomposition method and device of embodiments of the present application are described below with reference to the accompanying drawings. In view of the problems mentioned in the background art, the present application provides a bottom-up virtual power plant resource configuration-decomposition method, in which the operation model of the distributed resources configured by the target virtual power plant is constructed into a unified constraint form, and based on the unified constraint form, a polyhedral model of the port power of the target virtual power plant is established using a preset Gaussian elimination strategy, and a virtual power plant decomposition problem corresponding to the polyhedral model is constructed. Based on a preset feasible region affine transformation strategy, an upper-layer virtual power plant decomposition model and a lower-layer feasible region approximation model corresponding to the virtual power plant decomposition problem are constructed to construct a bi-level programming model through the upper-layer virtual power plant decomposition model and the lower-layer feasible region approximation model. Based on a preset Taylor expansion relaxation strategy, KKT condition and convex envelope relaxation strategy, the bi-level programming model is reconfigured into a linear programming model to obtain a target configuration decomposition scheme corresponding to the target virtual power plant according to the linear programming model, thereby meeting the flexibility requirement of the power grid. Thus, the problems of low utilization rate of resource regulation capability and easy loss of opportunity cost of the prior art are solved.

[0027] Specifically, Figure 1 A flowchart of a bottom-up virtual power plant resource configuration-decomposition method provided by embodiments of the present application.

[0028] As Figure 1 shown, the bottom-up virtual power plant resource configuration-decomposition method includes the following steps:

[0029] In step S101, the operation model of the distributed resources configured by the target virtual power plant is constructed into a unified constraint form, and based on the unified constraint form, a polyhedral model of the port power of the target virtual power plant is established using a preset Gaussian elimination strategy, and a virtual power plant decomposition problem corresponding to the polyhedral model is constructed.

[0030] Embodiments of the present application first construct the technical characteristics of the DER configured by the VPP into a unified constraint form, and construct a polyhedral model of the port power through Gaussian elimination, and construct a virtual power plant decomposition problem corresponding to the polyhedral model.

[0031] Optionally, in one embodiment of the present application, the operation model of the target virtual power plant configured distributed resource is constructed into a unified constraint form, and based on the unified constraint form, and a preset Gaussian elimination strategy is adopted to establish a polyhedral model of the port power of the target virtual power plant, and a virtual power plant decomposition problem corresponding to the polyhedral model is constructed, including: normalizing modeling of the single distributed resource, expressed as a unified constraint form; converting the unified constraint form into a closed polyhedron composed of port input and output power through the Gaussian elimination strategy; calculating the parameter average value of the target virtual power plant configured distributed resource, and constructing a basic isomorphic polyhedron corresponding to the closed polyhedron according to the parameter average value; approximating the feasible region of each single distributed resource by affine transformation on the basic isomorphic polyhedron, and performing algebraic summation to obtain the aggregate feasible region corresponding to the target virtual power plant, and inversely solving the aggregate feasible region to construct the virtual power plant decomposition problem corresponding to the polyhedral model.

[0032] Specifically, the embodiment of the present application constructs a polyhedral approximation decomposition model, and the process of the virtual power plant decomposition problem is as follows:

[0033] 1. Constructing an isomorphic polyhedral model:

[0034] The embodiment of the present application first normalizes the single DER model, expressed as a constraint form with a unified form, including power upper and lower limits, capacity upper and lower limits, and ramping constraints, as shown in the following formula:

[0035]

[0036] Wherein, p(t) in and p(t) out are the equivalent charging power and discharging power of the single DER at time t; p in and p out are the maximum and minimum values of the equivalent charging power and discharging power of the DER; is the maximum ramping value of the equivalent charging and discharging of the DER; e i (t) is the equivalent state of charge (SOC) at time t; and e i (t) is the maximum capacity and minimum capacity of the DER; θ i is the equivalent self-loss rate of the DER; κ in and κ out are the equivalent charging efficiency and discharging efficiency of the DER; ΔT is the set time interval.

[0037] Secondly, the embodiment of the present application can convert the intermediate SOC constraint in the above formula into a group linear inequality constraint composed of port input and output power through Gaussian elimination method, so that it can be regarded as a closed polyhedron in a high-dimensional space, and its compact form can be represented by the following matrix:

[0038]

[0039] wherein, represents the input power and output power of the BSS; A i and b i are the coefficient matrix of constraint conversion.

[0040] 2. Polyhedron scaling and translation approximation

[0041] The embodiment of the present application can aggregate the operation constraints of all single resources into a polyhedron operation constraint, i.e., VPP aggregated feasible region, by using Minkowski sum method, as shown in the following formula:

[0042]

[0043] In order to avoid the calculation complexity of calculating the accurate Minkowski sum of a large number of heterogeneous DERs, the embodiment of the present application can construct a basic isomorphic polyhedron, as shown in the following formula:

[0044]

[0045] Further, the embodiment of the present application can use the isomorphic polyhedron to approximate the polyhedron constraint of each DER, and then perform scaling and translation transformation (i.e., affine transformation) based on the basic isomorphic polyhedron, as shown in the following formula:

[0046]

[0047] wherein, β i is a scaling coefficient; t i is a translation coefficient.

[0048] It should be noted that, and are the coefficient matrixes for determining the feasible region polyhedron, and their structures are the same as M i and N i in the above formula. and can be obtained by averaging the parameters of the DERs configured in the virtual power plant, as shown in the following formula:

[0049] M0=∑M i / K DER , N0=∑N i / K DER

[0050] In addition, the determination of the monomer resource approximate polyhedral scaling coefficient can be converted into a linear programming problem, as shown in the following formula:

[0051] maxβ i

[0052]

[0053] β i >0

[0054] The above formula is converted according to Farkas's lemma, as shown in the following formula:

[0055] min s i

[0056] s.t.GM0=M i ,

[0057] GN0≤s i N i +M i r i

[0058] s i >0

[0059] G≥0

[0060] Where G is an intermediate coefficient matrix introduced in the conversion process.

[0061] After conversion by the above formula, the embodiment of the application can aggregate VPP according to the Brunn-Minkowski theory, and the process is as shown in the following formula:

[0062]

[0063] Therefore, according to the method, the above formula process can be inversely solved, that is, it can become a VPP decomposition problem, and the specific process can be as shown in the following formula:

[0064]

[0065] Therefore, the embodiment of the application constructs a polyhedral model of port power by Gaussian elimination method, thereby providing reliable data support for the construction of a double-layer planning model based on feasible region affine transformation.

[0066] In step S102, based on the preset feasible region affine transformation strategy, an upper virtual power plant decomposition model and a lower feasible region approximation model corresponding to the virtual power plant decomposition problem are constructed, so as to construct a double-layer planning model through the upper virtual power plant decomposition model and the lower feasible region approximation model.

[0067] Further, the embodiments of the present application also need to construct a bi-level programming model based on the feasible region affine transformation, wherein the upper layer virtual power plant decomposition model of the bi-level programming model meets the power grid regulation demand while minimizing the number of resource configurations, and the lower layer feasible region approximation model performs scaling and translation transformation of the single DER feasible region.

[0068] Optionally, in an embodiment of the present application, based on the preset feasible region affine transformation strategy, the upper layer virtual power plant decomposition model and the lower layer feasible region approximation model corresponding to the virtual power plant decomposition problem are constructed, including: determining the power grid regulation demand corresponding to the target virtual power plant, and based on the resource configuration scheme used for the target virtual power plant decomposition under the condition of meeting the power grid regulation demand, constructing a corresponding objective function to establish the upper layer virtual power plant decomposition model according to the power grid regulation demand and the objective function; based on the feasible region affine transformation strategy, constructing the lower layer feasible region approximation model.

[0069] It can be understood that since the number of resources in the VPP decomposition process of the embodiments of the present application is unknown, according to the above content, M0 and N0 belong to variables, and the decomposition model is still an optimization problem, and the feasible region approximation process is also an optimization problem. Therefore, the embodiments of the present application can establish a bi-level programming model of VPP decomposition, which aims to find the optimal configuration scheme of VPP meeting the power grid demand, and the specific process is as follows:

[0070] 1. Upper layer VPP decomposition model:

[0071]

[0072] s.t.H(N i ,β i ,t i ,M0,N0,N vpp )=0

[0073] P vpp ≥P demand

[0074] M vpp P vpp ≤N vpp

[0075] E(N i ,M0,M i )=0

[0076] F(N i ,N0,N i )=0

[0077] Wherein, H(·) represents the constraint expression for decomposition; E(·) and F(·) represent the constraint expression for calculating the basic similar polyhedral coefficient; P demandrepresents the grid regulation demand; and the objective function of the upper virtual power plant decomposition model is to minimize the number of resources used by the VPP decomposition while meeting the grid demand.

[0078] 2. Lower feasible region approximation model:

[0079]

[0080] s.t.C(G i ,N0,s i ,r i )≤0

[0081] G i ≥0

[0082] s i >0

[0083] wherein C(·) represents a constraint expression related to s.

[0084] Thus, the embodiments of the present application construct a bi-level programming model, thereby providing reliable data basis for subsequent model processing and implementation of virtual power plant configuration decomposition.

[0085] In step S103, the bi-level programming model is reconstructed into a linear programming model based on a preset Taylor expansion relaxation strategy, KKT condition and convex envelope relaxation strategy, so as to obtain a target configuration decomposition scheme corresponding to the target virtual power plant according to the linear programming model.

[0086] After that, the embodiments of the present application also need to use a Taylor expansion, Karush-Kuhn-Tucker condition and McCormick convex envelope relaxation joint method to reconstruct the bi-level programming model into a linear programming model, thereby accelerating the solving speed while obtaining the decomposition configuration scheme to maximize the resource regulation space while meeting the flexibility demand of the grid.

[0087] Optionally, in an embodiment of the present application, the bi-level programming model is reconstructed into a linear programming model based on a preset Taylor expansion relaxation strategy, KKT condition and convex envelope relaxation strategy, so as to obtain a target configuration decomposition scheme corresponding to the target virtual power plant according to the linear programming model, including: using a preset auxiliary variable to express a polynomial term in the bi-level programming model as a bilinear term, and linearizing the bilinear term through a convex envelope relaxation strategy to obtain a linear programming model corresponding to the bi-level programming model; performing linearization relaxation operation on a high-dimensional fractional constraint term of an upper virtual power plant decomposition model of the linear programming model through a Taylor expansion relaxation strategy to obtain an upper linearized relaxation model; converting a lower feasible region approximation model into a constraint equation based on a KKT condition, and embedding the constraint equation into the upper linearized relaxation model, and solving the upper linearized relaxation model to obtain the target configuration decomposition scheme.

[0088] Specifically, the specific process of the embodiment of the application for solving the bi-level programming model is as follows:

[0089] 1. McCormick convex envelope relaxation:

[0090] In order to solve the computational problem caused by high-dimensional non-convex nonlinear optimization with polynomial constraints in the bi-level programming model, the embodiment of the application can introduce auxiliary variables w i , re-express the polynomial term as a bilinear term, and linearize the bilinear variable through McCormick convex envelope relaxation. The specific process is as follows:

[0091] w i ≥ K i β i +K i β i - K i β i

[0092]

[0093] wherein, and K i , and β i are the upper and lower limits of the bilinear terms K i and β i .

[0094] 2. Taylor expansion relaxation:

[0095] Since M0 and N0 have high-dimensional fractional constraint terms in the optimization process, the embodiment of the application can use Taylor expansion to linearize and relax them. Take M0 as an example.

[0096]

[0097] The above formula is expanded at K0 and the gradient is calculated:

[0098]

[0099] Therefore, the original constraint can be approximated as:

[0100]

[0101] After Taylor expansion, it is arranged in the following linear form (i.e. the upper linear relaxation model):

[0102]

[0103] 3. Karush-Kuhn-Tucker condition derivation:

[0104] After linearizing the upper model by the above formula, the lower model can be transformed into a set of equations without objective function by using Karush-Kuhn-Tucker (KKT) condition, and then embedded into the upper minimization problem as constraints, so that it is easier to solve, as shown in the following formula:

[0105]

[0106] g j (s i )=0

[0107] h k (s i )≤0

[0108] μ k ≥0

[0109] μ k h k (s i )=0

[0110] Since the complementary slackness constraint in the above formula has a quadratic term, it still belongs to a non-convex function. In order to facilitate solving, the large-M method is introduced for linearization:

[0111]

[0112] Therefore, the embodiments of the present application improve the utilization efficiency of resource regulation capability by using the efficient VPP resource configuration and decomposition method.

[0113] According to the bottom-up virtual power plant resource configuration-decomposition method proposed in the embodiments of the present application, the operation model of the distributed resources configured by the target virtual power plant is constructed into a unified constraint form, and based on the unified constraint form, a polyhedral model of the port power of the target virtual power plant is established by using a preset Gaussian elimination strategy, and a virtual power plant decomposition problem corresponding to the polyhedral model is constructed; based on a preset feasible region affine transformation strategy, an upper virtual power plant decomposition model and a lower feasible region approximation model corresponding to the virtual power plant decomposition problem are constructed, so as to construct a bi-level programming model through the upper virtual power plant decomposition model and the lower feasible region approximation model; based on a preset Taylor expansion relaxation strategy, KKT condition and convex envelope relaxation strategy, the bi-level programming model is reconstructed into a linear programming model, so as to obtain a target configuration and decomposition scheme corresponding to the target virtual power plant according to the linear programming model, thereby meeting the flexibility requirement of the power grid.

[0114] Secondly, a bottom-up virtual power plant resource configuration-decomposition device is described with reference to the accompanying drawings according to an embodiment of the present application.

[0115] Figure 2 is a block schematic diagram of the bottom-up virtual power plant resource configuration-decomposition device of the embodiment of the present application.

[0116] As shown in Figure 2 the bottom-up virtual power plant resource configuration-decomposition device 10 comprises a first modeling module 100, a second modeling module 200 and a configuration decomposition module 300.

[0117] The first modeling module 100 is configured to construct an operation model of the distributed resources configured by the target virtual power plant into a unified constraint form, and based on the unified constraint form, establish a polyhedral model of port power of the target virtual power plant by using a preset Gaussian elimination strategy, and construct a virtual power plant decomposition problem corresponding to the polyhedral model.

[0118] The second modeling module 200 is configured to construct an upper-layer virtual power plant decomposition model and a lower-layer feasible region approximation model corresponding to the virtual power plant decomposition problem based on a preset feasible region affine transformation strategy, so as to construct a bi-level programming model by using the upper-layer virtual power plant decomposition model and the lower-layer feasible region approximation model.

[0119] The configuration decomposition module 300 is configured to reconstruct the bi-level programming model into a linear programming model based on a preset Taylor expansion relaxation strategy, KKT condition and convex envelope relaxation strategy, so as to obtain a target configuration decomposition scheme corresponding to the target virtual power plant according to the linear programming model.

[0120] Optionally, in an embodiment of the present application, the first modeling module 100 comprises a normalization modeling unit, a transformation unit, a calculation unit and a summation unit.

[0121] The normalization modeling unit is configured to perform normalization modeling on the single distributed resource, so as to be expressed in the unified constraint form.

[0122] The transformation unit is configured to transform the unified constraint form into a closed polyhedron composed of port input and output power by using the Gaussian elimination strategy.

[0123] The calculation unit is configured to calculate a parameter average value of the distributed resources configured by the target virtual power plant, and construct a basic isomorphic polyhedron corresponding to the closed polyhedron according to the parameter average value.

[0124] The summation unit is configured to approximate a feasible region of each single distributed resource by performing affine transformation on the basic isomorphic polyhedron, and perform algebraic summation to obtain an aggregated feasible region corresponding to the target virtual power plant, and inversely solve the aggregated feasible region to construct the virtual power plant decomposition problem corresponding to the polyhedral model.

[0125] Optionally, in an embodiment of the present application, the second modeling module 200 comprises a determining unit and a constructing unit.

[0126] The determining unit is configured to determine the grid regulation requirement corresponding to the target virtual power plant, and construct a corresponding objective function based on the resource configuration scheme used by the target virtual power plant for decomposing under the condition of meeting the grid regulation requirement, so as to establish the upper virtual power plant decomposition model according to the grid regulation requirement and the objective function.

[0127] The constructing unit is configured to construct the lower feasible region approximation model based on a feasible region affine transformation strategy.

[0128] Optionally, in an embodiment of the present application, the configuration decomposition module 300 comprises a linearization unit, a relaxation unit and a solving unit.

[0129] The linearization unit is configured to express the polynomial term in the bi-level programming model as a bilinear term by using a preset auxiliary variable, and linearize the bilinear term by a convex envelope relaxation strategy, so as to obtain a linear programming model corresponding to the bi-level programming model.

[0130] The relaxation unit is configured to perform linearization relaxation operation on the high-dimensional fractional constraint term of the upper virtual power plant decomposition model corresponding to the linear programming model by a Taylor expansion relaxation strategy, so as to obtain an upper linearization relaxation model.

[0131] The solving unit is configured to convert the lower feasible region approximation model into a constraint equation based on the KKT condition, embed the constraint equation into the upper linearization relaxation model, and solve the upper linearization relaxation model, so as to obtain the target configuration decomposition scheme.

[0132] It should be noted that the foregoing explanation and description of the top-down virtual power plant resource configuration-decomposition method embodiment are also applicable to the top-down virtual power plant resource configuration-decomposition device of the embodiment, which will not be described here again.

[0133] The bottom-up virtual power plant resource configuration-decomposition device provided by the embodiment of the present application comprises a first modeling module 100 configured to construct an operation model of a distributed resource configured by a target virtual power plant into a unified constraint form, and based on the unified constraint form, a preset Gaussian elimination strategy is adopted to establish a polyhedral model of a port power of the target virtual power plant, and a virtual power plant decomposition problem corresponding to the polyhedral model is constructed; a second modeling module 200 configured to construct an upper-layer virtual power plant decomposition model and a lower-layer feasible region approximate model corresponding to the virtual power plant decomposition problem based on a preset feasible region affine transformation strategy, so as to construct a double-layer programming model through the upper-layer virtual power plant decomposition model and the lower-layer feasible region approximate model; and a configuration decomposition module 300 configured to reconstruct the double-layer programming model into a linear programming model based on a preset Taylor expansion relaxation strategy, a KKT condition and a convex envelope relaxation strategy, so as to obtain a target configuration decomposition scheme corresponding to the target virtual power plant according to the linear programming model, thereby meeting the flexibility requirement of a power grid.

[0134] Figure 3 The structure schematic diagram of the electronic device provided by the embodiment of the present application is provided. The electronic device can comprise:

[0135] The memory 301, the processor 302 and the computer program stored in the memory 301 and executable on the processor 302.

[0136] The processor 302 implements the bottom-up virtual power plant resource configuration-decomposition method provided in the above embodiment when executing the program.

[0137] Further, the electronic device further comprises:

[0138] The communication interface 303 is configured to communicate between the memory 301 and the processor 302.

[0139] The memory 301 is configured to store the computer program executable on the processor 302.

[0140] The memory 301 can include a high-speed RAM memory, and can also include a non-volatile memory, for example, at least one disk memory.

[0141] If the memory 301, the processor 302 and the communication interface 303 are implemented independently, the communication interface 303, the memory 301 and the processor 302 can be connected with each other through a bus and complete communication between each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For convenience of representation, Figure 3 Only one thick line is used to represent the bus in the figure, but it does not mean that there is only one bus or only one type of bus.

[0142] Optionally, in a specific implementation, if the memory 301, the processor 302 and the communication interface 303 are integrated on a chip, the memory 301, the processor 302 and the communication interface 303 can complete communication between each other through an internal interface.

[0143] The processor 302 can be a Central Processing Unit (CPU), or an Application Specific Integrated Circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0144] The embodiments of the present application also provide a computer readable storage medium, which stores a computer program, and the program is executed by a processor to implement the above-described bottom-up virtual power plant resource configuration-decomposition method.

[0145] The embodiments of the present application also provide a computer program product, which includes a computer program, and the computer program is executed to implement the above-described bottom-up virtual power plant resource configuration-decomposition method.

[0146] In the description of the application, reference to "one embodiment", "some embodiments", "an example", "a specific example", or "some examples" means that a particular feature, structure, material, or characteristic being described is included in at least one embodiment or example of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment or example. Furthermore, the described specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples. In addition, the usage of "N" means at least two, for example, two, three or the like, unless explicitly stated otherwise.

[0147] Furthermore, the terms "first", "second", or the like, are used merely as a designation of certain elements or features of the application, and do not imply or connote relative importance or a specific order of precedence. Thus, features defined with "first", "second", etc. can include at least one of the features, either explicitly or implicitly.

[0148] Any process or method descriptions or blocks in flow charts or otherwise described herein represent embodiments of modules, segments, or portions of code which include one or more executable instructions for implementing specific logic functions or steps, and alternate implementations are possible. In some embodiments, the processes or methods described in flow charts or otherwise described herein are not necessarily performed in the order shown or discussed, including, for example, performing or depending from other operations or stages, in parallel, in reverse order, or in other orders.

[0149] The logic and / or steps represented in the flowcharts and / or described herein, for example, can be considered as a sequence of executable instructions stored in a computer readable medium, which can be executed by an instruction execution system, apparatus or device, such as a computer-based system, a processor-based system, or other system that can fetch the instructions from the instruction execution system, apparatus or device and execute the instructions, or a combination of the above. For the purposes of this specification, a "computer readable medium" can be any apparatus that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus or device. The computer readable medium can be a computer readable storage medium or a computer readable signal medium. The computer readable storage medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a propagation medium. The computer readable signal medium can include, but is not limited to, a computer readable medium that facilitates transfer of the program from one place to another. A specific example of a computer readable medium is a non-transitory computer-readable storage medium. A specific example of a computer readable signal medium is a source or destination of the computer readable medium. Another specific example of a computer readable signal medium is a computer readable signal travelling through space. Thus, a computer readable medium can take many forms of hardware to carry out the program for use by or in connection with the instruction execution system, apparatus or device.

[0150] It should be understood that aspects of the application can be implemented in hardware, software, firmware or a combination thereof. In the above embodiments, the N steps or methods can be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented in hardware and in another embodiment, the hardware can be implemented with any or a combination of the following technologies, which are all well known in the art: a discrete logic circuit(s) having logic gates for implementing logic functions upon an application of data signals, an application specific integrated circuit having appropriate combinational logic gates, a programmable gate array(s) (PGA), a field programmable gate array (FPGA), etc.

[0151] Those of skill in the art would understand that the steps carried out in the above-mentioned embodiments can be implemented by a program instructing the relevant hardware to complete all or part of the steps, and the program can be stored in a computer readable storage medium. When the program is executed, it includes one of the steps of the method embodiments or a combination thereof.

[0152] In addition, each of the functional units in the various embodiments of the present application can be integrated in one processing module, or each of the units can be physically present separately, or two or more units can be integrated in one module. The integrated module can be implemented in the form of hardware or in the form of a software functional module. When the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer readable storage medium.

[0153] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it should be understood that the above embodiments are exemplary and should not be construed as limiting the present application, and those skilled in the art can make changes, modifications, replacements and variations to the above embodiments within the scope of the present application.

Claims

1. A bottom-up virtual power plant resource configuration-decomposition method, characterized in that: The following steps are involved: The operation model of the distributed resources configured by the target virtual power plant is constructed as a unified constraint form, and based on the unified constraint form and using a preset Gaussian elimination strategy, a polyhedron model of the port power of the target virtual power plant is established, and a virtual power plant decomposition problem corresponding to the polyhedron model is constructed; Based on a preset feasible domain affine transformation strategy, an upper-level virtual power plant decomposition model and a lower-level feasible domain approximation model corresponding to the virtual power plant decomposition problem are constructed, so as to construct a two-level programming model through the upper-level virtual power plant decomposition model and the lower-level feasible domain approximation model; Based on the preset Taylor expansion relaxation strategy, KKT condition and convex envelope relaxation strategy, the two-level programming model is reconstructed into a linear programming model to obtain the target configuration decomposition scheme corresponding to the target virtual power plant according to the linear programming model.

2. The method according to claim 1, characterized in that The operation model of the distributed resources configured by the target virtual power plant is constructed into a unified constraint form, and based on the unified constraint form, a polyhedron model of the port power of the target virtual power plant is established by using a preset Gaussian elimination strategy, and a virtual power plant decomposition problem corresponding to the polyhedron model is constructed, including: Standardize modeling of single distributed resources to express them in a unified constraint form; Converting the unified constraint form into a closed polyhedron consisting of port input and output powers through the Gaussian elimination strategy; Calculating parameter averages of distributed resources configured by the target virtual power plant, and constructing a basic isomorphic polyhedron corresponding to the closed polyhedron based on the parameter averages; By performing an affine transformation on the basic isomorphic polyhedron to approximate the feasible domain of each monomer distributed resource, and performing algebraic summation on the feasible domain to obtain the aggregated feasible domain corresponding to the target virtual power plant, and inversely solving the aggregated feasible domain to construct the virtual power plant decomposition problem corresponding to the polyhedron model.

3. The method according to claim 2, characterized in that The method of constructing an upper-layer virtual power plant decomposition model and a lower-layer feasible domain approximation model corresponding to the virtual power plant decomposition problem based on a preset feasible domain affine transformation strategy includes: Determining a grid regulation demand corresponding to the target virtual power plant, and constructing a corresponding objective function based on a resource configuration scheme used for decomposing the target virtual power plant while satisfying the grid regulation demand, so as to establish the upper-layer virtual power plant decomposition model according to the grid regulation demand and the objective function; Based on the feasible domain affine transformation strategy, the lower layer feasible domain approximation model is constructed.

4. The method according to claim 3, characterized in that The method reconstructs the bi-level programming model into a linear programming model based on a preset Taylor expansion relaxation strategy, KKT condition, and convex envelope relaxation strategy, so as to obtain a target configuration decomposition scheme corresponding to the target virtual power plant according to the linear programming model, including: Using preset auxiliary variables, the polynomial terms in the bilevel programming model are expressed as bilinear terms, and the bilinear terms are linearized using the convex envelope relaxation strategy to obtain a linear programming model corresponding to the bilevel programming model; Performing a linearized relaxation operation on the high-dimensional fractional constraint terms of the upper-level virtual power plant decomposition model corresponding to the linear programming model through the Taylor expansion relaxation strategy to obtain an upper-level linearized relaxation model; Based on the KKT condition, the lower feasible domain approximation model is converted into a constraint equation, and the constraint equation is embedded in the upper linearized relaxation model, and the upper linearized relaxation model is solved to obtain the target configuration decomposition solution.

5. A bottom-up virtual power plant resource configuration-decomposition device, characterized in that: include: A first modeling module is configured to construct an operation model of distributed resources configured by a target virtual power plant into a unified constraint form, and based on the unified constraint form, a polyhedron model of the port power of the target virtual power plant is established using a preset Gaussian elimination strategy, and a virtual power plant decomposition problem corresponding to the polyhedron model is constructed; a second modeling module, configured to construct an upper-level virtual power plant decomposition model and a lower-level feasible domain approximation model corresponding to the virtual power plant decomposition problem based on a preset feasible domain affine transformation strategy, so as to construct a bi-level programming model through the upper-level virtual power plant decomposition model and the lower-level feasible domain approximation model; A configuration decomposition module is used to reconstruct the two-level programming model into a linear programming model based on a preset Taylor expansion relaxation strategy, KKT condition and convex envelope relaxation strategy, so as to obtain the target configuration decomposition scheme corresponding to the target virtual power plant according to the linear programming model.

6. The device according to claim 5, characterized in that The first modeling module includes: Normalized modeling unit, used to perform normalized modeling on single distributed resources to express them in a unified constraint form; a conversion unit, configured to convert the unified constraint form into a closed polyhedron consisting of port input and output powers through the Gaussian elimination strategy; a calculation unit, configured to calculate an average value of parameters of distributed resources configured by the target virtual power plant, and construct a basic isomorphic polyhedron corresponding to the closed polyhedron according to the average value of the parameters; A summation unit is used to approximate the feasible domain of each monomer distributed resource by performing an affine transformation on the basic isomorphic polyhedron, and to perform algebraic summation to obtain the aggregated feasible domain corresponding to the target virtual power plant, and to inversely solve the aggregated feasible domain to construct the virtual power plant decomposition problem corresponding to the polyhedron model.

7. The device according to claim 6, characterized in that The second modeling module includes: a determining unit, configured to determine a grid regulation demand corresponding to the target virtual power plant, and construct a corresponding objective function based on a resource configuration scheme used for decomposing the target virtual power plant while satisfying the grid regulation demand, so as to establish the upper-layer virtual power plant decomposition model according to the grid regulation demand and the objective function; A construction unit is used to construct the lower feasible domain approximation model based on the feasible domain affine transformation strategy.

8. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the bottom-up virtual power plant resource configuration-decomposition method according to any one of claims 1 to 4.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the bottom-up virtual power plant resource configuration-decomposition method as described in any one of claims 1 to 4.

10. A computer program product comprising a computer program, characterized in that The computer program is executed to implement the bottom-up virtual power plant resource configuration-decomposition method according to any one of claims 1 to 4.

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