Method, device, equipment and medium for determining feasible domain of distributed resource cluster

Abstract

The present invention relates to the field of microgrid static safety technology, and discloses a method, device, equipment and medium for determining a feasible domain of a distributed resource cluster. The method includes: constructing a microgrid constraint model including microgrid multi-period coupling flow constraints, distributed resource multi-period coupling operation constraints and distributed resource cost constraints; determining the distributed resource feasible domain based on the microgrid constraint model and using a convex hull vertex search algorithm with vertex tracing. The present invention realizes that the calculation of the feasible domain of distributed resources is simple by considering the multi-period coupling characteristics, and the distributed resources reflect the external power and cost characteristics of the distribution network after aggregation, solving the problem that the distributed resource feasible domain cannot be calculated without considering the multi-period coupling characteristics.

Description

Technical Field

[0001] The present invention relates to the field of microgrid static safety technology, and in particular to a method, device, equipment and medium for determining a feasible domain of a distributed resource cluster. Background Art

[0002] In the existing distributed resource flexibility feasible domain calculation method, only the power-cost relationship in a single time period is considered, ignoring the time period coupling characteristics of power in the distributed resource cluster. Therefore, the flexibility feasible domain is a two-dimensional convex hull under each time period. For resources with time period coupling constraints such as energy storage, the power coupling constraints between time periods must be considered. In this case, the feasible domain of the distributed resource cluster is a high-dimensional convex hull. Continuing to use the current distributed resource aggregation method will not be able to calculate the distributed resource feasible domain. Currently, distributed resource aggregation has the following problems:

[0003] (1) The feasible domain of the flexibility of distributed resources with time-slot coupling is a high-dimensional convex hull. However, the computational complexity of obtaining the convex hull from a high-dimensional vertex set in the PVE algorithm (Progressive vertex enumeration, PVE for short, based on ordered vertex search) is extremely large, which makes the calculation impossible to complete, and thus makes it impossible for the distributed resource cluster to aggregate.

[0004] (2) The PVE algorithm obtains new vertices by searching each hyperplane of the convex hull. As the number of loops increases, the number of hyperplanes grows exponentially and the amount of computation also increases rapidly.

[0005] (3) Since there are a large number of planes with approximate normal vectors in the hyperplane, when the vertex precision is constant (such as retaining four decimal places), each loop contains a large number of repeated vertices, resulting in a large amount of invalid calculations.

[0006] Therefore, there is an urgent need for a method to determine the feasible domain of distributed resources that takes into account the multi-period coupling characteristics. Summary of the invention

[0007] In view of this, the present invention provides a method, apparatus, device and medium for determining a feasible domain of a distributed resource cluster to solve the problem that the feasible domain of distributed resources cannot be calculated due to failure to consider multi-period coupling characteristics.

[0008] In a first aspect, the present invention provides a method for determining a feasible domain of a distributed resource cluster, the method comprising:

[0009] Construct a microgrid constraint model that includes microgrid multi-period coupled power flow constraints, distributed resource multi-period coupled operation constraints, and distributed resource cost constraints;

[0010] Based on the microgrid constraint model and the vertex tracing convex hull vertex search algorithm, the feasible domain of distributed resources is determined.

[0011] The method for determining the feasible domain of a distributed resource cluster provided by the present invention comprehensively considers a microgrid constraint model including a microgrid multi-period coupled flow constraint, a distributed resource multi-period coupled operation constraint, and a distributed resource cost constraint, and determines the distributed resource feasible domain based on the microgrid constraint model and a convex hull vertex search algorithm with vertex tracing, aggregates distributed resource clusters based on the distributed resource feasible domain, and realizes that the calculation of the distributed resource feasible domain is simple by considering the multi-period coupling characteristics. Finally, after the distributed resources are aggregated, the external power and cost characteristics of the distribution network are reflected, and the problem that the distributed resource feasible domain cannot be calculated due to failure to consider the multi-period coupling characteristics is solved.

[0012] In an optional implementation, constructing a microgrid constraint model including a microgrid multi-period coupled power flow constraint, a distributed resource multi-period coupled operation constraint, and a distributed resource cost constraint includes:

[0013] Acquire the multi-period operation data of the microgrid, and construct the multi-period coupled power flow constraints of the microgrid based on the multi-period operation data of the microgrid;

[0014] Obtaining multi-period operation data of distributed resources, and building multi-period coupling operation constraints of distributed resources based on the multi-period operation data of distributed resources;

[0015] Obtain the distributed resource power generation cost, determine the cost piecewise linear function based on the distributed resource power generation cost, and construct the distributed resource cost constraint based on the cost piecewise linear function;

[0016] The constraint model of the microgrid is determined based on the multi-period coupled power flow constraints of the microgrid, the multi-period coupled operation constraints of distributed resources and the cost constraints of distributed resources.

[0017] The method for determining the feasible domain of a distributed resource cluster provided by the present invention respectively constructs multi-period coupled flow constraints of a microgrid, multi-period coupled operation constraints of distributed resources and cost constraints of distributed resources, and determines a microgrid constraint model based on the above constraints. The multi-period coupling characteristics are taken into account when calculating the feasible domain of distributed resources, which provides a constraint model basis for the calculation of the feasible domain of distributed resources and indirectly improves the accuracy and precision of the calculation of the feasible domain of distributed resources.

[0018] In an optional implementation, determining the feasible domain of distributed resources based on the microgrid constraint model and using a convex hull vertex search algorithm with vertex tracing includes:

[0019] Based on the microgrid constraint model and using the vertex tracing convex hull vertex search algorithm, vertex search is performed to obtain the vertex set and the corresponding convex hull plane vertex index vector;

[0020] A clustered direction vector group is obtained based on the vertex index vector of the convex hull plane and a preset clustering algorithm;

[0021] Based on the clustered direction vector group and the preset optimization conditions, vertex search is performed to obtain a new vertex set and the corresponding new vertex index vector of the convex hull plane;

[0022] The feasible domain of distributed resources is determined based on the new vertex set and the corresponding new vertex index vector of the convex hull plane.

[0023] The method for determining the feasible domain of a distributed resource cluster provided by the present invention adopts a convex hull vertex search algorithm with vertex tracing to obtain the direction vectors of each surface of the convex hull without the need for convex hull calculation in the form of a hyperplane, and generalizes the convex hull vertex search algorithm with vertex tracing to high dimensions, and then generalizes the microgrid aggregation to multiple time periods. Vertex search is performed based on the clustered direction vector group and preset optimization conditions to obtain a new vertex set and a corresponding new vertex index vector of the convex hull plane, and the feasible domain of distributed resources is determined based on the new vertex set and the corresponding new vertex index vector of the convex hull plane, thereby achieving the purpose of calculating the feasible domain of distributed resources in multiple time periods.

[0024] In an optional implementation, based on the microgrid constraint model and using a vertex tracing convex hull vertex search algorithm to perform vertex search, the vertex set and the corresponding convex hull plane vertex index vector are obtained, including:

[0025] Based on the microgrid constraint model and using the vertex tracing convex hull vertex search algorithm, multiple direction vectors are randomly generated;

[0026] Perform vertex search on multiple direction vectors to obtain vertex sets corresponding to the multiple direction vectors;

[0027] The vertices in the vertex set are numbered, and multiple non-repeated vertices are selected for combination to obtain multiple groups of convex hull plane vertex index vectors containing multiple non-repeated vertices.

[0028] The method for determining the feasible domain of a distributed resource cluster provided by the present invention randomly generates multiple direction vectors based on a microgrid constraint model and adopts a vertex tracing convex hull vertex search algorithm; vertex searches are performed on multiple direction vectors to obtain vertex sets corresponding to multiple direction vectors; vertices in the vertex set are numbered, and multiple non-repeating vertices are selected for combination to obtain multiple groups of convex hull plane vertex index vectors containing multiple non-repeating vertices, thereby achieving the purpose of obtaining multiple groups of convex hull plane vertex index vectors containing multiple non-repeating vertices. This provides an index basis for subsequently obtaining a clustered direction vector group.

[0029] In an optional implementation, obtaining a clustered direction vector group based on the convex hull plane vertex index vector and a preset clustering algorithm includes:

[0030] Determine the outer normal direction vectors of multiple convex hull surfaces based on multiple groups of convex hull plane vertex index vectors;

[0031] A preset clustering algorithm is used to cluster the external normal direction vectors of multiple convex hull surfaces to obtain a clustered direction vector group.

[0032] In an optional implementation, a preset clustering algorithm is used to cluster the external normal direction vectors of multiple convex hull surfaces, and the clustered direction vector group obtained includes:

[0033] Get the density threshold and neighborhood radius of the outer normal direction vectors of multiple convex hull surfaces;

[0034] The preset clustering algorithm is used to cluster the density threshold and neighborhood radius to obtain a clustered direction vector group.

[0035] The method for determining the feasible domain of a distributed resource cluster provided by the present invention has a preset clustering algorithm that clusters data points that meet high density into a cluster, and can cluster direction vectors that point to a relatively concentrated area into a cluster, to obtain a final clustered direction vector group. The obtained direction vector group can concentrate on reflecting the direction vectors corresponding to the vertices of the convex hull plane, and provide a vector basis for subsequently obtaining a new vertex set.

[0036] In an optional implementation, vertex search is performed based on the clustered direction vector group and the preset optimization condition to obtain a new vertex set and a corresponding new vertex index vector of the convex hull plane, including:

[0037] According to the preset optimization conditions, the clustered direction vector group is searched for vertices in all directions of the convex hull surface. If a new vertex is found in a certain direction of the convex hull surface, the new vertex is added to the vertex set to obtain a new vertex set.

[0038] Number the new vertices and add them to the corresponding convex hull plane vertex index vector to obtain the new vertex index vector of the convex hull plane.

[0039] The method for determining the feasible domain of a distributed resource cluster provided by the present invention performs vertex search on the clustered direction vector group in various directions of the convex hull surface according to preset optimization conditions. If a new vertex is found in a certain direction of the convex hull surface, the new vertex is added to the vertex set to obtain a new vertex set; the new vertex is numbered and added to the corresponding convex hull plane vertex index vector to obtain a new vertex index vector of the convex hull plane, thereby ensuring the purpose of performing vertex search along all surfaces of the convex hull, improving the accuracy and comprehensiveness of the vertex search, and providing a vertex basis for calculating the feasible domain of distributed resources.

[0040] In a second aspect, the present invention provides a device for determining a feasible domain of a distributed resource cluster, the device comprising:

[0041] A construction module is used to construct a microgrid constraint model including microgrid multi-period coupled power flow constraints, distributed resource multi-period coupled operation constraints and distributed resource cost constraints;

[0042] The determination module is used to determine the feasible domain of distributed resources based on the microgrid constraint model and using a convex hull vertex search algorithm with vertex tracing.

[0043] In a third aspect, the present invention provides a computer device, comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the method for determining a feasible domain of a distributed resource cluster according to the first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0044] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the method for determining a feasible domain of a distributed resource cluster according to the first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0046] Figure 1 is a flow chart of a method for determining a feasible domain of a distributed resource cluster according to an embodiment of the present invention;

[0047] Figure 2 is a flow chart of another method for determining a feasible domain of a distributed resource cluster according to an embodiment of the present invention;

[0048] Figure 3 is a flow chart of another method for determining a feasible domain of a distributed resource cluster according to an embodiment of the present invention;

[0049] Figure 4 is a schematic flow chart of a convex hull vertex search method based on vertex tracing according to an embodiment of the present invention;

[0050] Figure 5 is a topological diagram of a 9-node microgrid according to an embodiment of the present invention;

[0051] Figure 6 According to the embodiment of the present invention i The logarithmic graph of the number of vertices in iterations;

[0052] Figure 7 is a schematic diagram of a micro-electric gateway power curve and a power deviation curve in each time period according to an embodiment of the present invention;

[0053] Figure 8 is a comparison diagram of the ratio of the number of repeated vertices to the total number of vertices searched in the current iteration according to an embodiment of the present invention;

[0054] Fig. 9 is a comparison diagram of the total cost deviation of the grid-microgrid over time according to an embodiment of the present invention;

[0055] Fig.10 is a structural block diagram of a device for determining a feasible domain of a distributed resource cluster according to an embodiment of the present invention;

[0056] Fig.11 It is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0058] According to an embodiment of the present invention, an embodiment of a method for determining a feasible domain of a distributed resource cluster is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0059] In this embodiment, a method for determining a feasible region of a distributed resource cluster is provided, which can be used in a microgrid. Figure 1 is a flowchart of a method for determining a feasible domain of a distributed resource cluster according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps:

[0060] Step S101, constructing a microgrid constraint model including microgrid multi-period coupled power flow constraints, distributed resource multi-period coupled operation constraints and distributed resource cost constraints.

[0061] Specifically, Micro-Grid, also known as microgrid, refers to a small power distribution system composed of distributed resources, energy storage devices, energy conversion devices, loads, monitoring and protection devices, etc. Distributed resources, energy storage devices, energy conversion devices, loads, monitoring and protection devices can be equivalent to each node, and the microgrid can be equivalent to a small power distribution system composed of multiple nodes, which can operate independently and be interconnected with the upper power grid.

[0062] The single period is extended to the T period, and the T period is set according to the actual situation. The value of T can be set from 1 to 24 hours. This allows the two-dimensional convex hull to be converted into a high-dimensional convex hull when the feasible domain of distributed resources is subsequently calculated, thereby improving the accuracy of the calculation of the feasible domain of distributed resources.

[0063] The multi-period coupled power flow constraint of microgrid refers to the power flow calculation performed when certain quantities are subject to optimization or other constraints in the microgrid distribution system. Including but not limited to the amplitude range of node voltage, the active power and reactive power restrictions of power supply nodes, and the constraints of voltage phase difference between nodes, the multi-period coupled power flow constraint of microgrid ensures the stability of the operation of microgrid distribution system.

[0064] The multi-period coupled operation constraints of distributed resources mainly involve the distributed power generation output of each node in the microgrid, etc., reflecting the parameter constraints such as power, line capacity, and charge state of distributed resources during power generation operation.

[0065] The distributed resource cost constraint refers to the cost involved in the operation of each distributed power generation resource, reflecting the constraints of distributed resources such as multi-time periods and transaction costs when generating electricity.

[0066] Step S102, based on the microgrid constraint model and using a convex hull vertex search algorithm with vertex tracing, the feasible domain of distributed resources is determined.

[0067] Specifically, the vertex tracing convex hull vertex search algorithm is an improvement of the PVE (Progressive vertex enumeration, PVE for short) algorithm. For an N-dimensional convex hull V, after knowing the external normal direction vectors of all surfaces of the convex hull, the vertex search can be performed to expand the convex hull. The core of calculating the external normal direction vector of the convex hull surface is to obtain the vertex index vectors of each surface of the convex hull. The vertex index vector refers to the index set of points on a convex hull surface, which corresponds one-to-one to each surface of the convex hull. The dimension of the convex hull is increased, and the huge amount of calculation of the convex hull is reduced.

[0068] Based on the microgrid constraint model, a convex hull vertex search algorithm with vertex tracing is used to determine the vertices of the feasible domain of distributed resources. The vertices constitute the boundary of the feasible domain of distributed resources, and finally the calculation of the feasible domain of distributed resources is realized.

[0069] The method for determining the feasible domain of a distributed resource cluster provided in this embodiment comprehensively considers a microgrid constraint model including a microgrid multi-period coupled flow constraint, a distributed resource multi-period coupled operation constraint, and a distributed resource cost constraint, and determines the distributed resource feasible domain based on the microgrid constraint model and a convex hull vertex search algorithm with vertex tracing, aggregates distributed resource clusters based on the distributed resource feasible domain, and realizes that the calculation of the distributed resource feasible domain is simple by considering the multi-period coupling characteristics. After the distributed resources are aggregated, the external power and cost characteristics of the distribution network are reflected, and the problem that the distributed resource feasible domain cannot be calculated due to failure to consider the multi-period coupling characteristics is solved.

[0070] In this embodiment, a method for determining a feasible region of a distributed resource cluster is provided, which can be used in a microgrid. Figure 2 is a flowchart of a method for determining a feasible domain of a distributed resource cluster according to an embodiment of the present invention. Figure 2 As shown, the process includes the following steps:

[0071] Step S201, constructing a microgrid constraint model including microgrid multi-period coupled power flow constraints, distributed resource multi-period coupled operation constraints and distributed resource cost constraints.

[0072] Specifically, the above step S201 includes:

[0073] Step S2011, obtaining multi-period operation data of the microgrid, and constructing multi-period coupled power flow constraints of the microgrid based on the multi-period operation data of the microgrid.

[0074] Specifically, the basic data of the multi-period operation of the microgrid includes: the number of nodes of the microgrid, the node voltages corresponding to the multi-periods, and the node phase angles corresponding to the multi-periods.

[0075] Specifically, the microgrid power flow equation constraint is expressed as follows:

[0076] (1);

[0077] (2);

[0078] in: , Node i and nodes j Distribution lines between ij Active power flow equation and reactive power flow equation; , Distribution lines ij The series susceptance and series conductance of , Node i and nodes j The node voltage amplitude of , Node i and nodes j Voltage phase angle; For distribution lines ij The voltage phase angle; is the node number set, i.e. the number of nodes, Representation and Node i A collection of node numbers that are directly connected via distribution lines.

[0079] Step S2012, obtaining the distributed resource multi-period operation data, and constructing the distributed resource multi-period coupling operation constraints based on the distributed resource multi-period operation data.

[0080] Exemplarily, the multi-period coupled operation constraints of distributed resources are represented by the operation constraints of distributed energy storage resources. Taking the multi-period from 1:00 to 24:00 as an example, the multi-period operation data of distributed resources include all energy storage active power, energy storage charge state, upper and lower limits of energy storage electrostatic discharge active power, and upper and lower limits of energy storage battery active power from 1:00 to 24:00.

[0081] The multi-period coupled operation constraints of distributed resources are expressed as follows:

[0082] (3);

[0083] in: for t Distributed energy storage static discharge active power during time period; is the active power of distributed energy storage generation during period t; is the active power generated by distributed resources during period t; , are time period t and t +1 time period energy storage charge state in distributed resources; and They are the energy storage power generation efficiency and the distributed energy storage electrostatic discharge efficiency in distributed resources respectively; is the initial electrical state of the distributed energy storage; , are weight coefficients respectively; and The lower and upper limits of the charging state of distributed energy storage generation; , They are the lower and upper limits of the active power of the distributed energy storage electrostatic discharge respectively; , They are respectively the lower and upper limits of the active power of distributed energy storage generation.

[0084] Step S2013, obtaining the distributed resource power generation cost, determining a cost piecewise linear function based on the distributed resource power generation cost, and constructing a distributed resource cost constraint based on the cost piecewise linear function.

[0085] Specifically, the cost of distributed resource generation is expressed as x Indicates that it is divided into K Segment, that is K Segment interval . K Segment interval ,use Ψ Represents a piecewise linear function operator, and let the piecewise linear function be The value at .

[0086] The piecewise linear function is expressed as follows:

[0087] (4);

[0088] in: , represents the slope of each piecewise linear function, Represents the intercept of the straight line corresponding to the piecewise linear function on the y-axis, Represents a piecewise linear function in The intercept at .

[0089] Distributed resource cost constraints are constructed based on the cost piecewise linear function. The distributed resource cost constraints are expressed as follows:

[0090] (5);

[0091] in: is the cost of distributed generation, For Node i Distributed generation power.

[0092] Step S2014, determining a microgrid constraint model based on the microgrid multi-period coupled power flow constraints, the distributed resource multi-period coupled operation constraints, and the distributed resource cost constraints.

[0093] Specifically, the multi-period coupled flow constraints of the microgrid, the multi-period coupled operation constraints of distributed resources and the cost constraints of distributed resources are placed in a three-dimensional coordinate system to obtain a polyhedron with a convex hull. The model represented by this polyhedron with a convex hull is used as the microgrid constraint model.

[0094] Step S202: Determine the feasible region of distributed resources based on the microgrid constraint model and using the convex hull vertex search algorithm of vertex tracing. Figure 1 Step S102 of the illustrated embodiment will not be described in detail here.

[0095] The method for determining the feasible domain of a distributed resource cluster provided in this embodiment respectively constructs a microgrid multi-period coupled flow constraint, a distributed resource multi-period coupled operation constraint, and a distributed resource cost constraint, and determines a microgrid constraint model based on the above constraints. The multi-period coupling characteristics are considered when calculating the feasible domain of distributed resources, which provides a constraint model basis for the calculation of the feasible domain of distributed resources and indirectly improves the accuracy and precision of the calculation of the feasible domain of distributed resources.

[0096] In this embodiment, a method for determining a feasible region of a distributed resource cluster is provided, which can be used in a microgrid. Figure 3 is a flowchart of a method for determining a feasible domain of a distributed resource cluster according to an embodiment of the present invention. Figure 3 As shown, the process includes the following steps:

[0097] Step S301, construct a microgrid constraint model including microgrid multi-period coupled power flow constraints, distributed resource multi-period coupled operation constraints and distributed resource cost constraints. Figure 2 Step S201 of the illustrated embodiment will not be described in detail here.

[0098] Step S302 , based on the microgrid constraint model and using a convex hull vertex search algorithm with vertex tracing, the feasible domain of distributed resources is determined.

[0099] Specifically, Figure 4 As shown, the above step S302 includes:

[0100] Step S3021, based on the microgrid constraint model and using the convex hull vertex search algorithm of vertex tracing, vertex search is performed to obtain a vertex set and a corresponding convex hull plane vertex index vector.

[0101] In an optional implementation, step S3021 includes:

[0102] Step a1: Based on the microgrid constraint model and using the vertex tracing convex hull vertex search algorithm, multiple direction vectors are randomly generated.

[0103] Specifically, it is assumed that a polyhedron with a convex hull represents the microgrid constraint model with N dimensional convex hull V ,for N dimensional convex hull V , after knowing the external normal direction vectors of all surfaces of the convex hull, we can search for vertices and then expand the convex hull. Ndimensional problem, generated by random generation N +1 direction vector.

[0104] Step a2, performing vertex search on multiple direction vectors to obtain vertex sets corresponding to the multiple direction vectors.

[0105] Specifically, along N +1 direction vector N +1 direction to search for vertices, and get N +1 vertex set V .

[0106] Step a3: number the vertices in the vertex set, and select multiple non-repeated vertices for combination to obtain multiple groups of convex hull plane vertex index vectors containing multiple non-repeated vertices.

[0107] Specifically, the number of convex hull surfaces is the number of combinations, which is expressed as: ; Vertex set V In N +1 vertex number, and then from the vertex set V Select N non-repeated vertices and record the vertex numbers, we can get N +1 set contains N The vertex index vectors of vertices, each set of vertex index vectors represents a set of vertex numbers on the convex hull plane.

[0108] Step S3022, obtaining a clustered direction vector group based on the convex hull plane vertex index vector and a preset clustering algorithm.

[0109] In an optional implementation, step S3022 includes:

[0110] Step b1, determining the external normal direction vectors of multiple convex hull surfaces based on multiple groups of convex hull plane vertex index vectors.

[0111] Specifically, for the i The vertex index vector can be obtained according to the formula i The external normal direction vector of the convex hull surface is calculated in sequence to obtain all the direction vectors of the convex hull.

[0112] The key to calculating the outer normal direction vector of the convex hull surface is to obtain the vertex index vectors of each surface of the convex hull The vertex index vector refers to the index set of points on a convex hull surface, which corresponds one-to-one to each surface of the convex hull. Since the number of vertices on each surface of the convex hull is at least equal to the dimension of the convex hull, in order to ensure that vertex searches are performed along all surfaces, specify The number of vertices indexed is N The outer normal direction vector of the convex hull surface can be calculated by the formula:

[0113] (6);

[0114] in, For the i’ The outer normal direction vector of the convex hull surface, For the i’ The vertex index vector of the convex hull surface, For a square array, The length is N The unit vector of .

[0115] Step b2: clustering the external normal direction vectors of multiple convex hull surfaces using a preset clustering algorithm to obtain a clustered direction vector group.

[0116] In an optional implementation, step b2 is implemented using the following process:

[0117] The density threshold and neighborhood radius of the external normal direction vectors of multiple convex hull surfaces are obtained; the density threshold and the neighborhood radius are clustered using a preset clustering algorithm to obtain a clustered direction vector group.

[0118] Specifically, the preset clustering algorithm adopts a density-based clustering algorithm, namely the DBSCAN algorithm (Density-Based Spatial Clustering of Applications with Noise, referred to as DBSCAN). The main idea of ​​the DBSCAN algorithm is to cluster data points that meet high density into a cluster, and to cluster direction vectors that point to a relatively concentrated direction into a cluster.

[0119] The DBSCAN algorithm has two input parameters: the density threshold of the external normal direction vector MinPts and neighborhood radius Eps In order to preserve the characteristics of the direction vector to the greatest extent, set MinPts =1, neighborhood radius Eps Set to one tenth of the maximum distance of the direction vector, that is . is the Euclidean distance function, is the direction vector group. After the clustering calculation is completed, the number of clusters is recorded as k D In each category, any Through clustering, the number of direction vectors and the number of vertex searches can be reduced, thereby reducing the amount of calculation and improving the calculation efficiency.

[0120] Step S3023, performing vertex search based on the clustered direction vector group and the preset optimization condition to obtain a new vertex set and a corresponding new vertex index vector of the convex hull plane.

[0121] In an optional implementation, step S3023 includes:

[0122] Step c1, performing vertex search on the clustered direction vector group in each direction of the convex hull surface according to preset optimization conditions. If a new vertex is found in a certain direction of the convex hull surface, the new vertex is added to the vertex set to obtain a new vertex set.

[0123] Specifically, taking the multi-period operation constraint of distributed resources as the constraint and the optimization condition of minimizing the cost of the microgrid as the goal as an example, the direction vector is substituted into the optimization condition for vertex search. i If a new vertex is found by searching the direction vector, the new vertex is added to the vertex set. V , get the new vertex set V’ .

[0124] Step c2: number the new vertices and add them to the corresponding convex hull plane vertex index vector to obtain a new vertex index vector of the convex hull plane.

[0125] Specifically, the new vertices are numbered and added to the i The vertex index vector is heavy, then i The vertex index vector contains N +1 vertex. If no new vertex is obtained, no operation is performed.

[0126] For all containing N +1 vertex index vectors are recombined to obtain the new vertex index vector of the convex hull plane; until the number of vertex concentration points exceeds a certain number M (number M Set according to actual situation), output new vertex set V’ .

[0127] Step S3024, determining the feasible domain of distributed resources based on the new vertex set and the corresponding new vertex index vector of the convex hull plane.

[0128] Specifically, assuming that k In the next cycle, search along the convex hull surface k p new vertices. Based on the new vertices and their vertex index vectors, at most new vertex index vectors (without considering duplicate vertex indices), that is A convex hull surface. The plane enclosed by the convex hull surfaces is the feasible domain of distributed resources.

[0129] In the convex hull vertex search algorithm based on vertex tracing, the number of new convex hull surfaces generated in each cycle increases linearly with the number of cycles, while the number of new convex hull surfaces in the PVE algorithm increases exponentially with the number of cycles. Based on the new vertex set and the corresponding new vertex index vector of the convex hull plane, high-dimensional distributed feasible domain calculation can be performed.

[0130] The method for determining the feasible domain of a distributed resource cluster provided in this embodiment adopts a convex hull vertex search algorithm with vertex tracing to obtain the direction vectors of each surface of the convex hull without the need for convex hull calculation in the form of a hyperplane, and generalizes the convex hull vertex search algorithm with vertex tracing to high dimensions, and then generalizes the microgrid aggregation to multiple time periods, and performs vertex search based on the clustered direction vector group and preset optimization conditions to obtain a new vertex set and the corresponding new vertex index vector of the convex hull plane, and determines the feasible domain of distributed resources based on the new vertex set and the corresponding new vertex index vector of the convex hull plane, thereby achieving the purpose of calculating the feasible domain of distributed resources in multiple time periods.

[0131] It should be noted that distributed resource clusters can be aggregated based on the feasible domain of distributed resources. Specifically, the high-dimensional vertex convex hull of the feasible domain of distributed resources is used as the aggregation model of distributed resource clusters, so as to aggregate distributed resource clusters in actual microgrids. The aggregation model of distributed resource clusters realizes the information encapsulation and elimination of internal variables of microgrid distribution systems and distributed resources, and can reflect the external power and cost characteristics of microgrid distribution systems.

[0132] After the distributed resource feasible region is calculated by the distributed resource cluster feasible region determination method provided in this embodiment, the obtained feasible region is used as a microgrid aggregation model to participate in the grid optimization scheduling, as follows:

[0133] like Figure 5 As shown, taking the topology of a 9-node microgrid as an example, the distributed resource cluster feasible domain determination method provided in this embodiment is used to calculate the feasible domain of the distributed resource cluster in the 9-node microgrid, and the feasible domain of each iteration is obtained. The feasible domain obtained from previous iterations is used as a microgrid aggregation model to participate in the grid optimization scheduling.

[0134] Specifically, the microgrid adopts the IEEE 9-node distribution network and calculates the aggregation model from 11:00 to 15:00. The IEEE 9-node distribution network has 2 adjustable buildings connected to nodes 2 and 4, 2 distributed photovoltaics connected to nodes 1 and 3, and 3 distributed energy storage connected to nodes 6, 8, and 9 respectively. The boundary operating conditions include power load and photovoltaic output. The hourly data is assumed based on the basic data collected from the actual distribution network for 24 hours of load and light irradiance data. The grid system connected to the microgrid is composed of centralized thermal power, and the grid-side network constraints are not considered.

[0135] The feasible domain information obtained from each iteration is shown in Table 1 below:

[0136] Table 1

[0137]

[0138] like Figure 6 As shown, it is the logarithmic graph of the number of vertices in the i-th iteration.

[0139] The high-dimensional feasible domain obtained by the iterative calculation is used as the microgrid aggregation model to participate in the grid operation and dispatching, and the power curve of the gateway and the power deviation of each period are obtained. Figure 7 As shown in Table 2, the total cost deviation of the grid-microgrid in each iteration changes over time:

[0140] Table 2

[0141]

[0142] Calculate the ratio of the number of repeated vertices searched in each iteration using the PVE algorithm (based on ordered vertex search, Progressive vertex enumeration, referred to as PVE) and the convex hull vertex search algorithm using vertex tracing to the total number of vertices searched in the iteration, such as Figure 8 As shown. In the second iteration and later, the proportion of repeated vertices searched by the convex hull vertex search algorithm using vertex tracing is lower than that of the PVE algorithm, indicating that the use of DBSCAN clustering direction vector effectively reduces the amount of invalid calculations. Plot the curve of the total cost deviation of the power grid-microgrid obtained by the PVE algorithm and the convex hull vertex search algorithm using vertex tracing as the calculation time decreases, as shown in Fig. 9 As shown in the figure. Since the time axis is of a large magnitude, the horizontal axis is logarithmic, and the initialization time is set to 0.01s. Fig. 9 It can be seen that the total cost error decreases faster when the convex hull vertex search algorithm with vertex tracing is used.

[0143] The method for determining the feasible domain of a distributed resource cluster provided in this embodiment uses a vertex tracing convex hull vertex search algorithm to obtain the direction vectors of each surface of the convex hull without the need for hyperplane form convex hull calculation, generalizes the vertex tracing convex hull vertex search algorithm to high dimensions, and then generalizes the microgrid aggregation to multiple time periods. Based on the clustered direction vector group and preset optimization conditions, vertex search is performed to obtain a new vertex set and the corresponding new vertex index vector of the convex hull plane. The feasible domain of distributed resources is determined based on the new vertex set and the corresponding new vertex index vector of the convex hull plane, thereby achieving the purpose of calculating the feasible domain of distributed resources in multiple time periods. The calculated multi-time period distributed resource feasible domain achieves the purpose of searching for high-dimensional convex hull vertices and constructing a distributed resource cluster aggregation model with energy storage. A 9-node microgrid case was selected to achieve the feasibility of the multi-time period distributed resource aggregation model participating in the coordinated optimization of the power grid.

[0144] In this embodiment, a device for determining a feasible domain of a distributed resource cluster is also provided, and the device is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware for a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0145] This embodiment provides a device for determining a feasible domain of a distributed resource cluster. Fig.10 As shown, including:

[0146] A construction module 1001 is used to construct a microgrid constraint model including a microgrid multi-period coupled power flow constraint, a distributed resource multi-period coupled operation constraint, and a distributed resource cost constraint;

[0147] The determination module 1002 is used to determine the feasible domain of distributed resources based on the microgrid constraint model and using a convex hull vertex search algorithm with vertex tracing.

[0148] In some optional implementations, building block 1001 includes:

[0149] The first construction unit is used to obtain multi-period operation data of the microgrid, and construct multi-period coupled power flow constraints of the microgrid based on the multi-period operation data of the microgrid.

[0150] The second construction unit is used to obtain the distributed resource multi-period operation data, and construct the distributed resource multi-period coupling operation constraints based on the distributed resource multi-period operation data.

[0151] The third construction unit is used to obtain the distributed resource power generation cost, determine the cost piecewise linear function based on the distributed resource power generation cost, and construct the distributed resource cost constraint based on the cost piecewise linear function.

[0152] The first determination unit is used to determine the microgrid constraint model based on the microgrid multi-period coupled power flow constraint, the distributed resource multi-period coupled operation constraint and the distributed resource cost constraint.

[0153] In some optional implementations, the determining module 1002 includes:

[0154] The first search unit is used to perform vertex search based on the microgrid constraint model and adopt a convex hull vertex search algorithm of vertex tracing to obtain a vertex set and a corresponding convex hull plane vertex index vector.

[0155] The clustering unit is used to obtain a clustered direction vector group based on the convex hull plane vertex index vector and a preset clustering algorithm.

[0156] The second search unit is used to perform vertex search based on the clustered direction vector group and preset optimization conditions to obtain a new vertex set and a corresponding new vertex index vector of the convex hull plane.

[0157] The second determining unit is used to determine the feasible domain of distributed resources based on the new vertex set and the corresponding new vertex index vector of the convex hull plane.

[0158] In some optional implementations, the first search unit includes:

[0159] The generating subunit is used for randomly generating a plurality of direction vectors based on the microgrid constraint model and adopting a convex hull vertex search algorithm of vertex tracing.

[0160] The first search subunit is used to perform vertex search on multiple direction vectors to obtain vertex sets corresponding to the multiple direction vectors.

[0161] A combination subunit is used to number the vertices in the vertex set and select multiple non-repeated vertices for combination to obtain multiple groups of convex hull plane vertex index vectors containing multiple non-repeated vertices. In some optional embodiments, the clustering unit includes:

[0162] The first determination subunit is used to determine the outer normal direction vectors of multiple convex hull surfaces based on multiple groups of convex hull plane vertex index vectors.

[0163] The clustering subunit is used to cluster the external normal direction vectors of multiple convex hull surfaces using a preset clustering algorithm to obtain a clustered direction vector group.

[0164] In some optional implementations, the second search unit includes:

[0165] The second search subunit is used to search for vertices in various directions of the convex hull surface for the clustered direction vector group according to preset optimization conditions. If a new vertex is found in a certain direction of the convex hull surface, the new vertex is added to the vertex set to obtain a new vertex set.

[0166] A subunit is added to number the new vertices and add them to the corresponding convex hull plane vertex index vector to obtain the new vertex index vector of the convex hull plane.

[0167] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0168] The distributed resource cluster feasible domain determination device in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0169] The embodiment of the present invention also provides a computer device having the above Fig.10 The distributed resource cluster feasible domain determination device is shown.

[0170] See also Fig.11 , Fig.11 is a schematic diagram of the structure of a computer device provided by an optional embodiment of the present invention, such as Fig.11 As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Fig.11 A processor 10 is taken as an example.

[0171] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0172] The memory 20 stores instructions executable by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.

[0173] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0174] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 20 may also include a combination of the above types of memory.

[0175] The computer device also includes an input device 30 and an output device 40. The processor 10, the memory 20, the input device 30 and the output device 40 may be connected via a bus or other means. Fig.11 The example of connecting through bus is taken in the following.

[0176] The input device 30 can receive input digital or character information, and generate key signal input related to the user settings and function control of the computer device, such as a touch screen, a keypad, a mouse, a track pad, a touch pad, an indicator bar, one or more mouse buttons, a trackball, a joystick, etc. The output device 40 may include a display device, an auxiliary lighting device (e.g., an LED) and a tactile feedback device (e.g., a vibration motor), etc. The above-mentioned display device includes but is not limited to a liquid crystal display, a light emitting diode, a display and a plasma display. In some optional embodiments, the display device may be a touch screen.

[0177] The embodiment of the present invention also provides a computer-readable storage medium. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0178] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A method for determining a feasible domain of a distributed resource cluster, characterized in that: The method comprises: Construct a microgrid constraint model that includes microgrid multi-period coupled power flow constraints, distributed resource multi-period coupled operation constraints, and distributed resource cost constraints; Based on the microgrid constraint model and using a vertex tracing convex hull vertex search algorithm, a feasible domain of distributed resources is determined; The construction of a microgrid constraint model including microgrid multi-period coupled power flow constraints, distributed resource multi-period coupled operation constraints and distributed resource cost constraints includes: Acquire multi-period operation data of the microgrid, and construct multi-period coupling power flow constraints of the microgrid based on the multi-period operation data of the microgrid; Acquire multi-period operation data of distributed resources, and construct multi-period coupling operation constraints of distributed resources based on the multi-period operation data of distributed resources; Obtain the distributed resource power generation cost, determine a cost piecewise linear function based on the distributed resource power generation cost, and construct a distributed resource cost constraint based on the cost piecewise linear function; use the distributed resource power generation cost as x Indicates that it is divided into K Segment, that is K Segment interval ; K Segment interval use Ψ Represents a piecewise linear function operator, and let the piecewise linear function be The value at ; The piecewise linear function is expressed as follows: ； in: , represents the slope of each piecewise linear function, Represents the intercept of the straight line corresponding to the piecewise linear function on the y-axis, Represents a piecewise linear function in The intercept at ; Distributed resource cost constraints are constructed based on the cost piecewise linear function. The distributed resource cost constraints are expressed as follows: ； in: is the cost of distributed generation, For Node i Distributed generation power; Determine a microgrid constraint model based on the microgrid multi-period coupled power flow constraint, the distributed resource multi-period coupled operation constraint, and the distributed resource cost constraint; The values ​​of the multiple time periods are set to be from 1 o'clock to 24 o'clock, so that the feasible domain of distributed resources is transformed from the two-dimensional convex hull to the high-dimensional convex hull.

2. The method according to claim 1, characterized in that Based on the microgrid constraint model and using the convex hull vertex search algorithm to trace the vertex source, the feasible domain of distributed resources is determined, including: Based on the microgrid constraint model and using a vertex tracing convex hull vertex search algorithm to perform vertex search, a vertex set and a corresponding convex hull plane vertex index vector are obtained; Obtaining a clustered direction vector group based on the convex hull plane vertex index vector and a preset clustering algorithm; Performing vertex search based on the clustered direction vector group and preset optimization conditions to obtain a new vertex set and a corresponding new vertex index vector of the convex hull plane; The distributed resource feasible domain is determined based on the new vertex set and the corresponding convex hull plane new vertex index vector.

3. The method according to claim 2, characterized in that Based on the microgrid constraint model and using the vertex tracing convex hull vertex search algorithm to perform vertex search, the vertex set and the corresponding convex hull plane vertex index vector are obtained, including: Based on the microgrid constraint model and using a vertex tracing convex hull vertex search algorithm, a plurality of direction vectors are randomly generated; Perform vertex search on multiple direction vectors to obtain vertex sets corresponding to the multiple direction vectors; The vertices in the vertex set are numbered, and a plurality of non-repeated vertices are selected for combination to obtain a plurality of groups of convex hull plane vertex index vectors containing a plurality of non-repeated vertices.

4. The method according to claim 3, characterized in that The clustered direction vector group obtained based on the convex hull plane vertex index vector and the preset clustering algorithm includes: Determine the outer normal direction vectors of multiple convex hull surfaces based on multiple groups of convex hull plane vertex index vectors; A preset clustering algorithm is used to cluster the external normal direction vectors of the multiple convex hull surfaces to obtain a clustered direction vector group.

5. The method according to claim 4, characterized in that The external normal direction vectors of the plurality of convex hull surfaces are clustered using a preset clustering algorithm, and the clustered direction vector group obtained includes: Get the density threshold and neighborhood radius of the outer normal direction vectors of multiple convex hull surfaces; The density threshold and the neighborhood radius are clustered using a preset clustering algorithm to obtain a clustered direction vector group.

6. The method according to claim 4, characterized in that Based on the clustered direction vector group and the preset optimization conditions, vertex search is performed to obtain a new vertex set and a corresponding new vertex index vector of the convex hull plane, including: According to the preset optimization conditions, the clustered direction vector group is searched for vertices in each direction of the convex hull surface. If a new vertex is found in a certain direction of the convex hull surface, the new vertex is added to the vertex set to obtain a new vertex set. The new vertices are numbered and added to the corresponding convex hull plane vertex index vector to obtain a new vertex index vector of the convex hull plane.

7. A device for determining a feasible domain of a distributed resource cluster, characterized in that: The device comprises: A construction module is used to construct a microgrid constraint model including microgrid multi-period coupled power flow constraints, distributed resource multi-period coupled operation constraints and distributed resource cost constraints; A determination module, used to determine the feasible domain of distributed resources based on the microgrid constraint model and using a convex hull vertex search algorithm of vertex tracing; The construction of a microgrid constraint model including microgrid multi-period coupled power flow constraints, distributed resource multi-period coupled operation constraints and distributed resource cost constraints includes: Acquire multi-period operation data of the microgrid, and construct multi-period coupling power flow constraints of the microgrid based on the multi-period operation data of the microgrid; Acquire multi-period operation data of distributed resources, and construct multi-period coupling operation constraints of distributed resources based on the multi-period operation data of distributed resources; Obtain the distributed resource power generation cost, determine a cost piecewise linear function based on the distributed resource power generation cost, and construct a distributed resource cost constraint based on the cost piecewise linear function; use the distributed resource power generation cost as x Indicates that it is divided into K Segment, that is K Segment interval ; K Segment interval use Ψ Represents a piecewise linear function operator, and let the piecewise linear function be The value at ; The piecewise linear function is expressed as follows: ； in: , represents the slope of each piecewise linear function, Represents the intercept of the straight line corresponding to the piecewise linear function on the y-axis, Represents a piecewise linear function in The intercept at ; Distributed resource cost constraints are constructed based on the cost piecewise linear function. The distributed resource cost constraints are expressed as follows: ； in: is the cost of distributed generation, For Node i Distributed generation power; Determine a microgrid constraint model based on the microgrid multi-period coupled power flow constraint, the distributed resource multi-period coupled operation constraint, and the distributed resource cost constraint; The values ​​of the multiple time periods are set to be from 1 o'clock to 24 o'clock, so that the feasible domain of distributed resources is transformed from the two-dimensional convex hull to the high-dimensional convex hull.

8. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the method for determining the feasible domain of a distributed resource cluster according to any one of claims 1 to 6 by executing the computer instructions.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the method for determining the feasible domain of a distributed resource cluster according to any one of claims 1 to 6.

Images