Operation envelope determination method and device, electronic equipment, readable storage medium and program product

Abstract

The invention relates to an operation envelope determination method and device, electronic equipment, a readable storage medium and a program product. A linear power flow model of the distributed energy distribution network is determined, a feasible region of the linear power flow model is obtained based on active power and reactive power of each distributed energy source, and sampling points of injection power of each distributed energy source are obtained through sampling based on output capacity of each distributed energy source. Sampling points in each distributed energy source are combined to form a plurality of vertex combinations, the vertex combinations which do not meet the safety constraint of the power distribution network are eliminated from the plurality of vertex combinations through an optimal vertex combination reduction method, so that target vertexes conforming to a feasible region are determined, and operation envelopes of each distributed energy source are generated based on the target vertexes. And safe operation of the power distribution network is ensured.

Description

Technical Field

[0001] The present application relates to the technical field of power system control, and particularly to a method, device, electronic device, readable storage medium, and program product for determining an operating envelope. Background Art

[0002] With the large-scale access of Distributed Energy Resources (DER) to the distribution network, the distribution network is prone to voltage over-limit and line overload problems. Generally, the active power and reactive power of DER are directly controlled to meet the safety operation constraints of the distribution network. However, since DER is usually privately owned by users and it is difficult to follow the regulation of the distribution network, the proposed operating envelope provides an effective indirect control method for DER regulation. The operating envelope is the safe operating range of DER active-reactive power, which is usually sent from the distribution network to DER. DER operating within the operating envelope can ensure the safe operation of the distribution network and promote the effective utilization of DER.

[0003] In traditional technologies, after sampling in the distribution network operating envelope calculation method based on Latin hypercube sampling, multiple groups of sampling point combinations are formed. After screening by the distribution network safety constraints, the point convex hull that meets the safety constraints is retained to approximately form the operating envelope. However, the operating envelope obtained by this method only shows that the currently retained combination meets the distribution network safety constraints, and it cannot guarantee that any point combination in each operating envelope meets the safety constraints. Therefore, the operating envelope obtained by the traditional method cannot guarantee that any point combination meets the distribution network safety constraints, thus affecting the safe operation of the distribution network. Summary of the Invention

[0004] Based on this, in view of the above technical problems, it is necessary to provide a method, device, electronic device, computer-readable storage medium, and computer program product for determining an operating envelope that can effectively ensure the safe operation of the distribution network.

[0005] In a first aspect, the present application provides a method for determining an operating envelope, which is applied to a distributed energy distribution network, and the method includes:

[0006] Determine the linear power flow model of the distributed energy distribution network, and obtain the feasible region of the linear power flow model based on the active power and reactive power of each distributed energy;

[0007] Based on the output capacity of each distributed energy, obtain the sampling points of the injection power of each distributed energy through sampling;

[0008] Combine the sampling points in each distributed energy to form multiple vertex combinations;

[0009] Determine the target vertices that meet the feasible region from multiple vertex combinations through the optimal vertex combination reduction method, and generate the operation envelope of the distributed energy based on the target vertices.

[0010] In one embodiment, the method for determining the linear power flow model of the distributed energy distribution network and obtaining the feasible region of the linear power flow model based on the active power and reactive power of each distributed energy source includes:

[0011] Establish the linear power flow model of the distributed energy distribution network;

[0012] Based on the node and branch incidence matrix in the distributed energy distribution network, convert the node injection power into line power, and determine the linear representation of the voltage amplitude of the nodes;

[0013] According to the preset constraint conditions of the voltage amplitude of the nodes and the number of nodes of the distributed energy sources in the distributed energy distribution network, determine the feasible region of the linear power flow model based on the active power and reactive power of each distributed energy source.

[0014] In one embodiment, the method for obtaining the sampling points of the injection power of each distributed energy source by sampling based on the output capacity of each distributed energy source includes:

[0015] Determine the power constraint range of each distributed energy source according to the rated capacity and rated power of each distributed energy source;

[0016] Based on the power and load of each distributed energy source, determine the node injection power of each distributed energy source;

[0017] Based on the power constraint range of each distributed energy source and the node injection power, determine the initial convex hull of each distributed energy source;

[0018] Discretize the initial convex hull of each distributed energy source through Latin hypercube sampling to obtain the active power sampling points and reactive power sampling points of the initial convex hull of each distributed energy source.

[0019] In one embodiment, the method for combining the sampling points in each distributed energy source to form multiple vertex combinations includes:

[0020] According to the active power sampling points and reactive power sampling points of each distributed energy source, generate an approximate convex hull of the initial convex hull of each distributed energy source based on the convex hull approximation function;

[0021] For each distributed energy source, determine the optimal vertex that maximizes the objective function in the corresponding approximate convex hull;

[0022] Combine the optimal vertices in each of the distributed energy sources to obtain multiple vertex combinations.

[0023] In one embodiment, the method of determining the target vertices that meet the feasible region from multiple vertex combinations by the optimal vertex combination reduction method includes:

[0024] In the case where it is determined that any one of the vertex combinations does not meet the feasible region, delete the vertices corresponding to the vertex combination in the corresponding approximate convex hull;

[0025] Determine the candidate vertices of the approximate convex hull after deleting the vertices;

[0026] Determine the candidate vertices and the remaining vertices of the approximate convex hull after deleting the vertices as the target vertices.

[0027] In one embodiment, the method of determining the target vertices that meet the feasible region from multiple vertex combinations by the optimal vertex combination reduction method includes:

[0028] In the case where it is determined that multiple vertex combinations all meet the feasible region, determine the vertices of the approximate convex hull corresponding to the multiple vertex combinations as the target vertices.

[0029] In a second aspect, the present application further provides a device for determining an operating envelope. The device is applied to a distributed energy distribution network, and the device includes:

[0030] A feasible region acquisition module, configured to determine a linear power flow model of the distributed energy distribution network, and obtain the feasible region of the linear power flow model based on the active power and reactive power of each distributed energy source;

[0031] A sampling point acquisition module, configured to obtain sampling points of the injection power of each distributed energy source by sampling based on the output capacity of each distributed energy source;

[0032] A vertex combination module, configured to combine the sampling points in each distributed energy source to form multiple vertex combinations;

[0033] An envelope generation module, configured to determine target vertices that meet the feasible region from multiple vertex combinations by the optimal vertex combination reduction method, and generate an operating envelope of the distributed energy based on the target vertices.

[0034] In a third aspect, the present application further provides an electronic device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps of the above method are implemented.

[0035] Fourthly, the present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned method are implemented.

[0036] Fifthly, the present application also provides a computer program product, including a computer program. When the computer program is executed by a processor, the steps of the above-mentioned method are implemented.

[0037] The above method, device, electronic device, computer-readable storage medium and computer program product for determining the operating envelope determine the linear power flow model of the distributed energy distribution network, obtain the feasible region of the linear power flow model based on the active power and reactive power of each distributed energy source, and based on the output capacity of each distributed energy source, sample to obtain the sampling points of the injection power of each distributed energy source, combine the sampling points in each distributed energy source to form multiple vertex combinations, eliminate the vertex combinations that do not meet the safety constraints of the distribution network from the multiple vertex combinations through the optimal vertex combination reduction method, thereby determining the target vertices that meet the feasible region, and generating the operating envelope of each distributed energy source based on the target vertices, so as to ensure the safe operation of the distribution network. Description of the Drawings

[0038] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following will briefly introduce the drawings required to be used in the description of the embodiments of the present application or related technologies. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0039] Figure 1 It is a schematic flowchart of the method for determining the operating envelope in an embodiment;

[0040] Figure 2 It is a schematic diagram of the distributed energy distribution network system in an embodiment;

[0041] Figure 3 It is a schematic diagram for comparing the operating envelopes in an embodiment;

[0042] Figure 4 It is a structural block diagram of the device for determining the operating envelope in an embodiment;

[0043] Figure 5 It is an internal structure diagram of an electronic device in an embodiment. Detailed Embodiments

[0044] To make the objectives, technical solutions and advantages of this application more clear and understandable, the following further details this application in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not used to limit this application.

[0045] In one embodiment, a method for determining an operating envelope is provided, and this method is applied to a distributed energy distribution network. Among them, distributed energy DER refers to small, decentralized energy generation, storage and management devices located at the user side or near the energy consumption point. DER can operate independently or be incorporated into the distribution network for operation. DER can provide backup power during grid failures, thereby improving the reliability of energy supply. DER can also optimize energy use and reduce power demand during peak hours, thereby reducing the energy cost of users. DER provides more opportunities for the access of renewable energy and supports the integration of renewable energy.

[0046] In this embodiment, as Figure 1 shown, this method may include the following steps:

[0047] Step 102, determine the linear power flow model of the distributed energy distribution network, and obtain the feasible region of the linear power flow model based on the active power and reactive power of each distributed energy.

[0048] Among them, the linear power flow model is a simplified model in power system analysis. It simplifies the power flow calculation process by assuming that the power flow distribution is linearly related to the voltage amplitude. This model is usually used for power flow calculation in the distribution network. The basic idea of the linear power flow model is to linearize the non-linear power flow equation for easy analysis and calculation. Specifically, the linear power flow model is usually established based on the branch power flow equation. In this way, a linear equation system can be established to describe the power flow distribution in the power system, so that the power flow distribution of the system can be quickly evaluated without complex non-linear calculations.

[0049] The feasible region refers to the space in the power system where the system state variables (such as voltage, power, etc.) can take values under the condition of satisfying all operating constraints. It defines the conditions under which the system can operate safely and is a region in a multi-dimensional space, where each point represents a possible operating state of the system.

[0050] In this embodiment, by constructing and determining the linear power flow model of the distributed energy distribution network, obtaining the feasible region of the linear power flow model based on the active power and reactive power of each distributed energy in the distribution network, and then determining the operating envelope of each distributed energy based on the subsequent steps, the safe operation of the distribution network is guaranteed.

[0051] Step 104, based on the output capacity of each distributed energy, obtain the sampling points of the injection power of each distributed energy through sampling.

[0052] Among them, the output capacity of distributed energy refers to the maximum electrical energy output that distributed energy can provide under certain conditions. It includes the output capabilities of the active power and reactive power of distributed energy, and is an important parameter for evaluating and planning the operation of the distribution network.

[0053] In a distributed energy distribution network, the sampling points of the injected power refer to the power values actually injected into the power grid by distributed energy systems (such as solar photovoltaics, wind power, etc.) under different operating conditions. These sampling points can be used to analyze and predict the output behavior of distributed energy, as well as evaluate its impact on the power grid. The acquisition of sampling points is usually based on the output capacity of each distributed energy, and multiple sets of sampling values of the injected power at each node are obtained through the Latin hypercube sampling algorithm. Through these sampling points, the output conditions of distributed energy under different operating states can be simulated, and then the impact on parameters such as the voltage and frequency of the power grid can be analyzed. This is of great significance for the planning, operation, and control of the power grid, and can help grid operators better manage and optimize the access and scheduling of distributed energy.

[0054] In this embodiment, based on the output capacity of each distributed energy, sampling points of the injected power of each distributed energy can be obtained through sampling.

[0055] Step 106: Combine the sampling points in each distributed energy to form multiple vertex combinations.

[0056] Specifically, the sampling points of the injected power of each distributed energy obtained above can be combined to obtain multiple combinations of the sampling points of the injected power of different distributed energies, that is, multiple vertex combinations are obtained.

[0057] Step 108: Determine the target vertices that meet the feasible region from multiple vertex combinations through the optimal vertex combination reduction method, and generate the operation envelope of distributed energy based on the target vertices.

[0058] Among them, the optimal vertex combination reduction method is a method used to process and reduce vertex combinations that do not meet the constraint conditions in optimization problems. In the evaluation of the output capacity of distributed energy in the distribution network, this method can be used to determine the operation envelope of each distributed energy node by finding the optimal solution within the convex hull or feasible region, that is, to determine the power output range within which each distributed energy node can operate safely and stably.

[0059] In the above method for determining the operating envelope, by determining the linear power flow model of the distributed energy distribution network, obtaining the feasible region of the linear power flow model based on the active power and reactive power of each distributed energy source, and based on the output capacity of each distributed energy source, sampling points of the injection power of each distributed energy source are obtained through sampling, and the sampling points in each distributed energy source are combined to form multiple vertex combinations. The vertex combinations that do not meet the safety constraints of the distribution network are excluded from the multiple vertex combinations through the optimal vertex combination reduction method, so as to determine the target vertices that meet the feasible region, and generate the operating envelopes of each distributed energy source based on the target vertices, thereby ensuring the safe operation of the distribution network.

[0060] In an exemplary embodiment, in step 102, to determine the linear power flow model of the distributed energy distribution network and obtain the feasible region of the linear power flow model based on the active power and reactive power of each distributed energy source, it may specifically include: establishing the linear power flow model of the distributed energy distribution network; converting the node injection power into line power based on the node and branch incidence matrix of the distributed energy distribution network, and determining the linear representation of the voltage amplitude of the nodes; according to the preset constraint conditions of the voltage amplitude of the nodes and the number of nodes of the distributed energy sources in the distributed energy distribution network, determining the feasible region of the linear power flow model based on the active power and reactive power of each distributed energy source.

[0061] Specifically, first, a linearized power flow model can be established, which takes the active power and reactive power of the distributed energy sources as variables.

[0062] In a radial distribution network, assuming that the distribution network power loss is not considered, the active power vector P L and the reactive power vector Q L can be linearly represented by the active power and reactive power injected by the nodes respectively, and the square vector V of the node voltage amplitude can be linearly represented by the active power and reactive power of the lines. Using the node-branch incidence matrix H, the node injection power can be converted into line power, and there is the following formula (1):

[0063] P L =H -1 p inj

[0064] Q L =H -1 q inj

[0065] V=V0+H -T (2R L P L +2X L Q L )

[0066] In the formula, p inj 、qinj The active and reactive power vectors injected into the nodes respectively. H is the node-branch incidence matrix of the distribution network, which describes the connection relationship between the nodes and branches in the distribution network. H -1 is the inverse matrix of H, used to calculate the line power from the node injection power. H T is the transpose matrix of H, H -T is H T 's inverse matrix, used to convert the line power into the node voltage change. V0 is the square vector of the reference node voltage amplitude, usually a known constant vector. P L , Q L are the active and reactive power vectors of the line respectively. R L and X L are the resistance matrix and reactance matrix of the line respectively, which describe the electrical characteristics of the line.

[0067] Therefore, the square vector of the node voltage amplitude can be linearly represented by the active and reactive power injected into the nodes. Then, through the matrices M P and M Q , the above expression V can be simplified to the following formula (2):

[0068] V = V0 + M P p inj + M Q q inj

[0069] where, M P and M Q can be expressed by the following formula (3):

[0070] M P = 2H -T R L H -1

[0071] M Q = 2H -T X L H -1

[0072] Due to the constraint of the square vector of the node voltage amplitude, that is, the following formula (4):

[0073] V min ≤ V ≤ V max , where, V min and V max represent the minimum and maximum allowable values of the node voltage respectively.

[0074] Therefore, the above constraint can be written in the following compact form, as the following formula (5):

[0075] Ax ≤ b

[0076] where A is an m×2N DER coefficient matrix, m is the number of rows of the constraints, and x = [P inj , Q inj is a 2N DER -dimensional variable vector representing the voltage magnitudes and phases of all nodes. And N DER is the number of nodes equipped with distributed energy in the distribution network. Since each node has two variables, a 2N DER -dimensional convex polyhedron feasible region can be obtained through the above constraints. b is an m-dimensional vector representing the constraint terms.

[0077] Specifically, by constructing the coefficient matrix A and the vector b, each constraint condition can be expressed in the form of Ax ≤ b. For example, for the constraint of the voltage magnitude, each row of A corresponds to the voltage constraint of a node, and the corresponding element of b is V min and V max (depending on whether it is the lower bound or the upper bound). Finally, all these constraints define a feasible region, that is, the set of all x that satisfy Ax ≤ b. In this case, the feasible region is a 2N DER -dimensional convex polyhedron representing all possible voltage configurations that satisfy the physical and operating constraints of the system.

[0078] In this embodiment, by establishing a linearized power flow model, the node injection power is converted into line power by using the node-branch incidence matrix, and the node voltage magnitudes are further represented. At the same time, the constraint conditions of the voltage magnitudes are defined and written in a compact form, so as to obtain a high-dimensional convex polyhedron feasible region. Furthermore, the operating state of the distribution network can be accurately described, laying a foundation for subsequent solving of the operating envelope.

[0079] In an exemplary embodiment, in step 104, based on the output capacity of each distributed energy, sampling points of the injection power of each distributed energy are obtained by sampling, which may specifically include: determining the power constraint range of each distributed energy according to the rated capacity and rated power of each distributed energy; determining the node injection power of each distributed energy based on the power and load of each distributed energy; determining the initial convex hull of each distributed energy based on the power constraint range and node injection power of each distributed energy; discretizing the initial convex hull of each distributed energy through Latin hypercube sampling to obtain the active sampling points and reactive sampling points of the initial convex hull of each distributed energy.

[0080] Specifically, the constraints of each distributed energy can be expressed by the following formula (6):

[0081]

[0082] Among them, the node h equipped with distributed energy can be represented by the set H. Suppose there are N DER nodes equipped with distributed energy, then respectively represent the active power and reactive power of the distributed energy h, is the maximum active output of h, and its capacity value, i.e., the rated power, can be taken, is the power factor angle of h, with the unit of rad, is the rated capacity of h.

[0083] The above constraints indicate that the active power of the distributed energy h is between 0 and its maximum active output , and the reactive power of the distributed energy h needs to satisfy the constraint of the power factor angle . At the same time, the sum of the squares of the active power and reactive power of the distributed energy h needs to be less than or equal to the square of its rated capacity . Through the above constraints, the reasonable configuration and operation of the distributed energy in the distribution network are ensured, and at the same time, the node power balance and system stability are guaranteed.

[0084] Therefore, the injection power of the node h equipped with distributed energy can be represented by the following formula (7):

[0085]

[0086] where, is the active load of the node h, is the reactive load of the node h. is the active injection power of the node h, is the reactive injection power of the node h.

[0087] The node w without distributed energy can be represented by the set W, and the injection power can be represented by the following formula (8):

[0088]

[0089] where, is the active load of the node w, is the reactive load of the node w. is the active injection power of the node w, is the reactive injection power of the node w.

[0090] Therefore, the active injection power and reactive injection power of all nodes in the distribution network can be respectively expressed as In this embodiment, since the output of each DER is independent and the node loads are all uncontrollable loads, only power regulation needs to be performed through the DER. That is to say, there is an operating envelope only for the set H of nodes equipped with DER. Then, for each h ∈ H (i.e., each node h in the set H), the initial convex hull of each node h in H can be obtained as shown in the following formula (9):

[0091]

[0092] This formula indicates that the initial convex hull of node h is composed of all pairs that satisfy the conditions of the above formulas (6) and (7).

[0093] Through the above formula (9), the initial convex hull of node h equipped with DER can be obtained Due to the initial convex hull of the two-dimensional continuous space it is difficult to determine a feasible constraint boundary to generate new vertices after vertex reduction. Therefore, the initial convex hull of the two-dimensional space can be discretized by using Latin hypercube sampling, so as to obtain the active-reactive sampling points of the initial convex hull

[0094]

[0095] In this embodiment, by defining the initial operating envelope of the nodes equipped with DER in the distribution network, this envelope describes all possible combinations of the active and reactive powers that the nodes can output under all constraint conditions. These constraints ensure the stability and security of the system, and at the same time allow the power demand to be met by adjusting the output of the DER. And the Latin hypercube sampling algorithm is used to discretely sample the output capabilities of each distributed energy source, so as to effectively capture the uncertainty characteristics of the distributed energy source output and improve the calculation accuracy of the operating envelope.

[0096] In an exemplary embodiment, in step 106, the sampling points in each distributed energy source are combined to form a plurality of vertex combinations, which may specifically include: based on the active sampling points and reactive sampling points of each distributed energy source, generating an approximate convex hull of the initial convex hull of each distributed energy source based on the convex hull approximation function; for each distributed energy source, determining the optimal vertex that maximizes the objective function in the corresponding approximate convex hull; combining the optimal vertices in each distributed energy source to obtain a plurality of vertex combinations.

[0096] In an exemplary embodiment, in step 108, the target vertices that meet the feasible region are determined from multiple vertex combinations by the optimal vertex combination reduction method, which may specifically include: in the case where it is determined that any vertex combination does not meet the feasible region, the vertices corresponding to the vertex combination are deleted in the corresponding approximate convex hull; the candidate vertices of the approximate convex hull after deleting the vertices are determined; the candidate vertices and the remaining vertices of the approximate convex hull after deleting the vertices are determined as the target vertices. Alternatively, in the case where it is determined that multiple vertex combinations all meet the feasible region, the vertices of the convex hulls corresponding to the multiple vertex combinations are determined as the target vertices.

[0097] Specifically, according to the active power sampling points and reactive power sampling points of each distributed energy source sampled above, based on the convex hull approximation function, the approximate convex hull of the initial convex hull of each distributed energy source can be generated, that is, as shown in the following formula (10):

[0098]

[0099] Wherein, represents the active power sampling points and reactive power sampling points obtained by Latin hypercube sampling. In the formula, the approximate convex hull generated by the sampling points can be denoted as the initial convex hull of node h can be approximately represented as the convex hull of all sampling points Specifically, it can be generated by the convex hull approximation function in the MPT toolbox.

[0100] After that can be used as the approximate convex hull of to approximate Ω through vertex reduction h . The Latin hypercube sampling method can convert the original two-dimensional convex set into a two-dimensional convex hull. The Cartesian product Λ of the vertices of the approximate convex hulls s of each node can be represented by the following formula (11):

[0101]

[0102] Wherein, Λ s is a matrix containing |Λ s | 2N pv -dimensional column vectors, and |Λ s | is the number of vertex combinations in Λ s , then there is the following formula (12):

[0103]

[0104] Wherein, is the number of vertices of the initial convex hull h. According to the convex optimization theory, if the approximate convex hulls All vertices in the Cartesian product set Λ of s are within the feasible region, that is when it satisfies the distribution network safe operation constraints. Therefore, Λ can be iteratively reduced s by removing the vertex combinations that do not satisfy the constraints until the operation envelope Ω is obtained.

[0105] It can be clearly seen from the above iterative reduction process that by traversing all vertex combinations although in theory all vertices violating the constraints can be deleted, as the number of nodes equipped with DER in the distribution network increases, the vertex combinations in Λ s will grow explosively, resulting in difficult solution and not meeting the control requirements. Based on this, this embodiment proposes an optimal vertex combination reduction method, which utilizes the property that the operation envelopes after Latin hypercube sampling are decoupled (that is, the initial convex hulls of the nodes equipped with DER are known and decoupled from each other), and judges the vertex combinations violating s the constraints in Λ through a few vertex combinations.

[0106] Specifically, to judge whether there are vertex combinations violating the constraints in Λ s it is only necessary to judge whether (maxAx) and b in the distribution network safe operation constraints satisfy the constraints of the following formula (13):

[0107] (max Ax) ≤ b

[0108] s.t. x ∈ Λ s

[0109] The objective function (maxAx) in the above formula (13) represents the maximum value of each row after multiplying matrix A and vector x. Then formula (13) means that the corresponding values of the calculated maximum values are all ≤ b, and the following has the same meaning. x ∈ Λ s means that the variable x belongs to the set Λ s , and this set is usually composed of all possible power combinations or other parameter combinations.

[0110] In addition, the variable is decoupled in constraint (5), so the compact form (5) can be written in the column form of the coefficient matrix A, as the following formula (14):

[0111]

[0112] Then formula (13) can be converted into the following formula (15):

[0113]

[0114] Equation (15) indicates that the optimization objective of the entire system can be decomposed into optimization problems for each node. Among them, A h is the coefficient matrix related to node h, and x h is the decision variable vector of node h, is the feasible region space of node h.

[0115] At this time, the objective function is equivalently decomposed due to the decoupling of the decision variable x. Therefore, it is only necessary to solve the maximum value of A h x h corresponding to each node respectively. For node h, there is the following optimization problem, as shown in Equation (16):

[0116] maxA h x h

[0117]

[0118] Equation (16) is the optimization problem for each node h, and the goal is to find the x within the feasible region space h such that A h x h is maximized.

[0119] Due to the decoupling of the decision variable x, the optimization problem of the entire system can be decomposed into independent optimization problems for multiple nodes. The optimization problem for each node only involves the decision variable x h of that node. Also, since is the vertex set of the convex hull , and its result has been obtained during the generation of the convex hull. Then, according to convex optimization theory, the optimal vertex of the linear optimization problem (16) must be on the vertex of its feasible region . Therefore, the vertex corresponding to the maximum value can be selected as the optimal vertex of this optimization problem. For example, for each node h, select the vertex corresponding to the maximum value of A h x h as the optimal vertex.

[0120] Since the optimization problem, namely Equation (16), represents m (the number of constraints of Ax ≤ b) optimization problems, the vertices corresponding to the optimal values of the optimization problems to be solved are m (i.e., m optimal vertices). That is, each row of A h corresponds to one optimal vertex in h . Therefore, for A pv corresponding to all nodes h ∈ H, m × N

[0121] Also, since the Ax corresponding to each optimal vertex combination is the maximum value of the current constraint row, there is no need to perform a Cartesian product on the vertices of the envelopes for all nodes. Instead, it is only necessary to determine whether the optimal vertex combinations corresponding to each constraint row satisfy this condition. If the constraint is satisfied, the vertices of the approximate convex hull corresponding to these multiple vertex combinations can be determined as the target vertices, and then the running envelope can be formed.

[0122] In one scenario, if the constraint is not satisfied, the optimal vertices that do not satisfy the constraint are deleted from each convex hull, and the candidate vertices of the convex hull after the deletion of the vertices are determined. The candidate vertices and the remaining vertices of the convex hull after the deletion of the vertices are determined as the target vertices until all optimal vertex combinations satisfy the constraint, and then a new convex hull is formed. This method can ensure that the finally obtained running envelope satisfies the operation constraints of the distribution network.

[0123] In the above embodiment, based on the optimal vertex combination reduction method, the output uncertainty characteristics of distributed energy are fully considered, which can provide strong support for the planning, operation, and control of the distribution network, and promote the efficient utilization of distributed energy in the distribution network. By determining whether the sampling point combination belongs to the feasible region, the vertex combinations that do not satisfy the safety constraints of the distribution network are eliminated, so as to obtain the effective running envelopes of each distributed energy node. The obtained running envelopes can ensure that any point combination of each running envelope satisfies the safety constraints of the distribution network, avoiding the combination problems that do not satisfy the safety constraints existing in the traditional method, and improving the reliability and safety of the distribution network operation.

[0124] In an exemplary embodiment, as Figure 2 shown, this embodiment verifies the above scheme with respect to an IEEE 33-node active distribution system involving 6 distributed energy sources. Among them, node 1 is the slack node connecting the distribution network to the superior grid, and its voltage amplitude is 1.0 p.u. The rated capacities of the 6 DER nodes (PVs in the figure) are all 0.4 MVA, and the power factor angle is arctan(0.5). They are connected to nodes 12, 14, 16, 18, 31, and 33 (i.e., DER nodes) respectively. The upper and lower limits of the node voltage are set to 0.95 p.u. and 1.05 p.u. respectively. Other data of the distribution network can refer to the original data of the IEEE 33-node system.

[0125] Then, as Figure 3 shown, among them, the green wireframe is the initial convex hull, the red dashed wireframe is the running envelopes of each DER node obtained by using the above method for determining the running envelope of the present application (i.e., the optimal vertex combination reduction method), and the blue dashed wireframe is the running envelope obtained by using the traditional KL decomposition (Karhunen-Loève decomposition). The comparison of the areas of the running envelopes obtained by the two is shown in the following table:

[0126]

[0127] By Figure 3 and the comparison in the above table, it can be intuitively seen that the method for determining the above operating envelope of the present application (i.e., the optimal vertex combination reduction method) has a larger range of the operating envelope than that obtained by KL decomposition.

[0128] It should be understood that although the steps in the flowcharts involved in the above-described embodiments are sequentially shown according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear indication in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-described embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same moment, but can be executed at different moments, and the execution order of these steps or stages is not necessarily sequential, but can be executed alternately or alternately with at least a part of other steps or steps or stages in other steps.

[0129] Based on the same inventive concept, an embodiment of the present application further provides a device for determining an operating envelope for implementing the method for determining the operating envelope involved above. The solution provided by this device for solving the problem is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the device for determining an operating envelope provided below can refer to the limitations on the method for determining an operating envelope in the above text, and will not be repeated here.

[0130] In an exemplary embodiment, as Figure 4 shown, a device for determining an operating envelope is provided. This device is applied to a distributed energy distribution network and includes: a feasible region acquisition module 402, a sampling point acquisition module 404, a vertex combination module 406, and an envelope generation module 408, where:

[0131] The feasible region acquisition module 402 is configured to determine a linear power flow model of the distributed energy distribution network and obtain a feasible region of the linear power flow model based on the active power and reactive power of each distributed energy.

[0132] The sampling point acquisition module 404 is configured to obtain sampling points of the injection power of each distributed energy by sampling based on the output capacity of each distributed energy.

[0133] The vertex combination module 406 is configured to combine the sampling points in each distributed energy to form a plurality of vertex combinations.

[0134] An envelope generation module 408 is configured to determine a target vertex that meets the feasible region from multiple vertex combinations through an optimal vertex combination reduction method, and generate an operation envelope of the distributed energy based on the target vertex.

[0135] In an exemplary embodiment, the feasible region acquisition module is specifically configured to: establish a linear power flow model of the distributed energy distribution network; convert the node injection power into line power based on the node and branch incidence matrix in the distributed energy distribution network, and determine a linear representation of the voltage magnitude of the node; according to the preset constraint conditions of the voltage magnitude of the node and the number of nodes of the distributed energy in the distributed energy distribution network, determine the feasible region of the linear power flow model based on the active power and reactive power of each distributed energy.

[0136] In an exemplary embodiment, the sampling point acquisition module is specifically configured to: determine the power constraint range of each distributed energy according to the rated capacity and rated power of each distributed energy; determine the node injection power of each distributed energy based on the power and load of each distributed energy; determine the initial convex hull of each distributed energy based on the power constraint range and the node injection power of each distributed energy; discretize the initial convex hull of each distributed energy through Latin hypercube sampling to obtain the active sampling points and reactive sampling points of the initial convex hull of each distributed energy.

[0137] In an exemplary embodiment, the vertex combination module is specifically configured to: generate an approximate convex hull of the initial convex hull of each distributed energy based on the convex hull approximation function according to the active sampling points and reactive sampling points of each distributed energy; for each distributed energy, determine the optimal vertex that maximizes the objective function in the corresponding approximate convex hull; combine the optimal vertices in each distributed energy to obtain multiple vertex combinations.

[0138] In an exemplary embodiment, the envelope generation module is specifically configured to: in the case where it is determined that any vertex combination does not meet the feasible region, delete the vertex corresponding to the vertex combination in the corresponding approximate convex hull; determine the candidate vertices of the approximate convex hull after deleting the vertices; determine the candidate vertices and the remaining vertices of the approximate convex hull after deleting the vertices as the target vertices.

[0139] In an exemplary embodiment, the envelope generation module is specifically further configured to: in the case where it is determined that multiple vertex combinations all meet the feasible region, determine the vertices of the approximate convex hulls corresponding to the multiple vertex combinations as the target vertices.

[0140] Each module in the above-mentioned determining device of the operating envelope can be implemented in whole or in part by software, hardware, or a combination thereof. Each of the above modules can be embedded in or independent of a processor in an electronic device in the form of hardware, or stored in a memory in the electronic device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0141] In an exemplary embodiment, an electronic device is provided, and its internal structure diagram can be as Figure 5 shown. The electronic device includes a processor, a memory, an input / output interface, a communication interface, a display unit, and an input device. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface, the display unit, and the input device are connected to the system bus through the input / output interface. Among them, the processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the electronic device is used for exchanging information between the processor and external devices. The communication interface of the electronic device is used for communicating with external terminals in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, near field communication (NFC), or other technologies. When the computer program is executed by the processor, it implements a method for determining an operating envelope. The display unit of the electronic device is used to form a visually visible picture, which can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen. The input device of the electronic device can be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the housing of the electronic device, or an external keyboard, a touchpad, or a mouse, etc.

[0142] Those skilled in the art can understand that Figure 5 the structure shown in

[0143] is only a block diagram of a part of the structure related to the solution of this application, and does not constitute a limitation on the electronic device to which the solution of this application is applied. The specific electronic device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0144] In an embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, it implements the steps in the above-mentioned method embodiments.

[0145] In one embodiment, a computer program product is provided, including a computer program which, when executed by a processor, implements the steps in the foregoing method embodiments.

[0146] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with relevant regulations.

[0147] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include Read-Only Memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, Resistive Random Access Memory (ReRAM), Magnetoresistive Random Access Memory (MRAM), Ferroelectric Random Access Memory (FRAM), Phase Change Memory (PCM), graphene memory, etc. Volatile memory can include Random Access Memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), etc. The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logic devices, data processing logics based on quantum computing, Artificial Intelligence (AI) processors, etc., without limitation.

[0148] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in the present application.

[0149] The above-described embodiments merely represent several implementation manners of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation on the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all fall within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.

Claims

1. A method for determining an operating envelope, characterized in that The method is applied to a distributed energy distribution network, and the method includes: Determine the linear power flow model of the distributed energy distribution network, and obtain the feasible region of the linear power flow model based on the active power and reactive power of each distributed energy source; Based on the output capacity of each distributed energy source, obtain the sampling points of the injection power of each distributed energy source by sampling; Combine the sampling points in each distributed energy source to form multiple vertex combinations; Determine the target vertex that meets the feasible region from multiple vertex combinations by the optimal vertex combination reduction method, and generate the operation envelope of the distributed energy source based on the target vertex.

2. The method according to claim 1, characterized in that, The step of determining the linear power flow model of the distributed energy distribution network and obtaining the feasible region of the linear power flow model based on the active power and reactive power of each distributed energy source includes: Establish the linear power flow model of the distributed energy distribution network; Based on the node and branch incidence matrix in the distributed energy distribution network, convert the node injection power into line power, and determine the linear representation of the voltage amplitude of the node; According to the preset constraint conditions of the voltage amplitude of the node and the number of nodes of the distributed energy source in the distributed energy distribution network, determine the feasible region of the linear power flow model based on the active power and reactive power of each distributed energy source.

3. The method according to claim 1 or 2, characterized in that, The step of obtaining the sampling points of the injection power of each distributed energy source by sampling based on the output capacity of each distributed energy source includes: Determine the power constraint range of each distributed energy source according to the rated capacity and rated power of each distributed energy source; Based on the power and load of each distributed energy source, determine the node injection power of each distributed energy source; Based on the power constraint range of each distributed energy source and the node injection power, determine the initial convex hull of each distributed energy source; Discretize the initial convex hull of each distributed energy source by Latin hypercube sampling to obtain the active sampling points and reactive sampling points of the initial convex hull of each distributed energy source.

4. The method according to claim 3, characterized in that, The step of combining the sampling points in each distributed energy source to form multiple vertex combinations includes: According to the active sampling points and reactive sampling points of each distributed energy source, generate an approximate convex hull of the initial convex hull of each distributed energy source based on the convex hull approximation function; For each distributed energy source, determine the optimal vertex that maximizes the objective function in the corresponding approximate convex hull; Combine the optimal vertices in each distributed energy source to obtain multiple vertex combinations.

5. The method according to claim 4, characterized in that, The step of determining the target vertex that meets the feasible region from multiple vertex combinations by the optimal vertex combination reduction method includes: In the case where it is determined that any vertex combination does not meet the feasible region, delete the vertex corresponding to the vertex combination in the corresponding approximate convex hull; Determine the candidate vertices of the approximate convex hull after deleting the vertices; Determine the candidate vertices and the remaining vertices of the approximate convex hull after deleting the vertices as the target vertices.

6. The method according to claim 4, characterized in that, The step of determining the target vertex that meets the feasible region from multiple vertex combinations by the optimal vertex combination reduction method includes: When it is determined that multiple vertex combinations all meet the feasible region, the vertices of the approximate convex hull corresponding to the multiple vertex combinations are determined as the target vertices.

7. A determining device for an operating envelope, characterized in that, The device is applied to a distributed energy distribution network, and the device includes: A feasible region acquisition module, configured to determine a linear power flow model of the distributed energy distribution network, and obtain the feasible region of the linear power flow model based on the active power and reactive power of each distributed energy source; A sampling point acquisition module, configured to obtain sampling points of the injection power of each distributed energy source by sampling based on the output capacity of each distributed energy source; A vertex combination module, configured to combine the sampling points in each distributed energy source to form multiple vertex combinations; An envelope generation module, configured to determine target vertices that meet the feasible region from the multiple vertex combinations by an optimal vertex combination reduction method, and generate an operation envelope of the distributed energy based on the target vertices.

8. An electronic device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product comprising a computer program, characterized in that, When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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