Power supply network reliability evaluation method considering power transmission loss

Abstract

The invention relates to a power supply network reliability evaluation method considering power transmission loss, and belongs to the field of power supply network reliability evaluation. The method comprises the following steps: firstly, acquiring basic parameters of a power supply network, and defining an initial state vector set C; judging the type of the C according to the relationship between the maximum flow M (L) and M (U) of the loss network under the minimum state vector L and the maximum state vector U in the C and the demand level d; for an uncertain state vector set, determining a loss network feasible vector of the uncertain state vector set, screening out a set A (X) according to the loss network feasible vector, decomposing C into a plurality of disjoint subsets by using a decomposition technology, and summing to calculate a network reliability LRd value; and checking whether a state vector subset of an undetermined type exists or not, if so, repeatedly calculating until the state vector subset of the undetermined type does not exist, and outputting a final LRd value. According to the method, all state vector sets meeting requirements are screened out through a decomposition technology, so that the reliability of the power supply network under the power transmission loss condition is obtained.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of power supply network reliability evaluation, and relates to a power supply network reliability evaluation method considering power transmission loss. BACKGROUND

[0002] With the acceleration of the process of building a new power system in China, the iteration and upgrading of smart grid technology, and the in-depth development of energy transformation, the power transmission network, as the "main artery" of energy transmission, has undergone structural innovation in recent years. With China's economy moving towards a high-quality development stage, the stability of power grid operation and power supply quality have become key indicators affecting the safety of the industrial chain. Compared with the expansion of power transmission capacity, the industry pays more attention to the improvement of the reliability of the power transmission network, especially the stability of power transmission under complex grid topology. However, affected by multiple uncertain factors such as weather conditions, equipment aging and sudden failures, the actual power transmission line may present a variety of states such as complete interruption, partial capacity reduction or normal operation, resulting in significant randomness of the transmission capacity of the power grid. Based on this random characteristic, the power transmission network can be modeled as a multi-state random flow network, where each line has independent, limited, and non-negative real random capacity, which usually follows a specific probability distribution based on historical operation data. Specifically, the reliability of the power transmission network can be defined as the probability that the power grid can continuously and stably transmit at least d units of power from the power generation end to the load center, and the evaluation results directly reflect the ability of the power grid to resist fault impact and maintain supply and demand balance.

[0003] It must be recognized that while the power industry is helping China's economic growth, power transmission loss is an important area for improving energy efficiency, and the challenge of energy loss is becoming increasingly severe, putting pressure on energy saving and energy transformation. The "2023 China Power Industry Energy Consumption and Development Report" points out that power transmission loss accounts for about 6% of total power production, second only to the power generation link. Long-distance transmission line loss accounts for more than 70%, which is the main part of power transmission loss and the focus of energy saving and consumption reduction. To reduce power transmission loss and ensure efficient energy use and achieve the "double carbon" goal, power companies are increasingly focusing on power transmission loss control. From the perspective of sustainable development, reducing power transmission loss and improving power supply reliability are equally important and help power companies save energy, increase efficiency, and green transition, and enhance competitiveness. Therefore, introducing power transmission loss constraints into the power supply network reliability index and considering power supply efficiency and energy saving benefits can more comprehensively and reasonably evaluate the operation and power supply quality of the power supply network. Based on this, the reliability of the power supply network considering power transmission loss refers to the probability that the network's sink can receive no less than d units of power under the condition of power transmission loss in the power transmission process. This reliability index is denoted by LRd.

[0004] However, traditional algorithms have revealed many shortcomings in calculating the reliability of stochastic power supply networks with transmission loss constraints. On the one hand, some algorithms cannot accurately calculate the reliability of power supply networks in the presence of transmission losses; on the other hand, calculation methods based on minimum paths are extremely inefficient when dealing with large-scale networks. Summary of the Invention

[0005] In view of this, the purpose of this invention is to provide a power supply network reliability assessment method that considers transmission losses. Given the demand and the transmission loss factors of each line, the method uses decomposition techniques to select all state vector sets that meet the demand, thereby obtaining the reliability of the power supply network.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A method for assessing the reliability of a power supply network considering transmission losses, the method comprising:

[0008] Set the basic parameters of the power supply network and define its initial state vector set C;

[0009] The type of state vector set C is determined by the relationship between the maximum flow M(L) and M(U) of the loss network under the minimum state vector L and the maximum state vector U in the initial state vector set C and the demand level d.

[0010] If C is determined to be an acceptable set of state vectors, then LR can be calculated directly based on the joint probability of the states in the set of state vectors C. d value;

[0011] If C is determined to be an uncertain state vector set, its feasible flow vectors for the loss network are determined. Based on the feasible flow vectors of the loss network, a set of state vectors to be decomposed, A(X), is selected. Decomposition techniques are used to decompose the state vector set C into several disjoint subsets. The probability logarithm (LR) is calculated based on the sum of the probabilities of all acceptable state vector subsets. d value;

[0012] If C is determined to be an unacceptable set of state vectors, then the set of state vectors is discarded and not included in subsequent calculations.

[0013] Check if there exists a subset of state vectors with an undetermined type. If so, set the subset of state vectors with an undetermined type as the initial state vector set, and repeat the process of determining the type of state vector set C and performing the corresponding logistic regression. d Value calculation continues until the types of all subsets of state vectors have been determined, and the final LR is output. d value.

[0014] Furthermore, in the process of setting the basic parameters of the power supply network, G(N,A,C,W) is used to represent the power supply network with transmission losses. N={s,1,2,...,n,t} is the set of all nodes in the network. Node s is the power plant and is the source of electricity, while node t is the demand side of electricity and is the destination of electricity. The other nodes mainly undertake the function of power transfer.

[0015] Let A = {a1, a2, ..., a} m Let} be the set of all power transmission edges in the network, m be the total number of edges, and a be the set of all power transmission edges. i (1≤i≤m) represents the i-th edge;

[0016] Let C = [L, U] be the initial state vector set, where L = (l1, l2, ..., l...). m )=(0,0,...,0) is the minimum state vector in C, U=(u1,u2,...,u m ) is the maximum state vector in C; the initial state vector set C can be further represented as:

[0017]

[0018] in, and Let represent the minimum and maximum capacity values ​​of the i-th edge in the initial state vector set C, respectively, where 1 ≤ i ≤ m;

[0019] Let W = (w1, w2, ..., w m ) is the transmission loss factor vector, w i It is the transmission loss factor of the i-th edge, 1≤i≤m, used to represent the degree of current loss when the current passes through this edge;

[0020] The state of each transmission edge is described by a random variable, with the variable's value ranging from l. i to u i The ones between include l i and u i The integers are assumed to be known and statistically independent of each other.

[0021] Let the demand level be d, and let the network reliability index LR under transmission loss conditions be... d =0.

[0022] Furthermore, when determining the type of the state vector set, the generalized maximum flow algorithm is used to calculate the maximum flow M(L) and M(U) of the loss network under the minimum state vector L and the maximum state vector U, respectively. Then, the type of its state vector set is determined as follows:

[0023] If M(L)≥d, it means that each state vector in C can satisfy the requirement d, and C is an acceptable set of state vectors;

[0024] If M(U) < d, it means that each state vector in C cannot satisfy the requirement d, and C is an unacceptable set of state vectors;

[0025] If M(L)<d≤M(U), then surface C is an uncertain state vector set.

[0026] Furthermore, when C is an acceptable set of state vectors, LR d The value is calculated as follows:

[0027] LR d =LR d +Pr(C)

[0028] Where Pr(C) is the joint probability of the states in the state vector set C, and it is calculated as follows:

[0029]

[0030] Where X represents the state vector. Is edge a i The state is x i The probability of that time.

[0031] Furthermore, when C is an uncertain set of state vectors, the process of calculating the feasible flow vector of the loss network is as follows:

[0032] Add a virtual sink t* to the initial network G(N,A,C,W) and add a virtual edge from the initial sink t to the virtual sink t*. Let the capacity state of the newly added virtual edge be d, the transmission loss factor be 0, and the other edges be a. i The state is equal to u i The transmission loss factor remains unchanged.

[0033] Given that M(U)≥d, and the capacity state of the virtual edge is d and the loss factor is 0, the maximum flow from the source s to the virtual sink t* must be equal to d;

[0034] Using the generalized maximum flow algorithm, namely the maximum flow algorithm for lossy networks, the maximum flow from the source node s to the virtual sink node t* is calculated. Then, the flow through each edge in the network G(N,A,C,W) constitutes the feasible flow vector of the lossy network.

[0035] Furthermore, when C is an uncertain set of state vectors, the set of state vectors to be decomposed, determined based on the feasible flow vectors of the loss network, is expressed as:

[0036]

[0037] Where A is the set of edges in the network, and the index z i ∈z1,z2,…,z q Denotes the z-th state in the set of state vectors to be decomposed. i There are q edges, where q is the total number of edges in the set of state vectors to be decomposed;

[0038] For each edge in A(X), its capacity interval in C is partitioned into two non-empty and disjoint subsets. and The specific definitions are as follows:

[0039]

[0040] in, Describe the edges in A(X) Capacity status, and Let each represent an edge in A(X). The minimum capacity state and the maximum capacity state in the state vector set C;

[0041] pass and The state vector set C is decomposed into C using decomposition techniques. 1 C 2 ,...,C q+1 There are a total of q+1 disjoint subsets, which are decomposed as follows:

[0042]

[0043] in:

[0044]

[0045] Furthermore, when C is an uncertain set of state vectors, C q+1 It must be the set of acceptable state vectors, and the rest of the subset C i The type , 1≤i≤q, needs further judgment; LR is calculated based on the decomposed subsets. d The process of valuing is as follows:

[0046] LR d =LR d +Pr(C q+1 )

[0047] Among them, Pr(C q+1 ) is a subset C q+1 The joint probability of the states is expressed as:

[0048]

[0049] in, and Representing edge a respectively i In subset C q+1 The minimum capacity state and the maximum capacity state in the system.

[0050] The beneficial effects of this invention are as follows:

[0051] The proposed power supply network reliability assessment method considering transmission losses demonstrates significant advantages in improving assessment accuracy and optimizing computational efficiency. By introducing transmission loss factors for each line and combining decomposition techniques to accurately select the state vector set that meets the requirements, this method comprehensively reflects the actual impact of transmission losses on network reliability. This multi-dimensional assessment approach not only quantifies the direct impact of losses on terminal power supply but also reveals the network performance under uncertainties such as different meteorological conditions and equipment aging levels. This significantly improves the accuracy and practicality of the assessment results, providing power companies and planning departments with more reliable decision-making support.

[0052] Secondly, regarding computational efficiency, traditional minimum path-based computation methods become inefficient due to state space explosion when dealing with large-scale power supply networks, making it difficult to meet practical needs. This invention uses decomposition techniques to split complex state vector sets into multiple disjoint subsets, calculating the reliability contribution of each subset separately, thus reducing computational complexity from exponential to polynomial level. This decomposition technique significantly reduces redundant calculations and substantially improves evaluation efficiency. This efficient computational approach not only shortens the evaluation cycle but also reduces the demand for computing resources, making real-time reliability assessment of large-scale networks possible and providing strong support for the stable operation and rapid response of power systems.

[0053] Other advantages, objectives, and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination, or may be learned from practice of the invention. The objectives and other advantages of the invention can be realized and obtained through the following description. Attached Figure Description

[0054] To make the objectives, technical solutions, and advantages of the present invention clearer, the preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, wherein:

[0055] Fig. 1 This is a flowchart illustrating the power supply network reliability assessment method considering transmission losses according to an embodiment of the present invention.

[0056] Fig. 2 This is a schematic diagram of an abstract structure of a power transmission network according to an embodiment of the present invention. Detailed Implementation

[0057] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0058] The accompanying drawings are for illustrative purposes only and are schematic diagrams, not actual pictures, and should not be construed as limiting the invention. To better illustrate the embodiments of the invention, some parts in the drawings may be omitted, enlarged, or reduced, and do not represent the actual product dimensions. It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0059] In the accompanying drawings of the embodiments of the present invention, the same or similar reference numerals correspond to the same or similar components. In the description of the present invention, it should be understood that if terms such as "upper," "lower," "left," "right," "front," and "rear" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, they are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, the terms used to describe positional relationships in the drawings are only for illustrative purposes and should not be construed as limiting the present invention. For those skilled in the art, the specific meaning of the above terms can be understood according to the specific circumstances.

[0060] Please see Figs. 1-2 This is a method for assessing the reliability of power supply networks that takes into account transmission losses.

[0061] Example 1

[0062] This embodiment provides a detailed implementation process for a power supply network reliability assessment method that considers transmission losses, such as... Fig. 1 As shown, it specifically includes the following steps:

[0063] Step 1: Set basic parameters: Let G(N,A,C,W) represent a power supply network with transmission losses. N={s,1,2,...,n,t} is the set of all nodes in the network. Node s is the power plant, the source of electricity, and node t is the electricity demand side, the destination of electricity. The remaining nodes mainly undertake the function of power transfer. A={a1,a2,...,a m Let} be the set of all power transmission edges in the network, m be the total number of edges, and a be the set of all power transmission edges. i(1≤i≤m) represents the i-th edge. C=[L,U] is the initial state vector set, where L=(l1,l2,...,l m )=(0,0,...,0) is the minimum state vector in C, U=(u1,u2,...,u m ) is the maximum state vector in C. The initial state vector set C can also be represented as In the form of, and Let W represent the minimum and maximum capacity values ​​of the i-th edge in the initial state vector set C, respectively, where 1 ≤ i ≤ m. W = (w1, w2, ..., w...) m ) is the transmission loss factor vector, w i Let be the transmission loss factor of the i-th edge, 1 ≤ i ≤ m, used to represent the degree of current loss when current passes through this edge. In a real network environment, the state of each transmission edge is constantly changing; therefore, its state can be described by a random variable, with values ​​ranging from l... i to u i Integers between (inclusive) i and u i Assume that the probability distributions of each side's state are known and statistically independent. Let the demand level be d, and let LR... d =0.

[0064] Step 2: Determine the type of state vector set: Use the generalized maximum flow algorithm (i.e., the loss network maximum flow algorithm) to calculate the loss network maximum flow M(L) and M(U) of the network under the minimum state vector L and the maximum state vector U.

[0065] If M(L) ≥ d, it means that each state vector in C satisfies requirement d, therefore C is an acceptable set of state vectors. Let LR d =LR d +Pr(C), where The joint probability of the states in the state vector set C is given by X, where X represents the state vector. Is edge a i The state is x i The probability of the time is determined, and then proceed to step 6.

[0066] If M(U) < d, it means that each state vector in C cannot meet the requirement d. In this case, C is an unacceptable set of state vectors, and no further calculation or processing is needed for this set of state vectors; it can be discarded directly. Then proceed to step 6.

[0067] If M(L)<d≤M(U), then C is classified as an uncertain state vector set, and proceed to step 3.

[0068] Step 3: Determine the feasible flow vector of the loss network: Add a virtual sink t* to the initial network G(N,A,C,W) and add a virtual edge from the initial sink t to the virtual sink t*. Let the capacity state of the newly added virtual edge be d, the transmission loss factor be 0, and the other edges be a. i The state is equal to u i The transmission loss factor remains unchanged. Given that M(U)≥d, and the capacity state of the virtual edge is d and the loss factor is 0, the maximum flow from the source s to the virtual sink t* must be equal to d. Using the generalized maximum flow algorithm, i.e., the maximum flow algorithm for lossy networks, to calculate the maximum flow from the source s to the virtual sink t*, the flow through each edge in the network G(N,A,C,W) constitutes the feasible flow vector F=(f1,f2,...,f m ).

[0069] Step 4: Decompose the state vector set. Based on the feasible flow vectors of the loss network determined in Step 3, obtain the set... Where A is the set of edges in the network, and the index z i ∈z1,z2,…,z q Denotes the z-th state in the set of state vectors to be decomposed. i There are q edges, where q is the total number of edges in the set of state vectors to be decomposed. For each edge in A(X), its capacity interval in C is partitioned into two non-empty and disjoint subsets. and The specific definitions are as follows:

[0070]

[0071] in, Here, Describe the edges in A(X) Capacity status, and Let each represent an edge in A(X). The minimum capacity state and the maximum capacity state in the state vector set C. and The state vector set C is decomposed into C using decomposition techniques. 1 C 2 ,...,C q+1 There are a total of q+1 disjoint subsets, which are decomposed as follows:

[0072]

[0073] in:

[0074]

[0075] Since any two subsets are disjoint, calculating LR...d In this case, it is sufficient to sum the probabilities of all acceptable state vector sets.

[0076] Step 5: Calculate LR d Value: In the subset obtained in step 4, C q+1 It must be the set of acceptable state vectors, while the remaining subset C i For sets of type 1 ≤ i ≤ q, further judgment is needed. Calculate subset C. q+1 The corresponding joint state probability Pr(C) q+1 ), and let LR d =LR d +Pr(C q+1 ).in and Representing edge a respectively i In subset C q+1 The minimum capacity state and the maximum capacity state in the system.

[0077] Step 6: Check if there is a subset of state vectors of undetermined type.

[0078] Each of the decomposed state vector subsets is checked to determine if there is an undetermined subset. If so, one of the undetermined state vector subsets is set as the new initial state vector set C = [L, U], and the process returns to step 2 to continue solving; otherwise, the final LR is output. d value.

[0079] Example 2

[0080] This embodiment describes the reliability assessment process using a specific power supply network as an example.

[0081] This embodiment provides a specific abstract structure of a power supply network, such as... Fig. 2 As shown, the network consists of 4 nodes and 6 transmission lines. Node s represents a power station, intermediate nodes 1 and 2 represent substations, and node t represents the demand location. Table 1 shows the capacity probability distribution of each line in the network and the transmission loss factor values ​​of each edge:

[0082] Table 1

[0083]

[0084] Assuming a demand of d = 310, this means that even with losses during power transmission, node t has a probability of receiving at least 310 units of power.

[0085] According to the present invention, the probability that the power transmission network can transmit at least 310 units of power demand to the demand location, taking into account transmission losses, is calculated; the specific steps are as follows:

[0086] 1) Set basic parameters.

[0087] Based on the known conditions, the initial values ​​are set as follows: d = 310, LR 310 =0, C=[L,U], where L=(0,0,0,0,0,0), U=(300,200,100,100,100,200), W=(0.10,0.12,0.15,0.11,0.09,0.08).

[0088] 2) Determine the type of the state vector set.

[0089] Using the generalized maximum flow algorithm, we calculate M(L) and M(U), and find that M(L) = 0 and M(U) = 314. Given that M(L) < 310 and M(U) > 310, we determine that the state vector set C belongs to the uncertain state vector set.

[0090] 3) Determine the feasible flow vector of the loss network.

[0091] An extended network is constructed by adding a virtual sink t* to the original network and introducing a virtual edge pointing from the original sink t to t*. The capacity state of this virtual edge is set to 310, and the transmission loss factor is 0. The states of edges a1, a2, a3, a4, a5, and a6 are 300, 200, 100, 100, 100, and 200, respectively, and the transmission loss factor remains unchanged. The generalized maximum flow algorithm is used to solve the extended network with transmission loss, and the feasible flow vector F = (293.66297, 200, 64.29668, 0, 100, 145.65217).

[0092] 4) Decompose the state vector set.

[0093] Based on the feasible flow vector F = (293.66297, 200, 64.29668, 0, 100, 145.65217) obtained in step 4), the set A(X) = {a1, a2, a3, a5, a6} can be obtained. After calculating the corresponding parameters of each edge in A(X), the state vector set C can be divided into the following subsets:

[0094]

[0095] C 1 =[(0,0,0,0,0,0),(200,200,100,100,100,200)]

[0096]

[0097] C 2 =[(300,0,0,0,0,0),(300,100,100,100,100,200)]

[0098]

[0099] C 3 =[(300,200,0,0,0,0),(300,200,0,100,100,200)]

[0100]

[0101] C 4 =[(300,200,100,0,0,0),(300,200,100,100,0,200)]

[0102]

[0103] C 5 =[(300,200,100,0,100,0),(300,200,100,100,100,100)]

[0104] C 6 =[(300,200,100,0,100,200),(300,200,100,100,100,200)]

[0105] 5) Calculate LR 310 value.

[0106] In the subset obtained in step 5), C 5 It is an acceptable state vector set, with corresponding probabilities Pr(C 5 )=0.96×0.96×0.96×1×0.96×0.95=0.83138, then LR 310 =LR 310 +Pr(C 5 = 0.83138. Further judgment is needed for the remaining subset types.

[0107] 6) Check if there is a subset of state vectors of undetermined type.

[0108] Subset C 1 C 2 C 3 C 4 C 5 The type has not been determined; let's discuss C first. 1 , put C 1Let C = [L, U] = C be the new initial state vector set. 1 Proceed to step 2) to continue solving.

[0109] 2) Determine the type of the state vector set.

[0110] Using the generalized maximum flow algorithm, we calculate M(L) and M(U), obtaining M(L) = 0 and M(U) = 242.12. Since M(U) < 310, we determine that the state vector set C belongs to the unacceptable state vector set and proceed to step 6).

[0111] 6) Check if there is a subset of state vectors of undetermined type.

[0112] Subset C 2 C 3 C 4 C 5 The type was not determined, so C was... 2 Let C = [L, U] = C be the new initial state vector set. 2 Proceed to step 2) to continue solving.

[0113] 2) Determine the type of the state vector set.

[0114] Using the generalized maximum flow algorithm, we calculate M(L) and M(U), obtaining M(L) = 0 and M(U) = 249.92. Since M(U) < 310, we determine that the state vector set C belongs to the unacceptable state vector set and proceed to step 6).

[0115] 6) Check if there is a subset of state vectors of undetermined type.

[0116] Subset C 3 C 4 C 5 The type was not determined, so C was... 3 Let C = [L, U] = C be the new initial state vector set. 3 (Continue to step 2) and then proceed to step 6.

[0117] 2) Determine the type of the state vector set.

[0118] Using the generalized maximum flow algorithm, M(L) and M(U) are calculated, yielding M(L) = 176 and M(U) = 259.72. Given that M(U) < 310, the state vector set C is determined to be an unacceptable state vector set.

[0119] 6) Check if there is a subset of state vectors of undetermined type.

[0120] Subset C 4 C 5 The type was not determined, so C was...4 Let C = [L, U] = C be the new initial state vector set. 2 Proceed to step 2) to continue solving.

[0121] 2) Determine the type of the state vector set.

[0122] Using the generalized maximum flow algorithm, we calculate M(L) and M(U), obtaining M(L) = 176 and M(U) = 230.74. Since M(U) < 310, we determine that the state vector set C belongs to the unacceptable state vector set and proceed to step 6).

[0123] 6) Check if there is a subset of state vectors of undetermined type.

[0124] Subset C 5 The type was not determined, so C was... 5 Let C = [L, U] = C be the new initial state vector set. 2 Proceed to step 2) to continue solving.

[0125] 2) Determine the type of the state vector set.

[0126] Using the generalized maximum flow algorithm, we calculate M(L) and M(U), obtaining M(L) = 176 and M(U) = 268. Since M(U) < 310, we determine that the state vector set C belongs to the unacceptable state vector set and proceed to step 6).

[0127] 6) Check if there is a subset of state vectors of undetermined type.

[0128] The type of all subsets is determined, and the final LR is output. 310 =0.80688.

[0129] Under the condition of demand d = 310, this invention uses decomposition technology to select the state vector set that meets the demand and calculates LR310 = 0.80688. This result not only reflects the reliability level of the network considering transmission losses, but also provides a quantitative basis for network optimization. For example, if it is necessary to improve the reliability to above 0.9, the critical lines that affect the LRd value (such as lines with high loss factors) can be upgraded or modified.

[0130] In summary, this invention achieves accurate assessment of the reliability of power supply networks considering transmission losses by screening state vector sets that meet the requirements through decomposition technology. It has multiple beneficial technical effects, including improving assessment accuracy, optimizing computational efficiency, comprehensively reflecting network performance, supporting energy conservation and emission reduction, enhancing grid stability, and providing decision support.

[0131] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for assessing the reliability of a power supply network considering transmission losses, characterized in that: The method includes: Set the basic parameters of the power supply network and define its initial state vector set C; The type of state vector set C is determined by the relationship between the maximum flow M(L) and M(U) of the loss network under the minimum state vector L and the maximum state vector U in the initial state vector set C and the demand level d. If C is determined to be an acceptable set of state vectors, then the network reliability (LR) under loss conditions can be calculated directly based on the joint probability of the states in the set of state vectors C. d value; If C is determined to be an uncertain state vector set, its feasible flow vectors for the loss network are determined. Based on the feasible flow vectors of the loss network, a set of state vectors to be decomposed, A(X), is selected. Decomposition techniques are used to decompose the state vector set C into several disjoint subsets. The probability logarithm (LR) is calculated based on the sum of the probabilities of all acceptable state vector subsets. d value; If C is determined to be an unacceptable set of state vectors, then the set of state vectors is discarded and not included in subsequent calculations. Check if there exists a subset of state vectors with an undetermined type. If so, set the subset of state vectors with an undetermined type as the initial state vector set, and repeat the process of determining the type of state vector set C and performing the corresponding logistic regression. d Value calculation continues until the types of all subsets of state vectors have been determined, and the final LR is output. d value.

2. The method for assessing the reliability of a power supply network considering transmission losses according to claim 1, characterized in that: In the process of setting the basic parameters of the power supply network, G(N,A,C,W) is used to represent the power supply network with transmission losses. N={s,1,2,...,n,t} is the set of all nodes in the network. Node s is the power plant and is the source of electricity. Node t is the demand side of electricity and is the destination of electricity. The other nodes mainly undertake the function of power transfer. Let A = {a1, a2, ..., a} m Let} be the set of all power transmission edges in the network, m be the total number of edges, and a be the set of all power transmission edges. i (1≤i≤m) represents the i-th edge; Let C = [L, U] be the initial state vector set, where L = (l1, l2, ..., ln) m )=(0,0,...,0) is the minimum state vector in C, U=(u1,u2,...,u m ) is the maximum state vector in C; the initial state vector set C can be further represented as: in, and Let represent the minimum and maximum capacity values ​​of the i-th edge in the initial state vector set C, respectively, where 1 ≤ i ≤ m; Let W = (w1, w2, ..., w m ) is the transmission loss factor vector, w i It is the transmission loss factor of the i-th edge, 1≤i≤m, used to represent the degree of current loss when the current passes through this edge; The state of each transmission edge is described by a random variable, with the variable's value ranging from l. i to u i The ones between include l i and u i The integers are assumed to be known and statistically independent of each other. Let the demand level be d, and let the network reliability index LR under transmission loss conditions be... d =0.

3. The method for assessing the reliability of a power supply network considering transmission losses according to claim 2, characterized in that: When determining the type of the state vector set, the generalized maximum flow algorithm is used to calculate the maximum flow M(L) and M(U) of the loss network under the minimum state vector L and the maximum state vector U, respectively. Then, the type of its state vector set is determined as follows: If M(L)≥d, it means that each state vector in C can satisfy the requirement d, and C is an acceptable set of state vectors; If M(U) < d, it means that each state vector in C cannot satisfy the requirement d, and C is an unacceptable set of state vectors; If M(L)<d≤M(U), then surface C is an uncertain state vector set.

4. The method for assessing the reliability of a power supply network considering transmission losses according to claim 2, characterized in that: When C is an acceptable set of state vectors, LR d The value is calculated as follows: LR d =LR d +Pr(C) Where Pr(C) is the joint probability of the states in the state vector set C, and it is calculated as follows: Where X represents the state vector. Is edge a i The state is x i The probability of that time.

5. The method for assessing the reliability of a power supply network considering transmission losses according to claim 2, characterized in that: When C is an uncertain set of state vectors, the process of calculating the feasible flow vector of the loss network is as follows: Add a virtual sink t* to the initial network G(N,A,C,W), and add a virtual edge from the initial sink t to the virtual sink t*. Let the capacity state of the newly added virtual edge be d, the transmission loss factor be 0, and the other edges be a. i The state is equal to u i The transmission loss factor remains unchanged. Given that M(U)≥d, and the capacity state of the virtual edge is d and the loss factor is 0, the maximum flow from the source s to the virtual sink t* must be equal to d; Using the generalized maximum flow algorithm, also known as the loss network maximum flow algorithm, the maximum flow from the source node s to the virtual sink node t* is calculated. Then, the flow through each edge in the network G(N,A,C,W) constitutes the feasible flow vector of the loss network.

6. The method for assessing the reliability of a power supply network considering transmission losses according to claim 5, characterized in that: When C is an uncertain set of state vectors, the set of state vectors to be decomposed, determined based on the feasible flow vectors of the loss network, is expressed as: Where A is the set of edges in the network, and the index z i ∈z1,z2,…,z q Denotes the z-th state in the set of state vectors to be decomposed. i There are q edges, where q is the total number of edges in the set of state vectors to be decomposed; For each edge in A(X), its capacity interval in C is partitioned into two non-empty and disjoint subsets. and The specific definitions are as follows: in, Describe the edges in A(X) Capacity status, and Let each represent an edge in A(X). The minimum capacity state and the maximum capacity state in the state vector set C; pass and The state vector set C is decomposed into C using decomposition techniques. 1 C 2 ,...,C q+1 There are a total of q+1 disjoint subsets, which are decomposed as follows: in:

7. The method for assessing the reliability of a power supply network considering transmission losses according to claim 6, characterized in that: When C is an uncertain set of state vectors, C q+1 It must be the set of acceptable state vectors, and the rest of the subset C i The type , 1≤i≤q, needs further judgment; LR is calculated based on the decomposed subsets. d The process of valuing is as follows: LR d =LR d +Pr(C q+1 ) Among them, Pr(C q+1 ) is a subset C q+1 The joint probability of the states is expressed as: in, and Representing edge a respectively i In subset C q+1 The minimum capacity state and the maximum capacity state in the system.

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