Distribution network two-stage region reconstruction method considering optimal operation

Abstract

A distribution network two-stage regional reconstruction method considering optimal operation comprises the steps that a transformer health assessment and risk assessment model is constructed, and the model comprehensively considers influence factors of dissolved gas, electrical tests and insulating oil of a distribution network transformer on transformer health; specifying a transformer maintenance strategy according to the results of the transformer health assessment and risk assessment indexes, and judging whether the transformer needs to be maintained or not; after the transformer is overhauled according to the transformer overhaul strategy, real-time data of power distribution network equipment and lines and the real-time operation state of the transformer are updated; a power distribution network area reconstruction model of optimal operation is constructed, the model takes the lowest risk index of each branch transformer and the minimum system line loss as a comprehensive optimization target, and a multi-target weighting method is adopted for optimization; the reconstruction model needs to meet the power flow constraint, the system operation constraint and the soft switching operation constraint. The method has the advantages that the safety and the reliability of a distribution network system are improved by implementing a distribution network two-stage regional reconstruction strategy.

Description

Technical Field

[0001] This invention relates to the field of regional reconfiguration technology, and in particular to a two-stage regional reconfiguration method for distribution networks that takes into account optimal operation. Background Technology

[0002] With the continuous growth of electricity demand and the increasing complexity of power systems, distribution networks, as a crucial link in power transmission, face a growing number of challenges. Distribution network equipment and lines typically operate for extended periods, inevitably encountering various factors during this time. Equipment itself ages over time, its performance parameters change, insulation performance declines, and mechanical strength decreases, increasing the probability of equipment failure. On the other hand, lines are affected by environmental factors (such as temperature, humidity, and chemical corrosion) and external forces (such as mechanical collisions and bird activity), leading to problems like line wear and insulation damage, thus affecting transmission capacity and safety. These factors interact, significantly increasing the operational risks of the distribution network system. These risks not only affect the safety and stability of the distribution network but can also lead to power outages. Power outages cause enormous losses to the socio-economic system, disrupting normal industrial production, commercial activities, and residents' daily lives. Therefore, effectively improving the reliability of distribution networks and reducing their operational risks is one of the most pressing issues to be addressed in the field of power system optimization.

[0003] Currently, research reports have described optimizations for distribution networks considering economic dispatch of distribution networks with network reconfiguration and resilient recovery of multi-energy distribution systems. The paper "Economic dispatch of distribution network with dispersed wind power considering network reconfiguration" (Frontiers in Energy Research, Lei J, Yuan Z, Bai H, et al. [J]., 2022, 10: 942350, July 11, 2022) proposes an economic dispatch method for distribution networks considering network reconfiguration. This method aims to minimize distribution network operating costs, reconfiguration costs, and total system network losses, and constructs a multi-objective collaborative optimization model. By rationally adjusting the network topology of the distribution network and optimizing the allocation of power sources and loads, economic operation is achieved under the condition of satisfying system operating constraints. This method improves the economy of the distribution network to a certain extent, and enhances the reliability of the system by optimizing the network structure, providing an effective approach for the optimized operation of distribution networks. The paper "Restoration of a multi-energy distribution system with joint district network reconfiguration via distributed stochastic programming" (IEEE Transactions on Smart Grid, Li Z, Xu Y, Wang P, et al. [J]., 2023, 15(3): 2667-2680, September 22, 2023) proposes a resilient recovery method for multi-energy distribution systems. This method addresses the recovery problem of multi-energy distribution systems after failures or disturbances by formulating a distributed, multi-stage scheduling decision-making mechanism. By comprehensively considering the coupling relationships between different energy sources and the dynamic characteristics of the system, it achieves rapid and reliable recovery of the multi-energy distribution system after a failure, effectively improving the system's resilience and reliability.

[0004] While the aforementioned studies have achieved some success in ensuring the economy and reliability of distribution networks, they all have significant limitations. Existing research, in constructing optimization models and formulating dispatch strategies, often overlooks the impact of equipment aging, a crucial factor, on distribution network operation. As the service life of distribution network equipment increases, equipment aging becomes increasingly prominent, leading to higher failure rates and performance degradation, which will have a significant impact on the safe and stable operation of the distribution network. However, existing methods fail to fully consider the risks brought about by equipment aging, still employing a uniform optimization strategy even when equipment has varying degrees of aging. This makes it difficult to effectively address the various problems caused by equipment aging in actual operation, and fails to meet the urgent needs of real-world distribution networks for efficient, safe, and reliable operation. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention proposes a two-stage regional reconfiguration method for distribution networks that considers optimal operation, thereby improving the security and reliability of the distribution network system.

[0006] The present invention adopts the following technical solution.

[0007] A method for two-stage regional reconfiguration of a distribution network considering optimal operation, characterized by comprising: S1: Construct a transformer health assessment and risk assessment model that comprehensively considers the influence factors of dissolved gas, electrical testing and insulating oil on transformer health in distribution network transformers; Based on the results of transformer health assessment and risk assessment indicators, a transformer maintenance strategy is specified to determine whether the transformer needs maintenance. S2: After the transformer is overhauled according to the transformer maintenance strategy, update the real-time data of the distribution network equipment and lines, as well as the real-time operating status of the transformer. S3: Construct an optimal distribution network regional reconfiguration model. This model takes the lowest risk index of each branch transformer and the lowest system line loss as the comprehensive optimization objectives and adopts a multi-objective weighted method for optimization. This reconfiguration model must meet power flow constraints, system operation constraints, and soft switching operation constraints.

[0008] Furthermore, the health index of each influencing factor for: ;

[0009] in, Initial health index; The aging coefficient; The current operating year of the transformer is in years. Transformer Comprehensive Health Index for: ;

[0010] in, As factors Weighting; The comprehensive health index is normalized, and the normalized health index is obtained. for: ;

[0011] Transformer health assessment and risk assessment indicators for: .

[0012] Furthermore, in step S3, the multi-objective weighted method for minimizing the risk index of each branch transformer and minimizing system line loss is as follows: ;

[0013] ;

[0014] Where j is a node; J is the set of nodes; t is time in hours; and n is the nth branch. For branch set; and They are respectively the first time at time t The resistance and current between nodes ij on the branch are respectively, with units of and A; The time interval is in hours.

[0015] The objective function is to minimize the risk index of each branch transformer. The objective function is to minimize the system line loss; n is the nth branch; For branch set; These are the transformer health assessment and risk assessment indicators at node i.

[0016] Furthermore, the transformer maintenance strategy is based on transformer health assessment and risk assessment indicators. The threshold division is as follows: when 0 ≤ < 0.25, the equipment operates normally and requires no maintenance; when 0.25 ≤ When <0.50, strengthen the operation of monitoring equipment; when 0.50≤ When <0.75, the equipment is operating abnormally and requires immediate repair; when 0.75≤ If the value is ≤1, the equipment has a serious problem and needs to be replaced immediately.

[0017] Furthermore, the overall objective function for: ;

[0018] in, The objective function is to minimize the risk index of each branch transformer. The objective function is to minimize the system line loss; and To optimize the weights.

[0019] Furthermore, in step S3, the power flow constraints include active power balance constraints, reactive power balance constraints, apparent power balance constraints, and node voltage constraints.

[0020] Furthermore, in step S2, the system operation constraints include upper limit constraints on power, upper and lower limits constraints on node voltage, and upper limit constraints on branch current.

[0021] Furthermore, in step S2, the soft-switching operation constraints include power flow balance constraints, active power loss constraints, and reactive power loss constraints.

[0022] The beneficial effects of this invention are: (1) By constructing a transformer health assessment model based on multi-dimensional factors such as dissolved gas, electrical tests, and insulating oil, the quantitative characterization of equipment status was realized. The introduction of the normalized health index and transformer health assessment and risk assessment indicators transformed implicit risks such as equipment aging degree and failure probability into calculable numerical indicators, solving the fuzziness problem of traditional qualitative assessment and providing a scientific basis for accurate maintenance decisions.

[0023] (2) A two-stage distribution network optimization is adopted. In the first stage, a four-level risk classification mechanism is established—normal operation, enhanced monitoring, immediate maintenance, and immediate replacement—to achieve efficient allocation of maintenance resources. In the second stage, with the dual objectives of "lowest risk index of each branch transformer + minimum system line loss," a multi-objective weighted optimization model is used to optimize topology reconfiguration and power distribution under physical constraints such as power flow constraints, node voltage limits, and SOP operating boundaries. By updating equipment status data in real time after maintenance, a closed-loop optimization chain of "evaluation-maintenance-reconfiguration" is formed to avoid cost waste caused by over-maintenance or fault risks caused by under-maintenance.

[0024] (3) By using a dual-objective weighted optimization method, network losses are significantly reduced while ensuring system security. Typical examples show that this method can reduce the risk index of the distribution network by 48.7% and reduce line losses by 12.7%.

[0025] In summary, this invention effectively solves the problems of existing power distribution network reconfiguration methods neglecting equipment aging risks and having a single optimization objective through innovative designs such as equipment health assessment and risk quantification, two-stage collaborative optimization, and integration of multiple constraints, thereby improving system safety and reliability. Attached Figure Description

[0026] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein: Figure 1 Example 1: Wiring diagram of a PG&E 69 node distribution system, which provides a two-stage regional reconfiguration method for distribution networks that takes into account optimal operation, as provided in this application embodiment; Figure 2 Wiring diagram of a PG&E 69 node distribution system, which is a case study of a two-stage regional reconfiguration method for distribution networks that takes into account optimal operation, provided in an embodiment of this application. Figure 3 The wiring diagram of the 3PG&E 69 node distribution system is a case study of a two-stage regional reconfiguration method for distribution networks that takes into account optimal operation, provided as an embodiment of this application. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of this invention. The embodiments described in this application are merely some embodiments of this invention, and not all embodiments. Based on the spirit of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the protection scope of this invention.

[0028] This invention provides a two-stage regional reconfiguration method for distribution networks that considers optimal operation, comprising the following steps: S1: Based on the factors affecting the health of transformers, including dissolved gas, electrical testing, and insulating oil, a transformer health assessment and risk assessment model is constructed. S101: During the reconfiguration of the distribution network area, the health status of the equipment is the foundation for improving the overall operating efficiency and reliability of the system; ensure that all lines and equipment in the reconfigured distribution network operate in the optimal state, obtain multi-dimensional data of transformers, and comprehensively assess the current operating status and potential fault risks of transformers; Transformer Health Comprehensive Index The calculation formula is:

[0029] in, As factors The health index of various influencing factors, including dissolved gases in oil. Electrical testing and insulating oil Health index; As factors The weighting of this invention is determined by the proportion of the weighting. .

[0030] S102: The comprehensive health index of transformers consists of dissolved gas health index in oil, electrical test health index, and insulating oil health index. factor The health index of each influencing factor can be expressed as:

[0031] in, Initial health index; The aging coefficient; The current operating year of the transformer is in years. To better measure the safety performance of transformers, the transformer health index is normalized:

[0032] in, The normalized transformer health index at node i; S103: Transformer health assessment and risk assessment indicators can be expressed as follows:

[0033] in, For the health assessment and risk assessment indicators of the transformer at node i; S2: Taking the lowest risk index of each branch transformer and the lowest system line loss as the comprehensive optimization objectives, and considering power flow constraints, system operation constraints, and soft switching (SOP) operation constraints, a multi-objective weighted method is used to construct a distribution network regional reconfiguration optimization model. S201: The two-stage regional reconfiguration strategy for the distribution network aims to optimize the operation of the power system, ensuring that line losses are reduced and the safe and stable operation of the power system is maintained while ensuring safety. In the first stage, the comprehensive optimization objectives are to minimize the risk index of each branch transformer and minimize the system line loss. A multi-objective weighted method is used for normalization, and then a regional reconfiguration optimization model for the distribution network is constructed. Furthermore, construct the objective function for the first stage:

[0034]

[0035]

[0036] in, The objective function is to minimize the risk index of each branch transformer. The objective function is to minimize the system line loss; For the first A side road; For branch set; For transformers at nodes Transformer health assessment and risk assessment indicators; and As a weighting factor, this invention takes ; The objective function is to minimize the risk index of the transformer. The objective function is to minimize the system line loss; For nodes; A set of nodes; Time, in hours; For the first A side road; For branch set; and They are respectively Time of the first On the side road The resistance and current between nodes are expressed in Ω and A, respectively. The time interval is in hours; S202: Power flow constraints can be expressed as:

[0037]

[0038]

[0039] in, , , and They are respectively Time Node Flow to Node , Time Node Substation injection, Distributed power injection and time nodes The active power of soft switching, measured in watts (W). , , and The flow from time node to node are respectively , Time Node Substation injection, Time Node Distributed power injection and time nodes The reactive power of soft switching, measured in var; and They are respectively Time Node Flow to Node and nodes The active power consumed by the load at the location is expressed in watts (W). and They are respectively Time Node Flow to Node and nodes The reactive power consumed by the load at the location is expressed in var. and They are respectively Time Node and nodes Voltage, measured in volts (V). For branch n nodes Reactance, measured in Ω.

[0040] S203: System operating constraints can be expressed as:

[0041] in, For nodes The maximum apparent power, in VA; and They are nodes The maximum and minimum values ​​of voltage, in V; For nodes The maximum value of the inter-channel current, in amperes (A).

[0042] S204: SOP operational constraints can be expressed as:

[0043] in, and They are respectively Time Node and nodes The active power at SOP, in watts (W). and They are respectively Time Node and nodes The active power loss at SOP, in W; and They are nodes and nodes Active power loss factor at SOP; and They are respectively Time Node and nodes The reactive power at the location is expressed in var.

[0044] The active and reactive power of each node must meet the capacity constraints:

[0045] in, and They are nodes and nodes SOP capacity, in VA; S3: Based on transformer health assessment and risk assessment indicators Based on the threshold division, a transformer maintenance strategy was proposed. When the risk index is greater than or equal to 0 and less than 0.25, the equipment is operating normally and requires no maintenance. When the equipment risk index is greater than or equal to 0.25 and less than 0.50, the equipment operation should be closely monitored. When the equipment risk index is greater than or equal to 0.50 and less than 0.75, the equipment is operating abnormally and requires immediate maintenance. When the equipment risk index is greater than or equal to 0.75 and less than or equal to 1, the equipment has a serious problem and needs to be replaced immediately.

[0046] S4: After maintenance, the real-time operating status of the transformer is updated by adjusting the real-time data of the cloud platform, and the transformer health assessment and risk assessment indicators after maintenance are re-output according to the transformer health assessment and risk assessment model of S1. and the normalized health index after maintenance ; S5: The optimal operating distribution network area reconfiguration model built based on S2, which will be updated to construct the optimal operating distribution network area reconfiguration model, i.e.

[0047]

[0048] in, This is the overall objective function for the second stage; The objective function is to minimize the risk index of the transformer after maintenance. The objective function is to minimize the system line loss after maintenance. and After maintenance Time of the first On the side road The resistance and current between nodes are expressed in Ω and A, respectively.

[0049] The following analysis examines the results of the two-stage regional reconfiguration of the distribution network.

[0050] This invention uses a complex distribution network system with PG&E 69 nodes for simulation analysis. The system bus voltage is set to 12.66kV, and the number of circuits is 21. Figure 1 As shown.

[0051] To verify the effectiveness of the two-stage regional reconfiguration method for distribution networks that considers optimal operation proposed in this invention, three reconfiguration optimization schemes were compared and analyzed.

[0052] Option 1: No regional reconfiguration of the distribution network. Option 2: The first phase of regional reconfiguration of the distribution network is carried out, with the comprehensive optimization goals of minimizing the risk index of each branch transformer and minimizing system line loss. Option 3: After maintenance of all equipment in the distribution network, the second phase of regional reconfiguration of the distribution network is carried out, with the same goals as Option 2.

[0053] The results of the distribution network area reconstruction for each scheme are shown in Table 1: Table 1. Results of Distribution Network Area Reconfiguration

[0054] Table 1 shows that the two-stage regional reconfiguration method for distribution networks significantly improved the health of the distribution network system and effectively reduced network losses. In Cases 1 to 3, the transformer health indices were 0.37, 0.23, and 0.19, respectively, and the line losses were 199.02 kW, 180.41 kW, and 173.81 kW, respectively. Through identification and maintenance, Case 3 achieved significant results in further optimizing the transformer health index and reducing line losses. Compared with Case 1, the risk index in Case 3 decreased by 48.7%, and the line loss decreased by 12.7%.

[0055] To effectively improve the reliability of distribution networks and reduce the operational risks of power systems, this invention proposes a two-stage regional reconfiguration method for distribution networks that considers optimal operation. Taking into account factors such as equipment aging and line degradation, a transformer health assessment and risk evaluation system is established. In the first stage, the regional reconfiguration scheme of the distribution network is optimized to reduce the transformer risk index and system line losses. In the second stage, a transformer maintenance strategy based on the risk index is proposed to further optimize the operating state of the distribution network and achieve optimal operation. Through a case study of the PG&E 69-node distribution network, the results show that the risk index was reduced by 48.7%, and line losses were reduced by 12.7%. This method significantly optimizes the operational performance of the distribution network and effectively improves the safety and reliability of the power system.

Claims

1. A method of distribution network two-stage area reconfiguration considering optimal operation, characterized in that: The method comprises the following steps: S1: a transformer health assessment and risk assessment model is constructed, which comprehensively considers the influence factors of dissolved gas, electrical test and insulating oil on the health of the transformer of the power distribution network; According to the results of the transformer health assessment and risk assessment indexes, the transformer maintenance strategy is specified to determine whether the transformer needs maintenance; S2: after the transformer is maintained according to the transformer maintenance strategy, the real-time data of the power distribution network equipment and lines and the real-time operation state of the transformer are updated; S3: an optimal operation power distribution network area reconstruction model is constructed, which takes the minimum risk index of each branch transformer and the minimum system line loss as the comprehensive optimization target, and adopts a multi-objective weighting method for optimization; the reconstruction model needs to meet the power flow constraint, the system operation constraint and the soft switch operation constraint.

2. The method for optimal operation considered distribution network two-stage area reconfiguration according to claim 1, characterized in that: Health index of each influencing factor is: ; Wherein, is the initial health index; is the aging coefficient; is the current running year of the transformer, in years; the comprehensive health index of the transformer is: Wherein, is the factor occupies the weight; The comprehensive health index is normalized, and the normalized health index is: ; Transformer health assessment and risk assessment indicators are: .

3. The method for optimal operation considered distribution network two-stage area reconfiguration according to claim 1, characterized in that: In step S3, the multi-objective weighted method of the lowest risk index of each branch transformer and the minimum system line loss and the model of the lowest risk index and the minimum system line loss are: ; ; Where j is a node; J is the set of nodes; t is time in hours; and n is the nth branch. For branch set; and They are respectively the first time at time t The resistance and current between nodes ij on the branch are respectively, with units of and A; The time interval is in hours. The objective function is to minimize the risk index of each branch transformer. The objective function is to minimize the system line loss; n is the nth branch; For branch set; These are the transformer health assessment and risk assessment indicators at node i.

4. The method for optimal operation considering two-stage regional reconfiguration of distribution network according to claim 1, characterized in that: Transformer maintenance strategies are based on transformer health assessment and risk assessment indicators. The threshold division is as follows: when 0 ≤ < 0.25, the equipment operates normally and requires no maintenance; when 0.25 ≤ When <0.50, strengthen the operation of monitoring equipment; when 0.50≤ When <0.75, the equipment is operating abnormally and requires immediate repair; when 0.75≤ If the value is ≤1, the equipment has a serious problem and needs to be replaced immediately.

5. The method for optimal operation considering two-stage regional reconfiguration of distribution network according to claim 1, characterized in that: Total objective function is: ; wherein, is the objective function for the lowest risk index of each branch transformer; is the objective function for the minimum system line loss; and is the optimization weight.

6. The method for optimal operation considering two-stage regional reconfiguration of distribution network according to claim 1, characterized in that: In step S3, the power flow constraint includes active power balance constraint, reactive power balance constraint, apparent power balance constraint and node voltage constraint.

7. The method for optimal operation considering two-stage regional reconfiguration of distribution network according to claim 1, characterized in that: In step S2, the system operation constraint includes power upper limit constraint, node voltage upper and lower limit constraint and branch current upper limit constraint.

8. The method for optimal operation considering two-stage regional reconfiguration of distribution network according to claim 1, characterized in that: In step S2, the soft switch operation constraint includes power flow balance constraint, active power loss constraint and reactive power loss constraint.

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