Power distribution network toughness improvement robust optimization method based on Holland model

Abstract

The invention discloses a Holland meteorological model-based robust optimization method for improving the toughness of a power distribution network, and the method comprises the steps: precisely simulating the wind speed distribution near a typhoon center through a Holland model, and converting the wind speed into the failure probability of each power transmission line in different time periods and different wind regions through a pre-fitted parameter. Based on the probability description, a multi-stage and multi-region uncertainty set is constructed for describing the impact on the power distribution network when the typhoon evolves along the path. According to the method, a Holland meteorological model is combined with power distribution network toughness planning, wind speed-broken line probability mapping, distributed robust optimization and a polyhedral robust set are unified in one model for the first time, and column and constraint generation (Camp; and CG) solving by an algorithm. And a brand new technical thought and a feasible scheme are provided for quickly recovering power supply in a micro-grid form and improving the toughness of the power distribution network under the invasion of an extreme storm.

Description

Technical Field

[0001] The present invention belongs to the field of power system optimization, and in particular relates to a robust optimization method for improving the resilience of a distribution network based on a Holland model. Background Art

[0002] In the operation and planning of modern distribution networks, resilience has been established as the third most important design objective, following reliability and safety. Given the frequent occurrence of extreme weather events such as typhoons, heavy rains, and snowstorms, as well as human disruptions such as cyberattacks and equipment failures, traditional "vulnerability minimization" strategies are unable to ensure power continuity. Because distribution networks are mostly radial structures with limited redundant channels, the failure of critical feeders or switches can trigger widespread power outages. Furthermore, the high proportion of wind power generation and distributed energy storage introduces power fluctuations and scheduling uncertainties. Therefore, it is necessary to develop an integrated resilience enhancement framework that encompasses pre-emptive protection, in-flight self-healing, and post-fault rapid recovery. In-flight self-healing is achieved through automated switching, microgrid islanding, and coordinated energy storage switching, while post-fault rapid recovery is based on restoration priority strategies and dynamic scheduling. Resilience can be quantified through metrics such as post-fault load retention, network-wide restoration time, and the area under the service level loss curve. Within limited investment budgets, scenario-based risk analysis or distributed robust optimization methods can be used to coordinate SOP and energy storage capacity allocation to maximize resilience gains while balancing economic efficiency. Therefore, the present invention proposes a robust optimization method for improving the resilience of distribution networks based on the Holland model, so as to effectively enhance the adaptability and recovery capabilities of distribution networks under various shocks. Summary of the Invention

[0003] The purpose of the present invention is to address the problems of few redundant channels in radial distribution networks, large fluctuations in wind and solar distributed resources, and insufficient self-healing and recovery capabilities after faults, and to propose a robust optimization method for improving the resilience of distribution networks based on the Holland model to achieve the above-mentioned purpose.

[0004] To achieve the above objectives, the present invention provides a robust optimization method for improving the resilience of a distribution network based on the Holland meteorological model, which comprises the following steps:

[0005] Step S1: Generate typhoon wind speed according to the Holland meteorological model, as follows:

[0006] Step S1.1: To accurately simulate the wind speed distribution near the typhoon center, the key meteorological parameters required by the Holland model must be input first.

[0007]

[0008] In the formula, the maximum wind speed V max: The wind speed measured in the area with the highest wind speed in the typhoon structure (usually near the typhoon eyewall); the maximum wind speed radius R max : The radius from the typhoon center to the maximum wind speed position, indicating the scale of the typhoon wind field; central air pressure P c : The lowest air pressure in the center of the typhoon; ambient air pressure P n : The standard atmospheric pressure outside the typhoon, usually 1010~1015hPa; Holland parameter B: controls the steepness of the wind speed distribution and is an adjustment factor for the shape of the typhoon wind field, often fitted based on historical experience or measured data; e: natural constant ≈2.71828.

[0009] Step S2: Obtain the distributed robust set of line damage probability and the wind power generation robust set according to the typhoon wind speed, as follows:

[0010]

[0011] In the formula, V ij,t : wind speed of branch (i, j) at time t; V safe : safety threshold wind speed; V max : Maximum wind speed in the simulation scenario; u ij,t,s : the state of branch (i, j) at time t in scenario s; ρij,t: the probability of damage of branch (i, j) at time t; s: scenario; ρ thre,s :Threshold; P wind : wind power; Predicted wind power; ΔP wind : Maximum fluctuation range of wind power; B+, B-: binary variables; B wind,t : Wind power cut-off binary variable, when the wind speed reaches 90km / h, wind power is cut off; Γ: disturbance budget; q s : The probability of adversarial distribution defined on S scenarios. st : The quality of transferring from scene s to adversarial scene t; p s : row marginal constraint; w: represents the element of the uncertain set; q: represents the element of the probability set; d(ξ s ,ξ t ) : “distance” or “inconsistency” measurement function between scene s and scene t; ξ s ,ξ t : The random parameter value vector corresponding to the s-th “scene” and the t-th “scene”.

[0012] The robust optimization method for improving the resilience of a distribution network based on the Holland meteorological model is characterized in that, in step S3, the robust set, line flow constraints, equipment configuration constraints, and partition constraints are added to the robust optimization model. The specific formula is as follows:

[0013]

[0014] In the formula, η n : The nth energy storage unit is counted into the position; Pos sop : Soft switch (SOP) access position; C BA , C sop : Cost coefficient: ω i : weight; P lsu,t : load shedding power; P ij,t ,Q ij,t : Branch active / reactive flow; V i,t ,θ i,t : node voltage amplitude / phase angle; SoC n,t : battery status; δ(j), π(j): lines starting and ending at node j, respectively; V: node set; {DG}: distributed generation (DG) node set; F jt , F ij : virtual power flow; W j : Virtual source; L: Branch set. F A , F B , F C They are line flow constraint, equipment configuration constraint, and partition constraint respectively; M: a sufficiently large constant; Pos sop : Sop access location; η n : nth cell efficiency; P soC,n,t : the power of the nth battery at time t; P sop,n ,Q sop,n : active and reactive power of the nth Sop;

[0015] Step S4: C&CG algorithm solves the robust optimization model, as follows:

[0016]

[0017] In the formula, λ, K1, K2, and K3 are auxiliary variables. Using the KKT condition on the subproblems, the maximum-minimum dual-layer problem is unified into a maximum problem. The upper layer passes the configuration to the lower layer, and the lower layer passes the worst-case environment to the upper layer, while also scheduling the existing configuration.

[0018] Step S5: Obtain configuration, zoning, and dispatching instruction results in the distribution network to improve the resilience of the distribution network.

[0019] An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the program, the robust optimization method for improving the resilience of a distribution network based on the Holland meteorological model is implemented.

[0020] A computer-readable storage medium stores computer instructions, which, when executed by a processor, implement the robust optimization method for improving the resilience of a distribution network based on a Holland meteorological model.

[0021] Beneficial effects:

[0022] By jointly optimizing the SOP location and energy storage configuration under the constraints of a limited investment budget, the present invention has the following beneficial effects: it can ensure the power supply of critical loads and reduce the scale of power outages in extreme weather and line failure scenarios; ensure excellent resilience in the worst case scenario through scenario-based risk analysis, and achieve efficient defense against operational uncertainties; the coordinated scheduling of energy storage systems not only improves the support capability during the event, but also speeds up the post-event recovery progress; combined with the economic trade-offs between equipment investment and operation and maintenance costs, the output configuration plan takes into account the project feasibility and promotion value, effectively enhancing the adaptability and resilience of the distribution network to natural disasters and man-made damage, and minimizing the overall investment and social power outage losses. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 is a flow chart of the present invention;

[0024] Figure 2 This is the distribution network zoning map. DETAILED DESCRIPTION

[0025] The present invention will be described in detail below with reference to the accompanying drawings.

[0026] Example: A robust optimization method for improving the resilience of distribution network based on Holland meteorological model, the process is as follows Figure 1 As shown, the specific steps include:

[0027] Step S1: Generate typhoon wind speed according to the Holland meteorological model, as follows:

[0028] Step S1.1: To accurately simulate the wind speed distribution near the typhoon center, the key meteorological parameters required by the Holland model must be input first.

[0029]

[0030] In the formula, the maximum wind speed V max : The wind speed measured in the area with the highest wind speed in the typhoon structure (usually near the typhoon eyewall); the maximum wind speed radius R max : The radius from the typhoon center to the maximum wind speed position, indicating the scale of the typhoon wind field; central air pressure P c : The lowest air pressure in the center of the typhoon; ambient air pressure P n: The standard atmospheric pressure outside the typhoon, usually 1010~1015hPa; Holland parameter B: controls the steepness of the wind speed distribution and is an adjustment factor for the shape of the typhoon wind field, often fitted based on historical experience or measured data; e: natural constant ≈2.71828.

[0031] Step S2: Obtain the distributed robust set of line damage probability and the wind power generation robust set according to the typhoon wind speed, as follows:

[0032]

[0033] In the formula, V ij,t : wind speed of branch (i, j) at time t; V safe : safety threshold wind speed; V max : Maximum wind speed in the simulation scenario; u ij,t,s : the state of branch (i, j) at time t in scenario s; ρij,t: the probability of damage of branch (i, j) at time t; s: scenario; ρ thre,s :Threshold; P wind : wind power; Predicted wind power; ΔP wind : Maximum fluctuation range of wind power; B+, B-: binary variables; B wind,t : Wind power cut-off binary variable, when the wind speed reaches 90km / h, wind power is cut off; Γ: disturbance budget; q s : The probability of adversarial distribution defined on S scenarios. st : The quality of transferring from scene s to adversarial scene t; p s : Row marginal constraints

[0034] Step S3: Add the robust set, line flow constraints, equipment configuration constraints, and partition constraints to the robust optimization model. The specific formula is as follows:

[0035]

[0036] In the formula, η n : The nth energy storage unit is counted into the position; Pos sop : Soft switch (SOP) access position; C BA , C sop : Cost coefficient: ω i : weight; P lsu,t : load shedding power; P ij,t ,Q ij,t : Branch active / reactive flow; V i,t ,θ i,t : node voltage amplitude / phase angle; SoC n,t: battery status; δ(j), π(j): lines starting and ending at node j, respectively; V: node set; {DG}: distributed generation (DG) node set; F jt , F ij : virtual power flow; W j : Virtual source; L: Branch set. F A , F B , F C They are line flow constraints, equipment configuration constraints, and partition constraints respectively; M: a sufficiently large constant

[0037] Step S4: C&CG algorithm solves the robust optimization model, as follows:

[0038]

[0039] In the formula, λ, K1, K2, and K3 are auxiliary variables. Using the KKT condition on the subproblems, the maximum-minimum dual-layer problem is unified into a maximum problem. The upper layer passes the configuration to the lower layer, and the lower layer passes the worst-case environment to the upper layer, while also scheduling the existing configuration.

[0040] Step S5: Obtain configuration, zoning, and dispatching instruction results in the distribution network to improve the resilience of the distribution network.

[0041] It should be noted that the above embodiments are not intended to limit the scope of protection of the present invention, and equivalent changes or substitutions made on the basis of the above technical solutions fall within the scope of protection of the claims of the present invention.

Claims

1. A robust optimization method for improving distribution network resilience based on the Holland meteorological model, characterized in that: The steps include: Step S1, generating typhoon wind speed according to the Holland meteorological model, Step S2, obtaining a distributed robust set of line damage probabilities and a wind power generation robust set according to the typhoon wind speed; Step S3, adding the robust set, line flow constraints, equipment configuration constraints, and partition constraints to the robust optimization model; Step S4, C&CG algorithm solves the robust optimization model, Step S5: Obtain configuration, zoning, and dispatching instruction results in the distribution network to improve the resilience of the distribution network.

2. The robust optimization method for improving the resilience of a distribution network based on the Holland meteorological model according to claim 1 is characterized in that: Step S1: Generate typhoon wind speed according to the Holland meteorological model, as follows: Step S1.1: To accurately simulate the wind speed distribution near the typhoon center, it is necessary to first input the key meteorological parameters required by the Holland model. In the formula, the maximum wind speed radius R max : The radius from the typhoon center to the maximum wind speed position, indicating the scale of the typhoon wind field; central air pressure P c : The lowest air pressure in the center of the typhoon; ambient air pressure P n : Standard atmospheric pressure outside a typhoon, usually 1010~1015hPa; Holland parameter B; e: natural constant ≈2.71828; r: maximum radius of a typhoon.

3. The robust optimization method for improving the resilience of distribution networks based on the Holland meteorological model according to claim 1 is characterized in that: Step S2: Obtain the distributed robust set of line damage probability and the wind power generation robust set according to the typhoon wind speed, as follows: In the formula, V ij,t : wind speed of branch (i, j) at time t; V safe : safety threshold wind speed; V max : Maximum wind speed in the simulation scenario; u ij,t,s : the state of branch (i, j) at time t in scenario s; ρij,t: the probability of damage of branch (i, j) at time t; s: scenario; ρ thre,s :Threshold; P wind : wind power; Predicted wind power; ΔP wind : Maximum fluctuation range of wind power; B + ,B - : binary variable; B wind,t : Wind power cut-off binary variable, when the wind speed reaches 90km / h, wind power is cut off; Γ: disturbance budget; q s : The probability of adversarial distribution defined on S scenarios, π st : The quality of transferring from scene s to adversarial scene t; p s : row marginal constraint; w: represents uncertain set elements; q: represents the probability set element; d(ξ s ,ξ t ) : the “distance” or “inconsistency” measurement function between scene s and scene t; ξ s ,ξ t : The random parameter value vector corresponding to the s-th "scene" and the t-th "scene".

4. The robust optimization method for improving the resilience of a distribution network based on the Holland meteorological model according to claim 1 is characterized in that: Step S3: Add the robust set, line flow constraints, equipment configuration constraints, and partition constraints to the robust optimization model. The specific formula is as follows: In the formula, η n : The nth energy storage unit is counted into the position; Pos sop : Soft switch (SOP) access position; C Ba , C sop : Cost coefficient: ω i : weight; P lsu,t : load shedding power; P ij,t ,Q ij,t : Branch active / reactive flow; V i,t V j,t ,θ i,t ,θ i,t : voltage amplitude / phase angle at node i, j; SoC t : battery status; δ(j), π(j): the line starting and ending at node j, respectively; V: node set; {DG}: distributed generation (DG) node set; F jt , F ij : virtual power flow; W j : virtual source; L: branch set, F A , F B , F C They are line flow constraints, equipment configuration constraints, and partition constraints; M: A sufficiently large constant; Pos sop : Sop access location; η n : nth cell efficiency; P soC,n,t : the power of the nth battery at time t; P sop,n ,Q sop,n : The active and reactive power of the nth Sop.

5. The robust optimization method for improving the resilience of distribution networks based on the Holland meteorological model according to claim 1 is characterized in that: Step S4: C&CG algorithm solves the robust optimization model, as follows: Main question: Sub-questions: In the formula, λ, K1, K2, and K3 are auxiliary variables. The KKT condition is used for the subproblems to uniformly transform the maximum-minimum dual-layer problem into a maximum problem. The upper layer transfers the configuration to the lower layer, and the lower layer transfers the worst environment to the upper layer, while scheduling the existing configuration.

6. The robust optimization method for improving the resilience of distribution networks based on the Holland meteorological model according to claim 1 is characterized in that: Step S5: Obtain configuration, zoning, and dispatching instruction results in the distribution network to improve the resilience of the distribution network.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, it implements the robust optimization method for improving the resilience of the distribution network based on the Holland meteorological model as described in any one of claims 1 to 5 above.

8. A computer-readable storage medium having computer instructions stored thereon, characterized in that: When the computer instructions are executed by the processor, the robust optimization method for improving the resilience of the distribution network based on the Holland meteorological model as described in any one of claims 1 to 5 is implemented.

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