Multi-stage toughness planning method and device for unexpected power-traffic coupling system

Abstract

The invention discloses a multi-stage toughness planning method and equipment for an unexpected power-traffic coupling system. The method comprises the following steps: establishing a toughness multi-stage planning model by taking the minimum total investment and operation cost in all planning periods as a target; constructing constraint conditions of power-traffic coupling under multi-stage toughness planning; establishing an uncertain set considering natural disasters, and converting the uncertain set into a multi-stage robust model according to the toughness multi-stage planning model; and solving to obtain a planning strategy. According to the invention, the coordinated operation mechanism of the power-traffic coupling system is described, and the strategic layout and capacity expansion of the charging stations in a plurality of planning periods and the reinforcement of the power line are determined at the same time. The decision of each period is based on the result and insight of the previous moment, and the strategic and adaptive development process is ensured. In order to cope with uncertain disasters occurring in each planning stage, the provided model is remodeled into a multi-stage robust optimization model.

Description

Technical Field

[0001] The present invention relates to the technical field of energy planning, and in particular to a multi-stage resilience planning method and device for an unexpected power-traffic coupling system. Background Art

[0002] Extreme events are occurring with increasing frequency and intensity, posing a threat to power system security. To enhance the resilience of power systems to uncertain extreme events, extensive research has been conducted on power system resilience planning methods. With the increasing popularity of electric vehicles, the establishment of a large number of electric vehicle charging stations has affected power flows in distribution networks and traffic flows in urban transportation networks. The interconnection between power and transportation networks highlights the need for coordinated operations to optimize economic efficiency and increase flexibility. Given that extreme events can have a compounding impact on interconnected networks—that is, failures in one system can affect the operation of another—resilience planning for coupled power-transportation systems is a significant research topic.

[0003] Previous research has introduced models for resilient planning of coupled power-transport systems under extreme events and discussed synergistic strategies for line hardening and road capacity expansion to enhance resilience. Despite these advances, the concept of multi-stage planning, which spans several years within each stage and adapts to changing resource demands, remains rarely applied to coupled power-transport systems. This approach, which provides flexibility for investment decisions across different years, has been applied to active distribution systems, transmission systems, and integrated electric-gas systems. However, the potential of multi-stage planning for enhancing the resilience of coupled power-transport systems remains an untapped research area.

[0004] A major challenge in implementing resilient multi-period planning for coupled power-transportation systems lies in managing the uncertainties inherent in natural hazards across planning horizons. Typical strategies for addressing these uncertainties include two-stage stochastic optimization and two-stage robust optimization. In these two-stage planning models, decisions for each period are made in the first period, assuming full knowledge of the uncertainties in future periods, which are aggregated and revealed in the second period. However, in practice, investors make decisions for each period based on the uncertainty information available in the current planning horizon. This leads two-stage models to produce overly optimistic planning decisions due to their neglect of unexpected constraints, a phenomenon that has been well-documented in previous research.

[0005] To address the problem of two-stage models ignoring the unexpected evolution of uncertainty, existing studies have extended two-stage stochastic optimization to multi-stage. In the multi-stage model structure, the investment problem in each period is expressed as a different stage problem, where the planning decision of the current period is associated with the uncertainty of the same stage. This multi-stage framework ensures unexpected constraints. However, the multi-stage stochastic optimization model faces the dimensionality curse caused by the large scale of the scenario tree. In addition, in terms of improving the resilience of energy systems in the event of disasters, the model cannot characterize the worst uncertain scenarios, making it difficult to obtain robust solutions. Multi-stage robust optimization models have been studied in the field of power system optimization and scheduling. However, multi-stage planning, especially resilience planning for power-transportation coupled systems, has not yet been explored. Summary of the Invention

[0006] The present invention mainly solves the problem that the current resilience planning of the power-transportation coupling system adopts a two-stage model to ignore the unexpected evolution of uncertainty, and the problem that the multi-stage model has not been explored. It provides a multi-stage resilience planning method and equipment for the unexpected power-transportation coupling system.

[0007] The above technical problems of the present invention are mainly solved by the following technical solutions: a multi-stage resilience planning method for an unexpected power-transportation coupling system, comprising the following steps:

[0008] The investment strategy of the power-transportation coupling system is described as a multi-stage resilience planning problem, and a resilient multi-stage planning model is established with the goal of minimizing the total investment and operating costs over all planning periods.

[0009] Construct constraints on power-transportation coupling under multi-stage resilience planning;

[0010] Establish an uncertainty set considering natural disasters and transform it into a multi-stage robust model based on the resilient multi-stage planning model;

[0011] Solve the multi-stage robustification model to obtain the planning strategy.

[0012] This paper characterizes the coordinated operation of a coupled power-transportation system and simultaneously determines the strategic placement and capacity expansion of charging stations and the reinforcement of power lines over multiple planning periods. Decisions in each period are based on the results and insights of the previous period, ensuring a strategic and adaptive development process. To account for the uncertainties that occur within each planning period, the proposed model is recast as a multi-stage robust optimization model, in which investment decisions are made sequentially within each planning period, depending only on the uncertainty of the current period.

[0013] As a preferred solution, the constraints of power-transportation coupling under multi-stage resilience planning are constructed, including:

[0014] Establishing a first constraint condition based on the grid power and voltage levels operating within an allowable range;

[0015] Establishing a second constraint condition based on the operating charging range of the electric vehicle;

[0016] The third constraint condition is established based on the logical accuracy of multi-stage investment decision-making.

[0017] As a preferred solution, the first constraint conditions include substation active and reactive power generation limitations, distributed power generation output limitations, voltage regulation constraints, electric vehicle charging demand constraints, power line capacity limitations, power line operating status constraints, and power imbalance and load reduction variable positive constraints.

[0018] As a preferred solution, the second constraint includes the relationship between traffic flow and electric vehicle charging demand, considering the restrictions of capacity expansion on charging demand, the charging flow balance constraint of the electric vehicle charging system, the charging flow of the electric vehicle charging system does not exceed the total flow limit of the intersecting lines, and the charging flow on the route does not exceed the total charging capacity limit of all electric vehicle charging systems on the route.

[0019] As a preferred option, the third constraint condition includes the change constraint of EVCS construction based on the existing indicators of the planned devices, the continuity of the status of charging piles and reinforced lines over time and the total reinforcement measures for reinforced lines in each period, the expansion constraint of charging stations, the dynamic adjustment constraint of charging station capacity in each period, and the expansion limit of charging pile capacity in each stage.

[0020] As a preferred solution, an uncertainty set considering natural disasters is established, including:

[0021] The power grid and transportation network take into account the impact of natural disasters and introduce an extreme event impact model based on the NK criterion.

[0022] As a preferred solution, the resilience multi-stage planning model is:

[0023] The minimum sum of investment costs and operating costs at each stage,

[0024] Among them, the investment cost at each stage is the sum of the investment costs of all newly built charging stations and charging station capacity expansion, and the sum of the investment costs of all reinforced power lines; the operating cost at each stage is the sum of the operating costs of all charging stations, the sum of the operating costs of all reinforced power lines, the operating costs of the power distribution system at each stage, and the operating costs of the transportation system at each stage.

[0025] As a preferred solution, the traffic demand of the traffic system is represented by an origin-destination pair. For each origin-destination pair, there are multiple paths, and each path consists of roads connecting the corresponding origin-destination pairs.

[0026] Establishing the constraints of the traffic system, including road travel time constraints, travel cost and time constraints for specified paths, traffic flow restrictions on roads, residual flow constraints, actual traffic flow constraints in OD pairs, matching constraints between the total traffic flow of each origin-destination pair and the demand in each time interval, and applying the supplementary condition constraints of Wardrop's user equilibrium principle;

[0027] The transportation system constraints are integrated to obtain the transportation system operation costs at each stage.

[0028] As a preferred solution, a multi-stage robust model is solved to obtain a planning strategy, including:

[0029] The multi-stage robustification model is iteratively solved using a dual integer dynamic programming method.

[0030] This solution preferably uses the dual integer dynamic programming method for iterative solution to obtain the system planning results of the power-transportation coupling system that follows the unexpected evolution of uncertain disasters. Alternatively, the linear-binary mixed affine rule can be used for iterative solution.

[0031] A multi-stage resilience planning device for an unexpected power-transportation coupling system, characterized in that it includes: a processor, a memory, and a multi-stage resilience planning program stored in the memory and running on the processor, wherein when the multi-stage resilience planning program is executed by the processor, it implements the multi-stage resilience planning method for an unexpected power-transportation coupling system as described in any one of claims 1 to 9.

[0032] Therefore, the advantages of this invention include: it characterizes the coordinated operation of the power-transportation coupling system and simultaneously determines the strategic layout and capacity expansion of charging stations and the reinforcement of power lines over multiple planning periods. Decisions in each period are based on the results and insights of the previous period, ensuring a strategic and adaptive development process. To address the uncertain disasters that occur within each planning period, the proposed model is reshaped into a multi-stage robust optimization model, in which investment decisions are made sequentially within each planning period, depending only on the uncertainty of the current period. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 IEEE 33-node power grid and 20-road traffic network topology diagram exemplified in the embodiment of the present invention.

[0034] Figure 2 Schematic diagram of multi-stage planning results exemplified in an embodiment of the present invention.

[0035] Figure 3 Schematic diagram of the charging capacity of each road in the traffic network during a typical operating period of an example in an embodiment of the present invention.

[0036] Figure 43 is a comparison diagram of the method of the present invention and a non-cooperative planning solution in an embodiment of the present invention.

[0037] Figure 5 Schematic diagram of an example of the method of the present invention using three planning periods. DETAILED DESCRIPTION

[0038] The technical solution of the present invention will be further specifically described below through embodiments and in conjunction with the accompanying drawings.

[0039] First, regarding the multi-stage resilience planning problem, in the power-transportation coupled system, substations and distributed power sources (such as wind turbines and photovoltaics) balance the power demand. Charging stations act as a link between the power grid and the transportation network. Specifically, the layout and capacity investment of charging stations determine the routing and charging power of electric vehicles in the transportation network, while also affecting the power flow distribution in the power grid. Investors subdivide their investment strategies into the required number of planning periods, each spanning one to several years. In each period, the occurrence of natural disasters is considered an uncertain variable. Investment decisions in each period include strengthening grid lines, investing in new charging stations, and expanding the capacity of existing charging stations.

[0040] Take three planning periods as an example, Figure 5 Figure 2 outlines the proposed multi-period resilient planning approach. In planning phase 1, investors make initial charging station investment decisions based on current traffic demand. In planning phase 2, to address the growing demand for electric vehicles and uncertain natural disasters that impact the power-transportation system, critical power lines are reinforced, existing charging station capacity is expanded, and new charging stations are installed on major roads. Moving into planning phase 3, the planning model considers new extreme events and further increases in traffic demand. Additional charging stations are constructed, existing ones are further expanded, and additional power lines are reinforced.

[0041] Example:

[0042] This embodiment provides a multi-stage resilience planning method for an unexpected power-transportation coupling system, including the following steps:

[0043] S1. The investment strategy of the power-transportation coupling system is described as a multi-stage resilience planning problem, and a resilient multi-stage planning model is established with the goal of minimizing the total investment and operating costs over all planning periods.

[0044] The multi-stage planning model for resilience is:

[0045] The minimum sum of investment costs and operating costs at each stage,

[0046] The investment cost at each stage is the sum of the investment costs of all new charging stations and charging station capacity expansion, and the sum of the investment costs of all reinforced power lines; the operating cost at each stage is the sum of the operating costs of all charging stations, the operating costs of all reinforced power lines, the operating costs of the power distribution system at each stage, and the operating costs of the transportation system at each stage. The formula is as follows:

[0047]

[0048] in, represents the investment cost at stage t, including the cost of building new charging stations, expanding the capacity of existing charging stations, and reinforcing power lines, represents the operating cost at stage t, It represents the sum of several operational factors in the power distribution system, including substation power costs and power imbalance penalties. represents the travel time cost of the transportation system, taking into account the semi-dynamic traffic flow, T represents the number of planning stages, μ represents the number of planning years, e represents the charging station index, c ec represents the investment and construction cost coefficient, c ep represents the cost coefficient for capacity expansion of charging stations, β e,t Indicates whether charging station e is newly built at stage t, which is a binary variable, y e,t Indicates whether the charging station e is expanded in stage t, which is a binary variable, Sc e,t represents the expansion capacity value of charging station e at stage t, ij represents the route index starting from / returning to node j, c lh represents the cost of power line reinforcement, Indicates whether the power line is reinforced, which is a binary variable, c me represents the operation and maintenance cost coefficient, z e,t Indicates whether charging station e exists at stage t, which is a binary variable, c ml represents the operating cost of the reinforcement circuit, τ represents the typical movement period index in the planning stage t, and r st represents the power generation cost coefficient of the substation, r pn Indicates the unbalanced power penalty coefficient of the substation, Pd b,t,τ represents the active power output of substation b in period τ during phase t, b represents the substation index, i represents the grid node index, represents the active positive unbalanced power of node i in phase t period τ, represents the active negative unbalanced power of node i in period τ during phase t, represents the reactive positive unbalanced power of node i in period τ during phase t, represents the reactive negative unbalanced power of node i in period τ during phase t, r cj represents the time-price coefficient, ta,t represents the commuting time of road a in stage t, x a,t represents the traffic flow of road a at stage t, a represents the traffic road index, rs represents the OD pair index of the traffic network commuting task, u rs,t,τ represents the equilibrium travel time of OD pair rs in period τ of stage t, represents the semi-dynamic traffic flow of OD pair rs in phase t and period τ.

[0049] The UTN is described as a series of directed graphs, where the traffic demand is represented by origin-destination (OD) pairs. For each OD pair There are multiple paths Each path consists of roads a∈A connecting corresponding OD pairs. For simplicity, without sacrificing generality, it is assumed that all vehicles are electric vehicles with charging requirements, and a semi-dynamic traffic flow model is adopted to solve the traffic assignment problem.

[0050] The constraints for establishing a traffic system include road travel time constraints, travel cost and time constraints for specified routes, traffic flow restrictions on roads, residual flow constraints, actual traffic flow constraints for OD pairs, and a constraint matching the total traffic flow of each origin-destination pair with the demand for each time interval. The supplementary condition constraints of the Wardrop user equilibrium principle are applied. The formula is as follows:

[0051]

[0052]

[0053] Among them, t a,t,τ represents the commuting time of road a in period τ during stage t, x a,t,τ represents the traffic flow of road a in period τ during stage t, represents the traffic capacity of road a in period τ during stage t, represents the travel time of path k in OD pair rs in stage t period τ, r cj represents the time-price coefficient, Λ rs,k,a represents the road-path association matrix, t rs,k,t,τ represents the travel time of path k in OD pair rs in stage t period τ, l represents the penalty coefficient, represents the uncertain variable of the impact of extreme events on road a during period τ in stage t, represents the initial road capacity a, Δq rs,t,τ represents the traffic flow of period τ in stage t of OD pair rs, ΔT represents the time interval between each period, Df rs,t,τ It represents the traffic demand of OD on rs in period τ during stage t.

[0054] Specifically, formula (18) is the road travel time constraint, which calculates the travel time of road a. Formulas (19) and (20) are the travel cost and time constraints of the specified path, respectively. Formula (21) is the traffic flow restriction on the road. Formula (22) is the residual flow constraint. Formula (23) is the actual traffic flow constraint in the OD pair. Formulas (24) and (25) are the total traffic flow matching constraints for each origin-destination pair and its demand in each time interval. Formula (26) is the supplementary condition constraint that applies the Wardrop user equilibrium principle.

[0055] By integrating formulas (18) to (26), we obtain formula (5) for the operating cost of the transportation system at each stage.

[0056] S2. Construct constraints for power-transportation coupling under multi-stage resilience planning, including:

[0057] Establishing a first constraint condition based on the grid power and voltage levels operating within an allowable range;

[0058] Establishing a second constraint condition based on the operating charging range of the electric vehicle;

[0059] The third constraint condition is established based on the logical accuracy of multi-stage investment decision-making.

[0060] The first constraint condition includes the substation active and reactive power generation limits, distributed generation output limits, voltage regulation constraints, electric vehicle charging demand constraints, power line capacity limits, power line operating state constraints, and power imbalance and load reduction variables remaining positive. The formula is as follows:

[0061]

[0062] in, Indicates the upper limit of active power output of substation b, Qd b,t,τ represents the reactive power output of substation b in period τ during stage t, represents the reactive power output upper limit of substation b, d represents the grid load index, g represents the distributed power supply index, Pu g Indicates the lower limit of the active output of distributed power supply g, Pu g,t,τ represents the active power output of distributed generation g in period τ during stage t, Indicates the upper limit of the active output of distributed power source g, Qu g,t,τ represents the reactive power output of distributed generation g in period τ during stage t, Qu g Indicates the lower limit of reactive output of distributed power source g, Indicates the upper limit of reactive power output of distributed power source g, V i,t,τrepresents the voltage amplitude of node i in period τ during phase t, V j,t,τ represents the voltage amplitude of node j in period τ during phase t, Pf ij,t,τ represents the active power overload of line ij during period t, Qf ij,t,τ represents the reactive power flow of line ij during period t, r ij represents the resistance in line ij, x ij Represents the reactance in line ij, Vn represents the nominal voltage amplitude, Auxiliary variable representing the voltage amplitude, V i represents the lower limit of the voltage amplitude at node i, Indicates the upper limit of the voltage amplitude at node i, Pcs a,t,τ represents the charging power of road a during period t, LD d,t,τ It represents the active power demand of load d in period τ during stage t, QD d,t,τ represents the reactive power demand of load d in period τ during stage t, ρ ij,t,τ Indicates whether line ij is damaged in period τ during stage t, which is a binary variable. Pf ij Indicates the lower limit of active power flow of line ij, Indicates the upper limit of the active power flow of line ij.

[0063] Specifically, formula (6) is the active and reactive power generation limit of the substation, formula (7) is the output limit of distributed generation, formulas (10) and (11) are voltage regulation constraints, formulas (12) and (13) are electric vehicle charging demand constraints, maintaining active and reactive power flow balance, formulas (14) and (15) are power line capacity constraints, considering natural disasters and line hardening decisions, formula (16) is the power line operation state constraint, indicating that if it is affected by extreme events before hardening, the line will experience a power outage, otherwise, it will operate normally, and formula (17) is the power imbalance and load reduction variable to maintain positive values.

[0064] The second constraint condition includes the relationship between traffic flow and electric vehicle charging demand, considering the restriction of capacity expansion on charging demand, the charging flow balance constraint of the electric vehicle charging system, the charging flow of the electric vehicle charging system does not exceed the total flow limit of the intersecting lines, and the charging flow on the route does not exceed the total charging capacity limit of all electric vehicle charging systems on the route. The formula is as follows:

[0065]

[0066] Among them, PCs e,t,τ represents the output power of charging station e in phase t period τ, Δp represents the charging power per unit charging flow, represents the charging flow of charging station e in period τ during stage t, Cc e,t represents the capacity of the charging station at stage t, X rs,k,e represents the electric vehicle charging location-path correlation matrix, f rs,k,t,τ represents the traffic flow of OD to rs path k in stage t period τ, and k represents the path index of OD to rs.

[0067] Specifically, formula (27) is the relationship between traffic flow and electric vehicle charging demand on a line with an electric vehicle charging system, formula (28) considers the restriction of charging demand on capacity expansion, formula (29) is the charging flow balance constraint of the electric vehicle charging system, formula (30) is the restriction that the charging flow of the electric vehicle charging system does not exceed the total flow of the intersecting lines, and formula (31) is the restriction that the charging flow on the route does not exceed the total charging capacity of all electric vehicle charging systems on the line.

[0068] To ensure the logical accuracy of multi-stage investment decisions, a third constraint is introduced. This constraint includes the change constraint of EVCS construction based on the existing indicators of planned devices, the continuity of the status of charging piles and reinforced lines over time, the total reinforcement measures for reinforced lines in each phase, the expansion constraint of charging stations, the dynamic adjustment constraint of charging station capacity in each period, and the expansion limit of charging pile capacity in each phase. The formula is as follows:

[0069]

[0070] Among them, Π hd Indicates the number of reinforced lines is limited. represents the initial capacity of charging station e, Indicates the upper limit of the capacity that the charging pile e can expand.

[0071] Specifically, formula (32) is the change constraint of existing indicators during the planning period for EVCS construction. Formula (33) is the constraint to ensure the continuity of the status of charging piles and reinforced lines over time and the total reinforcement measures for reinforced lines in each period. Formula (34) is the constraint for charging station expansion, which only allows the expansion of existing charging stations. Formula (35) is the constraint for the dynamic adjustment of charging station capacity in each period. Formula (36) is the expansion limit of charging pile capacity in each stage. When t-1=0, the relevant variables with the subscript t-1 degenerate into the initial parameters of the system.

[0072] S3. Establish an uncertainty set considering natural disasters and transform the resilient multi-stage planning model into a multi-stage robust model;

[0073] Establish an uncertainty set considering natural disasters, including:

[0074] The power grid and transportation network take into account the impact of natural disasters and adopt an extreme event impact model based on the NK criterion. The formula is as follows:

[0075]

[0076] in, represents the uncertain variable of line ij in period τ at stage t, Γ k,l represents the maximum planning of the impact of disasters on electricity, Γ k,a Indicates the maximum limit of the disaster's impact on the transportation network.

[0077] In formula (45), Representing the set of typical operating periods in planning stage t, we ensure that the power line and traffic road states do not overlap between different typical periods of the planning period. This prevents the coupling of uncertain variables between planning stages and maintains the stage independence of uncertainty when reformulating the model as a multi-stage robust solution.

[0078] In order to ensure that multi-period investment decisions are consistent with the uncertainty ξ2,ξ3,…,ξ T The sequential implementation of is consistent, that is, it complies with the unexpected constraints and transforms the deterministic T-stage planning model into a multi-stage robust optimization problem with T stages. The multi-stage robust optimization model is compactly expressed as follows:

[0079] stD1y1+F1p1≤g1

[0080]

[0081] Among them, y t represents the investment variable at stage t, including binary variables for line reinforcement and charging station construction, and continuous variable for charging station capacity expansion, p t represents the running variables of all typical exercise periods in stage t. These variables are continuous. t and p t All depend on the uncertainty variable ξ t , to ensure that unexpected constraints are taken into account; the coefficient c t and r t Represent investment cost and operating cost respectively; D t and F t is the coefficient matrix of technical constraints, which is composed of the coefficient constants of each variable in the constraints of power-transportation coupling under multi-stage resilience planning; G t represents the dynamic constraints across stages, specifically constraints (32), (33), and (35); the uncertainty set Ξ at each stage, including constraints (44)-(46); g t is established as a constant matrix for the initial stage problem, g tAccording to ξ t And change.

[0082] S4. Solve the multi-stage robust model to obtain the planning strategy.

[0083] The multi-stage planning model of the present invention is a multi-stage robustification problem involving discrete / binary recourse variables. Dual integer dynamic programming is used to iteratively solve the multi-stage robustification model to obtain system planning results for a coupled power-transportation system that complies with the unpredictable evolution of uncertain disasters. Alternatively, a linear-binary hybrid affine rule can be used for iterative solution.

[0084] This embodiment also discloses a multi-stage resilience planning device for an unexpected power-transportation coupling system, comprising: a processor, a memory, and a multi-stage resilience planning program stored in the memory and running on the processor. When the multi-stage resilience planning program is executed by the processor, a multi-stage resilience planning method for an unexpected power-transportation coupling system is implemented.

[0085] The method of the present invention is described below using actual examples.

[0086] The effectiveness of the proposed method is verified using the IEEE 33-node power grid and 20-road traffic network. The system topology is as follows: Figure 1 As shown in the figure, the construction and maintenance costs of the EVCS are set at $50,000 and $8,000, respectively, with a capacity expansion cost of $200 per kilowatt-hour. The cost of power line reinforcement is $10 million, with maintenance costs at $50,000 per line. The substation output cost is $0.3 per kilowatt-hour. The transportation network travel cost is $0.20 per minute. The test was run using the JuMP.jl toolkit in Julia and solved using Gurobi Optimizer 8.1.1 on a server equipped with a Xeon E5-2678 CPU and 64GB of RAM.

[0087] like Figure 2 As shown in the figure, the power line reinforcement, charging station investment decision, charging electric vehicle flow allocation and the worst uncertain disaster scenario selected by robust optimization are presented in three planning periods. Figure 2 In the study, line reinforcement decisions were primarily made at nodes with high charging demand to ensure connectivity with distributed resources and minimize load shedding. Conversely, natural disasters can affect power lines serving high-demand charging stations and roads connecting large EV traffic flows, complicating EV flow allocation and necessitating investment in charging stations. This demonstrates that planning decisions effectively account for potential extreme disaster scenarios, enhancing the resilience of the coupled power-transportation system.

[0088] Furthermore, the charging flow in the transportation network during the typical operation period of the third phase was screened out, and the 24-hour operation characteristics of the transportation network and the power grid were analyzed. The results are as follows: Figure 3 As shown in the figure, charging traffic is concentrated on roads with high capacity and equipped with charging stations. These charging stations are installed near grid nodes connected to distributed resources to support charging demand and promote clean energy consumption. This shows that investment in charging stations can effectively meet travel needs of the transportation network and promote the economic operation of the grid.

[0089] A non-cooperative planning scheme is introduced to compare with the proposed planning scheme. Specifically, the transportation network operation problem is first solved to obtain the charging flow, and then the power grid planning problem is solved separately to obtain the line reinforcement strategy and charging pile investment and construction strategy. The comparison results are as follows: Figure 4 shown. Figure 4 (a) and Figure 4 (b) shows that compared to the non-coordinated plan, this scheme has a higher total investment but lower total operating costs and substation power generation costs. This is because this scheme coordinates the operation of the power grid and transportation network, effectively promoting the generation of distributed resources, reducing the high power supply costs and load shedding at substations, and enabling the coupled power-transportation system to better cope with natural disasters.

[0090] The specific embodiments described herein are merely illustrative of the spirit of the present invention. Persons skilled in the art may make various modifications, additions, or substitutions to the described specific embodiments without departing from the spirit of the present invention or exceeding the scope of the appended claims.

[0091] Although this article frequently uses terms such as resilient multi-stage planning model, power-transportation coupling constraints, multi-stage robust model, first constraint, second constraint, and third constraint, the use of other terms is not excluded. These terms are used solely to facilitate the description and explanation of the present invention; interpreting them as additional limitations is contrary to the spirit of the present invention.

Claims

1. A multi-stage resilience planning method for an unexpected power-transportation coupling system, characterized by: The following steps are involved: The investment strategy of the power-transportation coupling system is described as a multi-stage resilience planning problem, and a resilient multi-stage planning model is established with the goal of minimizing the total investment and operating costs over all planning periods. Construct constraints on power-transportation coupling under multi-stage resilience planning; Establish an uncertainty set considering natural disasters and transform it into a multi-stage robust model based on the resilient multi-stage planning model; Solve the multi-stage robustification model to obtain the planning strategy.

2. The multi-stage resilience planning method for an unexpected power-transportation coupling system according to claim 1 is characterized in that: Construct constraints for power-transportation coupling under multi-stage resilience planning, including: Establishing a first constraint condition based on the grid power and voltage levels operating within an allowable range; Establishing a second constraint condition based on the operating charging range of the electric vehicle; The third constraint condition is established based on the logical accuracy of multi-stage investment decision-making.

3. The multi-stage resilience planning method for an unexpected power-transportation coupling system according to claim 2 is characterized in that: The first constraint conditions include substation active and reactive power generation limitations, distributed generation output limitations, voltage regulation constraints, electric vehicle charging demand constraints, power line capacity limitations, power line operating status constraints, and power imbalance and load reduction variables remaining positive.

4. The multi-stage resilience planning method for an unexpected power-transportation coupling system according to claim 2 is characterized in that: The second constraint condition includes the relationship between traffic flow and electric vehicle charging demand, considering the restrictions of capacity expansion on charging demand, the charging flow balance constraint of the electric vehicle charging system, the charging flow of the electric vehicle charging system does not exceed the total flow limit of the intersecting lines, and the charging flow on the route does not exceed the total charging capacity limit of all electric vehicle charging systems on the route.

5. The multi-stage resilience planning method for an unexpected power-transportation coupling system according to claim 2 is characterized in that: The third constraint condition includes the change constraint of existing indicators during the planning period for EVCS construction, the continuity of the status of charging piles and reinforced lines over time and the total reinforcement measures for reinforced lines in each period, the expansion constraint of charging stations, the dynamic adjustment constraint of charging station capacity in each period, and the expansion limit of charging pile capacity in each stage.

6. The multi-stage resilience planning method for an unexpected power-transportation coupling system according to any one of claims 1 to 5, characterized in that: The multi-stage planning model for resilience is: The minimum sum of investment costs and operating costs at each stage, Among them, the investment cost at each stage is the sum of the investment costs of all newly built charging stations and charging station capacity expansion, and the sum of the investment costs of all reinforced power lines; the operating cost at each stage is the sum of the operating costs of all charging stations, the sum of the operating costs of all reinforced power lines, the operating costs of the power distribution system at each stage, and the operating costs of the transportation system at each stage.

7. The multi-stage resilience planning method for an unexpected power-transportation coupling system according to claim 6 is characterized by: The traffic demand of a transportation system is represented by an origin-destination pair. For each origin-destination pair, there are multiple paths, and each path consists of roads connecting the corresponding origin-destination pairs. Establishing the constraints of the traffic system, including road travel time constraints, travel cost and time constraints for specified paths, traffic flow restrictions on roads, residual flow constraints, actual traffic flow constraints in OD pairs, matching constraints between the total traffic flow of each origin-destination pair and the demand in each time interval, and applying the supplementary condition constraints of Wardrop's user equilibrium principle; The transportation system constraints are integrated to obtain the transportation system operation costs at each stage.

8. The multi-stage resilience planning method for an unexpected power-transportation coupling system according to claim 7 is characterized by: Establish an uncertainty set considering natural disasters, including: The power grid and transportation network take into account the impact of natural disasters and introduce an extreme event impact model based on the NK criterion.

9. The multi-stage resilience planning method for an unexpected power-transportation coupling system according to claim 1 is characterized in that: Solve the multi-stage robust model to obtain the planning strategy, including: The multi-stage robustification model is iteratively solved using a dual integer dynamic programming method.

10. A multi-stage resilience planning device for an unexpected power-transportation coupling system, characterized in that: include: A processor, a memory, and a multi-stage resilience planning program stored in the memory and running on the processor. When the multi-stage resilience planning program is executed by the processor, it implements the multi-stage resilience planning method for the unexpected power-transportation coupling system as described in any one of claims 1 to 9.

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