Distributed transaction-driven power distribution network toughness improvement method and system

Abstract

The invention discloses a distributed transaction-driven power distribution network toughness improvement method and system, and relates to the technical field of demand side flexible resource regulation and control, and the method comprises the steps: obtaining public distributed energy resources, and carrying out the pre-construction of a distributed transaction-driven power distribution network toughness improvement model through the public distributed energy resources based on an upper layer and a lower layer; optimizing the distributed transaction-driven power distribution network toughness improvement model based on a VCG mechanism to obtain an optimized distributed transaction-driven power distribution network toughness improvement model; the distributed transaction-driven power distribution network toughness improvement model is solved through a distributed solution algorithm based on L (p)-Box ADMM, a power distribution network toughness improvement result is obtained, a producer and a consumer can be effectively stimulated to truly report own energy consumption cost and demand, and reasonable distribution of limited resources in an extreme scene is promoted.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of demand side flexible resource regulation, in particular to a distributed transaction driven power distribution network resilience improvement method and system. BACKGROUND

[0002] With the development of the decentralization of the power distribution system, non-public resources show potential adaptability in coping with extreme disasters. However, with the deregulation and privatization of the power industry, the ownership of potential resilience resources is increasingly transferred to individual entities, and the DSO lacks direct control over private resources. SUMMARY

[0003] To solve the problems mentioned in the background, the purpose of the present application is to provide a distributed transaction driven power distribution network resilience improvement method and system.

[0004] In the first aspect, the purpose of the present application can be achieved by the following technical solution: a distributed transaction driven power distribution network resilience improvement method, the method comprising the following steps:

[0005] Obtaining public distributed energy resources, and pre-constructing a distributed transaction driven power distribution network resilience improvement model based on the form of upper and lower layers;

[0006] Optimizing the distributed transaction driven power distribution network resilience improvement model based on the VCG mechanism to obtain an optimized distributed transaction driven power distribution network resilience improvement model;

[0007] Solving the distributed transaction driven power distribution network resilience improvement model by a distributed solving algorithm based on L(p)-Box ADMM to obtain a power distribution network resilience improvement result.

[0008] In combination with the first aspect, in some implementation manners of the first aspect, the method further comprises: the distributed transaction driven power distribution network resilience improvement model comprises an upper layer model and a lower layer model, wherein the upper layer model is established by a market driven framework between the DSO and the producers and consumers, at the DSO level, when a fault occurs, the DSO uses the deployed DG to restore power supply and coordinates the repair work of the fault line, when a certain energy transaction scheme violates the power flow safety constraint, the DSO will instruct the producers and consumers to adjust the plan, and if necessary, perform load shedding operation, the objective of the upper layer model is to minimize the overall service recovery cost, including the following five parts: the cost of purchasing electricity from the main power grid; the operating cost of the DG; the energy transaction cost with the producers and consumers; the load shedding cost; the transaction adjustment cost; the upper layer objective function is expressed as follows:

[0009]

[0010] In the formula: is the cost of purchasing electricity from the main grid; is the cost of DG generation; δ is the load reduction penalty; γ is the transaction adjustment penalty; ξ n,t is the node price;

[0011] In some implementations of the first aspect, the method further includes that the constraints of the upper-layer model include: power balance constraints, power flow constraints, power transmission constraints, power adjustment constraints, DG output constraints, and fault repair constraints.

[0012] In some implementations of the first aspect, the method further includes that the objective of the lower-layer model is to minimize the total cost of the prosumers, mainly including the following four parts: the use cost of renewable energy resources RES; the cost or benefit of P2P energy transaction; the cost of purchasing electricity from the DSO; and the utility cost of flexible load; and the lower-layer objective function is expressed as follows:

[0013]

[0014] wherein: A n , B n is the utility coefficient of the flexible load of the prosumer n; is the cost coefficient of the energy storage and new energy; is the charge and discharge capacity of the energy storage; is the new energy output of the prosumer n; is the P2P transaction price;

[0015] In some implementations of the first aspect, the method further includes that the constraints of the lower-layer model include: transaction constraints between the prosumers and the DSO, P2P transaction constraints, power balance constraints, energy storage charge and discharge constraints, and flexible load constraints.

[0016] In some implementations of the first aspect, the method further includes that the VCG mechanism can simultaneously satisfy the configuration efficiency and the incentive compatibility under the dominant strategy, each participant needs to pay for the total social welfare loss caused by its participation, and the pricing rule ensures that the truthful bidding becomes the optimal choice of each prosumer to maximize its income under the dominant strategy;

[0017] The prosumer conducts power transactions with the DSO and other prosumers, and the information set reported by the prosumer n to the DSO is: wherein The utility function of the prosumer n at time t is expressed as:

[0018]

[0019] wherein, K n,tis the agent fee paid by the producer-consumer to the trading platform, H n,t is the electricity purchase cost of the producer-consumer from the DSO;

[0020] Let denote the sealed power demand and cost parameter set submitted by the producer-consumer n to the trading platform of the DSO, based on the VCG mechanism, the payment function of the producer-consumer n can be expressed as follows:

[0021]

[0022]

[0023] where G(·) is the total social welfare of the producer-consumer, G -n (·) represents the social welfare obtained by other producer-consumers after the producer-consumer n makes a false bid, X n,t denotes the false bid strategy set of the producer-consumer n; L -n,t denotes the real bid strategy set of other producer-consumers;

[0024] The social welfare function of the producer-consumer n is the net income in the market, which can be expressed as:

[0025]

[0026] The VCG mechanism is allocated through equivalent liability:

[0027]

[0028]

[0029] The definition of the scarcity premium item is as follows:

[0030]

[0031]

[0032] where δ represents the load reduction penalty cost, i.e. the upper limit of electricity price, ξ n,t is the node marginal electricity price obtained by the upper model, R m represents the power scarcity margin of the system, which is used to measure the tightness of supply and demand, π LOLP is the loss of load probability of the node under similar disaster scenarios;

[0033] By combining the producer-consumer liability allocation mechanism with the scarcity pricing mechanism, the lower P2P energy transaction model is reconstructed, and the objective function is expressed as follows:

[0034]

[0035]

[0036]

[0037]

[0038] In a first aspect, in some implementations of the first aspect, the method further includes that the process of solving the distributed transaction-driven distribution network resilience improvement model by the L(p)-Box ADMM-based distributed solving algorithm includes:

[0039] L(p)-box ADMM is to approximate the binary set ψ = {0, 1} n Relaxing to an n-dimensional cube S b The intersection of an (n-1) dimensional spherical surface S p , thereby realizing continuous approximation of discrete variables;

[0040]

[0041] By defining the equality constraint by a typical binary variable S n,t

[0042]

[0043]

[0044]

[0045] The augmented Lagrangian function of the P2P energy transaction model is expressed as:

[0046]

[0047]

[0048] Wherein, the last two terms in formula (50) are respectively as the penalty term for P2P energy transaction and binary variable, the process is as follows:

[0049]

[0050]

[0051]

[0052] Define the residual threshold, which is the sum of the original residual and the dual residual, as the convergence criterion:

[0053]

[0054] ​Secondly, in order to achieve the above objectives, this invention discloses a distributed transaction-driven distribution network resilience enhancement system, comprising:

[0055] The model building module is used to acquire public distributed energy resources and pre-build a distributed transaction-driven distribution network resilience improvement model based on the public distributed energy resources in an upper and lower layer form.

[0056] The model optimization module is used to optimize the distributed transaction-driven distribution network resilience improvement model based on the VCG mechanism, so as to obtain the optimized distributed transaction-driven distribution network resilience improvement model.

[0057] The model solving module is used to solve the distributed transaction-driven distribution network resilience improvement model using a distributed solving algorithm based on L(p)-Box ADMM, and obtain the distribution network resilience improvement results.

[0058] In another aspect of the present invention, in order to achieve the above-mentioned objective, a terminal device is disclosed, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor. The memory stores the computer program capable of running on the processor. When the processor loads and executes the computer program, it employs a distributed transaction-driven distribution network resilience enhancement method as described above.

[0059] In another aspect of the present invention, in order to achieve the above objectives, a computer-readable storage medium is disclosed, wherein a computer program is stored in the computer-readable storage medium, and when the computer program is loaded and executed by a processor, a distributed transaction-driven distribution network resilience enhancement method as described above is employed.

[0060] The beneficial effects of this invention are:

[0061] Compared with traditional centralized control methods, this invention uses price signals as an incentive to regulate the user-side DER to participate in power restoration while ensuring user privacy, effectively improving the safety and resilience of the distribution network in extreme scenarios.

[0062] The pricing method of this invention can effectively incentivize producers and consumers to truthfully report their energy costs and needs, and promote the rational allocation of limited resources in extreme scenarios. Attached Figure Description

[0063] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0064] Figure 1 This is a schematic diagram of the method flow of the present invention;

[0065] Figure 2 This is a schematic diagram of the distributed-driven recovery framework of the present invention;

[0066] Figure 3 This is a schematic diagram of a simulation example of the present invention;

[0067] Figure 4 This is a schematic diagram of the power restoration simulation results of the present invention;

[0068] Figure 5 This is a schematic diagram of the power supply restoration simulation results under centralized control mode;

[0069] Figure 6 This is a schematic diagram showing the total power restoration cost under different calculation examples of the present invention;

[0070] Figure 7 This is a schematic diagram illustrating the 24-hour price payment mechanism for prosumers under the pricing mechanism of this invention;

[0071] Figure 8 This is a schematic diagram of the total payment of producers and consumers under the pricing mechanism of this invention;

[0072] Figure 9 This is a schematic diagram illustrating the convergence of the algorithm of this invention;

[0073] Figure 10 This is a schematic diagram of the system structure of the present invention. Detailed Implementation

[0074] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0075] Example 1:

[0076] like Figure 1 As shown, a distributed transaction-driven method for improving the resilience of a distribution network includes the following steps:

[0077] S101: Obtain public distributed energy resources and pre-build a distributed transaction-driven distribution network resilience improvement model based on upper and lower layers of public distributed energy resources;

[0078] The rise of the peer-to-peer (P2P) energy market allows producers and consumers (DERs) to trade electricity directly with other users at prices lower than those offered by distribution stations (DSOs), effectively reducing their dependence on the main grid. In the event of cascading failures (Nk), the DSO, acting as a coordinator between the main grid and producers and consumers, is responsible for issuing load shedding orders and providing economic incentives to DER owners. In this context, producers and consumers are no longer passive recipients of service restoration but active participants with flexibility and generation capacity, whose initiative can accelerate the service restoration process.

[0079] like Figure 2 As shown, a market-driven framework between the Distribution System Operator (DSO) and prosumers is proposed. At the DSO level, the DSO no longer issues direct control signals but instead optimizes the recovery process of outage loads and sets incentive prices based on power scarcity. Simultaneously, when energy trading becomes unfeasible due to power flow constraints, the DSO intervenes and adjusts the trading scheme. At the prosumer level, each prosumer actively shares energy with neighboring users while minimizing its own operating costs. Driven by scarcity pricing signals, prosumers can autonomously adjust their trading roles and energy dispatch plans. Therefore, using price signals as a coupling mechanism, this model can achieve safe and orderly energy trading under Nk fault scenarios, thereby promoting the integration of private distribution system derivatives (DERs) into distribution system resilience enhancement strategies.

[0080] At the DSO level, when a fault occurs, the DSO utilizes deployed DGs to restore power and coordinate maintenance work on the faulty lines. When an energy trading scheme violates grid power flow security constraints, the DSO will instruct producers and consumers to adjust their trading plans and, if necessary, perform load shedding operations. The upper-level model aims to minimize the overall service restoration cost, which mainly includes the following five parts: 1) the cost of purchasing electricity from the main grid; 2) the operating cost of DGs; 3) the energy trading costs with producers and consumers; 4) the load shedding costs; and 5) the trading adjustment costs. The upper-level objective function is specifically expressed as follows:

[0081]

[0082] In the formula: Main grid electricity purchase cost; δ represents the cost of DG generation; γ represents the load reduction penalty; ξ represents the transaction adjustment penalty; n,t This refers to the nodal electricity price.

[0083] The constraints are expressed as follows:

[0084] Power balance constraints

[0085]

[0086]

[0087] In the formula: The main network purchase volume at time t; For the output of node iDG; The base load of load i; For the flexible load of producer-consumer n; The transaction volume between producers and consumers (nm); The transaction status of producer-consumer n; For producer-consumer n and DSO transaction volume; Adjustment volume for P2P transactions of producer-consumer n; PL i,t Let i be the load reduction amount for node i. Let i be the outflow and inflow power of node i.

[0088] Current constraints

[0089]

[0090]

[0091]

[0092]

[0093] In the formula: U i,t The voltage at node i; r ij x ij Let be the resistance and reactance of branch ij; I represents the power and flow rate of branch ij; ij U is the current in branch ij; max U min υ represents the upper and lower limits of the node voltage; ij,t This represents the interruption status of branch ij.

[0094] Power transfer constraints

[0095]

[0096]

[0097] Power adjustment constraints

[0098]

[0099]

[0100]

[0101] DG output constraint

[0102]

[0103] Fault repair constraints

[0104]

[0105]

[0106]

[0107] In the formula: Δt f The time required to repair a single line; Q f This represents the maximum number of maintenance tasks that can be performed in a single time period.

[0108] At the producer-consumer level, producers can trade electricity with other users through P2P trading platforms. After the transaction is completed, producers report the equivalent load to the upper-level model for service restoration decisions. The lower-level model aims to minimize the total cost for producers, which mainly includes the following four parts: 1) the cost of using renewable energy resources (RES); 2) the cost or benefit of P2P energy trading; 3) the cost of purchasing electricity from DSOs; and 4) the utility cost of flexible loads. The lower-level objective function is specifically expressed as follows:

[0109]

[0110] In the formula: A n B n The utility coefficient for the flexible load of producer-consumer n; Cost coefficients for energy storage and new energy sources; The amount of charge and discharge for energy storage; To contribute to the new energy supply for producer-consumer n; This refers to the price of P2P transactions.

[0111] The constraints are expressed as follows:

[0112] Transaction constraints between prosumers and DSOs

[0113]

[0114]

[0115]

[0116]

[0117] In the formula: The upper limit for the buying and selling of electricity by producers and consumers and DSOs.

[0118] P2P Transaction Constraints

[0119]

[0120]

[0121] In the formula: This is the upper limit for P2P transactions.

[0122] Power balance constraints

[0123]

[0124]

[0125] Energy storage charge and discharge constraints

[0126]

[0127]

[0128]

[0129]

[0130] In the formula: The upper limit for energy storage charging and discharging for producers and consumers; It is in the energy storage and discharge state; For energy storage SOC; τ ch τ dis This refers to the energy storage charging and discharging efficiency.

[0131] Flexible load constraints

[0132]

[0133]

[0134] S102: The distributed transaction-driven distribution network resilience improvement model is optimized based on the VCG mechanism to obtain the optimized distributed transaction-driven distribution network resilience improvement model.

[0135] In the peer-to-peer (P2P) trading model, each producer-consumer (PDC) submits a sealed electricity demand quote and its cost function to the trading platform. The Distributed Supply Order (DSO) determines the transaction outcome and payment amount based on the collected information. However, this mechanism faces a key challenge: in situations of supply-demand imbalance, DSCs may strategically inflate their costs or demands to obtain more favorable transaction outcomes, thereby maximizing their personal utility. Therefore, especially in extreme scenarios, introducing a credible pricing mechanism that incentivizes truthful quotes is crucial.

[0136] The VCG mechanism is a mature monetary mechanism that, in theory, can simultaneously satisfy allocative efficiency and incentive compatibility under a dominant strategy. Under the VCG mechanism, each participant must pay for the total social welfare loss resulting from their participation. This pricing rule ensures that truthful pricing becomes the optimal choice for each producer-consumer to maximize their gains under a dominant strategy.

[0137] Based on the P2P energy trading model established by formulas (17) to (31), prosumers can conduct electricity transactions with DSOs and other prosumers through the trading platform. Let the set of information reported by prosumer n to the DSO be: in Therefore, the utility function of producer-consumer n at time t can be expressed as:

[0138]

[0139] Among them, K n,t It is the agency fee paid by the consumer to the trading platform, H n,t It is the cost of electricity purchased by producers and consumers from DSO.

[0140] set up Let n represent the set of sealed electricity demand and cost parameters submitted by producer-consumer n to the DSO trading platform. Then, based on the VCG mechanism, producer-consumer n's payment function can be expressed as follows:

[0141]

[0142]

[0143] Where G(·) is the total social welfare of producers and consumers, G -n (·) represents the social welfare received by other prosumers after prosumer n makes a false offer, X n,t L represents the set of false pricing strategies of producer-consumer n; -n,t This represents the set of actual pricing strategies of other prosumers.

[0144] Therefore, the social welfare function of producer-consumer n, i.e., its net income in the market, can be expressed as:

[0145]

[0146] Under normal operating conditions, the VCG mechanism determines the final payment amount based on each producer-consumer's marginal contribution to overall social welfare. However, in situations where extreme events lead to power scarcity, the traditional VCG mechanism struggles to effectively incentivize the supply of non-utility-based DERs (Demand Providers). Therefore, a "responsibility-sharing" mechanism should be introduced for producers-consumers. In reality, during normal operation, the peer-to-peer energy market erodes the profit margins of DSOs (Demand Solicitors); however, in the event of a sudden disaster causing power shortages, the resulting punitive costs are borne entirely by the DSOs. Rather than directly excluding producers-consumers from the market when calculating marginal contributions, we propose the concept of equivalent responsibility to distribute the responsibility for system stability in a fairer and more reasonable manner.

[0147]

[0148]

[0149] Secondly, the scarcity of electricity resources is particularly pronounced in extreme events. To incentivize producers and consumers to proactively adjust their energy consumption plans and trading roles, this invention introduces a scarcity-based pricing mechanism. This mechanism draws on the scarcity pricing concept in the reserve market, adding a scarcity surcharge item to the DLMP (Digital Distribution Management System) after market clearing, reflecting the progress of service recovery. This surcharge item comprehensively considers the node's load shedding probability and the overall power balance of the distribution system to quantify the current degree of electricity scarcity, thereby releasing a clearer price incentive signal to producers and consumers. The definition of the scarcity surcharge item is as follows:

[0150]

[0151]

[0152] Where δ represents the load reduction penalty cost, i.e., the electricity price cap, and ξ n,t R represents the nodal marginal electricity price obtained from the upper-level model. m π represents the power scarcity margin of the system and is used to measure the degree of supply and demand tension. LOLP This represents the probability of node failure under similar disaster scenarios.

[0153] By combining a producer-consumer responsibility-sharing mechanism with a scarcity pricing mechanism, the electricity purchase cost for producers and consumers is redefined in extreme scenarios. This revised pricing scheme effectively incentivizes producers and consumers to dynamically adjust their energy consumption plans based on system conditions, or to flexibly switch between buyer and seller roles to maximize their own interests. Therefore, the underlying P2P energy trading model is reconstructed, and its objective function is restated as follows:

[0154]

[0155]

[0156]

[0157]

[0158] S103: Solve the distributed transaction-driven distribution network resilience improvement model using a distributed solution algorithm based on L(p)-Box ADMM to obtain the distribution network resilience improvement results.

[0159] To address the aforementioned challenges, this section of the invention proposes a solution method based on L(p)-box ADMM. It should be noted that the implementation of P2P energy trading inevitably introduces a large number of binary variables, leading to the non-convexity of the optimization model. Therefore, we employ the L(p)-box ADMM algorithm to solve the non-convexity problem in P2P energy trading. The core idea of ​​L(p)-box ADMM is to use the binary set ψ = {0,1}... n Relaxed into an n-dimensional cube S b With an (n-1)-dimensional sphere S p The intersection of these points allows for continuous approximation of discrete variables.

[0160]

[0161] Clearly, with dimension p set to 2, we can use a typical binary variable S n,t To define these equality constraints.

[0162]

[0163]

[0164]

[0165] Subsequently, we can express the augmented Lagrangian function of the P2P energy trading model as:

[0166]

[0167]

[0168] In equation (50), the last two terms serve as penalty terms for P2P energy trading and binary variables, respectively. As an iterative algorithm, L(p)-box ADMM can decompose the optimization problem in a distributed manner. Therefore, the low-level subproblems have strong convexity and are very suitable for using the L(p)-box ADMM algorithm. The specific process is as follows:

[0169]

[0170]

[0171]

[0172] Then, we define a residual threshold, which is the sum of the original residual and the dual residual, as a convergence criterion.

[0173]

[0174] Specifically, the present invention will be further illustrated below through embodiments:

[0175] To verify the effectiveness of the proposed method and evaluate its performance, this section uses a modified IEEE 33 bus power distribution system (containing 5 tie lines). All simulations were performed on a computer equipped with an Intel Core i7-14700K CPU (3.80GHz) and 32GB of memory, using the Matlab platform with the Gurobi 12.0 solver.

[0176] The revised schematic diagram of the IEEE 33-node system is as follows: Figure 3 As shown. Assume the system experiences five branch disconnection faults, with the recovery time domain comprising 24 time steps, each lasting 15 minutes. Every 4 hours, the Distribution System Operator (DSO) will organize a repair team to repair one faulty branch. The DSO is located at node 0, and the distribution system contains six prosumers located at nodes 8, 13, 17, 22, and 29, with the other nodes designated as traditional consumers. The DSO's distributed generation (DG) output caps are set at 5.5MW, 4.5MW, 5MW, and 5MW, respectively. All lines have a capacity of 5MW, and node voltage is limited to ±8%. Penalty costs for load shedding and P2P transaction adjustments are set at 1000 and 800, respectively, with a penalty of 800 for two-layer interaction. Based on NordPool's wholesale market price, the external regulation price is set between $30 / MWh and $75 / MWh, and the generation cost of distributed generation is set at 0.67 RMB / kWh. The main input data for the market currently uses IEEE 33-node standard case data, and the flexible load limit for producers and consumers is set at 0.2MW.

[0177] To evaluate the effectiveness of the resilience enhancement for P2P trading pairs, we designed the following five case studies, which are explained in detail below:

[0178] Case 1 (Proposed Method): User-side resources are controlled through a P2P trading platform, and electricity trading pricing adopts an obligation-based VCG mechanism, combined with scarcity pricing and distributed linear marginal price (DLMP).

[0179] Case 2: DSO can only directly control distributed generation (DG) and load shedding. This case is used to verify the effect of P2P transactions on resilience improvement.

[0180] Case 3: User-side resources are controlled through a P2P trading platform, and electricity trading adopts the VCG pricing mechanism. This case is used to verify the incentive effect of scarcity prices on prosumers under the VCG mechanism.

[0181] Case 4: User-side resources are controlled through a P2P trading platform, and electricity trading pricing adopts DLMP and scarcity pricing. This case is used to verify the impact of the VCG mechanism on social welfare.

[0182] Case 5: User-side resources are controlled through a P2P trading platform, and electricity trading pricing adopts DLMP. This case is used to verify the incentive effect of scarcity pricing on prosumers under the DLMP mechanism.

[0183] (2) Analysis of the effectiveness of distributed transaction-driven distribution network resilience enhancement strategies

[0184] To assess the impact of producer-consumer P2P electricity trading on power restoration Figure 4 The total load and P2P reduction results of Case 1 and Case 2 were compared. In the graph, colors from white to red indicate a gradually increasing degree of load reduction. Figure 4 As shown, because the DSO cannot control the private electricity resources of prosumers, surplus electricity cannot be shared and is limited to its own use. Therefore, compared to Case 1, prosumers 2, 3, and 5 in Case 2 experienced more severe load shedding due to local power shortages. Furthermore, the total load shedding and the number of affected loads in Case 2 were significantly higher than in Case 1. In contrast, the DSO in Case 1 indirectly regulated energy allocation through the P2P electricity market to ensure power flow security, rather than directly controlling private resources. This not only improved system resilience but also protected the privacy of prosumers.

[0185] To further assess the impact of P2P electricity trading on power restoration Figure 5The composition of total recovery costs in Cases 1 through 5 is illustrated. Due to the persistent supply-demand imbalance and transmission capacity constraints, the expenditures of DSOs on grid purchases and DG remain relatively stable across the cases. Therefore, total recovery costs are primarily affected by load shedding and P2P transaction reductions. Compared to Case 2, although P2P transactions introduce additional adjustment penalties for DSOs, they release more electricity from prosumers, significantly reducing load shedding costs. Comparing Cases 1 and 3, the application of scarcity pricing provides a stronger incentive for prosumers to adjust their energy consumption plans, reducing total costs by approximately RMB 3,500. While the total recovery costs in Cases 1 and 4 are almost identical, the implementation of the VCG mechanism in Case 1 enhances the fairness of social welfare distribution; this will be discussed further in subsequent chapters.

[0186] (3) Analysis of transaction pricing results in extreme scenarios based on VCG mechanism

[0187] During extreme events, producers and consumers may manipulate the market to obtain more electricity by falsifying electricity consumption information or cost coefficients. To verify the anti-falsification capability of the proposed mechanism, we compared the impact of falsification on producers' own electricity costs under the VCG mechanism and the traditional transaction pricing mechanism.

[0188] Suppose that producer-consumer 4 attempts to increase their utility by falsely reporting bids during a P2P transaction. Figure 6 The 24-hour payment curve for producer-consumer 3 in Case 1 is shown. According to Theorem 1, false bidding under the VCG mechanism distorts individual returns and prevents producer-consumers from achieving maximum utility. Therefore, when a producer-consumer engages in false bidding, additional electricity costs are incurred at each time step. Notably, from time steps 16 to 18, false bidding negatively impacts social welfare, causing the producer-consumer's financial situation to shift from profit to loss.

[0189] like Figure 7 As shown, the impact of false bidding on total electricity costs is compared between the VCG mechanism and the traditional trading mechanism. In the absence of information oversight (i.e., Case 5 of the traditional pricing mechanism), producers and consumers can profit by falsely reporting cost coefficients or exaggerating energy demand, thereby obtaining a larger share of electricity in the market and ensuring uninterrupted power supply. In contrast, the VCG mechanism determines market payments based on each producer and consumer's marginal contribution to overall social welfare. Therefore, false bidding can actually lead to producers and consumers paying more. This demonstrates that the VCG mechanism effectively curbs false bidding and ensures the rational allocation of limited resources in extreme scenarios.

[0190] (4) Analysis of the distributed solution algorithm based on L(p)-Box ADMM

[0191] In our model, prosumers frequently adjust the binary variables of their trading roles in response to price incentives, and this frequent switching behavior affects the convergence of low-level P2P transactions. Furthermore, the VCG mechanism requires prosumers to submit transaction and energy consumption information to the DSO. To address these challenges, we propose the L(p)-box ADMM algorithm, which improves model convergence while protecting user privacy.

[0192] Since the algorithm's performance is quite sensitive to parameter settings, we conducted sensitivity analysis on the penalty coefficient and convergence criterion to determine the optimal values. Figure 8 The effects of the convergence criterion and penalty parameter on the algorithm's performance are demonstrated. Clearly, the number of iterations increases proportionally with the increase in stopping tolerance; however, the penalty parameter has little effect on the number of iterations. When the convergence criterion is set to 0.01, a better convergence speed is achieved within the range [1050, 1200] for the penalty parameter.

[0193] Therefore, we chose a suitable parameter combination ρ = 1100, ε = 0.01 to achieve a balance between convergence speed and computational accuracy in the algorithm. Figure 9 The convergence performance of the L(p)-box ADMM algorithm is demonstrated, with the y-axis using a base-10 logarithmic scale.

[0194] Example 2: To achieve the above objective, such as Figure 10 As shown, based on Embodiment 1, this invention discloses a distributed transaction-driven distribution network resilience enhancement system, comprising:

[0195] Model building module 11 is used to acquire public distributed energy resources and pre-build a distributed transaction-driven distribution network resilience improvement model based on the public distributed energy resources in the form of upper and lower layers.

[0196] Model optimization module 12 is used to optimize the distributed transaction-driven distribution network resilience improvement model based on the VCG mechanism to obtain the optimized distributed transaction-driven distribution network resilience improvement model.

[0197] Model solving module 13 is used to solve the distributed transaction-driven distribution network resilience improvement model using a distributed solving algorithm based on L(p)-Box ADMM, and obtain the distribution network resilience improvement results.

[0198] Based on the same inventive concept, this invention also provides a computer device, comprising: one or more processors, and a memory for storing one or more computer programs; the programs include program instructions, and the processor executes the program instructions stored in the memory. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, used to implement one or more instructions, specifically for loading and executing one or more instructions stored in a computer storage medium to implement the above-described method.

[0199] It should be further explained that, based on the same inventive concept, the present invention also provides a computer storage medium storing a computer program, which, when executed by a processor, performs the above-described method. This storage medium can be any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In the present invention, the computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0200] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this disclosure. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0201] The foregoing has shown and described the basic principles, main features, and advantages of this disclosure. Those skilled in the art should understand that this disclosure is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of this disclosure. Various changes and modifications can be made to this disclosure without departing from its spirit and scope, and all such changes and modifications fall within the scope of this disclosure as claimed.

Claims

1. A distributed transaction-driven method for improving the resilience of a distribution network, characterized in that, The method includes the following steps: Acquire public distributed energy resources and pre-build a distributed transaction-driven distribution network resilience enhancement model based on upper and lower layers of public distributed energy resources; The distributed transaction-driven distribution network resilience improvement model is optimized based on the VCG mechanism to obtain the optimized distributed transaction-driven distribution network resilience improvement model. The distributed transaction-driven distribution network resilience improvement model is solved using a distributed solution algorithm based on L(p)-Box ADMM, and the distribution network resilience improvement results are obtained.

2. The distributed transaction-driven distribution network resilience enhancement method according to claim 1, characterized in that, The distributed transaction-driven distribution network resilience enhancement model comprises an upper-layer model and a lower-layer model. The upper-layer model is established through a market-driven framework between the Distribution System Operator (DSO) and producers / consumers (PDs). At the DSO level, when a fault occurs, the DSO utilizes deployed distributed generation (DGs) to restore power and coordinate maintenance work on the faulty lines. When an energy trading scheme violates grid power flow security constraints, the DSO will instruct producers / consumers to adjust their plans and, if necessary, execute load shedding operations. The objective of the upper-layer model is to minimize the overall service restoration cost, which includes the following five parts: the cost of purchasing electricity from the main grid; the operating cost of DGs; the energy trading cost with producers / consumers; the load shedding cost; and the trading adjustment cost. The upper-layer objective function is expressed as follows: In the formula: Main grid electricity purchase cost; δ represents the cost of DG generation; γ represents the load reduction penalty; ξ represents the transaction adjustment penalty; n,t This refers to the nodal electricity price.

3. The distributed transaction-driven distribution network resilience enhancement method according to claim 2, characterized in that, The constraints of the upper-level model include: power balance constraints, power flow constraints, power transmission constraints, power adjustment constraints, DG output constraints, and fault detection constraints.

4. The distributed transaction-driven distribution network resilience enhancement method according to claim 3, characterized in that, The goal of the lower-level model is to minimize the total cost to prosumers, which mainly includes the following four parts: the cost of using renewable energy resources (RES); the cost or benefit of P2P energy trading; the cost of purchasing electricity from DSO; and the utility cost of flexible loads. The lower-level objective function is expressed as follows: In the formula: A n B n The utility coefficient for the flexible load of producer-consumer n; Cost coefficients for energy storage and new energy sources; The amount of charge and discharge for energy storage; To contribute to the new energy supply for producer-consumer n; This refers to the price of P2P transactions.

5. The distributed transaction-driven distribution network resilience enhancement method according to claim 4, characterized in that, The constraints of the lower-level model include: transaction constraints between prosumers and DSOs, P2P transaction constraints, power balance constraints, energy storage charging and discharging constraints, and flexible load constraints.

6. The distributed transaction-driven distribution network resilience enhancement method according to claim 1, characterized in that, The VCG mechanism can simultaneously satisfy allocation efficiency and incentive compatibility under the dominant strategy. Each participant must pay for the total social welfare loss caused by their participation. The pricing rules ensure that truthful pricing becomes the optimal choice for each producer-consumer to maximize their benefits under the dominant strategy. Prosumers engage in electricity transactions with DSOs and other prosumers. Let Z be the set of information reported by prosumer n to the DSO. n,t ,θ n,t ;in θ n =(A n B n The utility function of producer-consumer n at time t is expressed as: F n,t =Z n,t θ n -K n,t -H n,t (3) Among them, K n,t It is the agency fee paid by the consumer to the trading platform, H n,t It is the cost of electricity purchased by producers and consumers from DSOs; Let L n,t =(Z n,t ,θ n () represents the set of sealed electricity demand and cost parameters submitted by producer-consumer n to the DSO trading platform. Based on the VCG mechanism, the payment function of producer-consumer n can be expressed as follows: Where G(·) is the total social welfare of producers and consumers, G -n (·) represents the social welfare received by other prosumers after prosumer n makes a false offer, X n,t L represents the set of false pricing strategies of producer-consumer n; -n,t This represents the set of actual pricing strategies of other prosumers; The social welfare function of producer-consumer n, which is the net benefit in the market, can be expressed as: The VCG mechanism distributes responsibility through equivalent liability: The definition of the scarcity surcharge item is as follows: Where δ represents the load reduction penalty cost, i.e., the electricity price cap, and ξ n,t R represents the nodal marginal electricity price obtained from the upper-level model. m π represents the power scarcity margin of the system and is used to measure the degree of supply and demand tension. LOLP This represents the probability of node failure under similar disaster scenarios. By combining the producer-consumer responsibility sharing mechanism with the scarcity pricing mechanism, the underlying P2P energy trading model is reconstructed, and the objective function is expressed as follows:

7. The distributed transaction-driven distribution network resilience enhancement method according to claim 1, characterized in that, The process of solving the distributed transaction-driven distribution network resilience improvement model using a distributed solution algorithm based on L(p)-Box ADMM includes: L(p)-box ADMM is a binary set ψ={0,1} n Relaxed into an n-dimensional cube S b With an (n-1)-dimensional sphere S p The intersection of these points allows for a continuous approximation of discrete variables. Through a typical binary variable S n,t Define equality constraints: The augmented Lagrangian function of the P2P energy trading model is expressed as: In equation (50), the last two terms serve as penalty terms for P2P energy transactions and binary variables, respectively, as shown below: Define the residual threshold as the sum of the original residual and the dual residual, and use it as a convergence criterion:

8. A distributed transaction-driven distribution network resilience enhancement system, characterized in that, include: The model building module is used to acquire public distributed energy resources and pre-build a distributed transaction-driven distribution network resilience improvement model based on the public distributed energy resources in an upper and lower layer form. The model optimization module is used to optimize the distributed transaction-driven distribution network resilience improvement model based on the VCG mechanism, so as to obtain the optimized distributed transaction-driven distribution network resilience improvement model. The model solving module is used to solve the distributed transaction-driven distribution network resilience improvement model using a distributed solving algorithm based on L(p)-Box ADMM, and obtain the distribution network resilience improvement results.

9. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that, The memory stores a computer program that can run on a processor. When the processor loads and executes the computer program, it employs a distributed transaction-driven distribution network resilience enhancement method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is loaded and executed by the processor, it employs a distributed transaction-driven distribution network resilience enhancement method according to any one of claims 1 to 7.

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