Power distribution network toughness improvement method based on virtual power plant collaboration and transaction settlement

Abstract

The invention discloses a power distribution network toughness improvement method based on virtual power plant cooperation and transaction settlement, and the method comprises the steps: S1, constructing a multi-type virtual power plant which comprises a virtual power plant management platform, a power transaction platform and a scheduling platform, and enabling the multi-type virtual power plant to participate in a power grid scheduling and power transaction whole-process management frame, so as to carry out the plan of a VPP disaster accident; s2, the scheduling platform constructs a pre-disaster and post-disaster multi-type VPP collaborative optimization power distribution network power supply recovery model so as to formulate a network reconstruction strategy of each time period, a real-time scheduling strategy of emergency resources and an adjustment strategy of the VPP; and S3, according to the VPP actual response amount issued by the scheduling platform, and the declaration response amount and the VPP historical participation degree reported by the virtual power plant management platform, the power transaction platform calculates the power supply recovery participation excitation unit price and compensation income of each VPP. According to the invention, through the whole-process closed-loop management integrating pre-disaster plan generation, post-disaster dynamic adjustment and transaction settlement, the key problems of insufficient resource collaboration, stage splitting, excitation stiffness and the like are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of power system optimization and dispatching, and in particular to a method for improving the resilience of a distribution network based on virtual power plant collaboration and transaction settlement. Background Art

[0002] In recent years, the intensification of global climate change has led to frequent extreme disasters such as typhoons, freezing rain, and high temperatures, which have impacted the safety and security of power systems. Among power outages caused by extreme disasters, the majority of failures stem from damage to the distribution network. Traditional distribution network planning focuses on operational reliability, emphasizing the stable operation of the system under normal operating conditions. However, when responding to low-probability, high-destructiveness extreme events, it often exposes its high vulnerability and insufficient resilience. Against this backdrop, the concept of a "resilient distribution network" has emerged. Its core is to build a distribution system that is resilient to disasters, fault-tolerant, and self-recovering after disasters through the triple mechanisms of active defense, rapid response, and adaptive recovery.

[0003] Common measures to enhance distribution network resilience include network reconstruction, pre-deployment of emergency resources, and real-time dispatch. With the accelerated construction of new power systems, the integration of a high proportion of renewable energy, primarily wind and photovoltaic power, into distribution networks poses a significant challenge to their resilience. Virtual Power Plants (VPPs) aggregate geographically dispersed distributed resources (such as wind power, photovoltaic power, energy storage, flexible loads, and electric vehicles) into a virtual energy network. These VPPs can serve as fast-responding dynamic power restoration units in disaster scenarios, offering a new path to enhancing distribution network resilience.

[0004] Currently, research on improving distribution network resilience during extreme disasters has evolved into a traditional technology framework focused on network reconstruction and emergency resource scheduling. Meanwhile, virtual power plants, as a distributed resource aggregation vehicle, are increasingly being applied in disaster response scenarios. However, existing technologies still have significant shortcomings in multi-type resource coordination, full-process management, and dynamic incentives.

[0005] First, while traditional resilience-enhancing approaches, such as network reconfiguration, pre-deployment of emergency resources, and real-time scheduling, can partially restore power, they fail to fully tap the synergistic potential of distributed resources. In particular, they lack the integration of the dynamic response characteristics of multiple virtual power plant types, limiting post-disaster recovery efficiency. For example, single-stage optimization methods focus on pre-deploying mobile emergency power sources or reconfiguring distribution network topologies after a disaster, but overlook the complementary characteristics of demand-side, supply-side, and hybrid virtual power plants. Second, existing research on virtual power plant applications has largely focused on conventional electricity market transactions or single-scenario scheduling, while collaborative recovery mechanisms for virtual power plants designed for extreme disasters are still underdeveloped. Therefore, while existing technology-based resilience-enhancing approaches for virtual power plants can improve distribution network security, they lack economic incentives to balance the interests of virtual power plant operators. Pre-disaster scheduling models based on reservoir regulation optimize pre-deployment strategies but fail to address the response errors caused by the discrepancy between actual and predicted post-disaster output, resulting in insufficient robustness in recovery strategies.

[0006] Further analysis revealed that existing technologies are generally subject to the defects of phase separation and missing processes. Although the two-stage model in existing research integrates pre-disaster deployment and post-disaster recovery, it does not establish a transaction settlement mechanism to manage the response behavior of virtual power plants in a closed loop; the pre-disaster virtual power plant dispatch strategy does not take into account the dynamic changes in the actual fault scenario after the disaster, resulting in a serious disconnect between the reported response volume and actual demand, reducing recovery efficiency. In addition, existing incentive mechanisms mostly use fixed prices or simple segmented pricing, and do not dynamically adjust based on the historical participation of virtual power plants, response capacity, and the dynamic deviation between reported and actual responses. It is difficult to incentivize virtual power plants to improve response accuracy, and may even exacerbate the risk of load shedding due to imbalanced resource allocation.

[0007] It is worth noting that existing research also underutilizes the complementary characteristics of multiple types of virtual power plants. Research on the reverse power supply capabilities of electric vehicle-based virtual power plants focuses on the characteristics of a single resource. The output optimization model of renewable energy-based virtual power plants does not coordinate with demand-side resources. Discussions on the application of commercial building-based virtual power plants lack a resilience-enhancing framework for a combination of multiple resource types. The root cause of these problems lies in the fact that existing technologies do not systematically integrate the complementary characteristics of multiple types of VPPs and lack pre-disaster and post-disaster linkage optimization and dynamic incentive mechanism design. These limitations result in post-disaster recovery capabilities being limited to the characteristics of a single resource, making it difficult to realize the global optimization potential of resource synergy.

[0008] The statements herein merely provide background information related to the present invention and do not necessarily constitute prior art. Summary of the Invention

[0009] The purpose of this invention is to provide a method for improving the resilience of distribution networks based on virtual power plant collaboration and transaction settlement. By integrating the full-process closed-loop management of pre-disaster plan generation, post-disaster dynamic adjustment and transaction settlement, it systematically solves key problems such as insufficient resource coordination, stage fragmentation and incentive rigidity in the existing technology.

[0010] To achieve the above objectives, the present invention provides a method for improving distribution network resilience based on virtual power plant collaboration and transaction settlement, comprising:

[0011] Step S1, pre-disaster phase: Build a full-process management framework for multi-type virtual power plants participating in grid dispatching and power trading, including a virtual power plant management platform, power trading platform, and dispatching platform, to prepare for VPP disaster accidents;

[0012] Step S2, post-disaster phase: The dispatching platform constructs a distribution network power restoration model for pre- and post-disaster multi-type VPP collaborative optimization to formulate network reconstruction strategies for each time period, real-time dispatch strategies for emergency resources, and VPP adjustment strategies, and distributes these strategies to the power trading platform and virtual power plant management platform;

[0013] Step S3, transaction settlement phase: Based on the actual response volume of the VPPs issued by the dispatching platform, the declared response volume reported by the virtual power plant management platform, and the VPP historical participation rate, the power trading platform calculates the incentive unit price and compensation income of each VPP participating in power restoration.

[0014] Optionally, step S1 includes:

[0015] S1.1, the dispatching platform predicts wind speed, line failure rate, and fault disconnection scenarios in each area of ​​the distribution network based on a typhoon disaster model; wherein the typhoon disaster model includes the typhoon's landing time, wind speed, and impacted lines;

[0016] S1.2. Complete the registration of various virtual power plants (VPPs) participating in power restoration, along with the types and loads of their connected distributed power sources, on the virtual power plant management platform. Also, forecast the output and load demand of the distributed power sources connected to the VPPs, and report these to the dispatching platform.

[0017] S1.3, the virtual power plant management platform obtains a VPP declaration response quantity based on the output of the distributed power sources connected to the VPP and the load demand;

[0018] S1.4. The dispatching platform formulates a VPP disaster accident plan based on the reported output and load demand of the distributed power sources, as well as the predicted wind speed, line failure rate and fault disconnection scenarios in each area of ​​the distribution network.

[0019] Optionally, the VPP declaration response amount is:

[0020]

[0021] Where: The power restoration response quantity reported by the i-th VPP at time t, where I is the distributed resource access node within the i-th VPP; is the V2G reverse charging amount of the electric vehicle charging station coupled with the kth node of the distribution network at time t, is the renewable energy output of access node k at time t, is the load reduction of node k in the distribution network at time t; is the charge and discharge capacity of the energy storage element connected to the distribution network node k at time t; Optimize the load before and after transfer for node k at time t respectively.

[0022] Optionally, step S2 includes:

[0023] S2.1, the pre-disaster distribution network power supply restoration model determines a response plan for the minimum load shedding of the VPP based on the predicted fault disconnection scenario and the output of the distributed generation;

[0024] S2.2, the virtual power plant management platform obtains the actual VPP response amount according to the VPP response plan;

[0025] S2.3, the post-disaster distribution network power supply restoration model combines the real-time data of the distribution network to dynamically adjust network reconstruction, emergency resource scheduling, and VPP regulation.

[0026] Optionally, step S2.1 includes:

[0027] For the predicted fault disconnection scenario, combined with the predicted distributed power output and load demand, a first objective function for minimizing the load shedding amount of the distribution network is obtained:

[0028]

[0029] Where: V is the set of distribution network nodes; ω1, ω2, and ω3 are the primary, secondary, and tertiary load weight coefficients of the distribution network respectively; are the primary, secondary and tertiary load reductions of node k respectively; min f is the first objective function.

[0030] Optionally, the operation constraints of the pre-disaster distribution network power supply restoration model include operation constraints of controllable units, energy storage elements, renewable energy, electric vehicle charging stations, and demand response, and simultaneously satisfy network topology reconstruction constraints:

[0031] The operating constraints of the controllable unit are:

[0032]

[0033] Where: are the state, active power and reactive power output of the controllable unit connected to the distribution network node k at time t; For maximum output; is the maximum up and down climbing power; α is the power coefficient;

[0034] The operating constraints of the energy storage element are:

[0035]

[0036] Where: are the charge and discharge state and charge and discharge amount of the energy storage element connected to the distribution network node k at time t; For maximum charging power, is the maximum discharge power; is the remaining capacity, maximum capacity and minimum capacity of the energy storage element; η ch is the charging coefficient, η dis is the discharge coefficient, δ ESS is the self-loss coefficient;

[0037] The output constraints of renewable energy are:

[0038]

[0039] Where: The renewable energy output for the access node k; Contribute to renewable energy forecasting;

[0040] The power supply constraints for electric vehicle charging stations are:

[0041]

[0042] Where: is the V2G reverse charging amount of the electric vehicle charging station coupled with the kth node of the distribution network, To predict the amount of V2G reverse charging, it is determined by the number of EVs in the charging station, the remaining power, and the user’s willingness;

[0043] The demand response constraints are:

[0044]

[0045] Where: is the load reduction amount and the maximum load reduction amount of the k-node in the distribution network; t zy1 , t zy2 are the start and end time of load transfer respectively; are the loads of node k before and after the optimized transfer at time t;

[0046] The network topology constraints are:

[0047]

[0048] Where: are the active power flowing into node k, the active power flowing out of node k, the reactive power flowing into node k, and the reactive power flowing out of node k respectively; f and t are binary quantities representing the direction of the actual branch flow, and it is stipulated that the flow from node k to node j in branch kj is positive, and if it is positive, f = 1, t = 0; p f 、p b ,q f ,q b are the active power flow into node k, the active power flow out of node k, the reactive power flow into node k, and the reactive power flow out of node k respectively; r kj 、x kj are the branch kj resistance and reactance respectively; are the current flowing through branch kj, the upper limit and lower limit of the square of the current respectively; are the voltages at node k and node j respectively; are the lower and upper limits of the square voltage of node j respectively; p kj ,q kj are the active power and reactive power of branch kj respectively, are binary variables representing the actual flow direction of branch kj, The current is in the positive direction; Z, Z nor , Z bal are respectively the set of all branches of the distribution network, the set of branches without balancing nodes, and the set of branches with balancing nodes; M is an infinite auxiliary variable;

[0049] The network topology reconstruction constraints are:

[0050]

[0051] Where: E Pre It is the set of predicted fault disconnected branches.

[0052] Optionally, step S2.3 includes:

[0053] The second objective function is obtained by taking the minimum load shedding amount and the deviation between the actual VPP power restoration response amount and the declared response amount:

[0054]

[0055] Where: is the actual power restoration response of the i-th VPP at time t; R is the VPP set; T is the power restoration duration; min h is the second objective function;

[0056] Optionally, the operation constraints of the post-disaster distribution network power supply restoration model include energy storage element operation constraints, demand response operation constraints, post-disaster MEG / MESS emergency resource real-time scheduling constraints, controllable unit real-time regulation constraints, renewable energy real-time output constraints, V2G real-time reverse power supply constraints, and network topology reconstruction constraints;

[0057] The operating constraints of the energy storage element are:

[0058]

[0059] Where: are the charge and discharge state and charge and discharge amount of the energy storage element connected to the distribution network node k at time t; For maximum charging power, is the maximum discharge power; is the remaining capacity, maximum capacity and minimum capacity of the energy storage element; η ch is the charging coefficient, η dis is the discharge coefficient, δ ESS is the self-loss coefficient;

[0060] The demand response constraints are:

[0061]

[0062] Where: is the load reduction amount and the maximum load reduction amount of the k-node in the distribution network; t zy1 , t zy2 are the start and end time of load transfer respectively; are the loads of node k before and after the optimized transfer at time t;

[0063] The post-disaster MEG / MESS emergency resource real-time scheduling constraints:

[0064]

[0065]

[0066] Where: T is the set of time nodes for power supply restoration after a disaster; W MEG 、W MESS is the set of MEG and MESS; N MEG 、N MESS is the number of dispatchable MEGs and MESSs; are binary variables for MEG and MESS respectively. If the wth group of MEG and MESS is dispatched to the distribution network node k at time t, the variable is 1, otherwise it is 0; For MEG A binary variable that is dispatched to the distribution network node k at any given moment; is the output of the wth group of MEG and MESS at the distribution network node k at time t; p MEG,max ,q MESS,max The output limit of MEG and MESS; T is the time it takes for MEG and MESS to be dispatched from the emergency resource warehouse to the distribution network node k after a disaster; all The time when the disaster ends;

[0067] The real-time control constraints of the controllable units are:

[0068]

[0069] Where: are the upward and downward adjustment amounts of the controllable unit connected to node k at time t; They are the maximum upward adjustment amount and downward adjustment amount respectively; is the maximum up and down climbing power;

[0070] The real-time output constraint of renewable energy is:

[0071]

[0072] Where: Make real contributions to renewable energy; Providing real-time power for renewable energy;

[0073] Among them, the V2G real-time reverse power supply constraints are:

[0074]

[0075] Where: is the actual maximum reverse charging amount of V2G;

[0076] The network topology reconstruction constraints are:

[0077]

[0078] Where: E Act It is the set of actual fault disconnected branches; are binary variables representing the actual flow direction of branch kj.

[0079] Optionally, step S3 includes:

[0080] S3.1 uses the power restoration response decision coefficient and the logistic regression model to characterize the probability of VPP participation in power restoration and obtain the VPP historical participation rate;

[0081] S3.2, determining a power restoration incentive price based on the VPP's historical participation rate, the VPP's declared response volume, and the VPP's actual response volume;

[0082] S3.3, obtain the VPP compensation income based on the power restoration incentive price and the actual VPP response volume.

[0083] Optionally, the power restoration response decision coefficient is:

[0084] w d =αw e +βw s (29)

[0085] Where: α and β are weight coefficients, w e Considering the economic benefits of VPP, w s Considering social impact factors for VPP;

[0086] The probability of power restoration is:

[0087]

[0088] Where: P r The probability of power restoration;

[0089] The calculation formula for the power restoration incentive price is:

[0090]

[0091] Where: R i is the power restoration incentive price, q i 、f i are the capacity compensation coefficient and the response number compensation coefficient, respectively, which are calculated based on the capacity and frequency of the i-th VPP participating in power restoration in the past year; is the actual response of the ith VPP at time t, is the declared response amount; R min 、R max are the minimum and maximum subsidy prices, μ min 、μ max are the left and right boundaries of the power restoration response ratio;

[0092] The calculation formula for the VPP compensation income is:

[0093]

[0094] Where: Ci is the compensation income of the i-th VPP participating in power restoration, R i is the power restoration incentive price of the i-th VPP in period n, are the start time and end time of the i-th VPP participating in power supply restoration in period n, is the output power of the i-th VPP participating in power restoration at time t in period n.

[0095] In summary, compared with the prior art, the present invention has the following beneficial effects:

[0096] 1. In response to the limitations of traditional methods that rely on single resources or stage optimization, this invention, for the first time, constructs a full-process closed-loop management framework for pre-disaster plan generation, post-disaster dynamic response, and transaction settlement. In the pre-disaster phase, the resource characteristics of demand-side, supply-side, and hybrid virtual power plants (VPPs) are integrated based on the forecast scenario to generate an optimized plan with minimum load shedding. In the post-disaster phase, network reconstruction and resource scheduling strategies are dynamically adjusted based on real-time data to achieve rapid power restoration in non-faulty areas and power support in faulty areas. In the transaction settlement phase, a dynamic incentive pricing mechanism is used to balance the interests of VPP entities, forming a sustainable collaborative model of "prediction-response-incentive", significantly improving VPP participation enthusiasm and response accuracy.

[0097] 2. Traditional methods usually separate pre-disaster deployment from post-disaster recovery, resulting in a disconnect between plans and actual needs. This paper proposes a two-layer optimization model, namely a distribution network power supply recovery model that collaboratively optimizes multiple types of VPPs before and after a disaster: the pre-disaster distribution network power supply recovery model is based on pre-disaster forecast data, coordinates renewable energy output, V2G reverse power supply and load demand, and generates a global optimal plan; the post-disaster distribution network power supply recovery model introduces a dual-objective optimization of load shedding and response deviation after the disaster, dynamically coordinates energy storage, controllable units and demand response resources, achieves rapid response in the time dimension and resource complementarity in the spatial dimension, effectively addresses the deviation between prediction and actual scenarios, and enhances system robustness.

[0098] 3. To address the difficulty of balancing economic efficiency and resilience in traditional fixed incentive models, this paper proposes a dynamic pricing mechanism based on reported volume, actual response volume, and historical participation. By calculating VPP incentive unit prices in segments, this not only encourages VPPs to improve response accuracy but also ensures reasonable returns. Simultaneously, by integrating with a transaction settlement platform, it enables multi-party benefit sharing and fosters a healthy interactive ecosystem among power grids, VPP operators, and users. This mechanism not only reduces the marginal cost of emergency resources but also significantly improves renewable energy absorption capacity and the reliability of power supply to critical loads.

[0099] 4. Traditional research has focused on a single type of VPP. This invention integrates the complementary characteristics of demand-side resources, supply-side resources, and hybrid VPPs to build a collaborative optimization model. By combining the flexibility of demand-side resources with the stability of supply-side resources, it can quickly smooth out load fluctuations during disaster peaks. Hybrid VPPs achieve cross-time energy balance through reverse power supply and energy storage charging and discharging coordination, significantly improving overall recovery efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0100] Figure 1 The VPP of the present invention participates in the entire power restoration process;

[0101] Figure 2 Schematic diagram of the VPP structure of the present invention;

[0102] Figure 3 This is the full-process management framework for multi-type VPPs participating in grid dispatching and power trading in the present invention. DETAILED DESCRIPTION

[0103] The following will be combined with the attached Figures 1 to 3 , the present invention is further described in detail through preferred embodiments. According to the following description, the advantages and features of the present invention will be clearer. It should be noted that the drawings are in a very simplified form and are not in precise proportions, which are only used to conveniently and clearly assist in explaining the purpose of the embodiments of the present invention. In order to make the purposes, features and advantages of the present invention more obvious and easy to understand, please refer to the drawings. It should be noted that the structures, proportions, sizes, etc. illustrated in the drawings of this specification are only used to match the contents disclosed in the specification for people familiar with this technology to understand and read, and are not used to limit the conditions for the implementation of the present invention, so they have no technical significance. Any modification of the structure, change in the proportional relationship or adjustment of the size should still fall within the scope of the technical content disclosed by the present invention without affecting the efficacy and purpose that can be achieved by the present invention.

[0104] Aiming at the problem of distribution network resilience to extreme disasters, the present invention provides a distribution network resilience improvement method based on multi-type virtual power plant (VPP) collaboration and transaction settlement, such as Figure 1 As shown, the distribution network resilience improvement method includes the following steps:

[0105] Step S1, pre-disaster phase: Build a full-process management framework for multi-type virtual power plants participating in grid dispatching and power trading, including a virtual power plant management platform, power trading platform, and dispatching platform, to prepare for VPP disaster accidents;

[0106] Step S2, post-disaster phase: The dispatching platform constructs a distribution network power restoration model for pre- and post-disaster multi-type VPP collaborative optimization to formulate network reconstruction strategies for each time period, real-time dispatch strategies for emergency resources, and VPP adjustment strategies, and distributes these strategies to the power trading platform and virtual power plant management platform;

[0107] Step S3, transaction settlement phase: Based on the actual response volume of the VPPs issued by the dispatching platform, the declared response volume reported by the virtual power plant management platform, and the VPP historical participation rate, the power trading platform calculates the incentive unit price and compensation income of each VPP participating in power restoration.

[0108] The multi-type virtual power plant (VPP) is a system that virtually integrates distributed resources of different regions and types (including distributed generation, energy storage systems, controllable loads and electric vehicles, etc.). Its typical structure is as follows: Figure 2 As shown in the figure, the VPP uses a centralized control system to optimize the scheduling of a large number of small-capacity, diverse distributed resources, enabling them to function as a unified entity to provide ancillary services for grid operations and participate in electricity market transactions. Compared to microgrids, the VPP's core advantage lies in its ability to integrate resources across regions. Specifically, the VPP can coordinate and integrate multiple types of distributed resources in different locations to form a holistic resource that can flexibly respond to demand.

[0109] Furthermore, the types of VPPs include commercial building VPPs, EV (electric vehicle) power station VPPs, and energy station VPPs. Based on the different resources they aggregate, VPPs can be defined as demand-side resource-based, supply-side resource-based, and hybrid resource-based.

[0110] Among them, the commercial building VPP is a demand-side resource type with adjustable load, which can provide flexible and efficient demand response; the distributed resources aggregated by the commercial building VPP include: energy storage system, new energy power generation, EV, and flexible load (including lighting system, central air conditioning system, and fresh air system).

[0111] The EV power station VPP is a hybrid resource type. It uses orderly charging technology and vehicle-to-grid (V2G) technology to flexibly respond to demand and provide reverse power supply at special moments. The distributed resources aggregated by the EV power station VPP include: charging piles, electric energy storage, photovoltaic panels, and lighting systems.

[0112] The energy station VPP is a supply-side resource type, and the distributed resources it aggregates are new energy output units mainly based on photovoltaic and wind power.

[0113] Among them, the multi-type virtual power plant full process management framework is as follows Figure 3As shown, this full-process management framework enables VPP registration, trading, operation, and settlement. Specifically, the virtual power plant management platform is responsible for VPP access, load forecasting, distributed renewable energy output forecasting, regulation capacity reporting, and dispatch plan execution. The power trading platform communicates with the virtual power plant management platform to perform VPP registration, quotation, contract processing, and settlement. The dispatch platform communicates with both the virtual power plant management platform and the power trading platform. Based on the regulation capacity reported by the virtual power plant management platform, the dispatch platform formulates a dispatch plan and distributes it to the power trading platform and virtual power plant management platform.

[0114] Furthermore, the step S1 comprises:

[0115] S1.1, the dispatching platform predicts wind speed, line failure rate, and fault disconnection scenarios in each area of ​​the distribution network based on a typhoon disaster model; wherein the typhoon disaster model includes the typhoon's landing time, wind speed, and impacted lines;

[0116] S1.2. Complete the registration of various virtual power plants (VPPs) participating in power restoration, along with the types and loads of their connected distributed power sources, on the virtual power plant management platform. Also, forecast the output and load demand of the distributed power sources connected to the VPPs, and report these to the dispatching platform.

[0117] S1.3, the virtual power plant management platform obtains a VPP declaration response quantity based on the output of the distributed power sources connected to the VPP and the load demand;

[0118] S1.4. The dispatching platform formulates a VPP disaster accident plan based on the reported output and load demand of the distributed power sources, as well as the predicted wind speed, line failure rate and fault disconnection scenarios in each area of ​​the distribution network.

[0119] Furthermore, step S2 is implemented by introducing a loop mechanism, which can dynamically adjust power supply restoration to adapt to the ever-changing actual situation during the disaster process, thereby ensuring the stable operation and rapid recovery of the distribution network during the disaster; specifically, by judging whether the current time t is within the total duration T of the disaster (i.e., t>T), it is ensured that during the disaster, the dispatching platform can continuously monitor and respond to actual changes in network conditions; when t≤T, the dispatching platform will continue to execute the post-disaster process, including updating the network topology, formulating network reconstruction strategies, emergency resource scheduling strategies, and VPP adjustment strategies.

[0120] Specifically, step S2 includes:

[0121] S2.1, the pre-disaster distribution network power supply restoration model determines a response plan for the minimum load shedding of the VPP based on the predicted fault disconnection scenario and the output of the distributed generation;

[0122] S2.2, the virtual power plant management platform obtains the actual VPP response amount according to the VPP response plan;

[0123] S2.3, the post-disaster distribution network power supply restoration model combines real-time data of the distribution network to dynamically adjust network reconstruction, emergency resource scheduling, and VPP regulation;

[0124] Specifically, step S2.1 includes:

[0125] For the predicted fault disconnection scenario, combined with the predicted distributed power output and load demand, a first objective function (min f) for minimizing the load shedding amount of the distribution network is obtained:

[0126]

[0127] Where: V is the set of distribution network nodes; ω1, ω2, and ω3 are the primary, secondary, and tertiary load weight coefficients of the distribution network respectively; are the primary, secondary, and tertiary load reductions of node k, respectively.

[0128] It is worth noting that in the pre-disaster stage, when the distribution network formulates a power restoration plan, it must comprehensively consider the operating constraints of the connected controllable units, energy storage components, renewable energy, electric vehicle charging stations, and demand response, and at the same time meet network constraints such as network topology constraints, node power balance constraints, node voltage and branch current constraints.

[0129] The operating constraints of the controllable unit are:

[0130]

[0131] Where: are the state, active power and reactive power output of the controllable unit connected to the distribution network node k at time t; For maximum output; is the maximum up and down climbing power; α is the power coefficient.

[0132] The operating constraints of the energy storage element are:

[0133]

[0134] Where: are the charge and discharge state and charge and discharge amount of the energy storage element connected to the distribution network node k at time t; For maximum charging power, is the maximum discharge power; is the remaining capacity, maximum capacity and minimum capacity of the energy storage element; η ch is the charging coefficient, η dis is the discharge coefficient, δ ESS is the self-loss coefficient.

[0135] The renewable energy output constraint is:

[0136]

[0137] Where: The renewable energy output for the access node k; Contribute to renewable energy forecasting.

[0138] The power supply constraints of the electric vehicle charging station are:

[0139]

[0140] Where: is the V2G reverse charging amount of the electric vehicle charging station coupled with the kth node of the distribution network, To predict the amount of V2G reverse charging, it is determined by the number of EVs in the charging station, the remaining power, and the user's willingness.

[0141] The demand response constraint is:

[0142]

[0143] Where: is the load reduction amount and the maximum load reduction amount of the k-node in the distribution network; t zy1 , t zy2 are the start and end time of load transfer respectively; Optimize the load before and after transfer for node k at time t respectively.

[0144] In the pre-disaster phase, when developing a power restoration plan, the distribution network must comprehensively consider the following network topology constraints to ensure power balance at each node, maintain node voltages within specified ranges, and keep branch currents within rated values. The network maintains a radial structure to avoid loops, and performs second-order cone relaxation on nonlinear constraints to simplify calculations, thereby ensuring the safety and stability of distribution network operations. The network topology constraints are:

[0145]

[0146]

[0147] Where: are the active power flowing into node k, the active power flowing out of node k, the reactive power flowing into node k, and the reactive power flowing out of node k respectively; f and t are binary quantities representing the direction of the actual branch flow, and it is stipulated that the flow from node k to node j in branch kj is positive, and if it is positive, f = 1, t = 0; p f 、p b ,q f ,q b are the active power flow into node k, the active power flow out of node k, the reactive power flow into node k, and the reactive power flow out of node k respectively; r kj 、x kj are the branch kj resistance and reactance respectively; are the current flowing through branch kj, the upper limit and lower limit of the square of the current respectively; are the voltages at node k and node j respectively; are the lower and upper limits of the square voltage of node j respectively; p kj ,q kj are the active power and reactive power of branch kj respectively, are binary variables representing the actual flow direction of branch kj, The current is in the positive direction; Z, Z nor 、Z bal They are respectively the set of all branches of the distribution network, the set of branches without balancing nodes, and the set of branches with balancing nodes; M is an infinite auxiliary variable.

[0148] Based on the network topology constraints in the post-disaster recovery process, distribution network topology reconstruction constraints are set to ensure that after a disaster, the network structure of the distribution network can be reasonably adjusted to restore power supply while maintaining the network structure. The distribution network topology reconstruction constraint conditions are:

[0149]

[0150] Where: E Pre It is the set of predicted fault disconnected branches.

[0151] In the present invention, the components connected to the virtual power plant (VPP) mainly include four categories: electric vehicle charging stations, renewable energy power generation devices, demand response loads, and energy storage components. Before a disaster occurs, the control center takes the minimum load shedding as the objective function, comprehensively considers factors such as the predicted scenario of fault disconnection, the predicted output of renewable energy, and the predicted reverse power supply of V2G, and formulates a detailed pre-disaster emergency plan, which is promptly issued to the virtual power plant management platform. Each VPP participating in power restoration determines its declared response quantity for power restoration based on this plan:

[0152]

[0153] Where: The power restoration response quantity reported by the i-th VPP at time t, where I is the distributed resource access node within the i-th VPP; is the V2G reverse charging amount of the electric vehicle charging station coupled with the kth node of the distribution network at time t, is the renewable energy output of access node k at time t, is the load reduction of node k in the distribution network at time t.

[0154] Specifically, step S2.3 includes:

[0155] The second objective function (min h) is obtained by taking the minimum load shedding amount and the deviation between the actual VPP power restoration response amount and the declared response amount:

[0156]

[0157] Where: is the actual power restoration response of the i-th VPP at time t; R is the VPP set; T is the power restoration duration.

[0158] Among them, the operating constraints of the energy storage element are:

[0159]

[0160] Where: are the charge and discharge state and charge and discharge amount of the energy storage element connected to the distribution network node k at time t; For maximum charging power, is the maximum discharge power; is the remaining capacity, maximum capacity and minimum capacity of the energy storage element; η ch is the charging coefficient, η dis is the discharge coefficient, δ ESS is the self-loss coefficient.

[0161] The demand response constraint is:

[0162]

[0163]

[0164] Where: is the load reduction amount and the maximum load reduction amount of the k-node in the distribution network; t zy1 , t zy2 are the start and end time of load transfer respectively; Optimize the load before and after transfer for node k at time t respectively.

[0165] In the post-disaster phase, mobile emergency generators (MEGs) and mobile energy storage systems (MESSs) provide active and reactive power support to the distribution network, respectively. Based on the power restoration plan, MEGs and MESSs are dispatched from the emergency response resource warehouse to the corresponding distribution network nodes to provide necessary power support. The following constraints are also defined for the real-time dispatch of MEG / MESS emergency resources after the disaster:

[0166]

[0167] Where: T is the set of time nodes for power supply restoration after a disaster; W MEG 、W MESS is the set of MEG and MESS; N MEG 、N MESS is the number of dispatchable MEGs and MESSs; are binary variables for MEG and MESS respectively. If the wth group of MEG and MESS is dispatched to the distribution network node k at time t, the variable is 1, otherwise it is 0; For MEG A binary variable that is dispatched to the distribution network node k at any given moment; is the output of the wth group of MEG and MESS at the distribution network node k at time t; p MEG,max ,q MESS,max The output limit of MEG and MESS; T is the time it takes for MEG and MESS to be dispatched from the emergency resource warehouse to the distribution network node k after a disaster; all The time when the disaster ends.

[0168] Among them, the real-time control constraints of the controllable units are:

[0169]

[0170] Where: are the upward and downward adjustment amounts of the controllable unit connected to node k at time t; They are the maximum upward adjustment amount and downward adjustment amount respectively; It is the maximum uphill and downhill climbing power.

[0171] Among them, renewable energy real-time output The constraints are:

[0172]

[0173] Where: Make practical contributions to renewable energy.

[0174] Among them, V2G real-time reverse power supply The constraints are:

[0175]

[0176] Where: It is the actual maximum reverse charging amount of V2G.

[0177] Among them, the real-time topology reconstruction constraints of the distribution network are:

[0178]

[0179] Where: E Act It is the set of actual fault disconnected branches; are binary variables representing the actual flow direction of branch kj.

[0180] Further, the step S3 comprises:

[0181] S3.1 uses the power restoration response decision coefficient and the logistic regression model to characterize the probability of VPP participation in power restoration and obtain the VPP historical participation rate:

[0182] Specifically, when virtual power plants (VPPs) participate in power restoration in distribution networks during extreme disasters, both economic and social factors must be considered. Economic factors encompass the dispatching costs of distributed energy resources, the operating and maintenance costs of post-disaster power supply equipment, and the incentive compensation benefits the VPP receives from participating in power restoration. Social factors, on the other hand, include improving power restoration speed, enhancing the resilience of the distribution system, and ensuring stable power supply to critical loads. To comprehensively assess the various factors influencing the willingness of VPP entities to participate in power restoration, the power restoration response decision coefficient must consider both the economic benefits and social impact of the VPP.

[0183] The power restoration response decision coefficient is:

[0184] w d =αw e +βw s (29)

[0185] Where: α and β are weight coefficients, w e Considering the economic benefits of VPP, w s Consider social impact factors for VPP.

[0186] The logistic regression (logit) model is used to characterize the probability of VPP participating in power restoration (P r )for:

[0187]

[0188] S3.2, determining a power restoration incentive price based on the VPP's historical participation rate, the VPP's declared response volume, and the VPP's actual response volume;

[0189] Specifically, to optimize the response of virtual power plants (VPPs) in power restoration on distribution networks, this paper proposes an incentive pricing determination mechanism. This mechanism comprehensively considers multiple key factors, including the VPP's declared response volume, actual response volume, historical participation rate, and historical participation capacity. Through scientific and rational calculation methods, it determines an incentive price that can effectively improve the VPP's response enthusiasm and accuracy. This mechanism aims to balance the economic benefits and social impact of VPPs, ensuring that VPPs can efficiently and accurately participate in power restoration efforts on distribution networks in the event of extreme disasters.

[0190] Among them, the power supply restoration incentive price (R i ) is calculated as:

[0191]

[0192] Where: R i is the power restoration incentive price, q i 、f i are the capacity compensation coefficient and the response number compensation coefficient, respectively, which are calculated based on the capacity and frequency of the i-th VPP participating in power restoration in the past year; is the actual response of the ith VPP at time t, is the declared response amount; R min 、R max are the minimum and maximum subsidy prices, μ min 、μ max The left and right boundaries of the power restoration response ratio are set to 75% and 125% in this invention. The higher the VPP's historical participation rate, the greater the historical response capacity, and the closer the reported response volume is to the actual response volume, the higher the incentive price.

[0193] S3.3, obtain the VPP compensation income based on the power restoration incentive price and the actual VPP response volume.

[0194] Among them, the VPP compensation income (C i ) is calculated as:

[0195]

[0196] Where: C i is the compensation income of the ith VPP participating in power restoration, R i is the power restoration incentive price of the i-th VPP in period n, are the start time and end time of the i-th VPP participating in power supply restoration in period n, is the output power of the i-th VPP participating in power restoration at time t in period n.

[0197] To sum up, the method for improving the resilience of distribution networks based on virtual power plant collaboration and transaction settlement provided by the present invention can overcome the problem that insufficient resilience of distribution networks in extreme disaster scenarios poses a severe challenge to power supply security. Through full-process closed-loop management and dynamic resource optimization, the rapid response and self-healing capabilities of distribution networks to disasters can be significantly enhanced.

[0198] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or apparatus comprising the element.

[0199] In the description of the present invention, it should be understood that the terms "center," "height," "thickness," "up," "down," "vertical," "horizontal," "top," "bottom," "inside," "outside," "axial," "radial," "circumferential," and the like, indicating positions or location relationships, are based on the positions or location relationships shown in the accompanying drawings and are intended only to facilitate the description of the present invention and simplify the description. They do not indicate or imply that the devices or elements referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limiting the present invention. In the description of the present invention, unless otherwise specified, "plurality" means two or more.

[0200] In the description of the present invention, unless otherwise expressly specified or limited, the terms "mounted," "connected," "connected," and "fixed" should be understood in a broad sense. For example, they may refer to fixed or detachable connections, or integration; mechanical or electrical connections; direct or indirect connections through an intermediate medium; and internal communication between two components or interaction between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0201] In the present invention, unless otherwise expressly specified or limited, a first feature being "above" or "below" a second feature may include the first and second features being in direct contact, or may include the first and second features being in contact not directly but through another feature between them. Furthermore, a first feature being "above," "above," and "above" a second feature may include the first feature being directly above or obliquely above the second feature, or may simply mean that the first feature is higher in level than the second feature. A first feature being "below," "below," and "below" a second feature may include the first feature being directly below or obliquely below the second feature, or may simply mean that the first feature is lower in level than the second feature.

[0202] Although the present invention has been described in detail through the above preferred embodiments, it should be understood that the above description is not intended to limit the present invention. After reading the above description, various modifications and substitutions of the present invention will become apparent to those skilled in the art. Therefore, the scope of protection of the present invention should be defined by the appended claims.

Claims

1. A method for improving distribution network resilience based on virtual power plant collaboration and transaction settlement, characterized in that: The distribution network resilience improvement method includes the following steps: Step S1, pre-disaster phase: Build a full-process management framework for multi-type virtual power plants participating in grid dispatching and power trading, including a virtual power plant management platform, power trading platform, and dispatching platform, to prepare for VPP disaster accidents; Step S2, post-disaster phase: The dispatching platform constructs a distribution network power restoration model for pre- and post-disaster multi-type VPP collaborative optimization to formulate network reconstruction strategies for each time period, real-time dispatch strategies for emergency resources, and VPP adjustment strategies, and distributes these strategies to the power trading platform and virtual power plant management platform; Step S3, transaction settlement phase: Based on the actual response volume of the VPPs issued by the dispatching platform, the declared response volume reported by the virtual power plant management platform, and the VPP historical participation rate, the power trading platform calculates the incentive unit price and compensation income of each VPP participating in power restoration.

2. The method for improving the resilience of a distribution network according to claim 1, wherein: The step S1 comprises: S1.1, the dispatching platform predicts wind speed, line failure rate, and fault disconnection scenarios in each area of ​​the distribution network based on a typhoon disaster model; wherein the typhoon disaster model includes the typhoon's landing time, wind speed, and impacted lines; S1.

2. Complete the registration of various virtual power plants (VPPs) participating in power restoration, along with the types and loads of their connected distributed power sources, on the virtual power plant management platform. Also, forecast the output and load demand of the distributed power sources connected to the VPPs, and report these to the dispatching platform. S1.3, the virtual power plant management platform obtains a VPP declaration response quantity based on the output of the distributed power sources connected to the VPP and the load demand; S1.

4. The dispatching platform formulates a VPP disaster accident plan based on the reported output and load demand of the distributed power sources, as well as the predicted wind speed, line failure rate and fault disconnection scenarios in each area of ​​the distribution network.

3. The method for improving the resilience of a distribution network according to claim 2, wherein: The VPP declaration response amount is: Where: The power restoration response quantity reported by the i-th VPP at time t, where I is the distributed resource access node within the i-th VPP; is the V2G reverse charging amount of the electric vehicle charging station coupled with the kth node of the distribution network at time t, is the renewable energy output of access node k at time t, is the load reduction of node k in the distribution network at time t; is the charge and discharge capacity of the energy storage element connected to the distribution network node k at time t; Optimize the load before and after transfer for node k at time t respectively.

4. The method for improving the resilience of a distribution network according to claim 3, wherein: The step S2 comprises: S2.1, the pre-disaster distribution network power supply restoration model determines a response plan for the minimum load shedding of the VPP based on the predicted fault disconnection scenario and the output of the distributed generation; S2.2, the virtual power plant management platform obtains the actual VPP response amount according to the VPP response plan; S2.3, the post-disaster distribution network power supply restoration model combines the real-time data of the distribution network to dynamically adjust network reconstruction, emergency resource scheduling, and VPP regulation.

5. The method for improving the resilience of a distribution network according to claim 4, characterized in that: The step S2.1 comprises: For the predicted fault disconnection scenario, combined with the predicted distributed power output and load demand, a first objective function for minimizing the load shedding amount of the distribution network is obtained: Where: V is the set of distribution network nodes; ω1, ω2, and ω3 are the primary, secondary, and tertiary load weight coefficients of the distribution network respectively; are the primary, secondary and tertiary load reductions of node k respectively; min f is the first objective function.

6. The method for improving the resilience of a distribution network according to claim 5, characterized in that: The operation constraints of the pre-disaster distribution network power supply restoration model include the operation constraints of controllable units, energy storage components, renewable energy, electric vehicle charging stations, and demand response, and at the same time meet the network topology reconstruction constraints: The operating constraints of the controllable unit are: Where: are the state, active power and reactive power output of the controllable unit connected to the distribution network node k at time t; For maximum output; is the maximum up and down climbing power; α is the power coefficient; The operating constraints of the energy storage element are: Where: are the charge and discharge state and charge and discharge amount of the energy storage element connected to the distribution network node k at time t; For maximum charging power, is the maximum discharge power; is the remaining capacity, maximum capacity and minimum capacity of the energy storage element; η ch is the charging coefficient, η dis is the discharge coefficient, δ ESS is the self-loss coefficient; The output constraints of renewable energy are: Where: The renewable energy output for the access node k; Contribute to renewable energy forecasting; The power supply constraints for electric vehicle charging stations are: Where: is the V2G reverse charging amount of the electric vehicle charging station coupled with the kth node of the distribution network, To predict the amount of V2G reverse charging, it is determined by the number of EVs in the charging station, the remaining power, and the user’s willingness; The demand response constraints are: Where: is the load reduction amount and the maximum load reduction amount of the k-node in the distribution network; t zy1 , t zy2 are the start and end time of load transfer respectively; are the loads of node k before and after the optimized transfer at time t; The network topology constraints are: Where: are the active power flowing into node k, the active power flowing out of node k, the reactive power flowing into node k, and the reactive power flowing out of node k respectively; f and t are binary quantities representing the direction of the actual branch flow, and it is stipulated that the flow from node k to node j in branch kj is positive, and if it is positive, f = 1, t = 0; p f 、p b ,q f ,q b are the active power flow into node k, the active power flow out of node k, the reactive power flow into node k, and the reactive power flow out of node k respectively; r kj 、x kj are the branch kj resistance and reactance respectively; are the current flowing through branch kj, the upper limit and lower limit of the square of the current respectively; are the voltages at node k and node j respectively; are the lower and upper limits of the square voltage of node j respectively; p kj ,q kj are the active power and reactive power of branch kj respectively, are binary variables representing the actual flow direction of branch kj, The current is in the positive direction; Z, Z nor , Z bal are respectively the set of all branches of the distribution network, the set of branches without balancing nodes, and the set of branches with balancing nodes; M is an infinite auxiliary variable; The network topology reconstruction constraints are: Where: E Pre It is the set of predicted fault disconnected branches.

7. The method for improving the resilience of a distribution network according to claim 6, characterized in that: The step S2.3 comprises: The second objective function is obtained by taking the minimum load shedding amount and the deviation between the actual VPP power restoration response amount and the declared response amount: Where: is the actual power restoration response of the i-th VPP at time t; R is the VPP set; T is the power restoration duration; min h is the second objective function.

8. The method for improving the resilience of a distribution network according to claim 7, wherein: The operation constraints of the post-disaster distribution network power supply restoration model include energy storage element operation constraints, demand response operation constraints, post-disaster MEG / MESS emergency resource real-time scheduling constraints, controllable unit real-time regulation constraints, renewable energy real-time output constraints, V2G real-time reverse power supply constraints, and network topology reconstruction constraints; The operating constraints of the energy storage element are: Where: are the charge and discharge state and charge and discharge amount of the energy storage element connected to the distribution network node k at time t; For maximum charging power, is the maximum discharge power; is the remaining capacity, maximum capacity and minimum capacity of the energy storage element; η ch is the charging coefficient, η dis is the discharge coefficient, δ ESS is the self-loss coefficient; The demand response constraints are: Where: is the load reduction amount and the maximum load reduction amount of the k-node in the distribution network; t zy1 , t zy2 are the start and end time of load transfer respectively; are the loads of node k before and after the optimized transfer at time t; The post-disaster MEG / MESS emergency resource real-time scheduling constraints: Where: T is the set of time nodes for power supply restoration after a disaster; W MEG 、W MESS is the set of MEG and MESS; N MEG 、N MESS is the number of dispatchable MEGs and MESSs; are binary variables for MEG and MESS respectively. If the wth group of MEG and MESS is dispatched to the distribution network node k at time t, the variable is 1, otherwise it is 0; For MEG A binary variable that is dispatched to the distribution network node k at any given moment; is the output of the wth group of MEG and MESS at the distribution network node k at time t; p MEG,max ,q MESS,max The output limit of MEG and MESS; T is the time it takes for MEG and MESS to be dispatched from the emergency resource warehouse to the distribution network node k after a disaster; all The time when the disaster ends; The real-time control constraints of the controllable units are: Where: are the upward and downward adjustment amounts of the controllable unit connected to node k at time t; They are the maximum upward adjustment amount and downward adjustment amount respectively; is the maximum up and down climbing power; The real-time output constraint of renewable energy is: Where: Make real contributions to renewable energy; Providing real-time power for renewable energy; Among them, the V2G real-time reverse power supply constraint conditions are: Where: is the actual maximum reverse charging amount of V2G; The network topology reconstruction constraints are: Where: E Act is the set of actual fault-disconnected branches; are binary variables representing the actual flow direction of branch kj.

9. The method for improving the resilience of a distribution network according to claim 8, characterized in that: The step S3 comprises: S3.1 uses the power restoration response decision coefficient and the logistic regression model to characterize the probability of VPP participation in power restoration and obtain the VPP historical participation rate; S3.2, determining a power restoration incentive price based on the VPP's historical participation rate, the VPP's declared response volume, and the VPP's actual response volume; S3.3, obtain the VPP compensation income based on the power restoration incentive price and the actual VPP response volume.

10. The method for improving the resilience of a distribution network according to claim 9, characterized in that: The power restoration response decision coefficient is: w d =αw e +βw s (29) Where: α and β are weight coefficients, w e Considering the economic benefits of VPP, w s Considering social impact factors for VPP; The probability of power restoration is: Where: P r The probability of power restoration; The calculation formula for the power restoration incentive price is: Where: R i is the power restoration incentive price, q i 、f i are the capacity compensation coefficient and the response number compensation coefficient, respectively, which are calculated based on the capacity and frequency of the i-th VPP participating in power restoration in the past year; is the actual response of the ith VPP at time t, is the declared response amount; R min 、R max are the minimum and maximum subsidy prices, μ min 、μ max are the left and right boundaries of the power restoration response ratio; The calculation formula for the VPP compensation income is: Where: C i is the compensation income of the i-th VPP participating in power restoration, R i is the power restoration incentive price of the i-th VPP in period n, are the start time and end time of the i-th VPP participating in power supply restoration in period n, is the output power of the i-th VPP participating in power restoration at time t in period n.

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