Power distribution network dynamic scheduling method considering restoring force improvement

Abstract

The invention discloses a power distribution network dynamic scheduling method considering restoring force improvement, and relates to the field of electric power energy, and the method comprises the steps: firstly considering the influence of an extreme event on a traffic network, and building a mobile energy storage space-time dynamic scheduling model in a fault state; secondly, considering the dynamic scheduling potential of a data center in a communication network under an extreme event, and establishing a data load space-time flexibility transfer model of the data center; based on the above model, a power distribution network dynamic optimization model is established by taking minimization of power distribution network load shedding cost, mobile energy storage scheduling cost, data center operation cost and distributed photovoltaic light abandoning cost as an objective function, and the post-disaster recovery capability of the power distribution network is improved through dynamic scheduling of mobile energy storage and data loads; and finally, verifying the rationality and economy of the dynamic optimization model of the power distribution network by using an instance system. The method can improve the elastic response capability and rapid recovery capability of the power distribution network to cope with extreme events.

Description

Technical Field

[0001] This invention relates to the field of power energy technology, and in particular to a dynamic dispatching method for distribution networks that takes into account the improvement of resilience. Background Technology

[0002] In recent years, frequent extreme events such as natural disasters and cyberattacks have posed a significant threat to the safe operation of distribution networks. The establishment of a resilient distribution network with the ability to prevent, withstand, and recover from extreme events has received widespread attention. Resilience, as an important indicator of distribution network resilience, describes the ability of a distribution network to utilize various flexible and adaptable resources to participate in the elastic dispatching of resources during extreme events, maximizing its ability to withstand disturbances and recover rapidly.

[0003] Traditional power distribution network operation and dispatching methods typically only consider static flexible resources, resulting in insufficient dispatching flexibility. Furthermore, fault response usually requires a certain amount of time, potentially leading to untimely fault handling. In addition, due to the low probability and high loss characteristics of extreme events, configuring large static resources may waste significant investment costs and is detrimental to carbon emission reduction. Moreover, current research does not consider the coordinated use of mobile power sources and data loads for post-disaster recovery, nor does it account for the impact of extreme events on transportation and communication networks. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to overcome the shortcomings of the prior art and provide a dynamic scheduling method for distribution networks that takes into account resilience enhancement, thereby improving the elastic response capability and rapid recovery capability of distribution networks in the face of extreme events.

[0005] To solve the above-mentioned technical problems, the present invention adopts the following technical solution:

[0006] A dynamic dispatching method for distribution networks considering resilience enhancement, proposed according to the present invention, includes:

[0007] Based on the coupled architecture of power grid, communication network and transportation network in the distribution network, and considering the coupling effect of power grid and transportation network under extreme events, mobile energy storage is used as a flexible dynamic scheduling resource for the distribution network. A spatiotemporal dynamic scheduling model of mobile energy storage under fault conditions is established. The spatiotemporal dynamic scheduling model of mobile energy storage includes a mobile energy storage vehicle passage model and a mobile energy storage vehicle charging and discharging model.

[0008] Based on the coupled architecture of power grid, communication network and transportation network in the distribution network, considering the coupling effect of power grid and communication network under extreme events, and taking into account the dynamic scheduling potential of data center in communication network under extreme events, a spatiotemporal flexibility transfer model of data load of data center is established.

[0009] Based on the spatiotemporal dynamic scheduling model of mobile energy storage and the spatiotemporal flexibility transfer model of data load, a dynamic optimization model of distribution network is established with the objective function of minimizing the distribution network load shedding cost, mobile energy storage scheduling cost, data center operation cost and distributed photovoltaic curtailment cost. The dynamic scheduling of mobile energy storage and data load improves the post-disaster recovery capability of distribution network.

[0010] As a further optimization of the dynamic dispatching method for distribution networks that considers resilience enhancement as described in this invention, the coupled architecture of the power grid-communication network-transportation network in the distribution network includes:

[0011] During the dynamic dispatching of the distribution network under extreme events, considering the integration of distributed photovoltaic (PV), mobile energy storage vehicles, and data centers, the coupled architecture of the power grid, communication network, and transportation network is as follows: The power grid includes distribution busbars, distribution feeders, transformers, and distributed PV. Before the occurrence of extreme events, the distribution network is powered by the transmission network and distributed PV, with data load from the data center coordinating power load dispatching. The transportation network includes mobile energy storage vehicles, road corridors, and charging piles. After the occurrence of extreme events, mobile energy storage vehicles can flexibly move through the transportation network using optimal dynamic dispatching strategies to supply part of the power load to the distribution network, thereby improving the post-disaster resilience of the distribution network.

[0012] The communication network includes data centers, wired networks, servers, and smart terminals, enabling flexible spatial and temporal transfer of data loads before and after a disaster based on the actual operating status of the distribution network.

[0013] As a further optimization scheme of the dynamic dispatching method for distribution networks that considers resilience enhancement as described in this invention, the mobile energy storage vehicle passage model includes:

[0014] Let the dispatch distance of the mobile energy storage vehicle k from point x to point y in the distribution network be denoted as . The actual speed was The total scheduling time is then expressed as:

[0015]

[0016]

[0017]

[0018]

[0019] Where k is the mobile energy storage vehicle number, and x and y are the distribution network node numbers; Total scheduling time, This represents the time from when the scheduling instruction was issued, and is a constant. Represents a constant; Indicates the travel time of the mobile energy storage vehicle; Indicates the rated speed of the mobile energy storage vehicle; It is a constant that depends on the type of extreme event; This indicates the degree of damage to the transportation network under extreme events, where e is the base of the natural logarithm.

[0020] As a further optimization scheme of the dynamic dispatching method for distribution networks that considers resilience enhancement as described in this invention, the charging and discharging model of the mobile energy storage vehicle includes:

[0021] Mobile energy storage vehicle charging and discharging model: When the mobile energy storage vehicle is located at a certain node, its function is equivalent to fixed energy storage, sending or absorbing active power into the distribution network, thereby changing the power flow distribution of the grid and realizing dynamic power regulation; the mobile energy storage vehicle satisfies capacity constraints, power constraints, battery state of charge (SOC) constraints, and charging and discharging dynamic constraints during the charging and discharging process.

[0022] The charging and discharging model of the mobile energy storage vehicle is described as follows:

[0023]

[0024]

[0025]

[0026]

[0027]

[0028]

[0029]

[0030] in, This represents the decision variable for charging mobile energy storage vehicles. Denotes the discharge decision variables of a mobile energy storage vehicle; Indicates the charging power of the mobile energy storage vehicle. This indicates the maximum charging power of the mobile energy storage vehicle; Indicates the discharge power of the mobile energy storage vehicle This indicates the maximum discharge power of the mobile energy storage vehicle; Indicates the mobile energy storage vehicle's performance per unit time Change in internal power This indicates the rated capacity of the mobile energy storage vehicle. Indicates the mobile energy storage vehicle's performance per unit time The change in the state of charge within; Indicates that the mobile energy storage vehicle is in Energy storage capacity at any given time This represents the energy storage capacity of the mobile energy storage vehicle at time t; This represents the state of charge of the mobile energy storage vehicle at time t. and These are the lower and upper limits of the mobile energy storage vehicle's charged state, respectively. and These refer to the charging power and discharging power of the mobile energy storage vehicle, respectively. and These represent the upper and lower limits of the capacity of mobile energy storage vehicles.

[0031] As a further optimization of the dynamic dispatching method for distribution networks that considers resilience enhancement as described in this invention, the spatiotemporal flexibility transfer model for data center data load is as follows:

[0032] The data center data load spatiotemporal transfer model needs to meet the following constraints during each processing time period: 1) The sum of the data load allocated by each front-end server should equal the local user demand; 2) The data load of each data center should equal the sum of its own storage load and the spatial transfer load of other data centers; 3) The data load to be processed in each data center at each time should equal the difference between the data load received by spatial transfer and the time transfer load; the specific descriptions are as follows:

[0033] ;

[0034] ;

[0035] ;

[0036] Where s is the front-end server number, t is the time period number, and r and rsp are the compute node numbers; This represents the amount of data load that the s-th front-end server needs to allocate at time t. Indicates the total number of front-end servers; This represents the amount of data load that the s-th front-end server allocates to the r-th compute node at time t. This represents the total number of compute nodes; This represents the amount of data load that the r-th computing node transfers to the data storage at time t. This represents the amount of data load that the r-th computing node transfers to other computing nodes at time t. The time-space flexible data load transfer model is as follows:

[0037]

[0038]

[0039]

[0040]

[0041]

[0042] in, This represents the amount of data load transferred from other computing nodes to the space of the r-th computing node at time t. express The amount of data load transferred to the data storage by the r-th computing node within the time interval. Indicates that the r-th computing node is in The amount of data load that needs to be processed at all times. Indicates that the r-th computing node is in The amount of data load that needs to be processed at all times. For time interval identifiers, This indicates the maximum total storage capacity of the data center data load in the system; This indicates the upper limit of the amount of data load transferred per unit of time.

[0043] As a further optimization scheme of the dynamic dispatching method for distribution networks that considers resilience enhancement as described in this invention, the objective function of the dynamic optimization model for distribution networks includes:

[0044] 1) Cost of loss of load after extreme events :

[0045] in, This represents the cost per unit of load loss. Indicates the duration of an extreme event; This represents the rated active power demand of the load; This represents the actual load value under extreme events. Related to mobile energy storage and dynamic control schemes for data load, where X is the total number of distribution network nodes;

[0046] 2) Mobile energy storage dispatch cost :

[0047] in, Indicates the number of mobile energy storage vehicles. This represents the scheduling cost required per unit communication time for a mobile energy storage vehicle; Indicates the travel time of mobile energy storage vehicles under extreme events;

[0048] 3) Data center scheduling costs :

[0049] in, Indicates time-of-use electricity pricing; This represents the electrical energy required by a data center to process a unit of data load per unit of time.

[0050] 4) Distributed photovoltaic power generation costs :

[0051] in, This represents the actual active power consumption of distributed photovoltaic power at the x'-th node of the distribution network during time period t. This represents the set of nodes in a distribution network equipped with distributed photovoltaic systems.

[0052] The objective function of the distribution network dynamic optimization model is:

[0053] .

[0054] As a further optimization scheme of the dynamic dispatching method for distribution networks that considers resilience enhancement described in this invention, the dynamic optimization model for distribution networks includes the following constraints:

[0055] Mobile energy storage constraints: During the dynamic scheduling of mobile energy storage vehicles, the following constraints should be met: 1) Each mobile energy storage vehicle can be connected to at most one distribution network node in each time period; 2) Due to the limited charging power, there is an upper limit to the number of mobile energy storage vehicles that can be connected to a single node at any given time; 3) Mobile energy storage vehicles cannot connect to other nodes while traveling to their destination.

[0056] The constraints are described as follows:

[0057]

[0058]

[0059]

[0060] in, This indicates the connection status of the k-th mobile energy storage vehicle with the x-th distribution network node at time t. If it is connected to the x-th distribution network node... ,on the contrary ; This indicates the maximum number of mobile energy storage vehicles that can be connected to a single node; Indicates the total number of mobile energy storage vehicles;

[0061] Distribution network power flow constraints: For radial distribution networks, the following power flow equations are used to constrain their topology:

[0062]

[0063]

[0064] in, and Let be the resistance and reactance values ​​of branch ij; and Let be the conductance and susceptance values ​​of branch ij; This represents the active power absorbed by the mobile energy storage at the j-th node of the distribution network. Let be the active power of the load at the j-th node of the distribution network; Let be the reactive power of the load at the j-th node of the distribution network;

[0065] This represents the photovoltaic active power at the j-th node of the distribution network at time t. This represents the active power of the mobile energy storage vehicle at the j-th node of the distribution network at time t. This represents the amount of data load processed at the j-th node of the distribution network at time t. This represents the power consumed by the data center to process a unit of data load at time t. This indicates that the k-th node in the distribution network is an in-degree node of the j-th node. This represents the set of in-degree indices of all nodes with the j-th node. This represents the active power between the j-th and k-th nodes of the distribution network at time t. This indicates that the i-th node in the distribution network is an out-degree node of the j-th node. This represents the active power between the i-th and j-th nodes in the distribution network at time t. This represents the current between the i-th and j-th nodes in the distribution network at time t. This represents the voltage amplitude at the j-th node of the distribution network at time t. This represents the photovoltaic reactive power at the j-th node of the distribution network at time t. This represents the reactive power between the j-th and k-th nodes of the distribution network at time t. This represents the reactive power between the i-th and j-th nodes of the distribution network at time t;

[0066] Record the active power of the j-th node in the distribution network at time t. = The reactive power of the j-th node in the distribution network at time t = It has the following constraints:

[0067]

[0068]

[0069]

[0070]

[0071]

[0072] in, and These are the upper and lower limits of the branch current, respectively. and These are the upper and lower limits of the active power at the node; and These are the upper and lower limits of the reactive power at the node. This represents the voltage amplitude at the i-th node of the distribution network at time t. It represents the set of branches between all nodes in the distribution network.

[0073] Compared with the prior art, the present invention, employing the above technical solution, has the following technical effects:

[0074] This invention takes into account the coordinated efforts of mobile power supplies and data loads in disaster recovery, and considers the impact of extreme events on transportation and communication networks, thereby improving the resilience and rapid recovery capabilities of power distribution networks in the face of extreme events. Attached Figure Description

[0075] Figure 1 This is a flowchart of the method of the present invention;

[0076] Figure 2 This invention provides an improved IEEE-33 power distribution system model.

[0077] Figure 3 This is a power curve diagram of the photovoltaic system and load of the present invention;

[0078] Figure 4 A bar chart is set up for the data load of the data center in this invention. Detailed Implementation

[0079] 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.

[0080] Based on the goal of improving resilience under extreme events, this invention proposes a dynamic scheduling method for distribution networks that considers mobile energy storage and data loads as flexible resources.

[0081] like Figure 1 As shown, a dynamic dispatching method for distribution networks considering resilience enhancement includes the following steps:

[0082] Based on the coupled architecture of power grid, communication network and transportation network in the distribution network, considering the coupling effect of power grid and transportation network under extreme events, mobile energy storage is used as a flexible dynamic scheduling resource of distribution network. A spatiotemporal dynamic scheduling model of mobile energy storage under fault conditions is established, including mobile energy storage vehicle passage model and mobile energy storage vehicle charging and discharging model.

[0083] Based on the coupled architecture of power grid, communication network and transportation network in the distribution network, considering the coupling effect of power grid and communication network under extreme events, and taking into account the dynamic scheduling potential of data center in communication network under extreme events, a spatiotemporal flexibility transfer model of data load of data center is established.

[0084] Based on the spatiotemporal dynamic scheduling model of mobile energy storage and the spatiotemporal flexibility transfer model of data load, a dynamic optimization model of distribution network is established with the objective function of minimizing the distribution network load shedding cost, mobile energy storage scheduling cost, data center operation cost and distributed photovoltaic curtailment cost. The post-disaster recovery capability of distribution network is improved by the dynamic scheduling of mobile energy storage and data load.

[0085] Based on the optimization results of mobile energy storage and data load dynamic scheduling, the rationality and economy of the proposed distribution network dynamic optimization model are verified through a case study system.

[0086] The coupled architecture of power grid, communication network, and transportation network in a distribution network includes:

[0087] During the dynamic dispatching of the distribution network under extreme events, considering the integration of distributed photovoltaic (PV), mobile energy storage vehicles, and data centers, the coupled architecture of the power grid, communication network, and transportation network is as follows: The power grid includes distribution busbars, distribution feeders, transformers, and distributed PV. Before an extreme event, the distribution network is powered by the transmission network and distributed PV, with data center loads coordinating power load dispatching. The transportation network includes mobile energy storage vehicles, road corridors, and charging piles. After an extreme event, mobile energy storage vehicles can flexibly move via the transportation network using optimal dynamic dispatching strategies to supply part of the power load to the distribution network, improving its post-disaster resilience. The communication network includes data centers, wired networks, servers, and smart terminals, enabling flexible spatial and temporal transfer of data loads before and after a disaster based on the actual operating status of the distribution network. Under extreme events, some power lines in the distribution network may be damaged, resulting in insufficient power supply; the transportation network may experience road damage and insufficient traffic capacity due to extreme events; and the communication network may also be affected by faults, leading to communication congestion and interruptions in some communication channels.

[0088] The mobile energy storage vehicle traffic model in the spatiotemporal dynamic scheduling model of mobile energy storage includes:

[0089] Mobile energy storage vehicle traffic model: Due to the impact of extreme events, roads in the traffic network will suffer varying degrees of damage, resulting in different road conditions for mobile energy storage vehicles. The actual travel time of mobile energy storage vehicles may differ under different extreme event periods and road conditions. Furthermore, extreme events primarily affect the operating speed of mobile energy storage vehicles during dispatch, thus impacting their dispatch costs. Let the dispatch distance of mobile energy storage vehicle k from point x to point y be denoted as... The actual speed was The total scheduling time can then be expressed as:

[0090]

[0091]

[0092]

[0093]

[0094] Where k is the mobile energy storage vehicle number, and x and y are the distribution network node numbers; Total scheduling time, This represents the time from when the scheduling instruction was issued, and is a constant. Represents a constant; Indicates the travel time of the mobile energy storage vehicle; Indicates the rated speed of the mobile energy storage vehicle; It is a constant that depends on the type of extreme event; This indicates the extent of damage to the transportation network under extreme events.

[0095] The charging and discharging model of mobile energy storage vehicles in the spatiotemporal dynamic scheduling model of mobile energy storage is as follows:

[0096] Mobile energy storage vehicle charging and discharging model: When a mobile energy storage vehicle is located at a node, its function is equivalent to fixed energy storage, capable of sending or absorbing active power into the distribution network, thereby changing the power flow distribution and achieving dynamic power regulation. During charging and discharging, the mobile energy storage vehicle needs to meet constraints such as capacity, power, and battery state of charge (SOC). Due to the spatial transfer characteristics of charging and discharging, dynamic charging and discharging constraints must also be met. The mobile energy storage vehicle charging and discharging model is described below:

[0097]

[0098]

[0099]

[0100]

[0101]

[0102]

[0103]

[0104] in, This represents the decision variable for charging mobile energy storage vehicles. Denotes the discharge decision variables of a mobile energy storage vehicle; This indicates the maximum charging power of the mobile energy storage vehicle. This indicates the maximum discharge power of the mobile energy storage vehicle; This represents the energy storage capacity of the mobile energy storage vehicle at time t; This represents the change in the state of charge of a mobile energy storage vehicle during time period t. and These are the lower and upper limits of the mobile energy storage vehicle's charged state, respectively. and These refer to the charging power and discharging power of the mobile energy storage vehicle, respectively. and These represent the upper and lower limits of the capacity of mobile energy storage vehicles.

[0105] Data center load spatiotemporal flexibility transfer models include:

[0106] The data center data load spatiotemporal transfer model needs to meet the following constraints during each processing time period: 1) The sum of the data load allocated by each front-end server should equal the local user demand; 2) The data load of each data center should equal the sum of its own storage load and the spatial transfer load of other data centers; 3) The data load to be processed in each data center at any given time should equal the difference between the data load received by spatial transfer and the time transfer load. The specific details are as follows:

[0107]

[0108]

[0109]

[0110] Where s is the front-end server number, t is the time period number, and r and rsp are the compute node numbers; This represents the amount of data load that the s-th front-end server needs to allocate at time t. Indicates the total number of front-end servers; This represents the amount of data load that the s-th front-end server allocates to the r-th compute node at time t. This represents the total number of compute nodes; This represents the amount of data load that the r-th computing node transfers to the data storage at time t. This represents the amount of data load that the r-th computing node transfers to other computing nodes at time t.

[0111] Based on the above data center expression and constraints, the flexible time-space transfer model for data load can be represented as follows:

[0112]

[0113]

[0114]

[0115]

[0116]

[0117] in, For time interval identifiers, This indicates the maximum total storage capacity of the data center data load in the system; This indicates the upper limit of the amount of data load transferred per unit of time.

[0118] The objective function of the power distribution network dynamic optimization model is as follows:

[0119] Cost of loss of load after extreme events:

[0120] in, This represents the unit load loss cost (this invention does not take into account the different load importances; all loads have the same weight). Indicates the duration of an extreme event; This represents the active power demand of the load under normal conditions. This represents the actual load value under extreme events and is related to mobile energy storage and dynamic control schemes for data load.

[0121] Mobile energy storage dispatch cost : in, Indicates the number of mobile energy storage vehicles. This represents the scheduling cost required per unit communication time for a mobile energy storage vehicle; This indicates the travel time of mobile energy storage vehicles under extreme events.

[0122] Data center scheduling costs:

[0123] in, Indicates time-of-use electricity pricing; This represents the electrical energy required by a data center to process a unit of data load per unit of time.

[0124] Cost of distributed photovoltaic power generation:

[0125] in, This represents the actual active power consumption of distributed photovoltaic power at node x during time period t. This represents the set of nodes in the distribution network that are equipped with distributed photovoltaic systems.

[0126] The objective function of the distribution network dynamic optimization model is:

[0127]

[0128] The dynamic optimization model for the distribution network includes the following constraints:

[0129] Mobile energy storage constraints: During the dynamic scheduling of mobile energy storage vehicles, the following constraints must be met: 1) Each mobile energy storage vehicle can connect to at most one distribution network node in each time period; 2) Due to limited charging power, there is an upper limit to the number of mobile energy storage vehicles allowed to connect to a single node at any given time; 3) Mobile energy storage vehicles cannot connect to other nodes while traveling to their destination. The constraints are described below:

[0130]

[0131]

[0132]

[0133] in, This indicates the connection state of the mobile energy storage vehicle k with node x at time t. If it is connected to node x... ,on the contrary ; This represents the total number of nodes in the distribution network; This indicates the maximum number of mobile energy storage vehicles that can be connected to a single node; This represents the total number of mobile energy storage vehicles that can be flexibly deployed within an isolated island.

[0134] Distribution network power flow constraints: For radial distribution networks, the following power flow equations are used to constrain their topology:

[0135]

[0136]

[0137] in, and Let be the resistance and reactance values ​​of branch ij; and Let be the conductance and susceptance values ​​of branch ij; Let be the active power absorbed by the mobile energy storage at node j; Let J be the active power of the load at node j; Let J be the reactive power of the load at node j.

[0138] remember = , = It has the following constraints:

[0139]

[0140]

[0141]

[0142]

[0143]

[0144] in, and These are the upper and lower limits of the branch current, respectively. and These are the upper and lower limits of the active power at the node; and These are the upper and lower limits of the reactive power at the nodes.

[0145] The following is a detailed description of an optional embodiment of the present invention.

[0146] In one embodiment of the present invention: the above method is applied to, for example... Figure 2 The improved IEEE-33 distribution system model is shown. The rated voltage is 12.66 kV, and the rated active power of the distribution network is 4000 kW. The power curves of the photovoltaic system and the load are shown below. Figure 3 As shown, the power factor of all loads is 0.95. During extreme events, two severe fault points exist in the distribution network, dividing it into several isolated islands disconnected from the main grid. Each island requires power from its internal distributed generation and mobile energy storage vehicles. The degree of road damage on three of these islands is... The values ​​are 1.2, 1.5, and 1.7 respectively; the number of mobile energy storage vehicles is 3, 2, and 3 respectively; the photovoltaic access node numbers are 2, 6, 13, 21, 28, and 33; the data center access node numbers are 3, 12, and 27; and the charging pile node numbers are 11, 20, 23, and 29. The data center data load settings are as follows: Figure 4 As shown in the figure, the remaining parameter settings are shown in Table 1.

[0147] Table 1 Parameter Settings

[0148]

[0149] Based on the above example system, the dynamic scheduling results of the distribution network considering resilience enhancement are as follows:

[0150] Table 2 shows the scheduling schemes for different mobile energy storage vehicles during different time periods of failure, and Table 3 shows the spatiotemporal transfer of data load in the data center after an extreme event.

[0151] Table 2 Mobile Energy Storage Dispatch Scheme

[0152]

[0153] Table 3 Spatiotemporal Transfer of Data Load

[0154]

[0155] 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.

[0156] 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 dynamic dispatching method for distribution networks that considers enhancing resilience, characterized in that, include: Based on the coupled architecture of power grid, communication network and transportation network in the distribution network, and considering the coupling effect of power grid and transportation network under extreme events, mobile energy storage is used as a flexible dynamic scheduling resource for the distribution network. A spatiotemporal dynamic scheduling model of mobile energy storage under fault conditions is established. The spatiotemporal dynamic scheduling model of mobile energy storage includes a mobile energy storage vehicle passage model and a mobile energy storage vehicle charging and discharging model. Based on the coupled architecture of power grid, communication network and transportation network in the distribution network, considering the coupling effect of power grid and communication network under extreme events, and taking into account the dynamic scheduling potential of data center in communication network under extreme events, a spatiotemporal flexibility transfer model of data load of data center is established. Based on the spatiotemporal dynamic scheduling model of mobile energy storage and the spatiotemporal flexibility transfer model of data load, a dynamic optimization model of distribution network is established with the objective function of minimizing the distribution network load shedding cost, mobile energy storage scheduling cost, data center operation cost and distributed photovoltaic curtailment cost. The dynamic scheduling of mobile energy storage and data load improves the post-disaster recovery capability of distribution network.

2. The dynamic dispatching method for distribution networks considering resilience enhancement according to claim 1, characterized in that, The coupled architecture of power grid, communication network, and transportation network in a distribution network includes: During the dynamic dispatching of the distribution network under extreme events, considering the integration of distributed photovoltaic (PV), mobile energy storage vehicles, and data centers, the coupled architecture of the power grid, communication network, and transportation network is as follows: The power grid includes distribution busbars, distribution feeders, transformers, and distributed PV. Before the occurrence of extreme events, the distribution network is powered by the transmission network and distributed PV, with data load from the data center coordinating power load dispatching. The transportation network includes mobile energy storage vehicles, road corridors, and charging piles. After the occurrence of extreme events, mobile energy storage vehicles can flexibly move through the transportation network using optimal dynamic dispatching strategies to supply part of the power load to the distribution network, thereby improving the post-disaster resilience of the distribution network. The communication network includes data centers, wired networks, servers, and smart terminals, enabling flexible spatial and temporal transfer of data loads before and after a disaster based on the actual operating status of the distribution network.

3. The dynamic dispatching method for distribution networks considering resilience enhancement according to claim 1, characterized in that, The mobile energy storage vehicle access model includes: Let the dispatch distance of the mobile energy storage vehicle k from point x to point y in the distribution network be denoted as . The actual speed was The total scheduling time is then expressed as: ； ； ； ； Where k is the mobile energy storage vehicle number, and x and y are the distribution network node numbers; Total scheduling time, This represents the time from when the scheduling instruction was issued, and is a constant. Represents a constant; Indicates the travel time of the mobile energy storage vehicle; Indicates the rated speed of the mobile energy storage vehicle; It is a constant that depends on the type of extreme event; This indicates the degree of damage to the transportation network under extreme events, where e is the base of the natural logarithm.

4. The dynamic dispatching method for distribution networks considering resilience enhancement according to claim 1, characterized in that, The charging and discharging model of the mobile energy storage vehicle includes: Mobile energy storage vehicle charging and discharging model: When the mobile energy storage vehicle is located at a certain node, its function is equivalent to fixed energy storage, sending or absorbing active power into the distribution network, thereby changing the power flow distribution of the grid and realizing dynamic power regulation; the mobile energy storage vehicle satisfies capacity constraints, power constraints, battery state of charge (SOC) constraints, and charging and discharging dynamic constraints during the charging and discharging process. The charging and discharging model of the mobile energy storage vehicle is described as follows: ； ； ； ； ； ； ； in, This represents the decision variable for charging mobile energy storage vehicles. Denotes the discharge decision variables of a mobile energy storage vehicle; Indicates the charging power of the mobile energy storage vehicle. This indicates the maximum charging power of the mobile energy storage vehicle; Indicates the discharge power of the mobile energy storage vehicle This indicates the maximum discharge power of the mobile energy storage vehicle; Indicates the mobile energy storage vehicle's performance per unit time Change in internal power This indicates the rated capacity of the mobile energy storage vehicle. Indicates the mobile energy storage vehicle's performance per unit time The change in the state of charge within; Indicates that the mobile energy storage vehicle is in Energy storage capacity at any given time This represents the energy storage capacity of the mobile energy storage vehicle at time t; This represents the state of charge of the mobile energy storage vehicle at time t. and These are the lower and upper limits of the mobile energy storage vehicle's charged state, respectively. and These refer to the charging power and discharging power of the mobile energy storage vehicle, respectively. and These represent the upper and lower limits of the capacity of mobile energy storage vehicles.

5. A dynamic dispatching method for distribution networks considering resilience enhancement according to claim 1, characterized in that, The data center data load spatiotemporal flexibility transfer model is as follows: The data center data load spatiotemporal transfer model needs to meet the following constraints during each processing time period: 1) The sum of the data load allocated by each front-end server should equal the local user demand; 2) The data load of each data center should equal the sum of its own storage load and the spatial transfer load of other data centers; 3) The data load to be processed in each data center at each time should equal the difference between the data load received by spatial transfer and the time transfer load; the specific descriptions are as follows: ； ； ； Where s is the front-end server number, t is the time period number, and r and rsp are the compute node numbers; This represents the amount of data load that the s-th front-end server needs to allocate at time t. Indicates the total number of front-end servers; This represents the amount of data load that the s-th front-end server allocates to the r-th compute node at time t. This represents the total number of compute nodes; This represents the amount of data load that the r-th computing node transfers to the data storage at time t. This represents the amount of data load that the r-th computing node transfers to other computing nodes at time t. The time-space flexible data load transfer model is as follows: ； ； ； ； ； in, This represents the amount of data load transferred from other computing nodes to the space of the r-th computing node at time t. express The amount of data load transferred to the data storage by the r-th computing node within the time interval. Indicates that the r-th computing node is in The amount of data load that needs to be processed at all times. Indicates that the r-th computing node is in The amount of data load that needs to be processed at all times. For time interval identifiers, This indicates the maximum total storage capacity of the data center data load in the system; This indicates the upper limit of the amount of data load transferred per unit of time.

6. A dynamic dispatching method for distribution networks considering resilience enhancement according to claim 1, characterized in that, The objective function of the distribution network dynamic optimization model includes: 1) Cost of loss of load after extreme events : ; in, This represents the cost per unit of load loss. Indicates the duration of an extreme event; This represents the rated active power demand of the load; This represents the actual load value under extreme events. Related to mobile energy storage and dynamic control schemes for data load, where X is the total number of distribution network nodes; 2) Mobile energy storage dispatch cost : ; in, Indicates the number of mobile energy storage vehicles. This represents the scheduling cost required per unit communication time for a mobile energy storage vehicle; Indicates the travel time of mobile energy storage vehicles under extreme events; 3) Data center scheduling costs : ; in, Indicates time-of-use electricity pricing; This represents the electrical energy required by a data center to process a unit of data load per unit of time. 4) Distributed photovoltaic power generation costs : ; in, This represents the actual active power consumption of distributed photovoltaic power at the x'-th node of the distribution network during time period t. This represents the set of nodes in a distribution network equipped with distributed photovoltaic systems. The objective function of the distribution network dynamic optimization model is: 。 7. A dynamic dispatching method for distribution networks considering resilience enhancement according to claim 1, characterized in that, The dynamic optimization model for the distribution network includes the following constraints: Mobile energy storage constraints: During the dynamic scheduling of mobile energy storage vehicles, the following constraints should be met: 1) Each mobile energy storage vehicle can be connected to at most one distribution network node in each time period; 2) Due to the limited charging power, there is an upper limit to the number of mobile energy storage vehicles that can be connected to a single node at any given time; 3) Mobile energy storage vehicles cannot connect to other nodes while traveling to their destination. The constraints are described as follows: ； ； ； in, This indicates the connection status of the k-th mobile energy storage vehicle with the x-th distribution network node at time t. If it is connected to the x-th distribution network node... ,on the contrary ; This indicates the maximum number of mobile energy storage vehicles that can be connected to a single node; Indicates the total number of mobile energy storage vehicles; Distribution network power flow constraints: For radial distribution networks, the following power flow equations are used to constrain their topology: ； ； in, and Let be the resistance and reactance values ​​of branch ij; and Let be the conductance and susceptance values ​​of branch ij; This represents the active power absorbed by the mobile energy storage at the j-th node of the distribution network. Let be the active power of the load at the j-th node of the distribution network; Let be the reactive power of the load at the j-th node of the distribution network; This represents the photovoltaic active power at the j-th node of the distribution network at time t. This represents the active power of the mobile energy storage vehicle at the j-th node of the distribution network at time t. This represents the amount of data load processed at the j-th node of the distribution network at time t. This represents the power consumed by the data center to process a unit of data load at time t. This indicates that the k-th node in the distribution network is an in-degree node of the j-th node. This represents the set of in-degree indices of all nodes with the j-th node. This represents the active power between the j-th and k-th nodes of the distribution network at time t. This indicates that the i-th node in the distribution network is an out-degree node of the j-th node. This represents the active power between the i-th and j-th nodes in the distribution network at time t. This represents the current between the i-th and j-th nodes in the distribution network at time t. This represents the voltage amplitude at the j-th node of the distribution network at time t. This represents the photovoltaic reactive power at the j-th node of the distribution network at time t. This represents the reactive power between the j-th and k-th nodes of the distribution network at time t. This represents the reactive power between the i-th and j-th nodes of the distribution network at time t; Record the active power of the j-th node in the distribution network at time t. = The reactive power of the j-th node in the distribution network at time t = It has the following constraints: ； ； ； ； ； in, and These are the upper and lower limits of the branch current, respectively. and These are the upper and lower limits of the active power at the node; and These are the upper and lower limits of the reactive power at the node. This represents the voltage amplitude at the i-th node of the distribution network at time t. It represents the set of branches between all nodes in the distribution network.

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