A source-load-storage adjustable resource scheduling method, device and equipment in an extreme scenario

Abstract

The application discloses a source-load-storage adjustable resource scheduling method, device and equipment under an extreme scenario, and relates to the field of power system optimization scheduling. The method comprises the following steps: acquiring the power-off duration of a distribution network after a disaster under an extreme scenario and uniformly dividing the duration; based on the time period division result, combining the actual power generation resources in the distribution network, and establishing a distribution network post-disaster load recovery optimization model of source-load-storage coordination; in the early stage of distribution network post-disaster load recovery, the loads equipped with emergency power supplies are powered by the emergency power supplies; in the late stage of distribution network post-disaster load recovery, the distribution network after a fault under an extreme scenario is divided into islands; according to the divided islands and the reserves of various source-load-storage adjustable resources in the actual distribution network, the distribution network post-disaster load recovery optimization model of source-load-storage coordination is optimized and solved to obtain a post-disaster load recovery result. The application can coordinate various distributed resources under an extreme scenario, fully exert the advantages of the distributed resources, improve the flexibility of the distribution network, and ensure the safety of the distribution network.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of power system optimal dispatching, and in particular to a source-load-storage adjustable resource dispatching method, device and equipment under an extreme scenario. BACKGROUND

[0002] With the continuous advancement of urbanization and electrification, the demand for electric energy gradually increases with the development of society, and the global power system is rapidly scaled and complicated. Meteorological conditions such as typhoon, high temperature, plum rain, direct current blocking and system fault operation scenarios will bring great challenges to the safe and stable operation of the power grid. Influenced by extreme bad weather, major natural disasters and deliberate attacks, large-scale power outages of global power systems occur frequently, and the economic losses caused by them increase sharply. As the end of the power system, the distribution network directly affects the power demand of the end user. Due to its wide coverage and low equipment redundancy, the distribution network is more prone to long-time power outages and other problems under extreme conditions.

[0003] Single power generation resources cannot meet diversified demands and are prone to failure under extreme conditions, making it difficult to maintain power supply stability. At the same time, there are problems such as difficulty in coordinating various dynamic changes, lack of flexibility, limited dispatching strategy, etc. Moreover, only focusing on a single time period, such as short-term recovery, ignores the recovery demand of other time periods, which leads to unbalanced global recovery strategy, cannot optimize the overall power supply effect, ignores the stability and sustainability of the system, and cannot adjust the strategy according to real-time conditions under extreme scenarios, making it difficult to coordinate multi-stage resource allocation. It can be seen that under extreme scenarios, only considering a kind of distributed power or only considering the load recovery method of a single time period has obvious limitations, mainly in the lack of reliability, flexibility and global optimization ability, which cannot guarantee the ability of the distribution network to respond to disturbances and the safe and stable operation of the system. SUMMARY

[0004] The purpose of the present application is to provide a source-load-storage adjustable resource dispatching method, device and equipment under an extreme scenario, which can coordinate various distributed resources under an extreme scenario, fully utilize the advantages of distributed resources, and improve the flexibility and safety of the distribution network.

[0005] To achieve the above purpose, the present application provides the following solutions:

[0006] In a first aspect, the application provides a method for adjusting source-load-storage adjustable resources in an extreme scenario, comprising: obtaining a power outage duration of a power distribution network after a disaster in an extreme scenario; uniformly dividing the power outage duration to obtain a time period division result; based on the time period division result, in combination with power generation resources in an actual power distribution network, establishing a source-load-storage coordinated power distribution network post-disaster load restoration optimization model; in an early stage of power distribution network post-disaster load restoration, relying on emergency power supply for loads equipped with emergency power supply; in a later stage of power distribution network post-disaster load restoration, performing island division on the power distribution network after a fault in the extreme scenario, with the goal that loads equipped with emergency power supply are supplied by at least one distributed resource; based on the divided islands and reserves of various source-load-storage adjustable resources in the actual power distribution network, optimizing and solving the source-load-storage coordinated power distribution network post-disaster load restoration optimization model to obtain a post-disaster load restoration result; the post-disaster load restoration result comprises a restoration state of each load node in the power distribution network in each time period and network active power loss of the power distribution network in each time period.

[0007] In a second aspect, the application provides a device for adjusting source-load-storage adjustable resources in an extreme scenario, comprising: a prediction module configured to predict a power outage duration of a power distribution network after a disaster in an extreme scenario; a division module configured to uniformly divide the power outage duration to obtain a time period division result; a model establishment module configured to, based on the time period division result, in combination with power generation resources in an actual power distribution network, establish a source-load-storage coordinated power distribution network post-disaster load restoration optimization model; an early stage power supply module configured to, in an early stage of power distribution network post-disaster load restoration, rely on emergency power supply for loads equipped with emergency power supply; a later stage planning module configured to, in a later stage of power distribution network post-disaster load restoration, perform island division on the power distribution network after a fault in the extreme scenario, with the goal that loads equipped with emergency power supply are supplied by at least one distributed resource; an optimization and solution module configured to, based on the divided islands and reserves of various source-load-storage adjustable resources in the actual power distribution network, optimize and solve the source-load-storage coordinated power distribution network post-disaster load restoration optimization model to obtain a post-disaster load restoration result; the post-disaster load restoration result comprises active power and reactive power of a line in each time period and active power output and reactive power output of a distributed power source in each time period.

[0008] In a third aspect, the application provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for adjusting source-load-storage adjustable resources in an extreme scenario according to any one of the above embodiments.

[0009] According to the embodiments provided in the application, the following technical effects are achieved.

[0010] This application provides a method, device, and equipment for scheduling adjustable resources of source, load, and storage under extreme scenarios. The established optimization model for post-disaster load recovery of distribution networks with source, load, and storage coordination considers multiple distributed resources of source, load, and storage. These distributed resources coordinate with each other in multiple time periods divided by the power outage duration, enabling rapid response and emergency power supply to the distribution network when a power outage occurs. Compared with load recovery methods that only consider one distributed power source or only consider a single time period, this method can fully leverage the advantages of distributed power sources and energy storage systems, exhibiting significant superiority in both spatial and temporal scales. It can better improve the resilience of the distribution network and ensure its security. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 A flowchart illustrating a method for scheduling adjustable source-load-storage resources in an extreme scenario, provided as an embodiment of this application;

[0013] Figure 2 A schematic diagram illustrating the time period division of a source-load-storage coordinated recovery strategy provided in another embodiment of this application;

[0014] Figure 3 A schematic diagram of an improved IEEE 33-node system provided for another embodiment of this application;

[0015] Figure 4 This application provides a schematic diagram of emergency measures following an extreme disaster, as part of another embodiment of the present application.

[0016] Figure 5 A schematic diagram of an equivalent generator and energy storage device model provided for another embodiment of this application;

[0017] Figure 6 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0018] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0019] The above and other objects, features and advantages of the present application will become more apparent from the following detailed description when taken in conjunction with the accompanying drawings in which:

[0020] Various distributed resources (distributed Energy resource, DER), i.e., adjustable resources, including distributed generators (distributed generator, DG), distributed energy storage systems (energy storage system, ESS), electric vehicles (electric vehicle, EV) and controllable loads, etc., can enhance the flexibility and security of the distribution network. The diversity of distributed resources provides more recovery schemes for the power grid, and the optimal scheduling of adjustable resources also becomes an important means to ensure the safe operation of the distribution network. In extreme scenarios, coordinating these resources helps to improve the ability of the distribution network to respond to disturbances and ensure the safe and stable operation of the system.

[0021] Therefore, in one exemplary embodiment, as shown in Figure 1 An extreme scenario source-load-storage adjustable resource scheduling method is provided, including the following steps 101 to 106. Among them:

[0022] Step 101: Obtain the outage duration of the distribution network after the disaster in the extreme scenario.

[0023] Step 102: Uniformly divide the outage duration to obtain a time period division result.

[0024] Step 103: Based on the time period division result, in combination with the actual power generation resources in the distribution network, an optimization model for post-disaster load restoration of the distribution network is established in coordination with the source, load and storage.

[0025] Step 104: In the early stage of post-disaster load restoration of the distribution network, the load equipped with emergency power is powered by the emergency power.

[0026] Step 105: In the later stage of post-disaster load restoration of the distribution network, the load equipped with emergency power is powered by at least one distributed resource.

[0027] Step 106: According to the divided islands and the reserves of various source-load-storage adjustable resources in the actual distribution network, the optimization model for post-disaster load restoration of the distribution network in coordination with the source, load and storage is optimized and solved to obtain a post-disaster load restoration result; the post-disaster load restoration result includes the restoration state of each load node in the distribution network in each time period and the network active power loss of the distribution network in each time period.

[0028] The source-load-storage coordinated power distribution network post-disaster load recovery optimization model is established by implementing steps 101-106, and the model considers multiple source-load-storage distributed resources. The multiple source-load-storage distributed resources are coordinated with each other in multiple time periods divided according to the power outage duration, and quickly respond to power outages to provide emergency power supply to the power distribution network. Compared with the load recovery method considering only one type of distributed power supply or the load recovery method considering only a single time period, the advantages of the distributed power supply and the energy storage system can be fully utilized, and the spatial scale and the time scale have obvious advantages, so that the flexibility of the power distribution network can be improved and the safety of the power distribution network can be ensured.

[0029] In another exemplary embodiment of the present application, in order to discretize the power outage duration (power distribution network recovery power supply time) after the disaster, the entire power outage time is defined as the total duration from the power outage time to the complete recovery of the power transmission network before the power transmission path, denoted as T0. In order to more accurately describe and optimize the load recovery process, the entire power outage time T0 is divided into several time periods, and the time period set T after discretization of the power outage time is obtained, wherein the interval of any time period is ΔT. Assuming that the shortest standby time of the emergency power supply (EPS) of the important load in the target power distribution network is T min , then T min The set of all time periods after T0 is T2, which is specifically divided as shown in Figure 2 .

[0030] Under the invasion of extreme disasters, the transmission lines, transportation networks and other facilities in the disaster area are severely damaged, and users cannot normally obtain electric energy, so a large-scale power outage accident will occur. In the present example, the line (4, 5) is faulty and has been quickly isolated to prevent further damage. The power distribution network will gradually restore power supply capability after about T0=3.5h, and the time interval is 30min, i.e. ΔT=0.5h, and a total of 7 time periods are divided.

[0031] In another exemplary embodiment of the present application, considering the correlation between various influencing factors, in order to continuously guarantee the power supply of important load nodes, based on the time period division result, the maximum load weighted power supply time is taken as the core objective function, and the minimization of the total network loss in the whole time period is taken as the auxiliary objective function. In order to unify the dimension and facilitate comparison, the two objective functions are normalized to obtain the core objective function and the auxiliary objective function. The reference value of the core objective function is the calculated value when all nodes of the target power distribution network are recovered; and the reference value of the auxiliary objective function is artificially set according to the power distribution system that needs to be recovered.

[0032] To unify the core objective function and the auxiliary objective function, a weighting coefficient λ0 is introduced. λ0 can be flexibly set according to the decision-maker's preferences, facilitating the acquisition of load restoration strategies that meet various requirements. It is worth noting that load restoration is the primary task, while reducing system network losses is a secondary task. Therefore, when setting the weighting coefficient, its value should not be too large to ensure the priority achievement of the core objective function.

[0033] Therefore, step 103 above can be replaced by the following steps 201 to 203:

[0034] Step 201: Based on the time period division results, and with the goal of maximizing the load-weighted power supply time, the core objective function is established as follows:

[0035]

[0036] In the formula, F1 is the load-weighted power supply time, and n b Let T be the set of load nodes, and T be the set of time periods after the power outage time is discretized. i Let ξ be the weighting coefficient for load node i. i,t ΔT represents the recovery status of load node i in the corresponding time period t, and ΔT is the time interval between each time period in the time period division result.

[0037] Step 202: With the goal of minimizing the total network loss over all time periods, the auxiliary objective function is established as follows:

[0038]

[0039] In the formula, F2 is the total network loss over the entire time period, and P t L S represents the active power loss of the distribution network during time period t. b This is the base power of the power distribution system.

[0040] Step 203: Combining the core objective function and auxiliary objective function, and introducing weighting coefficients, the post-disaster load recovery optimization model for distribution networks with source-load-storage coordination is obtained as follows:

[0041] maxF=F1-λ0F2 (3)

[0042] In the formula, F is the overall objective and λ0 is the weighting coefficient.

[0043] by Figure 3 The improved IEEE 33-bus system shown is used as a case study to verify and illustrate the proposed source-load-storage coordinated distribution network post-disaster load recovery optimization model. Figure 3The dashed line in the figure indicates the feeder added to the improved IEEE 33-node, and the solid line indicates the feeder originally possessed by the IEEE 33-node. The loads are divided into two levels according to the importance, and the weight coefficient of the important load is 1. The important load is located at nodes 3, 15, 22, and 30, and the EPS is installed for the important power users. The weight coefficient of the ordinary load is 0.3. The present application focuses on the restoration of the important load.

[0044] In the context of coping with extreme disasters, the emergency restoration power supply capability of the distribution network can be effectively measured by the numerical value of the post-disaster load restoration optimization model of the source-load-storage collaborative distribution network, which can be used as an important reference for evaluating the elasticity of the power system.

[0045] In another exemplary embodiment of the present application, in the early stage of post-disaster load restoration of the distribution network, the load equipped with the emergency power supply is powered by the emergency power supply; in the late stage of post-disaster load restoration of the distribution network, all important loads are integrated into the distribution network from the off-grid state.

[0046] After the occurrence of extreme failure, the four important load nodes set in the present application are powered by the EPS in the first two periods, i.e., all important loads are in an offline state. From the third period, all important loads are integrated into the distribution network from the off-grid state. It is assumed that distributed power sources DG, EV charging and discharging stations, and energy storage systems ESS are set at nodes 4, 12, 20, 21, and 29.

[0047] In another exemplary embodiment of the present application, in the late stage of post-disaster load restoration of the distribution network, a method for dividing the radial network topology is proposed in combination with network graph theory:

[0048] The node connection state variable α ij and the node parent-child relationship variable β ij are introduced for decision-making of the topology of the island after power failure, where α ij is a 0-1 variable representing the connection relationship between two nodes, β ij and β ji are 0-1 variables, if node i is the parent node of node j, then β ij = 0 and β ji = 1, otherwise, β ij = 1 and β ji = 0, if node i and node j are not connected, then α ij = β ij = β ji = 0.

[0049] Following a large-scale power outage in a distribution network caused by an extreme disaster, the primary objective is load restoration, which in this application is characterized as maximizing the weighted supply time of the load. Under this condition, a radial network can more directly and efficiently utilize various distributed generation resources within the system to provide emergency power to local loads. Since there are no ring networks within the system, network losses in the distribution system are also effectively reduced. The aforementioned radial topology constraints are used to autonomously disconnect the distribution network from the main grid, creating microgrid islands. However, for the stability of the microgrid islands, the islands need to be as large as possible. After network topology reconstruction (topology reconstruction i.e., autonomous disconnection using topology constraints to form microgrid islands) of the distribution network after the disconnection fault, the network topology in each time period satisfies the radial topology constraints. Therefore, the distribution network topology in each time period is radial. Directly connected distributed generation (DG) and distributed energy storage (ESS) are set up at important load nodes to ensure that each local important load can be stably and quickly supplied.

[0050] The islanding process in step 105 above can be described as follows: Based on network graph theory, with the goal that the load equipped with emergency power supply is powered by at least one distributed resource, islanding is performed on the distribution network after a fault in extreme scenarios under the constraint of radial topology reconfiguration.

[0051] The radial topology reconstruction constraint is:

[0052]

[0053]

[0054]

[0055] Formula (4) indicates whether two load nodes are directly connected and their corresponding parent-child relationship.

[0056] In the formula, β ij and β ji Let β be the parent-child relationship variable between load node i and load node j. ij and β ji Let β be a 0-1 variable. If load node i is the parent node of load node j, then β ij =0 and β ji =1, if load node j is the parent node of load node i, then β ij =1 and β ji =0; α ij Let α be the connection state variable between load node i and load node j. ij β is a 0-1 variable. 1j Let n be the parent-child relationship variable between the load node i and load node j; N(i) is the set of all nodes associated with load node i, n b1a node set consisting of the remaining load nodes of the first load node; n l a line set connected to the load nodes of the distribution network, n b a load node set.

[0057] Figure 4 An emergency measure after an extreme disaster occurs is shown.

[0058] In another exemplary embodiment of the present application, when discussing the load restoration problem of the distribution network in which various source-load-storage adjustable resources cooperate with each other, a series of complex constraint conditions must be comprehensively considered, including the Distflow power flow model, the limited remaining energy, the radial topology structure of the distribution network, and the load state change. Comprehensive consideration of these constraint conditions is crucial for efficient and stable load restoration of the distribution network. Therefore, when solving the post-disaster load restoration optimization model of the source-load-storage cooperative distribution network in step 106, energy constraints, power flow constraints, and safe operation constraints must be met.

[0059] In another exemplary embodiment of the present application, the energy transmission line of the area of the distribution network affected by the extreme disaster may be severely damaged, and various DERs in the system cannot be completely supplied, such as coal and oil required by the thermal power unit cannot be transported, and the gas pipeline is damaged. Therefore, the remaining limited energy constraint of each distributed power source and energy storage needs to be considered. The energy constraint is:

[0060]

[0061] In the formula, is the active power output of the distributed power source connected to the load node i in the time period t; ΔT is the time interval of the time period division result, E i,0 is the remaining available power generation energy of the distributed power source connected to the corresponding load node i before starting to restore the load node, T is the time period set after discretization of the outage time, G∪C∪B is the set of the distributed diesel generator, the charging and swapping station of the electric vehicle, and the distributed energy storage system accessed to the distribution network. The equivalent generator and energy storage device model is shown in Figure 5 .

[0062] In an extreme scenario, when the distribution network encounters a large-scale power outage accident, the charging and swapping station of the electric vehicle can be converted into a critical energy storage unit to access the distribution network to provide emergency power support for important load nodes, ensuring that important facilities can still maintain normal operation during power interruption. In the subsequent application, the charging and swapping station C of the electric vehicle and the distributed energy storage system B are collectively referred to as the energy storage system.

[0063] In order to simplify the calculation process, in the examples of the present application, various energy forms such as natural gas and fuel oil are converted into electric energy in kilowatt hours according to specific conversion efficiency coefficients p, so as to ensure the consistency and accuracy of the calculation.

[0064] In another exemplary embodiment of the present application, the power flow constraints are:

[0065]

[0066]

[0067]

[0068]

[0069]

[0070]

[0071]

[0072]

[0073] In the formula, p j,t is the active power of the load node j at the time period t, k:j→k represents a set composed of all downstream load nodes k of the load node j, i:i→j represents a set composed of all downstream load nodes j of the load node i, P jk,t and P ij,t are the active powers of the line (j, k) and the line (i, j) at the time period t, r ij is the resistance of the line (i, j), l ij,t is the square of the current amplitude of the line (i, j) at the time period t, n b is a set of load nodes, T2 is a set T of time periods after the discretization of the outage time minus a set of all time periods after T min is a set of time periods of the power distribution network; T min is the shortest standby time of the emergency power supply of the load assembly in the power distribution network; q j,t is the reactive power of the load node j at the time period t, Q jk,t and Q ij,t are the reactive powers of the line (j, k) and the line (i, j) at the time period t; p i,t and q i,t are the active power and the reactive power of the load node i at the time period t, and are the active power and the reactive power of the distributed power supply connected to the load node i at the time period t, The active power output of the energy storage system connected to load node i during time period t. and ξ represents the active power demand and reactive power demand of load node i, respectively. i,t For the recovery status of load node i in the corresponding time period t, η i,t The connection status between the load and load node i; v i,t v is the square of the voltage at load node i during time period t. j,t α is the square of the voltage at load node j during time period t. ij,t Let z be the connection state variable between load node i and load node j during time period t, where M is a positive real number. ij Let z be the impedance of line (i,j). ij =r ij +jx ij x ij Let (i,j) be the reactance of the line. For z ij The conjugate of the complex number. Re() represents the real part of the complex number; P t L Let n be the network active power loss of the distribution network during time period t, and G∪C∪B be the set consisting of distributed diesel generator sets, electric vehicle charging and swapping stations, and distributed energy storage systems connected to the distribution network. l S is the set of lines connected to the load nodes of the distribution network. ij,t For complex power, For S ij,t The conjugate of S ij,t =P ij,t +jQ ij,t .

[0074] P jk,t and P ij,t For two continuous optimization variables, Q jk,t and Q ij,t For two continuous optimization variables, and Both are two continuous optimization variables. In the initial stage of emergency load restoration in the distribution network, critical loads rely on the installed EPS for power supply and are not connected to the distribution network. After the minimum standby time of the EPS is exceeded, load node i switches to connected state. Therefore, η i,t Depends on the shortest standby time T of EPS min α is a known quantity; M is a very large positive real number. ij It is a 0-1 variable, which takes the value 0 when load node i is not connected to load node j, and takes the value 1 when load node i is connected to load node j.

[0075] Equations (8) and (9) are the node power balance constraints, which represent the conservation of active and reactive power flowing into and out of node j; Equations (10) and (11) are the annotations of the complex power of load node i in period t, whose value is equal to the difference between the output of the DER set and the power consumed by the load connected to the load node, without considering the reactive power output of the energy storage system; Equations (12) and (13) are the transformations of the voltage balance equation; Equation (14) is a transformation of the definition of system complex power; Equation (15) is an annotation of the active power loss of the distribution network, whose value is equal to the sum of the active power output of all DERs connected to the distribution network in period t minus the sum of the power of all loads connected to the distribution network.

[0076] In another exemplary embodiment of the present application, in an extreme scenario, the primary prerequisite for seeking an optimal load restoration strategy is to ensure that the power system always maintains a safe and stable operating state during the restoration process. Therefore, the following distribution network safety and safe operation constraints must be considered:

[0077] Equation (16) is the limit current constraint that the line can flow; Equation (17) is the upper and lower voltage constraints that the load node can withstand; Equations (18) and (19) represent the upper and lower constraints of the active power and reactive power of the distributed power supply; Equation (20) represents the maximum charging power and maximum discharging power constraints of the energy storage device; Equation (21) represents that if the line (i, j) is not connected, the power is 0.

[0078]

[0079]

[0080]

[0081]

[0082]

[0083]

[0084] In the formula, l ij,t is the square of the current amplitude of line (i, j) in period t, is the maximum current that line (i, j) can flow; α ij is the load node i and the load node j connection state variable, α ij is a 0-1 variable; n l is the line set connected to the load node of the distribution network; T2 is the set T of time periods after the power outage time is discretized minus the set of all time periods T min after the power outage time is discretized, T min is the shortest standby time of the emergency power supply installed in the load of the distribution network; Vi max and V i min are the maximum and minimum voltage level that the load node i can withstand, respectively, and are the active and reactive power output of the distributed generator connected at load node i at time period t, P i g,max and are the maximum active and reactive power output of the distributed generator connected at load node i, G is the set of distributed diesel generators connected to the distribution network, P i c,max and P i d,max are the maximum charging and discharging power of the distributed energy storage system connected to load node i, C∪B is the set of the charging and swapping stations of electric vehicles and the distributed energy storage systems connected to the distribution network; M is a positive real number, α ij,t is the connection state variable of load node i and load node j at time period t.

[0085] For battery-type power sources such as electric vehicles and distributed energy storage devices, the state of charge SOC needs to be limited to ensure their normal operation during the restoration process:

[0086]

[0087] wherein, and are the upper and lower limits of the state of charge SOC to ensure the normal operation of the energy storage device b, κ b,0 is the initial state of charge of the energy storage device b, ρ b is the conversion factor of the state of charge and energy of the energy storage device b, is the active power output of the distributed generator connected to the energy storage device b at time period t, ΔT is the time interval of each time period divided by the time period. κ∈[0,1] represents the state of charge of the energy storage device.

[0088] In the design of the restoration strategy, a strict limit on the number of changes in the state of the load is introduced, allowing each load to have only one opportunity for state change during the entire restoration period, effectively preventing frequent fluctuations in the state of the load:

[0089]

[0090] When an extreme scenario occurs, a large-scale power outage accident occurs, and the application can cooperate with various types of power sources and energy storage devices to quickly restore the load for emergency power supply and improve the safety of the distribution network. First, the role of source-load-storage adjustable resources in improving the safety of the distribution network is analyzed. Various source-load-storage distributed resources have fast response capability and can provide emergency power supply to the distribution network when a power outage accident occurs, thereby reducing the peak load of the power grid and reducing the redundant investment of the power system. The cooperation of source-load-storage can improve the ability of various DERs to restore the load of the distribution network, greatly improving the safety of the distribution network. On this basis, a post-disaster load restoration optimization model of the distribution network is established. The model takes the maximum load weighted power supply time and the minimum total loss of the distribution network as the target, considers energy constraints, power flow constraints, and safe operation constraints, and solves a mixed integer second order cone programming (MISOCP) model for the multi-time period optimization problem of the distribution network considering the cooperation of various DERs. The method proposed in the application has universality. The weight coefficient can be flexibly set according to the preference of the decision maker, so as to obtain a load restoration strategy that meets various requirements, and the load restoration and the reduction of the network loss of the whole system are taken into account.

[0091] For the post-disaster load restoration problem of the distribution network side after a large-scale power outage accident of the power system in an extreme scenario, a post-disaster load restoration optimization model of the distribution network side considering the cooperation of various source-load-storage distributed resources such as distributed diesel generator sets, electric vehicles, and distributed energy storage units in multiple time scales is proposed, and a corresponding optimization decision method and relaxation and solution method are proposed. Compared with the load restoration method considering only one type of distributed power source or the load restoration method considering only a single time period, the restoration method proposed in the application considering the cooperation of various types of DERs in multiple time scales can fully utilize the advantages of distributed power sources and energy storage systems, has obvious advantages in spatial scale and time scale, and can better improve the flexibility of the distribution network and ensure the safety of the distribution network. It has very important application value for scientific research institutions and the business community to realize emergency load restoration in an extreme scenario.

[0092] Based on the same inventive concept, the embodiments of the application also provide an extreme scenario source-load-storage adjustable resource scheduling device for implementing the above-mentioned extreme scenario source-load-storage adjustable resource scheduling method. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme described in the above method, so the specific limitations in one or more extreme scenario source-load-storage adjustable resource scheduling device embodiments provided below can be referred to the limitations of the extreme scenario source-load-storage adjustable resource scheduling method in the above text, which will not be repeated here.

[0093] In one example embodiment, a device for scheduling source-load-storage adjustable resources in an extreme scenario is provided, comprising a prediction module, a division module, a model establishment module, an early power supply module, a later planning module, and an optimization solution module.

[0094] The prediction module is configured to predict the power outage duration of the power distribution network after a disaster in an extreme scenario.

[0095] The division module is configured to evenly divide the power outage duration to obtain a time period division result.

[0096] The model establishment module is configured to establish a source-load-storage collaborative power distribution network post-disaster load recovery optimization model based on the time period division result and in combination with actual power generation resources in the power distribution network.

[0097] The early power supply module is configured to supply power to loads equipped with emergency power supplies by using the emergency power supplies in an early stage of power distribution network post-disaster load recovery.

[0098] The later planning module is configured to divide islands of the power distribution network after a fault in an extreme scenario to supply power to loads equipped with emergency power supplies by at least one distributed resource in a later stage of power distribution network post-disaster load recovery.

[0099] The optimization solution module is configured to optimize and solve the source-load-storage collaborative power distribution network post-disaster load recovery optimization model according to the divided islands and reserves of various source-load-storage adjustable resources in the actual power distribution network, and obtain a post-disaster load recovery result, which includes active power and reactive power of lines in each time period, and active power output and reactive power output of distributed power sources in each time period.

[0100] In one example embodiment, a computer device, which can be a server or a terminal, has an internal structure as shown in Figure 6 The computer device includes a processor, a memory, an input / output interface (I / O), and a communication interface. The processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for running the operating system and the computer program in the non-volatile storage medium. The database of the computer device is configured to store a post-disaster load recovery result. The input / output interface of the computer device is configured to exchange information between the processor and external devices. The communication interface of the computer device is configured to communicate with external terminals through a network connection. The computer program is executed by the processor to implement a method for scheduling source-load-storage adjustable resources in an extreme scenario.

[0101] Those skilled in the art can understand that, Figure 6 The structure shown in the foregoing embodiments is merely a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. A specific computer device can include more or fewer components than those shown in the diagram, or combine certain components, or have a different arrangement of components. In an exemplary embodiment, a computer device is provided, which includes a memory and a processor. The memory stores a computer program. The processor implements the steps of the above-mentioned method embodiments when executing the computer program.

[0102] The technical features of the above embodiments can be combined in any manner. To make the description concise, not all possible combinations of the technical features in the above embodiments are described, but it should be considered that any combination of the technical features is within the scope of the present disclosure, as long as there is no contradiction.

[0103] The principles and implementation manners of the present application are described by using specific examples in the present application. The above embodiments are merely used to help understand the method of the present application and its core idea. Meanwhile, for those skilled in the art, the specific implementation manners and application ranges can be changed according to the idea of the present application. In conclusion, the content of the present description should not be understood as a limitation on the present application.

Claims

1. A method for source-load-storage adjustable resource scheduling under extreme scenarios, characterized in that, The application relates to a source-load-storage collaborative post-disaster load recovery optimization model for a power distribution network under an extreme scenario. The application comprises the following steps: acquiring the power outage duration of the power distribution network after a disaster under an extreme scenario; uniformly dividing the power outage duration to obtain a time period division result; establishing the source-load-storage collaborative post-disaster load recovery optimization model for the power distribution network based on the time period division result and in combination with power generation resources in the actual power distribution network; in the early stage of post-disaster load recovery of the power distribution network, the loads equipped with emergency power supplies are powered by the emergency power supplies; in the late stage of post-disaster load recovery of the power distribution network, island division is performed on the power distribution network after a fault under an extreme scenario, with the target that the loads equipped with emergency power supplies are powered by at least one distributed resource; the source-load-storage collaborative post-disaster load recovery optimization model is solved and optimized according to the divided islands and the reserves of various source-load-storage adjustable resources in the actual power distribution network, and a post-disaster load recovery result is obtained; the post-disaster load recovery result comprises the recovery state of each load node in the power distribution network in each time period and the network active power loss of the power distribution network in each time period; Based on the time period division results, and with the goal of maximizing the load-weighted power supply time, the core objective function is established as follows: In the formula, For load-weighted power supply time, For the set of load nodes, This is the set of time intervals after the power outage time has been discretized. For load nodes The weighting coefficients, For the corresponding time period Load Node The recovery state The time interval for each time period is divided into time periods; An auxiliary objective function is established to minimize the total network loss over the whole time period as follows: ; wherein, is the total network loss over the whole time period, is the time period, is the network active loss of the distribution network, is the reference power of the distribution system; The power distribution network post-disaster load recovery optimization model of source-load-storage coordination is obtained by combining the core objective function and the auxiliary objective function and introducing a weight coefficient: ; in the formula, is the total objective, is the weight coefficient.

2. The method of claim 1, wherein, the source-load-storage collaborative post-disaster load recovery optimization model is established based on the time period division result and in combination with power generation resources in the actual power distribution network, and specifically comprises the following steps:

3. The method of claim 1, wherein, the loads connected to the power distribution network are divided into important loads and ordinary loads; the important loads are the loads equipped with emergency power supplies, and the ordinary loads are the loads not equipped with emergency power supplies. the island division is performed on the power distribution network after a fault under an extreme scenario, with the target that the loads equipped with emergency power supplies are powered by at least one distributed resource, and specifically comprises the following steps: based on network graph theory, the island division is performed on the power distribution network after a fault under an extreme scenario, with the target that the loads equipped with emergency power supplies are powered by at least one distributed resource, and under the condition of meeting the radial topology reconstruction constraint; ， ； ， ； ， ； wherein, and is a load node with load node is a parent node of load node and is a 0-1 variable, if load node is a parent node of load node , then and , if load node is a parent node of load node , then and ; is a connection state variable between load node and load node , is a 0-1 variable; is a parent node variable between load head node and load node ; is a node set associated with load node , is a node set consisting of the remaining load nodes after removing the head load node; is a line set connected to load node is a load node set.

4. The method of claim 1, wherein, the radial topology reconstruction constraint is that:

5. The method of claim 4, wherein, when the source-load-storage collaborative post-disaster load recovery optimization model is solved and optimized, the energy constraint, the power flow constraint and the safe operation constraint also need to be met. ， ； In the formula, is the time period is the active power output of the distributed power source connected to the load node; is the time interval of each time period, is the remaining power generation energy in the distributed power source connected to the corresponding load node before the load node is restored, is the set of time periods after discretization of the outage time, is the set of time periods after discretization of the outage time, is the set of distributed diesel generator sets, electric vehicle charging and battery swap stations, and distributed energy storage systems connected to the distribution network.

6. The method of claim 4, wherein, the energy constraint is that: ， ； ， ； ， ； ， ； ， ； ， ； ； ； wherein, is the load node In time period active power, denotes the set of all downstream load nodes of the load node , denotes the set of all downstream load nodes of the load node , and are the line and line active power in time period , is the resistance of line , is the square of line current amplitude in time period , is the set of load nodes is the set of time periods after power outage time discretization , is the set of all time periods after , is the minimum backup time of emergency power supply of load assembly in distribution network; is the load node reactive power in time period and are the line and line reactive power in time period ; and are the active power and reactive power of load node in time period , and are the active power and reactive power of distributed power supply connected to load node in time period , is the active power of energy storage system connected to load node in time period , and are the active load demand and reactive load demand of load node , is the recovery state of load node in corresponding time period , is the connection state of load and load node ; is the time period load node square of voltage, for time period load node square of voltage, for load node with load node in time period connection state variable, for a positive real number, for line impedance, for conjugate, denotes the real part of a complex number; for time period network active power loss of distribution network, for a set of distributed diesel generator units, electric vehicle charging stations and distributed energy storage systems connected to the distribution network, for a set of lines connected to the load node of the distribution network, for complex power, for conjugate.

7. The method of claim 4, wherein, the power flow constraint is that: ， ； ， ； ， ； ， ； ， ； ， ； ， ； ， ； wherein, is the time period is the line is the square of the current amplitude, is the line that can flow through it; is the load node is the load node is the connection state variable, is a 0-1 variable; is the set of lines connected to the load node in the distribution network; is the set of time periods after discretization of the outage time is subtracted from the set of all time periods, is the minimum standby time of the emergency power supply installed in the load of the distribution network; and are the maximum voltage and minimum voltage per unit value that the load node can withstand, and are the active power and reactive power of the distributed power supply connected to the load node in the time period , and are the maximum active power and maximum reactive power values of the distributed power supply connected to the load node , is the set of distributed diesel generator sets connected to the distribution network, and are the limit charging power and limit discharging power of the distributed energy storage system connected to the load node , is the set of charging and swapping stations of electric vehicles and distributed energy storage systems combined to access the distribution network; is a positive real number, is the connection state variable of the load node and the load node in the time period , and are the upper limit and lower limit of the state of charge to ensure the normal operation of the energy storage device , is the initial state of charge of the energy storage device , is the conversion coefficient of the state of charge and energy conversion of the energy storage device , is the active power of the distributed power supply connected to the energy storage device in the time period , for the time period division result for the corresponding time period a recovery state of the load node for the corresponding time period a recovery state of the load node for the corresponding time period a recovery state of the load node for the recovery period for the complex power 8. A device for source-load-storage adjustable resource scheduling in an extreme scenario, characterized in that, the safe operation constraint is that: the source-load-storage adjustable resource scheduling device under an extreme scenario comprises: a prediction module configured to predict the power outage duration of the power distribution network after a disaster under an extreme scenario; a division module configured to uniformly divide the power outage duration to obtain a time period division result; a model establishment module configured to establish the source-load-storage collaborative post-disaster load recovery optimization model for the power distribution network based on the time period division result and in combination with power generation resources in the actual power distribution network; an early stage power supply module configured to, in the early stage of post-disaster load recovery of the power distribution network, power the loads equipped with emergency power supplies by the emergency power supplies; a late stage planning module configured to, in the late stage of post-disaster load recovery of the power distribution network, perform island division on the power distribution network after a fault under an extreme scenario, with the target that the loads equipped with emergency power supplies are powered by at least one distributed resource; an optimization solving module configured to solve and optimize the source-load-storage collaborative post-disaster load recovery optimization model according to the divided islands and the reserves of various source-load-storage adjustable resources in the actual power distribution network, and obtain a post-disaster load recovery result; the post-disaster load recovery result comprises the active power and the reactive power of a line in each time period and the active power output and the reactive power output of a distributed power supply in each time period. Based on the time period division result, in combination with actual power generation resources in the power distribution network, a source-load-storage coordinated post-disaster load restoration optimization model of the power distribution network is established, and specifically includes: Based on the time period division results, and with the goal of maximizing the load-weighted power supply time, the core objective function is established as follows: In the formula, For load-weighted power supply time, For the set of load nodes, This is the set of time intervals after the power outage time has been discretized. For load nodes The weighting coefficients, For the corresponding time period Load Node The recovery state The time interval for each time period is divided into time periods; An auxiliary objective function is established to minimize the total network loss over the whole time period as follows: ; wherein, is the total network loss over the whole time period, is the time period, is the network active loss of the distribution network, is the reference power of the distribution system; The power distribution network post-disaster load recovery optimization model of source-load-storage coordination is obtained by combining the core objective function and the auxiliary objective function and introducing a weight coefficient: ; in the formula, is the total objective, is the weight coefficient.

9. A computer device comprising: The memory, the processor, and the computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the source-load-storage adjustable resource scheduling method in the extreme scenario in any one of claims 1-7.

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