Source-load-storage adjustable resource scheduling method, device and equipment in extreme scene

Abstract

The invention discloses a source-load-storage adjustable resource scheduling method, device and equipment in an extreme scene, and relates to the field of power system optimization scheduling. The method comprises the following steps: acquiring post-disaster power failure duration of a power distribution network in an extreme scene, and uniformly dividing; establishing a power distribution network post-disaster load recovery optimization model based on source-load-storage coordination by combining actual power generation resources in the power distribution network on the basis of a time period division result; in the early stage of power distribution network post-disaster load recovery, the load equipped with the emergency power supply is powered by the emergency power supply; in the post-disaster load recovery later period of the power distribution network, carrying out island division on the power distribution network after the fault in the extreme scene; and according to the divided islands and the reserve of various source-load-storage adjustable resources in the actual power distribution network, optimizing and solving the power distribution network post-disaster load recovery optimization model based on the source-load-storage cooperation, and obtaining a post-disaster load recovery result. According to the invention, various distributed resources can be coordinated in an extreme scene, the advantages of the distributed resources are brought into full play, the elasticity of the power distribution network is improved, and the safety of the power distribution network is ensured.

Description

Technical Field

[0001] The present application relates to the field of power system optimization and scheduling, and in particular to a method, device and equipment for scheduling adjustable source-load-storage resources under extreme scenarios. Background Art

[0002] As urbanization and electrification continue to advance, the demand for electricity for social development is gradually increasing, and the scale and complexity of the global power system are rapidly increasing. Meteorological conditions such as typhoons, high temperatures, plum rains, DC interlocking, and system failure operation scenarios will bring great challenges to the safe and stable operation of the power grid. Affected by extreme weather, major natural disasters and deliberate attacks, large-scale power outages in the global power system have occurred frequently, and the economic losses caused have increased sharply. As the end of the power system, the distribution network directly affects the power demand of end users. Due to its wide coverage and low equipment redundancy, the distribution network is more prone to problems such as long-term power outages under extreme conditions.

[0003] A single power generation resource cannot meet diverse needs. It is easy to fail in extreme cases and it is difficult to maintain power supply stability. At the same time, it will be difficult to coordinate multiple dynamic changes, lack of flexibility, and limited scheduling strategies. In addition, only focusing on a single time period, such as short-term recovery, ignores the recovery needs of other time periods, which will lead to an unbalanced global recovery strategy, fail to optimize the overall power supply effect, ignore the stability and sustainability of the system, and fail to adjust the strategy according to real-time conditions in extreme scenarios, making it difficult to coordinate multi-stage resource allocation. It can be seen that in extreme scenarios, if only one distributed power source or only a single time period is considered, the load recovery method has obvious limitations, mainly manifested in insufficient reliability, flexibility and global optimization capabilities, and cannot guarantee the distribution network's ability to cope with interference and the safe and stable operation of the system. Summary of the invention

[0004] The purpose of this application is to provide a method, device and equipment for scheduling adjustable source-load-storage resources in extreme scenarios, which can coordinate multiple distributed resources in extreme scenarios, give full play to the advantages of distributed resources, improve the elasticity of the distribution network, and ensure the safety of the distribution network.

[0005] To achieve the above objectives, this application provides the following solutions:

[0006] In a first aspect, the present application provides a method for adjusting source-load-storage adjustable resources under extreme scenarios, including: obtaining the power outage duration after a distribution network disaster under extreme scenarios; evenly dividing the power outage duration to obtain a time period division result; based on the time period division result, in combination with the actual power generation resources in the distribution network, establishing a source-load-storage coordinated distribution network post-disaster load recovery optimization model; in the early stage of the distribution network post-disaster load recovery, the load equipped with an emergency power supply relies on the emergency power supply; in the late stage of the distribution network post-disaster load recovery, with the goal of the load equipped with an emergency power supply being powered by at least one distributed resource, the distribution network after a fault in an extreme scenario is islanded; based on the divided islands and the reserves of a variety of source-load-storage adjustable resources in the actual distribution network, the source-load-storage coordinated distribution network post-disaster load recovery optimization model is optimized to obtain a post-disaster load recovery result; the post-disaster load recovery result includes the recovery status 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.

[0007] In the second aspect, the present application provides a source-load-storage adjustable resource adjustment device under extreme scenarios, including: a prediction module, used to predict the power outage duration after a distribution network disaster under extreme scenarios; a division module, used to evenly divide the power outage duration and obtain a time period division result; a model building module, used to establish a source-load-storage coordinated distribution network post-disaster load recovery optimization model based on the time period division result and in combination with the actual power generation resources in the distribution network; an early power supply module, used to rely on the emergency power supply to supply power to the load equipped with an emergency power supply in the early stage of the distribution network post-disaster load recovery; a late planning module, used to island the distribution network after a fault in an extreme scenario in the late stage of the distribution network post-disaster load recovery, with the goal that the load equipped with an emergency power supply is supplied by at least one distributed resource; an optimization solution module, used to optimize and solve the source-load-storage coordinated distribution network post-disaster load recovery optimization model according to the divided islands and the reserves of multiple source-load-storage adjustable resources in the actual distribution network to obtain a post-disaster load recovery result; the post-disaster load recovery result includes the active power and reactive power of the line in each time period, and the active output and reactive output of the distributed power source in each time period.

[0008] In a third aspect, the present 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 source-load-storage adjustable resource adjustment method in any of the extreme scenarios described above.

[0009] According to the specific embodiments provided in this application, this application has the following technical effects:

[0010] The present application provides a method, device and equipment for scheduling adjustable resources of source, load and storage under extreme scenarios. The established distribution network post-disaster load recovery optimization model with coordinated source, load and storage takes into account a variety of distributed resources of source, load and storage. The multiple distributed resources of source, load and storage cooperate with each other in a plurality of time periods divided by the duration of the power outage, and respond quickly when a power outage occurs to provide emergency power supply to the distribution network. Compared with the load recovery method that only considers one distributed power source or the load recovery method that only considers a single time period, the advantages of distributed power sources and energy storage systems can be fully utilized, and it has obvious superiority in spatial and temporal scales, and can better improve the elasticity of the distribution network and ensure the safety of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0012] Figure 1 A schematic diagram of a flow chart of a method for scheduling resources with adjustable source, load and storage in an extreme scenario provided by an embodiment of the present application;

[0013] Figure 2 A schematic diagram of time period division of a restoration strategy for source-load-storage collaboration provided by another embodiment of the present application;

[0014] Figure 3 A schematic diagram of an improved IEEE 33 node provided by another embodiment of the present application;

[0015] Figure 4 A schematic diagram of emergency measures after an extreme disaster occurs provided by 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 the present application;

[0017] Figure 6 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0019] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0020] A variety of distributed energy resources (DER), that is, adjustable resources, including distributed generators (DG), distributed energy storage systems (ESS), electric vehicles (EV) and controllable loads, can enhance the flexibility and security of the distribution network. The diversity of distributed resources provides more recovery solutions for the power grid, and the optimal scheduling of coordinated adjustable resources has also become an important means to ensure the safe operation of the distribution network. In extreme scenarios, coordinating these resources can help improve the distribution network's ability to cope with interference and ensure the safe and stable operation of the system.

[0021] In view of this, in an exemplary embodiment, Figure 1 As shown, a method for scheduling resources with adjustable source, load and storage in extreme scenarios is provided, including the following steps 101 to 106. Among them:

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

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

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

[0025] Step 104: In the early stage of the load recovery after the disaster of the distribution network, the load equipped with the emergency power supply is supplied by the emergency power supply.

[0026] Step 105: In the later stage of the post-disaster load recovery of the distribution network, the distribution network after the fault in the extreme scenario is islanded with the goal that the load equipped with the emergency power supply 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, optimize and solve the distribution network post-disaster load recovery optimization model with source-load-storage collaboration to obtain the post-disaster load recovery result; the post-disaster load recovery result includes the recovery status 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] By implementing the above steps 101 to 106, the source-load-storage coordinated distribution network post-disaster load recovery optimization model is established, which takes into account a variety of source-load-storage distributed resources. The various source-load-storage distributed resources cooperate with each other in multiple time periods divided by the power outage duration, and respond quickly when a power outage occurs to provide emergency power supply to the distribution network. Compared with the load recovery method that only considers one distributed power source or the load recovery method that only considers a single time period, it can give full play to the advantages of distributed power sources and energy storage systems, has obvious superiority in spatial and temporal scales, can better improve the elasticity of the distribution network, and ensure the safety of the distribution network.

[0029] In another exemplary embodiment of the present application, in order to discretize the power outage duration after the power outage of the distribution network (the time for the distribution network to restore power), the entire power outage time is defined as the total time from the time when the power outage occurs to the time when the power transmission path of the transmission network is fully restored, which is recorded as T 0 In order to more accurately describe and optimize the load recovery process, the entire power outage time T 0 Divide the power outage time into several time periods, and discretize the time period set T, where the interval of any time period is ΔT. Assume that the shortest backup time of the emergency power supply (EPS) of the important loads in the target distribution network is T min , then T min The set of all time periods after is T 2 , specifically divided into Figure 2 shown.

[0030] Under the attack of extreme disasters, transmission lines, transportation networks and other facilities in the affected areas have suffered serious damage. As a result, users cannot obtain electricity normally, and large-scale power outages are imminent. Specifically, in this example, line (4,5) fails and has been quickly isolated to prevent further damage. The distribution network will be in about T 0 =3.5h later, the power supply capacity is gradually restored, with a time interval of 30min, i.e. ΔT = 0.5h, which is divided into 7 time periods.

[0031] In another exemplary embodiment of the present application, taking into account the correlation between various influencing factors, in order to continuously ensure the power supply of important load nodes, based on the time period division results, maximizing the load-weighted power supply time is used as the core objective function, and minimizing the total network loss in the entire period is set as the auxiliary objective function. In order to unify the dimensions 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 distribution network are restored; while the reference value of the auxiliary objective function is artificially set according to the distribution system that needs to be restored.

[0032] In order to unify the core objective function and the auxiliary objective function, a weight coefficient λ is introduced 0 . 0 It can be flexibly set according to the decision maker's preferences to facilitate the acquisition of load restoration strategies that meet various requirements. It is worth noting that load restoration is the primary task, while the reduction of system network losses is a secondary task. Therefore, when setting the weight coefficient, it should be ensured that its value is not too large to ensure the priority realization of the core objective function.

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

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

[0035]

[0036] In the formula, F 1 is the load-weighted power supply time, n b is the set of load nodes, T is the set of time periods after the power outage time is discretized, and w i is the weight coefficient of load node i, ξ i,t is the recovery state of load node i in the corresponding period t, and ΔT is the time interval between each period in the period division result.

[0037] Step 202: With the goal of minimizing the total network loss over the entire period, an auxiliary objective function is established:

[0038]

[0039] In the formula, F 2 is the total network loss during the entire period, P t L is the network active power loss of the distribution network in period t, S b It is the base power of the power distribution system.

[0040] Step 203: Combining the core objective function and the auxiliary objective function, and introducing the weight coefficient, the post-disaster load recovery optimization model of the distribution network with source-load-storage coordination is obtained as follows:

[0041] maxF=F 1 -λ 0 F 2 (3)

[0042] In the formula, F is the overall goal, λ 0 is the weight coefficient.

[0043] by Figure 3The improved IEEE 33-bus system shown is used as an example to verify and illustrate the proposed distribution network post-disaster load recovery optimization model with source-load-storage coordination. Figure 3 The dotted lines in the figure represent the feeders added after the IEEE 33 node is improved, and the solid lines represent the feeders originally possessed by the IEEE 33 node. The loads are divided into two levels according to their importance. The weight coefficient of important loads is 1. Important loads are located at nodes 3, 15, 22, and 30. EPS is installed for important power users. The weight coefficient of ordinary loads is 0.3. This application focuses on the recovery of important loads.

[0044] In the context of responding to extreme disasters, the emergency power supply restoration capability of the distribution network can be effectively measured through the numerical value of the distribution network post-disaster load recovery optimization model with source-load-storage coordination. This indicator can serve as an important reference for evaluating the resilience of the power system.

[0045] In another exemplary embodiment of the present application, in the early stage of post-disaster load recovery of the distribution network, the loads equipped with emergency power supplies rely on the emergency power supply; in the late stage of post-disaster load recovery of the distribution network, all important loads are merged into the distribution network from an off-grid state.

[0046] After an extreme fault occurs, the four important load nodes set in this application rely on EPS for power supply in the first two periods, that is, all important loads are offline. From the third period, all important loads are connected to the distribution network from the off-grid state. It is assumed that distributed power generation DG, EV charging and discharging stations, and energy storage systems ESS are set at nodes 4, 12, 20, 21, and 29 respectively.

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

[0048] Introduce node connection state variable α ij and node parent-child relationship variable β ij , used to decide the topology of the island after power outage, where α ij is a 0-1 variable, indicating the connection relationship between two nodes, β ij and β ji is a 0-1 variable. 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] After an extreme disaster causes a large-scale power outage in the distribution network, the primary goal is to restore the load, which is characterized in this application as maximizing the weighted power supply time of the load. Under this condition, the radial network can more directly and efficiently utilize the various distributed power generation resources in the system to provide emergency power supply to local loads. Since there is no ring network in the system, the network loss of the distribution system is also effectively reduced. The above-mentioned radial topology constraints are used to autonomously disconnect the distribution network disconnected from the main grid and divide the microgrid islands. However, for the stability of the operation of the microgrid islands, the divided islands need to be as large as possible. After the network topology of the distribution network after the disconnection fault is reconstructed (topology reconstruction is to autonomously disconnect the line using topology constraints to form microgrid islands), the network topology of each time period meets the radial topology constraints. Therefore, the distribution network topology of each time period is radial, and directly connected distributed power sources DG and distributed energy storage ESS are set at important load nodes to ensure that each local important load can be stably and quickly supplied.

[0050] The process of island division in step 105 above can be as follows: based on network graph theory, with the goal of ensuring that the load of the emergency power supply is powered by at least one distributed resource, and under the constraint of radial topology reconstruction, the distribution network after a fault in an extreme scenario is islanded.

[0051] The radial topology reconstruction constraints are:

[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 is the parent-child relationship variable between load node i and load node j, β ij and β ji is 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 is the connection state variable between load node i and load node j, α ij is a 0-1 variable; β 1j is the parent-child relationship variable between the first load node and load node j; Ν(i) is the set of all nodes associated with load node i, n b1is the node set consisting of the remaining load nodes after removing the first load node; n l is the set of lines connected to the load nodes of the distribution network, n b is the set of load nodes.

[0057] Figure 4 Demonstrate emergency measures after extreme disasters occur.

[0058] In another exemplary embodiment of the present application, when discussing the problem of load recovery of a distribution network with multiple source-load-storage adjustable resources working together, a series of complex constraints must be fully considered, including Distflow flow model, limited remaining energy, radial topology of distribution network, and load state change. The comprehensive consideration of these constraints is crucial to achieving efficient and stable load recovery of distribution network. When solving the post-disaster load recovery optimization model of the distribution network with source-load-storage collaboration in the above step 106, energy constraints, flow constraints, and safe operation constraints must also be met.

[0059] In another exemplary embodiment of the present application, the energy transmission lines in the distribution network affected by extreme disasters may be severely damaged, and various DERs in the system cannot be fully supplied, such as the coal and fuel oil required by thermal power units cannot be transported, and the gas pipeline is damaged, etc. Therefore, it is necessary to consider the remaining limited energy constraints of each distributed power source and energy storage. Then the energy constraint is:

[0060]

[0061] In the formula, is the active output of the distributed generation connected to the load node i in time period t; ΔT is the time interval between each time period after the time period division, E i,0 is the remaining available power generation energy in the distributed power source connected to the corresponding load node i before the load node is restored, T is the set of time periods after the power outage time is discretized, and G∪C∪B is the set composed of distributed diesel generator sets connected to the distribution network, charging and swapping stations for electric vehicles, and distributed energy storage systems. The equivalent generator and energy storage device model is as follows: Figure 5 shown.

[0062] In extreme scenarios, when the distribution network encounters a large-scale power outage, the electric vehicle charging and swapping station can be converted into a key energy storage unit and connected to the distribution network to provide emergency power supply support for important load nodes, ensuring that important facilities can maintain normal operation during power outages. This application will subsequently refer to the electric vehicle charging and swapping station C and the distributed energy storage system B as the energy storage system.

[0063] In order to simplify the calculation process, this application converts various energy forms such as natural gas and fuel oil into electric energy in kilowatt-hours according to a specific conversion efficiency coefficient ρ in the calculation example to ensure the consistency and accuracy of the calculation.

[0064] In another exemplary embodiment of the present application, the power flow constraint is:

[0065]

[0066]

[0067]

[0068]

[0069]

[0070]

[0071]

[0072]

[0073] In the formula, p j,t is the active power of load node j in period t, k:j→k represents the set of all downstream load nodes k of load node j, i:i→j represents the set of all downstream load nodes j of load node i, P jk,t and P ij,t are the active power of line (j, k) and line (i, j) in time period t, r ij is the resistance of line (i, j), l ij,t is the square of the current amplitude of line (i, j) in time period t, n b is the load node set, T 2 is the discretized time period set T minus T min The set of all time periods after T min The shortest backup time of emergency power supply for loads in the distribution network; j,t is the reactive power of load node j in period t, Q jk,t and Q ij,t are the reactive power of line (j, k) and line (i, j) in time period t respectively; p i,t and q i,t are the active power and reactive power of load node i in time period t, and are the active and reactive outputs of the distributed generation connected to load node i in time period t, is the active output of the energy storage system connected to load node i in time period t, and are the active load demand and reactive load demand of load node i respectively, ξ i,t is the recovery state of load node i in the corresponding period t, η i,t is the connection status between the load and load node i; v i,t is the square of the voltage at load node i in period t, v j,t is the square of the voltage at load node j in time period t, α ij,t is the connection state variable between load node i and load node j in time period t, M is a positive real number, z ij is the impedance of line (i, j), z ij =r ij +jx ij , x ij is the reactance of line (i,j), For z ij Re() represents the real part of a complex number; P t L is the network active power loss of the distribution network in time period t, G∪C∪B is the set of distributed diesel generator sets, electric vehicle charging and swapping stations and distributed energy storage systems connected to the distribution network, n l is the set of lines connected to the load nodes of the distribution network, S ij,t is the complex power, For S ij,t The conjugate, S ij,t =P ij,t +jQ ij,t .

[0074] P jk,t and P ij,t are two continuous optimization variables, Q jk,t and Q ij,t are two continuous optimization variables, and They are also two continuous optimization variables. In the initial stage of emergency load recovery of the distribution network, the important load relies on the installed EPS for power supply and is not connected to the distribution network. After exceeding the shortest backup time of the EPS, the load node i is switched to the access state. Therefore, η i,t Depends on the shortest backup time T of EPS min , is a known quantity; M is a large positive real number. ij It is a 0-1 variable. It 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] Formula (8) and Formula (9) are node power balance constraints, which represent the conservation of active power and reactive power flowing into and out of node j; Formula (10) and Formula (11) are annotations on the complex power of load node i in period t, whose values ​​are equal to the difference between the output of the set DER and the power consumed by the load connected to the load node, without considering the reactive output of the energy storage system; Formula (12) and Formula (13) are deformations of the voltage balance equation; Formula (14) is a deformation of the system complex power definition; Formula (15) is an annotation on the active power loss of the distribution network, whose value is equal to the sum of the active outputs 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 extreme scenarios, the primary premise for seeking the optimal load restoration strategy is to ensure that the power system always maintains a safe and stable operating state during the restoration process. Then, the following distribution network security must be considered, and the safe operation constraints are:

[0077] Formula (16) is the limit current constraint that the line can flow; Formula (17) is the upper and lower limit constraints of the voltage that the load node can withstand; Formula (18) and Formula (19) represent the upper and lower limit constraints of the active output and reactive output of the distributed power source; Formula (20) represents the maximum charging power and maximum discharging power constraints of the energy storage device; Formula (21) indicates 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 time period t, is the maximum current that can flow through line (i, j); α ij is the connection state variable between load node i and load node j, α ij is a 0-1 variable; n l is the set of lines connected to the load nodes of the distribution network; T 2 is the discretized time period set T minus T min The set of all time periods after T minThe shortest backup time of emergency power supply for loads in the distribution network; V i max and V i min are the highest voltage and lowest voltage per unit that load node i can withstand, respectively. and are the active and reactive outputs of the distributed generation connected to load node i in time period t, P i g,max and are the maximum active output and maximum reactive output of the distributed power source connected to the load node i, G is the set of distributed diesel generator sets connected to the distribution network, P i c,max and P i d,max are the limit charging power and limit discharging power of the distributed energy storage system connected to the load node i, respectively; C∪B is the set of charging and swapping stations for electric vehicles connected to the distribution network and distributed energy storage systems; M is a positive real number, α ij,t is the connection state variable between load node i and load node j in time period t.

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

[0086]

[0087] In the formula, and are the upper and lower limits of the state of charge to ensure the normal operation of the energy storage device b, κ b,0 is the initial state of charge of energy storage device b, ρ b is the conversion coefficient between the state of charge and energy of the energy storage device b, is the active output of the distributed generation connected to the energy storage device b in time period t, ΔT is the time interval of each time period after the time period division. κ∈[0,1] represents the state of charge of the energy storage device.

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

[0089]

[0090] When a large-scale power outage occurs in an extreme scenario, this application can coordinate various types of power sources and energy storage equipment to quickly restore loads for emergency 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 the ability to respond quickly and can provide emergency power to the distribution network when a power outage occurs, thereby reducing the peak load of the power grid and reducing redundant investment in the power system. The coordination of source-load-storage can enhance 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 recovery optimization model for the distribution network with source-load-storage coordination is established. The goal is to maximize the load-weighted power supply time and minimize the total loss of the distribution network. Considering energy constraints, flow constraints, safe operation constraints, etc., the multi-period optimization problem of the distribution network considering the coordination of multiple DERs is relaxed into a mixed-integer second order cone programming (MISOCP) model. The method proposed in this application is universal. The method can flexibly set the weight coefficient according to the decision maker's preferences, so as to obtain a load recovery strategy that meets various requirements, taking into account the goals of load recovery and reducing the network loss of the entire system.

[0091] In view of the problem of post-disaster load recovery considering the safety of the distribution network side after a large-scale power outage in the power system under extreme scenarios, this application proposes a post-disaster load recovery optimization model for the distribution network side at multiple time scales that takes into account the coordination of multiple source-load-storage distributed resources such as distributed diesel generator sets, electric vehicles, and distributed energy storage units, and proposes corresponding optimization decision methods as well as relaxation and solution methods. Compared with the load recovery method that only considers one distributed power source or the load recovery method that only considers a single time period, the recovery method proposed in this application that coordinates multiple types of DER at multiple time scales can give full play to the advantages of distributed power sources and energy storage systems, and has obvious superiority in spatial and temporal scales, which can better improve the elasticity of the distribution network and ensure the safety of the distribution network. It has very important application value for scientific research institutions and the industrial and commercial sectors to realize emergency load recovery in extreme scenarios.

[0092] Based on the same inventive concept, the embodiment of the present application also provides a source-load-storage adjustable resource scheduling device for extreme scenarios for implementing the source-load-storage adjustable resource scheduling method for extreme scenarios involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in the embodiments of the source-load-storage adjustable resource scheduling device for extreme scenarios provided below can refer to the limitations of the source-load-storage adjustable resource scheduling method for extreme scenarios above, and will not be repeated here.

[0093] In an exemplary embodiment, a source-load-storage adjustable resource scheduling device for extreme scenarios is provided, including: a prediction module, a division module, a model building module, an early power supply module, a late planning module and an optimization solution module.

[0094] The prediction module is used to predict the duration of power outages after a distribution network disaster in extreme scenarios.

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

[0096] The model building module is used to establish a distribution network post-disaster load recovery optimization model with source-load-storage coordination based on the time period division results and combined with the actual power generation resources in the distribution network.

[0097] The early power supply module is used to supply power to the loads equipped with emergency power supply by relying on emergency power supply in the early stage of load recovery after the distribution network is hit by a disaster.

[0098] The post-planning module is used to island the distribution network after a fault in an extreme scenario, with the goal of ensuring that the load equipped with emergency power supply is powered by at least one distributed resource in the later stage of post-disaster load recovery of the distribution network.

[0099] The optimization solution module is used to optimize and solve the distribution network post-disaster load recovery optimization model with source-load-storage coordination according to the divided isolated islands and the reserves of various source-load-storage adjustable resources in the actual distribution network, and obtain the post-disaster load recovery results; the post-disaster load recovery results include the active power and reactive power of the line in each time period, and the active output and reactive output of the distributed power source in each time period.

[0100] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 6 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as 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 used 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 the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store post-disaster load recovery results. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a source-load-storage adjustable resource scheduling method in an extreme scenario is implemented.

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

[0102] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0103] The present application uses specific examples to illustrate the principles and implementation methods of the present application. The above examples are only used to help understand the method and core ideas of the present application. At the same time, for those skilled in the art, according to the ideas of the present application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present application.

Claims

1. A method for scheduling adjustable source-load-storage resources in extreme scenarios, characterized in that: include: Obtain the duration of power outages after a power distribution network disaster in extreme scenarios; Evenly divide the power outage duration to obtain a time period division result; Based on the time period division results and combined with the actual power generation resources in the distribution network, a distribution network post-disaster load recovery optimization model with source-load-storage coordination is established; In the early stage of post-disaster load recovery of the distribution network, the loads equipped with emergency power supply rely on emergency power supply; In the later stage of post-disaster load recovery of the distribution network, the load equipped with emergency power supply is powered by at least one distributed resource, and the distribution network after the fault in extreme scenarios is islanded; According to the divided islands and the reserves of various source-load-storage adjustable resources in the actual distribution network, the post-disaster load recovery optimization model of the distribution network with source-load-storage collaboration is optimized and solved to obtain the post-disaster load recovery results; the post-disaster load recovery results include the recovery status 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.

2. The method for scheduling adjustable source-load-storage resources in extreme scenarios according to claim 1 is characterized in that: Based on the time period division results and combined with the actual power generation resources in the distribution network, a distribution network post-disaster load recovery optimization model with source-load-storage coordination is established, which specifically includes: Based on the time period division results, with the goal of maximizing the load-weighted power supply time, the core objective function is established as follows: Where F1 is the load weighted power supply time, n b is the set of load nodes, T is the set of time periods after the power outage time is discretized, and w i is the weight coefficient of load node i, ξ i,t is the recovery state of load node i in the corresponding period t, ΔT is the time interval between each period in the period division result; With the goal of minimizing the total network loss over the entire period, the auxiliary objective function is established as: Where F2 is the total network loss during the entire period, P t L is the network active power loss of the distribution network in period t, S b is the reference power of the power distribution system; Combining the core objective function and the auxiliary objective function, and introducing the weight coefficient, the post-disaster load recovery optimization model of the distribution network with source-load-storage coordination is obtained: maxF=F1-λ0F2; where F is the overall objective and λ0 is the weight coefficient.

3. The method for scheduling adjustable source-load-storage resources in extreme scenarios according to claim 2 is characterized in that: The loads connected to the distribution network are divided into important loads and ordinary loads; the important loads are loads equipped with emergency power supplies, and the ordinary loads are loads not equipped with emergency power supplies.

4. The method for scheduling adjustable source-load-storage resources in extreme scenarios according to claim 1 is characterized in that: With the goal of ensuring that the loads equipped with emergency power are powered by at least one distributed resource, the distribution network after a fault in an extreme scenario is islanded, including: Based on network graph theory, the load of the emergency power supply is powered by at least one distributed resource, and the distribution network after the fault in extreme scenarios is islanded under the constraint of radial topology reconstruction. The radial topology reconstruction constraints are: In the formula, β ij and β ji is the parent-child relationship variable between load node i and load node j, β ij and β ji is 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 is the connection state variable between load node i and load node j, α ij is a 0-1 variable; β 1j is the parent-child relationship variable between the first load node and load node j; Ν(i) is the set of all nodes associated with load node i, n b1 is the node set consisting of the remaining load nodes after removing the first load node; n l is the set of lines connected to the load nodes of the distribution network, n b is the set of load nodes.

5. The method for scheduling adjustable source-load-storage resources in extreme scenarios according to claim 1, characterized in that: When optimizing and solving the source-load-storage coordinated distribution network post-disaster load recovery optimization model, energy constraints, flow constraints and safe operation constraints must also be met.

6. The method for scheduling adjustable source-load-storage resources in extreme scenarios according to claim 5 is characterized in that: The energy constraint is: In the formula, is the active output of the distributed generation connected to the load node i in time period t; ΔT is the time interval between each time period after the time period division, E i,0 is the remaining available power generation energy in the distributed power source connected to the corresponding load node i before the load node is restored, T is the set of time periods after the power outage time is discretized, and G∪C∪B is the set composed of distributed diesel generator sets connected to the distribution network, charging and swapping stations for electric vehicles, and distributed energy storage systems.

7. The method for scheduling adjustable source-load-storage resources in extreme scenarios according to claim 5, characterized in that: The power flow constraint is: In the formula, p j,t is the active power of load node j in period t, k:j→k represents the set of all downstream load nodes k of load node j, i:i→j represents the set of all downstream load nodes j of load node i, P jk,t and P ij,t are the active power of line (j, k) and line (i, j) in time period t, r ij is the resistance of line (i, j), l ij,t is the square of the current amplitude of line (i, j) in time period t, n b is the set of load nodes, T2 is the set of time periods after the power outage time is discretized, T minus T min The set of all time periods after T min The shortest backup time of emergency power supply for loads in the distribution network; j,t is the reactive power of load node j in period t, Q jk,t and Q ij,t are the reactive power of line (j, k) and line (i, j) in time period t respectively; p i,t and q i,t are the active power and reactive power of load node i in time period t, and are the active and reactive outputs of the distributed generation connected to load node i in time period t, is the active output of the energy storage system connected to load node i in time period t, and are the active load demand and reactive load demand of load node i respectively, ξ i,t is the recovery state of load node i in the corresponding period t, η i,t is the connection status between the load and load node i; v i,t is the square of the voltage at load node i in period t, v j,t is the square of the voltage at load node j in time period t, α ij,t is the connection state variable between load node i and load node j in time period t, M is a positive real number, z ij is the impedance of line (i,j), For z ij The conjugate of , Re() represents the real part of the complex number; P t L is the network active power loss of the distribution network in time period t, G∪C∪B is the set of distributed diesel generator sets, electric vehicle charging and swapping stations and distributed energy storage systems connected to the distribution network, n l is the set of lines connected to the load nodes of the distribution network, S ij,t is the complex power, For S ij,t The conjugation of.

8. The method for scheduling adjustable source-load-storage resources in extreme scenarios according to claim 5 is characterized in that: The safe operation constraints are: In the formula, l ij,t is the square of the current amplitude of line (i, j) in time period t, is the maximum current that can flow through line (i, j); α ij is the connection state variable between load node i and load node j, α ij is a 0-1 variable; n l is the set of lines connected to the load nodes of the distribution network; T2 is the set of time periods after the power outage time is discretized, T minus T min The set of all time periods after T min The shortest backup time of emergency power supply for loads in the distribution network; V i max and V i min are the highest voltage and lowest voltage per unit that load node i can withstand, respectively. and are the active and reactive outputs of the distributed generation connected to load node i in time period t, P i g,max and are the maximum active output and maximum reactive output of the distributed power source connected to the load node i, G is the set of distributed diesel generator sets connected to the distribution network, P i c,max and P i d,max are the limit charging power and limit discharging power of the distributed energy storage system connected to the load node i, respectively; C∪B is the set composed of the charging and swapping stations of electric vehicles connected to the distribution network and the distributed energy storage system; M is a positive real number, α ij,t is the connection state variable between load node i and load node j in time period t; and are the upper and lower limits of the state of charge to ensure the normal operation of the energy storage device b, κ b,0 is the initial state of charge of energy storage device b, ρ b is the conversion coefficient between the state of charge and energy of the energy storage device b, is the active output of the distributed power source connected to the energy storage device b in time period t, ΔT is the time interval of each time period after the time period division; ξ i,t is the recovery state of load node i in the corresponding period t, ξ i,t-1 is the recovery state of load node i in the corresponding period t-1, T1 is the recovery period, S ij,t is the complex power.

9. A resource scheduling device with adjustable source, load and storage in extreme scenarios, characterized in that: The source-load-storage adjustable resource scheduling device in the extreme scenario includes: The prediction module is used to predict the duration of power outages after a power distribution network disaster in extreme scenarios; A division module, used to evenly divide the power outage duration and obtain a time period division result; A model building module is used to establish a distribution network post-disaster load recovery optimization model with source-load-storage coordination based on the time period division results and combined with the actual power generation resources in the distribution network; The early power supply module is used to supply power to the loads equipped with emergency power supply by emergency power supply in the early stage of load recovery after the distribution network disaster; The post-planning module is used to island the distribution network after a fault in an extreme scenario, with the goal that the load equipped with emergency power supply is powered by at least one distributed resource in the post-disaster load recovery stage of the distribution network; The optimization solution module is used to optimize and solve the distribution network post-disaster load recovery optimization model with source-load-storage coordination according to the divided isolated islands and the reserves of various source-load-storage adjustable resources in the actual distribution network, and obtain the post-disaster load recovery results; the post-disaster load recovery results include the active power and reactive power of the line in each time period, and the active output and reactive output of the distributed power source in each time period.

10. 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 source-load-storage adjustable resource scheduling method in the extreme scenario described in any one of claims 1-8.

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