A method and system for post-disaster power supply restoration of a micro-grid considering fuel supply elasticity

Abstract

The application discloses a kind of micro-grid post-disaster power supply recovery method and system considering fuel supply elasticity.First, the motion path model of fuel vehicle in traffic network is constructed, then the fuel consumption model of fuel vehicle and gas turbine is respectively constructed, then the micro-grid power supply recovery optimization model considering fuel reserve limit is constructed, finally the linearization treatment is carried out to the model constructed and is solved, and the micro-grid power supply recovery optimization result is obtained.The technical scheme of the application considers fuel supply elasticity in the process of micro-grid power supply recovery, ensures the continuous and reliable operation of gas turbine, and effectively improves the reliability of post-disaster micro-grid power supply recovery scheme.

Description

Technical Field

[0001] The present invention belongs to the technical field of power grids, and in particular to a method and system for restoring power supply after a disaster in a microgrid taking fuel supply elasticity into account. Background Art

[0002] Frequent natural disasters in recent years have posed unprecedented challenges to the safe and reliable operation of power systems. Natural disasters often exceed normal power system design standards, making power infrastructure susceptible to widespread physical failures and, in turn, power outages. The large-scale integration of distributed energy resources into distribution systems has provided a foundation for resilient post-disaster operations. In areas where no faults have occurred, microgrids built using distributed energy sources can be used to restore power to critical loads, effectively reducing the scale and duration of power outages.

[0003] To ensure reliable power supply to critical loads after a power outage, researchers have begun focusing on microgrid-based power restoration methods, conducting extensive research on microgrid structure optimization and resilient microgrid operation control. However, existing research primarily focuses on optimizing the dispatch of power within microgrids, with little attention paid to the fuel availability of distributed power sources such as gas turbines. In fact, insufficient fuel supply can prevent gas turbines from operating normally, leading to further power outages for critical loads already restored within the microgrid. Therefore, it is necessary to consider fuel supply elasticity when developing microgrid power restoration strategies to improve the reliability of microgrid operations after disasters. Summary of the Invention

[0004] The purpose of the present invention is to address the problems existing in the above-mentioned prior art, take into account the impact of fuel supply elasticity on the effectiveness of the microgrid power supply restoration strategy, and provide a microgrid post-disaster power supply restoration method that takes fuel supply elasticity into account. First, a movement path model of the fuel vehicle in the traffic network is constructed, and then the fuel consumption models of the fuel vehicle and the gas turbine are constructed respectively. Subsequently, a microgrid power supply restoration optimization model that takes into account the fuel reserve limit is constructed. Finally, the constructed model is linearized and solved to obtain the microgrid power supply restoration optimization result. Compared with the traditional microgrid power supply restoration method that does not consider fuel supply elasticity, the proposed method can effectively ensure the continuous power supply of the power source in the microgrid and improve the problem of insufficient reliability of the traditional power supply restoration strategy.

[0005] The technical solution to achieve the purpose of the present invention is: a method for restoring power supply after a disaster in a microgrid taking into account fuel supply elasticity, the method comprising the following steps:

[0006] Step 1: Construct a fuel vehicle motion path model;

[0007] Step 2: Construct fuel consumption models for fuel vehicles and gas turbines;

[0008] Step 3: Construct a microgrid power restoration optimization model taking into account the fuel reserve limit;

[0009] Step 4: Linearize and solve the model to obtain the optimized microgrid power restoration plan.

[0010] Furthermore, the step 1 of constructing the fuel vehicle motion path model specifically includes:

[0011] A fuel vehicle can be in motion or parked, and can only be in one state at any given moment:

[0012]

[0013] The conversion relationship between the stationary state and the moving state of the fuel vehicle is expressed as:

[0014]

[0015] The running time required by the fuel vehicle at each moment is expressed as:

[0016]

[0017] The remaining movement time of the fuel vehicle during movement is expressed as:

[0018]

[0019] The motion state of the fuel vehicle is limited by the remaining motion time, which is expressed as:

[0020]

[0021] During each movement, the movement target of the fuel vehicle must remain unchanged, which can be expressed as:

[0022]

[0023] Among them, N M is the set of nodes in the transportation network; F is the set of fuel vehicles; T is the set of scheduling time scales; x f.n.t 、x f.n.t+1 、x f.n.t-1 are all 0-1 variables, indicating whether the fuel vehicle f stops at node n at time t, time t+1, and time t-1 respectively; v f.n.t 、v f.n.t+1 、v f.n.t-1 are all 0-1 variables, indicating whether the fuel vehicle f is heading to node n at time t, time t+1, and time t-1 respectively; v f.n′.1 Indicates whether the fuel vehicle f is heading to node n' at the first moment, v f.n′.t Indicates whether the fuel truck f is heading to node n' at time t; Cf.t Indicates the number of time steps that the fuel vehicle f needs to consume at time t, C f.1 Indicates the number of time steps that the fuel vehicle f needs to consume at the first moment; R f.t represents the remaining movement time of the fuel vehicle f at time t, R f.1 represents the remaining motion time of the fuel vehicle f at the first moment; φ is a constant; ε is a positive number; α f.t is a 0-1 variable, indicating whether the fuel vehicle f is in motion between the time t-1 and the time t; T fnn’ It represents the number of time steps required for fuel truck f to move from node n to node n'.

[0024] Furthermore, in step 2, a fuel consumption model of a fuel vehicle is constructed, specifically including:

[0025] When the fuel truck is refueling at the warehouse, and only considers its external fuel supply at other nodes, the fuel consumption model of the fuel truck is modeled as:

[0026]

[0027] in, represents the remaining fuel of fuel vehicle f at time t and time t-1 respectively; F is the set of fuel vehicles; T is the set of scheduling time scales; N M is the set of nodes in the transportation network; V f.t represents the fuel replenishment amount of fuel vehicle f at time t; U f.n.t represents the fuel consumption of fuel vehicle f at node n at time t; represents the fuel loading capacity of fuel vehicle f; x f.n.t 、x f.n-k.t Both are 0-1 variables, indicating whether the fuel vehicle f stops at node n or node nk at time t.

[0028] Furthermore, in step 2, a fuel consumption model of the gas turbine is constructed, which specifically includes:

[0029] Gas turbines consume fuel to generate electricity. The relationship between fuel consumption and power output per unit time is expressed as:

[0030] B g.t =a g.r P g.t +b g.r , if P g.t ∈[p g.r-1 , p g.r ]

[0031] Among them, B g.t represents the fuel consumption of engine g at time t; P g.trepresents the active power output of the engine g at time t; a g.r is the coefficient of the rth piecewise linear term of the fuel consumption function of the engine g; b g.r is the rth piecewise constant coefficient of the fuel consumption function of the engine g; p g.r The rth split point of the fuel consumption function of the combustion engine g, p g.r-1 The r-1th split point of the fuel consumption function of the combustion engine g;

[0032] The remaining fuel quantity of the gas turbine is modeled as:

[0033]

[0034] Where G is the set of gas turbines; T is the set of scheduling time scales; represents the remaining fuel quantity of the gas turbine g at time t and time t-1, U f.n＝g.t It represents the fuel consumption of fuel vehicle f at node n, namely, combustion engine g, at time t.

[0035] Furthermore, step 3 of constructing a microgrid power restoration optimization model taking into account the fuel remaining limit specifically includes:

[0036] Taking the maximum weighted load recovery amount within the fault duration as the power supply restoration goal, the objective function is established:

[0037]

[0038] Among them, w l represents the weight of load l; P l.t represents the active power recovery of load l at time t, T represents the set of scheduling time scales, and above represents the load set;

[0039] The gas turbine output model taking into account the fuel margin limitation is modeled as:

[0040]

[0041] in, represents the active power output of gas turbine g at time t; represents the reactive power output of gas turbine g at time t; and They represent the lower and upper limits of the active power output of the gas turbine g respectively; and They represent the lower and upper limits of the reactive output of the gas turbine g respectively; represents the remaining fuel amount of the gas turbine g at time t, represents the upper limit of fuel that can be stored in gas turbine g; G is the set of gas turbines;

[0042] The amount of fuel a fuel truck can carry is limited by its loading capacity:

[0043]

[0044] in, represents the remaining fuel amount of fuel vehicle f at time t, represents the fuel loading capacity of fuel truck f; F is the set of fuel trucks;

[0045] During power restoration, the load demand should meet the following constraints:

[0046]

[0047] Among them, the above is the set of loads; D l represents the active power demand of load l; γ l is a power coefficient, which indicates the relationship between the reactive power recovery and the active power recovery of load l; Q l.t represents the reactive power recovery amount of load l at time t;

[0048] The power flow constraints that need to be met during the microgrid power restoration process are as follows:

[0049]

[0050] Where N is the set of nodes; B is the set of lines; represents the active power flowing through line (h, i) and line (i, j) at time t; represents the reactive power flowing through line (h, i) and line (i, j) at time t; represents the active power output of node i at time t; represents the reactive power output of node i at time t; P i.t Qi.t represents the amount of active power restored by the load at node i at time t; Used to indicate whether the line (h, i) is in a fault state. Indicates that the line is in fault state, otherwise Used to indicate whether the line (i, j) is in a fault state. Indicates that the line is in fault state, otherwise Indicates the minimum active power allowed to flow on line (h, i); Indicates the maximum active power allowed to flow on line (h, i); Indicates the minimum reactive power allowed to flow on line (h, i); Indicates the maximum reactive power allowed to flow on line (h, i).

[0051] Furthermore, the model is linearized and solved as described in step 4 to obtain an optimized microgrid power supply restoration plan, which specifically includes:

[0052] Linearize the fuel consumption model of the gas turbine:

[0053]

[0054] Where φ is a constant; τ g.t.r It is a 0-1 variable, used to indicate whether the output of the gas turbine g at time t is within the interval [p g.r-1 , p g.r ]; R represents the total number of segmentation points;

[0055] Based on the linearized constraints, the models constructed in steps 1, 2, and 3 are solved to obtain the optimal microgrid post-disaster power supply restoration result taking into account fuel supply elasticity.

[0056] In another aspect, a microgrid post-disaster power restoration system taking fuel supply resilience into account is provided, the system comprising:

[0057] The first module is used to build a fuel vehicle motion path model;

[0058] The second module is used to build fuel consumption models for fuel vehicles and gas turbines;

[0059] The third module is used to build a microgrid power restoration optimization model taking into account fuel reserve limitations;

[0060] The fourth module is used to linearize and solve the model to obtain the optimized microgrid power supply restoration plan.

[0061] On the other hand, a computer device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the microgrid post-disaster power supply restoration method taking into account fuel supply elasticity is implemented.

[0062] On the other hand, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the microgrid post-disaster power supply restoration method taking into account fuel supply elasticity is implemented.

[0063] Compared with the prior art, the present invention has the following significant advantages:

[0064] (1) The technical solution of the present invention takes into account the fuel supply elasticity during the power supply restoration process. Compared with the traditional microgrid power supply restoration method that does not consider the fuel supply elasticity, the proposed method can effectively ensure the continuous power supply of the power supply in the microgrid and ensure the reliable operation of important loads in the microgrid.

[0065] (2) When establishing the gas turbine output model, the present invention first establishes a mapping relationship between the fuel consumption and power output of the gas turbine per unit time, and then establishes a residual fuel model of the gas turbine. In addition, when establishing the microgrid power supply recovery optimization model, the fuel surplus limit of the gas turbine is taken into account, thereby achieving continuous power supply of the gas turbine during the power supply recovery process.

[0066] The present invention is further described in detail below with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 This is a flow chart of the microgrid post-disaster power restoration method taking fuel supply elasticity into account according to the present invention.

[0068] Figure 2 Schematic diagram of an IEEE 14-node test system in an embodiment of the present invention.

[0069] Figure 3 Schematic diagram of load recovery under different recovery strategies in an embodiment of the present invention. DETAILED DESCRIPTION

[0070] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0071] It should be noted that if the embodiments of the present invention involve directional indications (such as up, down, left, right, front, back, etc.), the directional indications are only used to explain the relative position relationship, movement status, etc. between the various components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indications will also change accordingly.

[0072] In addition, if there are descriptions involving "first", "second", etc. in the embodiments of the present invention, the descriptions of "first", "second", etc. are only for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features specified as "first" and "second" may explicitly or implicitly include at least one of such features. In addition, the technical solutions between the various embodiments can be combined with each other, but this must be based on the fact that ordinary technicians in this field can implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0073] In one embodiment, combined Figure 1 , provides a microgrid post-disaster power supply restoration method taking into account fuel supply elasticity, the method comprising the following steps:

[0074] Step 1: Construct a fuel vehicle motion path model;

[0075] Step 2: Construct fuel consumption models for fuel vehicles and gas turbines;

[0076] Step 3: Construct a microgrid power restoration optimization model taking into account the fuel reserve limit;

[0077] Step 4: Linearize and solve the model to obtain the optimized microgrid power restoration plan.

[0078] Furthermore, in one embodiment, the step 1 of constructing the fuel vehicle motion path model specifically includes:

[0079] A fuel vehicle can be in motion or parked, and can only be in one state at any given moment:

[0080]

[0081] The conversion relationship between the stationary state and the moving state of the fuel vehicle is expressed as:

[0082]

[0083] The running time required by the fuel vehicle at each moment is expressed as:

[0084]

[0085] The remaining movement time of the fuel vehicle during movement is expressed as:

[0086]

[0087] The motion state of the fuel vehicle is limited by the remaining motion time, which is expressed as:

[0088]

[0089] During each movement, the movement target of the fuel vehicle must remain unchanged, which can be expressed as:

[0090]

[0091] Among them, N M is the set of nodes in the transportation network; F is the set of fuel vehicles; T is the set of scheduling time scales; x f.n.t 、x f . n.t+1 、x f.n.t-1 are all 0-1 variables, indicating whether the fuel vehicle f stops at node n at time t, time t+1, and time t-1 respectively; v f.n.t 、v f.n.t+1 、vf.n.t-1 are all 0-1 variables, indicating whether the fuel vehicle f is heading to node n at time t, time t+1, and time t-1 respectively; v f.n′.1 Indicates whether the fuel vehicle f is heading to node n' at the first moment, v f.n′.t Indicates whether the fuel truck f is heading to node n' at time t; C f.t Indicates the number of time steps that the fuel vehicle f needs to consume at time t, C f.1 Indicates the number of time steps that the fuel vehicle f needs to consume at the first moment; R f.t represents the remaining movement time of the fuel vehicle f at time t, R f.1 represents the remaining motion time of the fuel vehicle f at the first moment; φ is a large constant; ε is a small positive number; α f.t is a 0-1 variable, indicating whether the fuel vehicle f is in motion between the time t-1 and the time t; T f.nn’ It represents the number of time steps required for fuel truck f to move from node n to node n'.

[0092] Furthermore, in one embodiment, constructing a fuel consumption model of a fuel vehicle in step 2 specifically includes:

[0093] When the fuel truck is refueling at the warehouse, and only considers its external fuel supply at other nodes, the fuel consumption model of the fuel truck is modeled as:

[0094]

[0095] in, represents the remaining fuel of fuel vehicle f at time t and time t-1 respectively; F is the set of fuel vehicles; T is the set of scheduling time scales; N M is the set of nodes in the transportation network; V f.t represents the fuel replenishment amount of fuel vehicle f at time t; U f.n.t represents the fuel consumption of fuel vehicle f at node n at time t; represents the fuel loading capacity of fuel vehicle f; x f.n.t 、x f.n-k.t Both are 0-1 variables, indicating whether the fuel vehicle f stops at node n or node nk at time t.

[0096] Furthermore, in one embodiment, constructing a fuel consumption model of the gas turbine in step 2 specifically includes:

[0097] Gas turbines consume fuel to generate electricity. The relationship between fuel consumption and power output per unit time is expressed as:

[0098] B g.t =a g.r Pg.t +b g.r , if P g.t ∈[p g.r-1 , p g.r ]

[0099] Among them, B g.t represents the fuel consumption of engine g at time t; P g.t represents the active power output of the engine g at time t; a g.r is the coefficient of the rth piecewise linear term of the fuel consumption function of the engine g; b g.r is the rth piecewise constant coefficient of the fuel consumption function of the engine g; p g.r The rth split point of the fuel consumption function of the combustion engine g, p g.r-1 The r-1th split point of the fuel consumption function of the combustion engine g;

[0100] The remaining fuel quantity of the gas turbine is modeled as:

[0101]

[0102] Where G is the set of gas turbines; T is the set of scheduling time scales; represents the remaining fuel quantity of the gas turbine g at time t and time t-1, U f.n＝g.t It represents the fuel consumption of fuel vehicle f at node n, namely, combustion engine g, at time t.

[0103] Furthermore, in one embodiment, step 3 of constructing a microgrid power restoration optimization model taking into account fuel remaining limit specifically includes:

[0104] Taking the maximum weighted load recovery amount within the fault duration as the power supply restoration goal, the objective function is established:

[0105]

[0106] Among them, w l represents the weight of load l; P l.t represents the active power recovery of load l at time t, T represents the set of scheduling time scales, and above represents the load set;

[0107] The gas turbine output model taking into account the fuel margin limitation is modeled as:

[0108]

[0109] in, represents the active power output of gas turbine g at time t; represents the reactive power output of gas turbine g at time t; and They represent the lower and upper limits of the active power output of the gas turbine g respectively; and They represent the lower and upper limits of the reactive output of the gas turbine g respectively; represents the remaining fuel amount of the gas turbine g at time t, represents the upper limit of fuel that can be stored in gas turbine g; G is the set of gas turbines;

[0110] The amount of fuel a fuel truck can carry is limited by its loading capacity:

[0111]

[0112] in, represents the remaining fuel amount of fuel vehicle f at time t, represents the fuel loading capacity of fuel truck f; F is the set of fuel trucks;

[0113] During power restoration, the load demand should meet the following constraints:

[0114]

[0115] Among them, the above is the set of loads; D l represents the active power demand of load l; γ l is a power coefficient, which indicates the relationship between the reactive power recovery and the active power recovery of load l; Q l.t represents the reactive recovery amount of the load at time t;

[0116] The power flow constraints that need to be met during the microgrid power restoration process are as follows:

[0117]

[0118] Where N is the set of nodes; B is the set of lines; represents the active power flowing through line (h, i) and line (ij) at time t; represents the reactive power flowing through line (h, i) and line (i, j) at time t; represents the active power output of node i at time t; represents the reactive power output of node i at time t; P i.t Qi.t represents the amount of active power restored by the load at node i at time t; Used to indicate whether the line (h, i) is in a fault state. Indicates that the line is in fault state, otherwise Used to indicate whether the line (i, j) is in a fault state. Indicates that the line is in fault state, otherwise Indicates the minimum active power allowed to flow on line (h, i); Indicates the maximum active power allowed to flow on line (h, i); Indicates the minimum reactive power allowed to flow on line (h, i); Indicates the maximum reactive power allowed to flow on line (h, i).

[0119] Furthermore, in one embodiment, the model is linearized and solved in step 4 to obtain an optimized microgrid power restoration solution, which specifically includes:

[0120] Linearize the fuel consumption model of the gas turbine:

[0121]

[0122] Where φ is a constant; τ g.t.r It is a 0-1 variable, used to indicate whether the output of the gas turbine g at time t is within the interval [p g.r-1 , p g.r ]; R represents the total number of segmentation points;

[0123] Based on the linearized constraints, the models constructed in steps 1, 2, and 3 are solved to obtain the optimal microgrid post-disaster power supply restoration result taking into account fuel supply elasticity.

[0124] In one embodiment, a microgrid post-disaster power restoration system taking fuel supply resilience into account is provided, the system comprising:

[0125] The first module is used to build a fuel vehicle motion path model;

[0126] The second module is used to build fuel consumption models for fuel vehicles and gas turbines;

[0127] The third module is used to build a microgrid power restoration optimization model taking into account fuel reserve limitations;

[0128] The fourth module is used to linearize and solve the model to obtain the optimized microgrid power supply restoration plan.

[0129] Regarding the specific definition of the microgrid post-disaster power supply restoration system taking into account fuel supply elasticity, please refer to the definition of the microgrid post-disaster power supply restoration method taking into account fuel supply elasticity above, which will not be repeated here. The various modules in the above-mentioned microgrid post-disaster power supply restoration system taking into account fuel supply elasticity can be implemented in whole or in part through software, hardware and their combination. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0130] In one embodiment, a computer device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the following is achieved:

[0131] Step 1: Construct a fuel vehicle motion path model;

[0132] Step 2: Construct fuel consumption models for fuel vehicles and gas turbines;

[0133] Step 3: Construct a microgrid power restoration optimization model taking into account the fuel reserve limit;

[0134] Step 4: Linearize and solve the model to obtain the optimized microgrid power restoration plan.

[0135] For the specific limitations of each step, please refer to the above limitations on the microgrid post-disaster power supply restoration method taking into account fuel supply elasticity, which will not be repeated here.

[0136] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the computer program implements:

[0137] Step 1: Construct a fuel vehicle motion path model;

[0138] Step 2: Construct fuel consumption models for fuel vehicles and gas turbines;

[0139] Step 3: Construct a microgrid power restoration optimization model taking into account the fuel reserve limit;

[0140] Step 4: Linearize and solve the model to obtain the optimized microgrid power restoration plan.

[0141] For the specific limitations of each step, please refer to the above limitations on the microgrid post-disaster power supply restoration method taking into account fuel supply elasticity, which will not be repeated here.

[0142] As a specific example, in one of the embodiments, the present invention is further verified and explained in detail.

[0143] The improved IEEE14 node test system is used as an example to verify the effectiveness of the microgrid post-disaster power supply restoration method proposed in this invention. The grid topology is as follows: Figure 2 shown.

[0144] Suppose the microgrid loses main grid power due to an extreme natural disaster. The microgrid is equipped with two gas turbines, G1 and G2, with fuel tank capacities of 600L and 400L, respectively. The hourly fuel consumption of gas turbine G1 at 1 / 4 rated power, 1 / 2 rated power, 3 / 4 rated power, and full load is 60L, 180L, 360L, and 600L; the hourly fuel consumption of gas turbine G2 at 1 / 4 rated power, 1 / 2 rated power, 3 / 4 rated power, and full load is 25L, 100L, 225L, and 400L. Other gas turbine parameters are shown in Table 1. In addition, the system is equipped with a fuel truck.

[0145] Table 1 Gas turbine parameters

[0146]

[0147] The power restoration decision-making process requires defining the variables and constraints for each time step. Therefore, the number of time steps chosen can affect the efficiency of power restoration decisions. Too few time steps can lead to local optimal solutions, while too large a time step can slow down the calculation. In this example, assume that the interval between two consecutive time steps in the sequential restoration process is set to 30 minutes, resulting in a total restoration time of eight time steps.

[0148] Through optimization, the fuel truck's trajectory is obtained, as shown in Table 2. The fuel truck departs from the warehouse (node ​​1), arrives at node 6 at time step 3 to refuel gas turbine G1, returns to the warehouse at time step 5 to refuel, and arrives at node 3 at time step 7 to refuel gas turbine G2.

[0149] Table 2 Movement trajectory of fuel vehicles

[0150]

[0151]

[0152] The proposed microgrid post-disaster power restoration method takes fuel supply elasticity into account. To illustrate the advantages of the proposed method, this example compares a microgrid power restoration strategy that takes fuel supply elasticity into account with a traditional power restoration strategy that does not.

[0153] Figure 3The expected and actual load restoration values ​​for the two power restoration strategies are presented. The expected load restoration value is the restoration value obtained through model optimization, while the actual load restoration value is the load restoration value after considering the remaining fuel constraints of the gas turbine. Because the proposed method considers the remaining fuel constraints of the gas turbine, the actual load restoration value in the proposed method is equivalent to the expected load restoration value.

[0154] from Figure 3 It can be seen that although the power restoration model that does not consider fuel supply elasticity can obtain a better objective function, there is a significant deviation between the actual load recovery and the expected load recovery. When the remaining fuel of the gas turbine is exhausted, the gas turbine will stop operating, which will cause a large number of secondary power outages. Therefore, the proposed power restoration strategy that considers fuel supply elasticity can effectively ensure the continuous and reliable operation of the gas turbine, thereby improving the reliability of the post-disaster microgrid power restoration plan.

[0155] The above embodiments illustrate and describe the basic principles and main features of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention, and such changes and modifications fall within the scope of the invention as claimed.

Claims

1. A method for restoring power after a disaster in a microgrid taking fuel supply elasticity into account, characterized in that: The method comprises the following steps: Step 1: Construct a fuel vehicle motion path model; Step 2: Construct fuel consumption models for fuel vehicles and gas turbines; Step 3: Construct a microgrid power restoration optimization model that takes into account the fuel reserve limit; specifically, it includes: Taking the maximum weighted load recovery amount within the fault duration as the power supply restoration goal, the objective function is established: Among them, w l represents the weight of load l; P l.t represents the active power recovery of load l at time t, T represents the set of scheduling time scales, and L represents the load set; The gas turbine output model taking into account the fuel margin limitation is modeled as: in, represents the active power output of gas turbine g at time t; represents the reactive power output of gas turbine g at time t; and They represent the lower and upper limits of the active power output of the gas turbine g respectively; and They represent the lower and upper limits of the reactive output of the gas turbine g respectively; represents the remaining fuel amount of the gas turbine g at time t, represents the upper limit of fuel that can be stored in gas turbine g; G is the set of gas turbines; The amount of fuel a fuel truck can carry is limited by its loading capacity: in, represents the remaining fuel amount of fuel vehicle f at time t, represents the fuel loading capacity of fuel truck f; F is the set of fuel trucks; During power restoration, the load demand should meet the following constraints: Where L is the set of loads; D l represents the active power demand of load l; γ l is a power coefficient, which indicates the relationship between the reactive power recovery and the active power recovery of load l; Q l.t represents the reactive power recovery amount of load l at time t; The power flow constraints that need to be met during the microgrid power restoration process are as follows: Where N is the set of nodes; B is the set of lines; represents the active power flowing through line (h, i) and line (i, j) at time t; represents the reactive power flowing through line (h,i) and line (i,j) at time t; represents the active power output of node i at time t; P i.t represents the active power recovery of the load on node i at time t; Q i.T represents the active power recovery amount of the load on node i at time t; represents the reactive power output of node i at time t; Used to indicate whether the line (h,i) is in a fault state. Indicates that the line is in fault state, otherwise Used to indicate whether the line (i, j) is in a fault state. Indicates that the line is in fault state, otherwise Indicates the minimum active power allowed to flow on line (h,i); Indicates the maximum active power allowed to flow on line (h,i); Indicates the minimum reactive power allowed to flow on line (h,i); Indicates the maximum reactive power allowed to flow on line (h,i); Step 4: Linearize and solve the model to obtain the optimized microgrid power restoration plan.

2. The microgrid post-disaster power supply restoration method taking fuel supply elasticity into account according to claim 1, characterized in that: Step 1 of constructing the fuel vehicle motion path model specifically includes: A fuel vehicle can be in motion or parked, and can only be in one state at any given moment: The conversion relationship between the stationary state and the moving state of the fuel vehicle is expressed as: The running time required by the fuel vehicle at each moment is expressed as: The remaining movement time of the fuel vehicle during movement is expressed as: The motion state of the fuel vehicle is limited by the remaining motion time, which is expressed as: During each movement, the movement target of the fuel vehicle must remain unchanged, which can be expressed as: Among them, N M is the set of nodes in the transportation network; F is the set of fuel vehicles; T is the set of scheduling time scales; x f.n.t 、x f.n.t+1 、x f.n.t-1 are all 0-1 variables, indicating whether the fuel vehicle f stops at node n at time t, time t+1, and time t-1 respectively; v f.n.t 、v f.n.t+1 、v f.n.t-1 are all 0-1 variables, indicating whether the fuel vehicle f is heading to node n at time t, time t+1, and time t-1 respectively; v f.n'.1 Indicates whether the fuel vehicle f is heading to node n' at the first moment, v f.n'.t Indicates whether the fuel truck f is heading to node n' at time t; C f.t Indicates the number of time steps that the fuel vehicle f needs to consume at time t, C f.1 Indicates the number of time steps that the fuel vehicle f needs to consume at the first moment; R f.t represents the remaining movement time of the fuel vehicle f at time t, R f.1 represents the remaining motion time of the fuel vehicle f at the first moment; φ is a constant; ε is a positive number; α f.t is a 0-1 variable, indicating whether the fuel vehicle f is in motion between the time t-1 and the time t; T f.nn’ It represents the number of time steps required for fuel truck f to move from node n to node n'.

3. The microgrid post-disaster power supply restoration method taking fuel supply elasticity into account according to claim 1, characterized in that: In step 2, the fuel consumption model of the fuel vehicle is constructed, which specifically includes: When the fuel truck is refueling at the warehouse, and only considers its external fuel supply at other nodes, the fuel consumption model of the fuel truck is modeled as: in, represents the remaining fuel of fuel vehicle f at time t and time t-1 respectively; F is the set of fuel vehicles; T is the set of scheduling time scales; N M is the set of nodes in the transportation network; V f.t represents the fuel replenishment amount of fuel vehicle f at time t; U f.n.t represents the fuel consumption of fuel vehicle f at node n at time t; represents the fuel loading capacity of fuel vehicle f; x f.n.t 、x f.n-k.t Both are 0-1 variables, indicating whether the fuel vehicle f stops at node n or node nk at time t.

4. The microgrid post-disaster power supply restoration method taking fuel supply elasticity into account according to claim 1, characterized in that: In step 2, a fuel consumption model of the gas turbine is constructed, which specifically includes: Gas turbines consume fuel to generate electricity. The relationship between fuel consumption and power output per unit time is expressed as: B g.t =a g.r P g.t +b g.r ,ifP g.t ∈[p g.r-1 ,p g.r ] Among them, B g.t represents the fuel consumption of engine g at time t; P g.t represents the active power output of the engine g at time t; a g.r is the coefficient of the rth piecewise linear term of the fuel consumption function of the engine g; b g.r is the rth piecewise constant coefficient of the fuel consumption function of the engine g; p g.r The rth split point of the fuel consumption function of the combustion engine g, p g.r-1 The r-1th split point of the fuel consumption function of the combustion engine g; The remaining fuel quantity of the gas turbine is modeled as: Where G is the set of gas turbines; T is the set of scheduling time scales; represents the remaining fuel quantity of the gas turbine g at time t and time t-1, U f.n＝g.t It represents the fuel consumption of fuel vehicle f at node n, namely, combustion engine g, at time t.

5. The microgrid post-disaster power supply restoration method taking fuel supply elasticity into account according to claim 4, characterized in that: In step 4, the model is linearized and solved to obtain the optimized microgrid power restoration plan, which specifically includes: Linearize the fuel consumption model of the gas turbine: Where φ is a constant; τ g.t.r It is a 0-1 variable, used to indicate whether the output of the gas turbine g at time t is within the interval [p g.r-1 ,p g.r ]; R represents the total number of segmentation points; Based on the linearized constraints, the models constructed in steps 1, 2, and 3 are solved to obtain the optimal microgrid post-disaster power supply restoration result taking into account fuel supply elasticity.

6. A microgrid post-disaster power supply restoration system taking fuel supply elasticity into account based on the method according to any one of claims 1 to 5, characterized in that: The system comprises: The first module is used to build a fuel vehicle motion path model; The second module is used to build fuel consumption models for fuel vehicles and gas turbines; The third module is used to build a microgrid power restoration optimization model taking into account fuel reserve limitations; The fourth module is used to linearize and solve the model to obtain the optimized microgrid power supply restoration plan.

7. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the method according to any one of claims 1 to 5 is implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

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