Micro-grid post-disaster power supply recovery method and system considering fuel supply flexibility

Abstract

The invention discloses a micro-grid post-disaster power supply recovery method and a micro-grid post-disaster power supply recovery system considering fuel supply flexibility. The method comprises the following steps: firstly, constructing a motion path model of a fuel vehicle in a traffic network, then respectively constructing fuel consumption models of the fuel vehicle and a gas turbine, then constructing a micro-grid power supply recovery optimization model considering fuel residual limit, and finally, carrying out linearization processing and solving on the constructed model to obtain a micro-grid power supply recovery optimization result. According to the technical scheme, the fuel supply flexibility is considered in the power supply recovery process of the micro-grid, continuous and reliable operation of the gas turbine is guaranteed, and the reliability of the post-disaster micro-grid power supply recovery scheme is effectively improved.

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 into account the elasticity of fuel supply. Background Art

[0002] In recent years, natural disasters have occurred frequently, posing unprecedented challenges to the safe and reliable operation of power systems. Natural disasters often exceed the normal design standards of power systems, and power infrastructure is prone to mass physical failures, which in turn cause power outages for users. With the large-scale access of distributed energy to the distribution system, it provides a basis for achieving post-disaster resilient operation of the power grid. In areas where no failures occur, using distributed power sources to build microgrids and restore power supply to important loads can effectively reduce the scale of power outages and shorten the duration of power outages.

[0003] In order to achieve reliable power supply for important loads after power outages, some scholars have begun to pay attention to power supply restoration methods based on microgrids, and have carried out a lot of research work from the aspects of microgrid structure optimization and microgrid elastic operation control. However, existing research mainly focuses on the optimal scheduling of electric energy in microgrids, and few studies focus on the fuel abundance of distributed power sources such as gas turbines during operation. In fact, when the fuel supply is insufficient, the gas turbine will not be able to operate normally, which will cause power outages to important loads that have been restored in the microgrid. Therefore, it is necessary to consider the elasticity of fuel supply when formulating microgrid power supply restoration strategies, thereby improving the reliability of microgrid operation 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, and to provide a microgrid post-disaster power supply restoration method taking into account the impact of fuel supply elasticity on the effectiveness of the microgrid power supply restoration strategy. First, a movement path model of the fuel vehicle in the transportation 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 taking into account the fuel remaining limit is constructed, and 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 the fuel supply elasticity, the proposed method can effectively ensure the continuous power supply of the power supply 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 the elasticity of fuel supply, the method comprising the following steps:

[0006] Step 1, constructing a fuel vehicle motion path model;

[0007] Step 2, constructing the fuel consumption model of the fuel vehicle and the gas turbine;

[0008] Step 3, construct a microgrid power supply restoration optimization model taking into account the fuel surplus limit;

[0009] Step 4: Linearize and solve the model to obtain the optimized microgrid power supply 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 stationary state, and can only be in one state at any time:

[0012]

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

[0014]

[0015] The running time required by the fuel truck 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, expressed as:

[0022]

[0023] Among them, N M is the set of nodes in the traffic 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; 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 truck 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 represents the number of time steps that the fuel vehicle f needs to consume at time t, C f.1 represents the number of time steps that the fuel vehicle f needs to consume at the first moment; R f.t represents the remaining running 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 t-1th moment and the tth moment; T fnn’ It represents the number of time steps required for the 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, which specifically includes:

[0025] When the fuel truck is refueled in 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 the fuel truck 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 need to consume fuel to generate electricity. The relationship between fuel consumption per unit time and power output 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 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 coefficient of the rth piecewise constant term of the fuel consumption function of the engine g; p g.r The rth split point of the fuel consumption function of the engine g, p g.r-1 The r-1th split point of the fuel consumption function of the gas 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 constructs a microgrid power supply restoration optimization model taking into account the fuel remaining limit, specifically including:

[0036] Taking the maximum weighted load recovery amount within the fault duration as the power supply restoration target, 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 considering the fuel surplus 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 output of the gas turbine g respectively; and They represent the lower and upper limits of reactive power output of gas turbine g respectively; represents the remaining fuel quantity 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 carries 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 the power supply restoration process, 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 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 supply 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 output of node i at time t; represents the reactive power output of node i at time t; P i.t represents the amount of active power restored by the load on node i at the tth moment; Qi.t represents the amount of active power restored by the load on node i at the tth moment; 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); It indicates the maximum reactive power allowed to flow on line (h, i).

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

[0052] Linearize the fuel consumption model for a gas turbine:

[0053]

[0054] Among them, φ is a constant; τ g.t.r is a 0-1 variable, used to indicate whether the output of the gas turbine g at time t is in 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 post-disaster power supply restoration result of the microgrid taking into account the fuel supply elasticity.

[0056] On the other hand, a microgrid post-disaster power supply restoration system taking into account fuel supply elasticity is provided, the system comprising:

[0057] The first module is used to construct 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 supply restoration optimization model taking into account the fuel surplus limitation;

[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, including 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 method for restoring power supply after a disaster in a microgrid 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 limitation 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 in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 This is a flow chart of a method for restoring power supply after a disaster in a microgrid taking fuel supply elasticity into account according to the present invention.

[0068] Figure 2 It is a schematic diagram of an IEEE14 node test system in an embodiment of the present invention.

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

[0070] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present 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 components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication 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 used for descriptive purposes and cannot be understood as indicating or suggesting their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in the field to 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, in combination Figure 1 , a method for restoring power supply after a disaster in a microgrid taking into account fuel supply elasticity is provided, the method comprising the following steps:

[0074] Step 1, constructing a fuel vehicle motion path model;

[0075] Step 2, constructing the fuel consumption model of the fuel vehicle and the gas turbine;

[0076] Step 3, construct a microgrid power supply restoration optimization model taking into account the fuel surplus limit;

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

[0078] Further, 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 parking state and the moving state of the fuel vehicle is expressed as:

[0082]

[0083] The running time required by the fuel truck 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, expressed as:

[0090]

[0091] Among them, N M is the set of nodes in the traffic 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; 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 truck 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 represents the number of time steps that the fuel vehicle f needs to consume at time t, C f.1 represents the number of time steps that the fuel vehicle f needs to consume at the first moment; R f.t represents the remaining running 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 t-1th moment and the tth moment; T f.nn’ It represents the number of time steps required for the fuel truck f to move from node n to node n'.

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

[0093] When the fuel truck is refueled in 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 the fuel truck 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] Further, in one embodiment, constructing a fuel consumption model of a gas turbine in step 2 specifically includes:

[0097] Gas turbines need to consume fuel to generate electricity. The relationship between fuel consumption per unit time and power output 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 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 coefficient of the rth piecewise constant term of the fuel consumption function of the engine g; p g.r The rth split point of the fuel consumption function of the engine g, p g.r-1 The r-1th split point of the fuel consumption function of the gas 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] Further, in one embodiment, the step 3 of constructing a microgrid power supply restoration optimization model taking into account the fuel remaining limit specifically includes:

[0104] Taking the maximum weighted load recovery amount within the fault duration as the power supply restoration target, 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 considering the fuel surplus 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 output of the gas turbine g respectively; and They represent the lower and upper limits of reactive power output of gas turbine g respectively; represents the remaining fuel quantity 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 carries 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 the power supply restoration process, 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 active power recovery of load l; Q l.t represents the reactive power recovery amount of the load at time t;

[0116] The power flow constraints that need to be met during the microgrid power supply 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 output of node i at time t; represents the reactive power output of node i at time t; P i.t represents the amount of active power restored by the load on node i at the tth moment; Qi.t represents the amount of active power restored by the load on node i at the tth moment; 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); It indicates the maximum reactive power allowed to flow on line (h, i).

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

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

[0121]

[0122] Among them, φ is a constant; τ g.t.r is a 0-1 variable, used to indicate whether the output of the gas turbine g at time t is in 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 post-disaster power supply restoration result of the microgrid taking into account the fuel supply elasticity.

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

[0125] The first module is used to construct 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 supply restoration optimization model taking into account the fuel surplus limit;

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

[0129] For the specific definition of the microgrid post-disaster power supply restoration system taking into account the fuel supply elasticity, please refer to the definition of the microgrid post-disaster power supply restoration method taking into account the fuel supply elasticity in the above text, which will not be repeated here. Each module in the above-mentioned microgrid post-disaster power supply restoration system taking into account the fuel supply elasticity can be implemented in whole or in part through software, hardware and a combination thereof. 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, including 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, constructing a fuel vehicle motion path model;

[0132] Step 2, constructing the fuel consumption model of the fuel vehicle and the gas turbine;

[0133] Step 3, construct a microgrid power supply restoration optimization model taking into account the fuel surplus limit;

[0134] Step 4: Linearize and solve the model to obtain the optimized microgrid power supply 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, constructing a fuel vehicle motion path model;

[0138] Step 2, constructing the fuel consumption model of the fuel vehicle and the gas turbine;

[0139] Step 3, construct a microgrid power supply restoration optimization model taking into account the fuel surplus limit;

[0140] Step 4: Linearize and solve the model to obtain the optimized microgrid power supply 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] Assume that the microgrid loses power from the main grid due to an extreme natural disaster. The microgrid is equipped with two gas turbines, and the fuel tank capacities of G1 and G2 are 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 G1 at 1 / 4 rated power, 1 / 2 rated power, 3 / 4 rated power and full load is 25L, 100L, 225L and 400L. Other parameters of the gas turbine are shown in Table 1. In addition, the system is also equipped with a fuel truck.

[0145] Table 1 Gas turbine parameters

[0146]

[0147] In the power restoration decision-making process, the variables and constraints of each time step need to be defined. Therefore, the selection of the number of time steps will affect the efficiency of the power restoration decision. If the number of time steps is too small, it will lead to the generation of a local optimal solution; if the time step length is too large, the calculation speed will be slowed down. In this example, it is assumed that the interval between two consecutive time steps in the sequential restoration process is set to 30 minutes, and the total restoration time is 8 time steps.

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

[0149] Table 2 Movement trajectory of fuel vehicles

[0150]

[0151]

[0152] The microgrid post-disaster power supply restoration method proposed in the present invention takes into account the fuel supply elasticity. In order to compare and illustrate the advantages of the proposed method, this example compares the microgrid power supply restoration strategy that takes into account the fuel supply elasticity with the traditional power supply restoration strategy that does not take into account the fuel supply elasticity.

[0153] Figure 3The expected load recovery amount and the real load recovery amount under the two power supply restoration ideas are shown. The expected load recovery amount is the recovery amount obtained by model optimization, and the real load recovery amount is the load recovery amount after considering the remaining fuel constraint of the gas turbine. Since the proposed method takes the remaining fuel constraint of the gas turbine into consideration, the real load recovery amount in the proposed method is equal to the expected load recovery amount.

[0154] from Figure 3 It can be seen that although the power supply restoration model without considering the elasticity of fuel supply 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 be shut down, resulting in a large number of secondary power outages. Therefore, the proposed power supply restoration strategy considering the elasticity of fuel supply can effectively ensure the continuous and reliable operation of the gas turbine, thereby improving the reliability of the post-disaster microgrid power supply restoration plan.

[0155] The above embodiments show 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 by the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements all fall within the scope of the present invention to be protected.

Claims

1. A method for restoring power supply after a disaster in a microgrid taking into account the elasticity of fuel supply, characterized in that: The method comprises the following steps: Step 1, constructing a fuel vehicle motion path model; Step 2, constructing the fuel consumption model of the fuel vehicle and the gas turbine; Step 3, construct a microgrid power supply restoration optimization model taking into account the fuel surplus limit; Step 4: Linearize and solve the model to obtain the optimized microgrid power supply restoration plan.

2. The method for restoring power supply after a disaster in a microgrid taking into account fuel supply elasticity 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 parking state and the moving state of the fuel vehicle is expressed as: The running time required by the fuel truck 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, expressed as: Among them, N M is the set of nodes in the traffic 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; 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 truck 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 represents the number of time steps that the fuel vehicle f needs to consume at time t, C f.1 represents the number of time steps that the fuel vehicle f needs to consume at the first moment; R f.t represents the remaining running 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 t-1th moment and the tth moment; T f.nn’ Represents the number of time steps required for fuel truck f to move from node n to node n'.

3. The method for restoring power supply after a disaster in a microgrid taking into account fuel supply elasticity according to claim 1, characterized in that: In step 2, a fuel consumption model of a fuel vehicle is constructed, which specifically includes: When the fuel truck is refueled in 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 the fuel truck 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 method for restoring power supply after a disaster in a microgrid taking into account fuel supply elasticity according to claim 1, characterized in that: In step 2, a fuel consumption model of a gas turbine is constructed, which specifically includes: Gas turbines need to consume fuel to generate electricity. The relationship between fuel consumption per unit time and power output is expressed as: B g.t =a g.r P g.t +b g.r ,if P 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 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 coefficient of the rth piecewise constant term of the fuel consumption function of the engine g; p g.r The rth split point of the fuel consumption function of the engine g, p g.r-1 The r-1th split point of the fuel consumption function of the gas 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 method for restoring power supply after a disaster in a microgrid taking into account fuel supply elasticity according to claim 1, characterized in that: Step 3 constructs a microgrid power supply restoration optimization model taking into account the fuel surplus limit, specifically including: Taking the maximum weighted load recovery amount within the fault duration as the power supply restoration target, 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 considering the fuel surplus limitation is modeled as: in, represents the active 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 output of the gas turbine g respectively; and They represent the lower and upper limits of reactive power output of gas turbine g respectively; represents the remaining fuel quantity 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 carries 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 the power supply restoration process, 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 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 supply 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 amount 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); It indicates the maximum reactive power allowed to flow on line (h,i).

6. The method for restoring power supply after a disaster in a microgrid taking into account the flexibility of fuel supply according to claim 4, characterized in that: In step 4, the model is linearized and solved to obtain an optimized microgrid power supply restoration plan, which specifically includes: Linearize the fuel consumption model of a gas turbine: Among them, φ is a constant; τ g.t.r is a 0-1 variable, used to indicate whether the output of the gas turbine g at time t is in 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 post-disaster power supply restoration result of the microgrid taking into account the fuel supply elasticity.

7. A microgrid post-disaster power supply restoration system taking into account fuel supply elasticity based on the method according to any one of claims 1 to 6, characterized in that: The system comprises: The first module is used to construct 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 supply restoration optimization model taking into account the fuel surplus limitation; The fourth module is used to linearize and solve the model to obtain the optimized microgrid power supply restoration plan.

8. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 6 is implemented.

9. 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 6 is implemented.

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