Mobile energy storage based power outage system coordinated restoration strategy optimization method

Abstract

The application discloses a power-off system cooperative recovery strategy optimization method based on mobile energy storage, and the method comprises the following steps: establishing a mobile energy storage scheduling model; establishing a linear model for cooperative optimization of black-start partition and recovery path; coupling the above models to establish a power-off system cooperative recovery strategy optimization model considering mobile energy storage; and processing nonlinear quantities in the model and converting the model into a mixed integer linear programming model. The method determines the position of the unit started by the mobile energy storage and the time when the system starts recovery, realizes cooperative optimization of the mobile energy storage scheduling, the black-start partition and the recovery path, maximizes the power generation capacity of the power-off system, improves the recovery efficiency of the power-off system, and has certain theoretical value and engineering value.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of power grid, and particularly relates to a power-off system cooperative recovery strategy optimization method based on mobile energy storage. BACKGROUND

[0002] With the continuous expansion of the interconnection scale of modern power grids, the operation state of power systems becomes more complex. Although many advanced operation and control technologies have been applied to power systems to improve their reliability and security, large-scale power outages still pose a serious risk to modern power systems, as shown by the large-scale power outages that have occurred worldwide in recent years.

[0003] In recent years, large-scale new energy has been connected to the grid, bringing more instability factors to the already complex power system, and the safe operation of the power grid is facing new challenges. State Grid has proposed to transform and upgrade to a new power system dominated by new energy, and the power system gradually presents the characteristics of high proportion of new energy and high proportion of power electronics. Extreme risks such as frequent power fluctuations of new energy, extreme weather disasters, and gas-electricity combined operation are more prominent, and the inertia of highly power-electronized systems is decreasing, which reduces the ability of the power grid to resist fault impacts. Under this background, local faults caused by natural or human factors may cause regional power system outages or even large-scale power outages.

[0004] Actual operation experience shows that formulating correct and effective power system outage recovery strategies can ensure the safe and rapid recovery of power supply and reduce the impact on the public and the economy. On the contrary, it may delay the recovery process, cause the outage area to expand, and cause more serious impacts. Therefore, systematically studying power system outage recovery strategies is of great significance to the security protection of power systems.

[0005] Traditional outage recovery strategies are based on hydroelectric generators as black-start power sources to provide power support for other units in the outage area to achieve the recovery of the entire power grid. However, most provincial power grids in China lack hydroelectric generators, making it difficult to further improve the system recovery efficiency. With the development of mobile energy storage technology, as a flexible resource in the power system, if mobile energy storage is dispatched to provide start-up power support for thermal power units that cannot start up, the system outage recovery efficiency will be greatly improved. However, when considering the dispatch of mobile energy storage, the location of thermal power units started by mobile energy storage is uncertain, which makes the starting node of the recovery path uncertain, making it difficult for the existing outage recovery strategies to decouple and optimize the partition and recovery path. SUMMARY

[0006] The present application aims at the problems of the prior art, and provides a power outage system cooperative recovery strategy optimization method based on mobile energy storage, which establishes a mobile energy storage scheduling model, considers the selection of black-start units and the scheduling time of mobile energy storage, finds the coupling relationship of the mobile energy storage scheduling optimization model, the black-start region division and the path recovery cooperative optimization model, and establishes the model as a mixed integer linear programming model.

[0007] The technical solution for achieving the object of the present application is a power outage system cooperative recovery strategy optimization method based on mobile energy storage, which comprises the following steps:

[0008] Step 1: establishing a mobile energy storage scheduling model;

[0009] Step 2: establishing a linear model for the cooperative optimization of black-start region division and recovery path;

[0010] Step 3: coupling the above models to establish a power outage system cooperative recovery strategy optimization model considering mobile energy storage;

[0011] Step 4: processing the nonlinear quantities in the power outage system cooperative recovery strategy optimization model, and converting the model into a mixed integer linear programming model.

[0012] Further, the mobile energy storage scheduling model established in Step 1 specifically comprises:

[0013] Step 1-1: limiting the number of black-start units:

[0014]

[0015] In the formula, m represents the mobile energy storage, j represents the region, g represents the unit, and B represents whether the mobile energy storage m is dispatched to the unit g in the region j; B sum represents the number of MESS in the system;

[0016] Step 1-2: making the sum of the total power provided by the mobile energy storage dispatched to the unit g in the region j greater than or equal to the starting power required by the unit g in the region j:

[0017]

[0018] In the formula, P g represents the starting power of the unit g; represents the rated power of the mobile energy storage m; Ω G represents the set of units; J represents the set of regions;

[0019] Step 1-3, let the dispatch go to the sum of the total capacity provided by the mobile energy storage of the unit g in the partition j be greater than or equal to the capacity required by the unit g in the partition j:

[0020]

[0021] In the formula, E g represents the starting capacity of the unit g; represents the rated capacity of the mobile energy storage m;

[0022] Step 1-4, let the mobile energy storage m be in one of the two states of being dispatched to the black start unit and not being dispatched to stay in place:

[0023]

[0024] Further, the linear model of step 2 for establishing the black start partition and the recovery path cooperative optimization specifically includes:

[0025] Step 2-1, establish a partition model considering node recovery time;

[0026] Step 2-2, establish a generator starting linearization model;

[0027] Step 2-3, couple the models established in steps 2-1 and 2-2 to establish a linearization model.

[0028] Further, the partition model considering node recovery time in step 2-1 specifically includes the following steps:

[0029] Step 2-1-1, let all recovery paths start from the node where the black start unit is located:

[0030]

[0031] In the formula, represents that node a in partition j is the node where the black start unit is located; V B represents the set of nodes where the black start unit is located;

[0032] Step 2-1-2, specify that the recovery of the path has only one direction:

[0033]

[0034] In the formula, represents whether the virtual power-on agent in partition j can flow from node a to node b, if it can flow, then otherwise 0; represents whether the virtual power-on agent in partition j can flow from node b to node a, if it can flow, then otherwise 0;

[0035] Step 2-1-3, stipulate that the connection between two nodes can be restored:

[0036]

[0037] In the formula, L ab represents whether node a is connected to node b, if not, L ab = 0, otherwise L ab ≠ 0;

[0038] Step 2-1-4, stipulate that a node cannot be accessed by multiple recovery paths:

[0039]

[0040] In the formula, V represents the node set;

[0041] Step 2-1-5, stipulate that a node can only be powered on by one agent, i.e. a node can only belong to one partition:

[0042]

[0043] Step 2-1-6, stipulate that only when the starting node corresponding to the arrival node is restored, the arrival node can be restored:

[0044]

[0045] In the formula, |V| represents the total number of nodes, represents whether the virtual power-on agent in partition j can flow from node c to node a, if it can flow, then otherwise 0; represents all nodes reaching node a; represents all nodes starting from node a;

[0046] Step 2-1-7, limit the restoration time of the node where the black start unit is located in each partition:

[0047]

[0048] In the formula, T aa represents the restoration time of the node where the black start unit is located; represents the restoration time of node a, and M represents the maximum value;

[0049] Step 2-1-8, calculate the restoration time of other nodes, where the node that has not been restored is assigned a maximum restoration time:

[0050]

[0051] In the formula, T Mis a maximum value, which limits the time of the non-recovered nodes;

[0052] where the recovered nodes need to calculate the recovery time of the nodes:

[0053]

[0054] where T ca represents the movement time of node c to node a; represents the recovery time of node c.

[0055] Further, the establishment of the generator start-up linearization model in step 2-2 includes the following specific steps:

[0056] Step 2-2-1, according to the start-up characteristics of the unit, the start-up process is modeled as a piecewise linear function P Gg (t) about time t, which has four stages:

[0057]

[0058] where t Ag represents the start-up time of unit g; the time period after t Ag is T Bg , during which the unit g only consumes constant start-up power P Cg , without generating any power; the time period T Bg after T Cg is the ramp-up time of unit g, during which the generator starts to generate power and ramps up at a rate of K g , until it reaches the maximum output power P Mg determined by the installed capacity of the generator g;

[0059] Step 2-2-2, linearize the piecewise model obtained in step 2-2-1:

[0060]

[0061]

[0062]

[0063]

[0064]

[0065]

[0066] where, are four auxiliary binary variables; t j is the recovery time of zone j; M Srepresents a maximum value, s = 1, 2,...; T represents the end time of power recovery.

[0067] Further, the model established in steps 2-1 and 2-2 is coupled to establish a linearized model in step 2-3, and the specific steps include:

[0068] Step 2-3-1, taking the maximization of system generation capacity as the optimization objective function:

[0069]

[0070] In the formula, is the energy generated by the unit g at the time of system recovery, is the energy required by the unit g at the time of startup, E sys is the total amount of unit recovery power generation, Ω Gj is the set of units in the jth partition, and J is the set of partitions;

[0071] Step 2-3-2, the system active power generation is described as follows:

[0072]

[0073] In the formula, t j represents the recovery time in the jth partition; p g (t j ) represents the active power of the unit g in the jth partition;

[0074] Check whether there exists j for each t n , n ∈ N The system power generation capacity constraint is expressed as the following constraint set:

[0075]

[0076]

[0077]

[0078]

[0079]

[0080]

[0081] In the formula, is still four auxiliary binary variables; t n is the recovery time of the unit n to be recovered; and N represents the set of all units to be recovered;

[0082] Step 2-3-3, the restoration time of the generator is greater than or equal to the node start-up time multiplied by the binary variable indicating the partition where the generator is located, and the unit node start-up constraint is expressed as:

[0083]

[0084] where t g represents the restoration time of the unit g; represents the restoration time of the node where the unit g is located; represents whether the virtual power-on agent is from the node a to the node where the unit g is located;

[0085] Step 2-3-4, linearize the objective function, the unit power generation E ggen and the active power consumption E gcrank in the objective function are respectively expressed as:

[0086]

[0087]

[0088] Based on this, the objective function is expressed as:

[0089]

[0090] where, is regarded as a constant, so:

[0091]

[0092] That is, maximizing the system power generation capacity is equivalent to minimizing the product of the unit start-up time and the maximum power;

[0093] Step 2-3-5, add a minimum value to the restoration time of the unit to avoid discontinuities, and the active power constraint set of the unit is expressed as:

[0094]

[0095]

[0096]

[0097]

[0098] where ε is a minimum positive value;

[0099] Step 2-3-6, let and linearize the constraint in step 2-3-2 as:

[0100]

[0101]

[0102]

[0103]

[0104]

[0105] Step 2-3-7, assuming Linearize the constraints in Step 2-3-3 as:

[0106]

[0107]

[0108]

[0109] Further, the above model is coupled to establish a power outage system collaborative recovery strategy optimization model considering mobile energy storage in Step 3, and the specific steps include:

[0110] Step 3-1, add constraints to limit the number of black start units and the scheduling of mobile energy storage, specifically including:

[0111] Step 3-1-1, each unit can only belong to one partition:

[0112]

[0113] In the formula, indicates whether unit g is a black start unit in partition j, if yes, 0 otherwise;

[0114] Step 3-1-2, there is and only one black start unit in each partition:

[0115]

[0116] Step 3-1-3, limit mobile energy storage to be scheduled only to black start units:

[0117]

[0118] In the formula, M 13 is a maximum value;

[0119] Step 3-1-4, limit the black start unit start time, only when the last mobile energy storage reaches the black start unit, the unit can be black started:

[0120]

[0121] In the formula, denotes the start-up time of unit g selected as the black start unit in partition j; t m,g denotes the dispatch time of mobile energy storage m from the initial position to unit g;

[0122] Step 3-2, modify the partial formula in step 1-step 2, specifically including:

[0123] Step 3-2-1, it is stipulated that each partition has and only has one black start unit, then step 2-1-1 is modified as follows:

[0124]

[0125] Step 3-2-2, it is stipulated that each unit can belong to at most one partition:

[0126]

[0127] Step 3-2-3, limit the mobile energy storage to only go to the node where the black start unit is located:

[0128]

[0129] Step 3-2-4, modify step 2-1-7 to:

[0130]

[0131] Further, in step 4, the non-linear quantity in the power system coordinated restoration strategy optimization model is processed, and the model is converted into a mixed integer linear programming model, and the specific steps include:

[0132] Step 4-1, linearize step 3-1-4, and is rewritten as Then step 3-2-4 becomes:

[0133]

[0134]

[0135]

[0136] In the formula, denotes the last mobile energy storage m arriving at unit g in partition j;

[0137] Step 4-2, linearize the non-linear quantity P Cg and T Bg in step 2-3-6, let and

[0138]

[0139]

[0140]

[0141]

[0142] Then the fifth formula in step 2-3-6 is changed to:

[0143]

[0144] Compared with the prior art, the present application has the following advantages:

[0145] 1) The method utilizes the thermal power units in the mobile energy storage start-stop system, and partitions the system for recovery according to the number of units to be started.

[0146] 2) The method optimizes the mobile energy storage scheduling, black start partitioning and recovery path by determining the position of the units to be started by the mobile energy storage and the time for starting the system recovery, thereby shortening the recovery time of each unit, maximizing the power generation capacity of the power outage system, improving the system recovery efficiency, and having certain theoretical and engineering values.

[0147] The present application will be further described in detail below with reference to the accompanying drawings. BRIEF DESCRIPTION OF DRAWINGS

[0148] Figure 1 It is a flow chart of the mobile energy storage-based power outage system coordinated recovery strategy optimization method of the present application.

[0149] Figure 2 It is a layout diagram of the IEEE39 power grid topology and mobile energy storage.

[0150] Figure 3 It is a partitioned result topology diagram.

[0151] Figure 4 It is a system available active power comparison diagram.

[0152] Figure 5 It is a system total power generation comparison diagram. DETAILED DESCRIPTION

[0153] In order to make the purpose, technical scheme and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific examples described herein are only used to explain the present application, and are not used to limit the present application.

[0154] It should be noted that if the description of "first", "second" and the like is involved in the embodiments of the present application, the description of "first", "second" and the like is only for the purpose of description, and cannot be understood as indicating or implying the relative importance of the indicated technical features or implicitly indicating the number of the indicated technical features. Therefore, the features limited by "first", "second" can be explicitly or implicitly included at least one of the features. In addition, the technical solutions of various embodiments can be combined with each other, but it must be based on the realization of ordinary skilled in the art, when the combination of technical solutions appears contradictory or cannot be realized, it should be considered that the combination of technical solutions does not exist, nor in the protection scope required by the present application.

[0155] In one embodiment, in conjunction with Figure 1 A mobile energy storage based power outage system coordinated recovery strategy optimization method is provided. For a power outage system lacking black start power supply, mobile energy storage is used to start the power outage thermal power unit, and mobile energy storage scheduling, black start partitioning and recovery path are optimized simultaneously, and are combined into a mixed integer linear programming model for solving. By deciding the number and position of the units started by the mobile energy storage, the mobile energy storage scheduling, partitioning results and corresponding recovery path are obtained simultaneously, so as to improve the recovery efficiency of the power outage system.

[0156] The method comprises the following steps:

[0157] Step 1, establishing a mobile energy storage scheduling model;

[0158] Step 2, establishing a linear model for coordinated optimization of black start partitioning and recovery path;

[0159] Step 3, coupling the above models to establish a power outage system coordinated recovery strategy optimization model considering mobile energy storage;

[0160] Step 4, processing the nonlinear quantities in the power outage system coordinated recovery strategy optimization model, and converting the model into a mixed integer linear programming model.

[0161] Further, in one of the embodiments, the mobile energy storage scheduling model in step 1 specifically comprises:

[0162] Step 1-1, limiting the number of black start units (because the number of mobile energy storage in the system is limited):

[0163]

[0164] In the formula, B indicates whether the mobile energy storage m is dispatched to the unit g in the partition j; B sum B indicates the number of MESS in the system;

[0165] Step 1-2, let the dispatch go to the mobile energy storage of the unit g in the partition j to provide the sum of the total power greater than or equal to the starting power required by the unit g in the partition j:

[0166]

[0167] In the formula, P g Indicates the starting power of the unit g; Indicates the rated power of the mobile energy storage m; Ω G Indicates the set of units; J is the set of partitions;

[0168] Step 1-3, let the dispatch go to the mobile energy storage of the unit g in the partition j to provide the sum of the total capacity greater than or equal to the capacity required by the unit g in the partition j:

[0169]

[0170] In the formula, E g Indicates the starting capacity of the unit g; Indicates the rated capacity of the mobile energy storage m;

[0171] Step 1-4, let the mobile energy storage m be in one of the two states of being dispatched to the black start unit and not being dispatched to stay in place:

[0172]

[0173] Further, in one of the embodiments, the step 2 establishes a linear model for the coordinated optimization of black start partition and recovery path, specifically comprising:

[0174] Step 2-1, establish a partition model considering node recovery time;

[0175] Step 2-2, establish a generator starting linearization model;

[0176] Step 2-3, couple the models established in steps 2-1 and 2-2 to establish a linearization model.

[0177] Further, in one of the embodiments, the step 2-1 establishes a partition model considering node recovery time, and the specific steps include:

[0178] Step 2-1-1, let all recovery paths start from the node where the black start unit is located:

[0179]

[0180] In the formula, Indicates that the node a in the partition j is the node where the black start unit is located; V B Indicates the set of nodes where the black start unit is located;

[0181] Step 2-1-2, it is stipulated that the recovery of the path has only one direction:

[0182]

[0183] In the formula, represents whether the virtual energized agent in the partition j can flow from node a to node b, if it can flow, then 0 otherwise; represents whether the virtual energized agent in the partition j can flow from node b to node a, if it can flow, then 0 otherwise;

[0184] Step 2-1-3, it is stipulated that the connection between two nodes can be recovered:

[0185]

[0186] In the formula, L ab represents whether node a and node b are connected, if not connected, L ab = 0, otherwise L ab ≠ 0;

[0187] Step 2-1-4, it is stipulated that a node cannot be accessed by multiple recovery paths:

[0188]

[0189] In the formula, V represents a set of nodes;

[0190] Step 2-1-5, it is stipulated that a node can only be energized by one agent, that is, a node can only belong to one partition:

[0191]

[0192] Step 2-1-6, it is necessary to ensure the actuality of the recovery path, and it is stipulated that only when the starting node corresponding to the arrival node is recovered, the arrival node can be recovered:

[0193]

[0194] In the formula, |V| represents the total number of nodes, represents whether the virtual energized agent in the partition j can flow from node c to node a, if it can flow, then 0 otherwise; represents all nodes reaching node a; represents all nodes starting from node a;

[0195] Step 2-1-7, limit the recovery time of the node where the black start unit of each partition is located:

[0196]

[0197] where T aa represents the restoration time of the node where the black-start unit is located; represents the restoration time of node a, and M represents a maximum value;

[0198] Step 2-1-8, calculate the restoration time of other nodes, wherein the nodes not restored are assigned a maximum restoration time:

[0199]

[0200] where T M is a maximum value, limiting the time of the nodes not restored;

[0201] where the restored nodes need to calculate the restoration time of the nodes:

[0202]

[0203] where T ca represents the moving time from node c to node a; represents the restoration time of node c.

[0204] Further, in one of the embodiments, the establishment of the generator start linearization model in step 2-2 includes the following specific steps:

[0205] Step 2-2-1, according to the start-up characteristics of the unit, model its start-up process as a piecewise linear function P Gg (t) about time t, which has four stages:

[0206]

[0207] where t Ag represents the start-up time of the unit g; the time period after t Ag is T Bg , during which the unit g only consumes constant start-up power P Cg without generating any power; the time period T Bg after T Cg is the ramp-up time of the unit g, during which the generator starts to generate power and ramps up at a rate of K g until it reaches the maximum output power P Mg determined by the installed capacity of the generator g;

[0208] Step 2-2-2, linearize the piecewise model obtained in step 2-2-1:

[0209]

[0210]

[0211]

[0212]

[0213]

[0214]

[0215] wherein, are four auxiliary binary variables, wherein g represents a unit to be restored, j represents a partition to which the unit g belongs, each variable is associated with one of the four stages of the generator output curve; t j is the restoration time of the partition j; M S (s = 1, 2,...) represents a maximum value, which is large enough to make the corresponding constraint redundant (limiting part of the integer variable, when multiplied by some 0-1 variable, can lose its constraint ability), but cannot be too large to affect the calculation efficiency.

[0216] Further, in one embodiment, the model established in steps 2-1 and 2-2 is coupled to establish a linear model in step 2-3, and the specific steps include:

[0217] Step 2-3-1, in order to restore the outage system to normal operation state in a short time and reduce the negative impact of outage, the non-black start unit in the system needs to be restored as soon as possible, which can provide power to the lines in the system and restructure the network as soon as possible to facilitate the subsequent full recovery of power supply. The maximum system power generation capacity is taken as the optimization objective function:

[0218]

[0219] wherein, is the energy generated by the unit g during system restoration, is the energy required by the unit g during startup, E sys is the total amount of unit restoration power generation, Ω Gj is the set of units in the j partition, and J is the set of partitions;

[0220] Step 2-3-2, the total power generation in the system needs to be considered, and the energy of the system needs to be ensured to be non-negative. The active power generation of the system is described in the following form:

[0221]

[0222] wherein, t j represents the restoration time of the partition j; p g (t jrepresents the active power sent out by unit g in partition j;

[0223] Check whether there exists j = t n , n∈N The system generation capacity constraint is expressed as the following constraint set:

[0224]

[0225]

[0226]

[0227]

[0228]

[0229]

[0230] In the formula, Still four auxiliary binary variables; t n is the restoration time of unit n to be restored; N represents the set of all units to be restored;

[0231] Step 2-3-3, for a determined partition, if a node does not belong to the partition or belongs to the partition but cannot be restored, the restoration time of the node will be assigned a maximum value, which will affect the start-up time of the unit. In order to ensure that the generator start-up time is not affected by the maximum value, the restoration time of the generator needs to be greater than or equal to the node start-up time multiplied by the binary variable indicating the partition, then the unit node start-up constraint is expressed as:

[0232]

[0233] In the formula, t g represents the restoration time of unit g; represents the restoration time of the node where unit g is located; represents whether the virtual power-on agent is from node a to the node where unit g is located;

[0234] Step 2-3-4, linearize the objective function, the unit generation capacity E ggen and the active power consumption E gcrank in the objective function are respectively expressed as:

[0235]

[0236]

[0237] The active power output and the active power consumption in the generator starting process are expressed in the form of linearization through the above process. Based on this, the objective function is expressed as:

[0238]

[0239] wherein, is regarded as a constant, thus:

[0240]

[0241] That is, maximizing the system power generation capacity is equivalent to minimizing the product of the unit starting time and the maximum power;

[0242] In step 2-3-5, the above linearization process does not handle the jump point problem of the generator output curve, so a small value needs to be added to the recovery time of the unit to avoid the discontinuity point. The active power constraint set of the unit is expressed as:

[0243]

[0244]

[0245]

[0246]

[0247] In the formula, ε is a small positive value;

[0248] In step 2-3-6, since the unit output constraint in step 2-3-2 is and are the multiplication of two variables, which can be linearized by the McCormick linearization technology, let and The constraint in step 2-3-2 is linearized as:

[0249]

[0250]

[0251]

[0252]

[0253]

[0254] In step 2-3-7, it is assumed that The constraint in step 2-3-3 is linearized as:

[0255]

[0256]

[0257]

[0258] Further, in one of the embodiments, the coupling of the above-mentioned model in step 3 establishes a power outage system collaborative recovery strategy optimization model considering mobile energy storage, and the specific steps include:

[0259] Step 3-1, adding constraint limits the number of black start units and the scheduling of mobile energy storage, specifically including:

[0260] Step 3-1-1, each unit can only belong to one partition:

[0261]

[0262] In the formula, Indicates whether the unit g is a black start unit in the partition j, if yes, Otherwise, 0;

[0263] Step 3-1-2, there is and only one black start unit in each partition:

[0264]

[0265] Step 3-1-3, limit mobile energy storage to be scheduled only to black start units:

[0266]

[0267] In the formula, M 13 is a maximum value;

[0268] Step 3-1-4, limit the black start unit start time, only when the last mobile energy storage reaches the black start unit, the unit can be black started:

[0269]

[0270] In the formula, Indicates the start time of the unit g in the partition j selected as the black start unit; t m,g Indicates the scheduling time of the mobile energy storage m from the initial position to the unit g;

[0271] Step 3-2, modify part of the formula in steps 1-2, specifically including:

[0272] Step 3-2-1, specify that each partition has and only one black start unit, then modify step 2-1-1 as follows:

[0273]

[0274] Step 3-2-2, stipulate that each unit can belong to at most one partition:

[0275]

[0276] Step 3-2-3, limit the mobile energy storage to only go to the node where the black start unit is located:

[0277]

[0278] Step 3-2-4, modify step 2-1-7 to:

[0279]

[0280] Further, in one of the embodiments, the non-linear quantities in the power system coordinated restoration strategy optimization model in step 4 are processed to convert the model into a mixed integer linear programming model, and the specific steps include:

[0281] Step 4-1, linearize step 3-1-4, and is rewritten as Then step 3-2-4 becomes:

[0282]

[0283]

[0284]

[0285] In the formula, indicates that the last mobile energy storage m arrives in the unit g in the partition j;

[0286] Step 4-2, linearize the non-linear quantities P Cg and T Bg in step 2-3-6, and let and

[0287]

[0288]

[0289]

[0290]

[0291] Then the fifth formula in step 2-3-6 is changed to:

[0292]

[0293] In one embodiment, a mobile energy storage based power outage system collaborative recovery strategy optimization system is provided, the system comprising:

[0294] A first module for establishing a mobile energy storage scheduling model;

[0295] A second module for establishing a linear model for black start partition and recovery path collaborative optimization;

[0296] A third module for coupling the above models to establish a mobile energy storage considering power outage system collaborative recovery strategy optimization model;

[0297] A fourth module for processing nonlinear quantities in the power outage system collaborative recovery strategy optimization model, and converting the model into a mixed integer linear programming model.

[0298] The specific limitations of the mobile energy storage based power outage system collaborative recovery strategy optimization system can be referred to the limitations of the mobile energy storage based power outage system collaborative recovery strategy optimization method described above, which will not be repeated here. The various modules in the above mobile energy storage based power outage system collaborative recovery strategy optimization system can be realized by software, hardware and their combinations, in whole or in part. The above modules can be embedded in or independent of the processor in the computer device in hardware form, or stored in the memory in the computer device in software form, so as to be called and executed by the processor to perform the operations corresponding to the above modules.

[0299] As a specific example, in one embodiment, the present application is further described in detail.

[0300] In this embodiment, the England IEEE10 machine 39 node system is selected as the simulation scene of the nonlinear partition optimization method considering path recovery. The topology structure diagram of the 39 node system and the layout of the mobile energy storage are shown in Figure 2 The generator parameters are shown in Table 1, and the mobile energy storage reaches each unit in Table 2. In this simulation, it is agreed that the unit number is consistent with the node number, the start-up time of the unit is the same as the recovery time of the connected bus, and the recovery time of each transmission line is uniformly set to 4 minutes.

[0301] Table 1 Generator parameters

[0302]

[0303]

[0304] Table 2 Time (min) of mobile energy storage reaching each unit

[0305]

[0306] The method is solved based on the CPLEX professional mathematical solver. The solution result is shown in Table 3. It can be seen that the thermal power units started by the mobile energy storage are unit 35 and unit 39. Here, the partition to which the unit started by unit 39 belongs is defined as partition 1, and the partition to which the unit started by unit 35 belongs is defined as partition 2. It can be seen from the table that units 30, 31, 37 and 38 belong to partition 1, and units 32, 33, 34 and 36 belong to partition 2. The mobile energy storage scheduling result is shown in Figure 3 . In the figure, the solid line represents the mobile energy storage scheduling route, the dashed line represents the recovery path, the solid line box represents partition 1, and the dashed line box represents partition 2. Mobile energy storages M1, M3 and M7 are dispatched to unit 39 to provide starting power for it, and mobile energy storages M3 and M6 are dispatched to unit 35 to provide starting power for it.

[0307] Table 3 Partition and recovery path optimization result

[0308]

[0309]

[0310] The system available active power compared with the collaborative optimization method without considering the mobile energy storage is shown in Figure 4 . It can be seen from the figure that the active power generation of the method of the present application is better than that of the collaborative optimization method without considering the mobile energy storage in most of the time. The total power generation of the two methods within 350 minutes is shown in Figure 5 . The method proposed in the present application is 508.88 MWh higher than the non-linear method.

[0311] In summary, the method of the present application uses mobile energy storage to supply power to the outage units, can flexibly adjust the position of the black start unit, obtains more reasonable partition result and recovery path of the unit to be started, and thus restores the system power generation capacity as soon as possible and maximizes the system power generation efficiency.

[0312] The basic principles, main features and advantages of the present application are shown and described above. It should be understood by those skilled in the art that the present application is not limited by the above examples, and the above examples and descriptions in the specification are only to illustrate the principles of the present application. Any modification, equivalent replacement, improvement, etc. within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A method for optimizing the collaborative recovery strategy of a power outage system based on mobile energy storage, characterized in that, The method includes the following steps: Step 1: Establish a mobile energy storage scheduling model; Step 2: Establish a linear model for the collaborative optimization of the black boot partition and the recovery path; Step 3: Couple the above models to establish an optimization model for collaborative recovery strategies of power outage systems that considers mobile energy storage; Step 4: Process the nonlinear quantities in the power outage system collaborative recovery strategy optimization model and convert the model into a mixed integer linear programming model; Step 2, which establishes a linear model for the collaborative optimization of the black boot partition and the recovery path, specifically includes: Step 2-1: Establish a partitioning model that considers node recovery time; Step 2-2: Establish a linearized model for generator startup; Step 2-3: Couple the models established in Step 2-1 and Step 2-2 to establish a linearized model; Step 2-1 describes establishing a partitioning model that considers node recovery time. The specific steps include: Step 2-1-1: Ensure all recovery paths begin from the node where the black boot unit is located: In the formula, This indicates that node a within partition j is the node where the black-start unit is located; V B This represents the set of nodes where the black-start generator set is located; Step 2-1-2 specifies that the path recovery has only one direction: In the formula, This indicates whether the virtual power-on agent in partition j can flow from node a to node b. If it can flow, then... Otherwise, it is 0; This indicates whether the virtual power-on agent in partition j can flow from node b to node a. If it can flow, then... Otherwise, it is 0; Step 2-1-3 stipulates that connectivity between the two nodes is required for recovery: In the formula, L ab Indicates whether node a and node b are connected. If they are not connected, L ab =0, otherwise L ab ≠0; Step 2-1-4 stipulates that a node cannot be accessed by multiple recovery paths: In the formula, V represents the set of nodes; Step 2-1-5 stipulates that a node can only be powered by one agent, meaning a node can only belong to one partition: Step 2-1-6 stipulates that the arrival node can only be restored after the departure node corresponding to the arrival node has been restored: In the formula, |V| represents the total number of nodes. This indicates whether the virtual power-on agent in partition j can flow from node c to node a. If it can flow, then... Otherwise, it is 0; This represents all nodes that can reach node a; This represents all nodes that originate from node a; Step 2-1-7: Limit the recovery time of the node where each partition's black boot unit is located: In the formula, T aa Indicates the recovery time of the node where the black-start unit is located; M represents the recovery time of node a, and M represents the maximum value. Step 2-1-8: Calculate the recovery time of other nodes, where unrecovered nodes are assigned a very large recovery time. In the formula, T M It is a maximum value that limits the time during which unrecovered nodes remain unrecovered; Among these, the recovery time of the node to be recovered needs to be calculated: In the formula, T ca This represents the travel time from node c to node a; This represents the recovery time of node c.

2. The method for optimizing the collaborative recovery strategy of a power outage system based on mobile energy storage according to claim 1, characterized in that, The establishment of the mobile energy storage scheduling model in step 1 specifically includes: Step 1-1, limit the number of black start units: In the formula, Indicates whether the mobile energy storage m is dispatched to unit g within zone j; B sum Indicates the number of MESSes in the system; Steps 1-2: Ensure that the total power provided by the mobile energy storage dispatched to unit g within partition j is greater than or equal to the starting power required by unit g within partition j. In the formula, P g This indicates the starting power of unit g; Indicates the rated power of mobile energy storage m; Ω G J represents the set of generator units; J represents the set of partitions. Steps 1-3: Ensure that the total capacity provided by mobile energy storage dispatched to unit g within partition j is greater than or equal to the capacity required by unit g within partition j. In the formula, E g This indicates the starting capacity of unit g; This indicates the rated capacity of the mobile energy storage unit m. Steps 1-4: Place the mobile energy storage m in one of two states: either it is dispatched to the black-start unit, or it remains stationary and is not dispatched.

3. The method for optimizing the collaborative recovery strategy of a power outage system based on mobile energy storage according to claim 2, characterized in that, The steps for establishing the generator starting linearization model as described in step 2-2 include: Step 2-2-1: Based on the unit's startup characteristics, model its startup process as a piecewise linear function P related to time t. Gg (t) has four stages: In the formula, t Ag Indicates the start-up time of unit g; at t Ag The following time period is T Bg During this period, unit g only consumes a constant starting power P. Cg without generating any power; T Bg The following time period T Cg This is the ramp-up time of unit g, during which the generator begins to output power, and at K... g The rate of increase climbs until the maximum output power P, determined by the installed capacity of generator g, is reached. Mg ; Step 2-2-2, linearize the piecewise model obtained in step 2-2-1: In the formula, There are four auxiliary binary variables; t j M represents the recovery time for partition j; S Let s represent a maximum value, where s = 1, 2, ...; T represents the end time of power outage restoration.

4. The method for optimizing the collaborative recovery strategy of a power outage system based on mobile energy storage according to claim 3, characterized in that, Step 2-3 describes coupling the models established in steps 2-1 and 2-2 to create a linearized model. The specific steps include: Step 2-3-1, with maximizing the system's power generation capacity as the optimization objective function: In the formula, The energy generated by unit g during system recovery. E is the energy required by unit g during startup. sys To restore the generating units to their total power output, Ω Gj Let J be the set of units within partition j, and J be the set of partitions. Step 2-3-2, the system's active power generation is described in the following form: In the formula, t j Indicates the recovery time in partition j; p g (t j ) indicates the active power generated by unit g in partition j; Verification for each t j =t n Does n∈N exist? The system power generation constraint is represented by the following set of constraints: In the formula, Still four auxiliary binary variables; t n Let N be the recovery time for unit n to be restored; N represents the set of all units to be restored. Step 2-3-3: The generator recovery time must be greater than or equal to the product of the node start-up time and the binary variable indicating the partition. Therefore, the unit node start-up constraint is expressed as: In the formula, t g Indicates the recovery time in unit g; This indicates the recovery time of the node where unit g is located; Indicates whether the virtual power-on agent travels from node a to the node where unit g is located; Steps 2-3-4: Linearize the objective function, where the unit's power generation E is in the objective function. ggen With active power consumption E gcrank They are represented as follows: Based on this, the objective function is expressed as: in, Treating it as a constant, therefore: That is, maximizing the system's power generation capacity is equivalent to minimizing the product of the unit's start-up time and maximum power. Steps 2-3-5 involve adding a minimum value to the unit's recovery time to avoid discontinuities. The unit's active power constraint set is represented as follows: In the formula, ε is a local positive value; Steps 2-3-6, let and The constraints in step 2-3-2 are linearized as follows: Steps 2-3-7, assuming The constraints in step 2-3-3 are linearized as follows:

5. The method for optimizing the collaborative recovery strategy of a power outage system based on mobile energy storage according to claim 4, characterized in that, Step 3, which involves coupling the above models to establish an optimization model for collaborative recovery strategies of power outage systems that considers mobile energy storage, includes the following specific steps: Step 3-1, add constraints to limit the number of black-start units and the scheduling of mobile energy storage, specifically including: Step 3-1-1: Each unit can only belong to one partition. In the formula, This indicates whether unit g is a black-start unit within partition j. If so, Otherwise, it is 0; Step 3-1-2: Each partition contains exactly one black boot machine group. Step 3-1-3: Restrict mobile energy storage to be dispatched only to black-start units: In the formula, M 13 It is a maximum value; Step 3-1-4: Limit the start-up time of the black-start unit. The unit can only be black-started when the last mobile energy storage unit arrives. In the formula, This indicates the startup time of unit g, where partition j is selected as the black-start unit; t m,g This represents the scheduling time for the mobile energy storage m to travel from its initial position to the generating unit g; Step 3-2, modify some formulas in Steps 1 and 2, specifically including: Step 3-2-1 stipulates that each partition has one and only one black boot machine group, so step 2-1-1 is modified as follows: Step 3-2-2 stipulates that each unit can belong to at most one partition: Step 3-2-3: Restrict mobile energy storage to the node where the black starter unit is located: Step 3-2-4, modify step 2-1-7 as follows:

6. The method for optimizing the collaborative recovery strategy of a power outage system based on mobile energy storage according to claim 5, characterized in that, Step 4 involves processing the nonlinear quantities in the power outage system collaborative recovery strategy optimization model and converting the model into a mixed-integer linear programming model. Specific steps include: Step 4-1, linearize step 3-1-4, and... Rewritten as Then step 3-2-4 becomes: In the formula, The mobile energy storage unit m is the one that finally arrives at unit g within zone j. Step 4-2, regarding the nonlinear quantity P from step 2-3-6 Cg and T Bg Linearization, let and Then the fifth formula in step 2-3-6 is changed to:

7. A power outage system collaborative recovery strategy optimization system based on mobile energy storage, based on the method of any one of claims 1 to 6, characterized in that, The system includes: The first module is used to establish a mobile energy storage scheduling model; The second module is used to establish a linear model for the collaborative optimization of the black boot partition and the recovery path; The third module is used to couple the above models to establish an optimization model for collaborative recovery strategies of power outage systems that takes into account mobile energy storage. The fourth module is used to handle the nonlinear quantities in the optimization model of the collaborative recovery strategy of the power outage system, and to convert the model into a mixed integer linear programming model.

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