Distribution network recovery control system and method based on electric vehicle battery swap station

Abstract

The invention discloses a distribution network recovery control system and method based on an electric vehicle battery swap station, and belongs to the technical field of power systems. The method comprises the following steps: obtaining operation information of all energy storage batteries and battery swap stations in a region; based on the operation information, establishing an optimal scheduling model considering the energy storage battery in the transportation battery swap station; the system operation cost in the region is used as a target function, constraint conditions of the energy storage batteries and the battery swap station are combined, and energy storage battery position information and output power information with the lowest system operation cost are obtained through an optimization scheduling model; and scheduling the energy storage batteries in the battery swap station based on the energy storage battery position information and the output power information. According to the method, the operation constraint of the battery swap station and the characteristics that the energy storage batteries are large in number and small in capacity are combined, cross-region circulation of the energy storage batteries of the battery swap station is achieved by dispatching the battery-replaceable electric vehicles, the operation cost of a power system under the disaster splitting condition is effectively reduced, the rapid recovery capacity after disasters is improved, and the reliability of the power system is improved. And safe operation of the important load is ensured to the greatest extent.

Description

Technical Field

[0001] The invention relates to a distribution network recovery control system and method based on an electric vehicle battery replacement station, and belongs to the technical field of power systems. Background Art

[0002] In recent years, the frequent occurrence of extreme natural disasters such as typhoons and rainstorms beyond expectations has seriously endangered the normal operation of the distribution network, and the existing distribution network construction is difficult to cope with the failure scenarios of large-scale natural disasters. Therefore, how to improve the rapid recovery capability after the disaster and maximize the safe operation of important loads is the key to enhancing the emergency resilience of the distribution network. There are two main methods that can be taken at present. One is to increase infrastructure construction, that is, to add a certain number of uninterruptible power supplies (UPS) for users; the other is to use existing equipment on site and flexibly dispatch various types of responsive distributed power resources in the power grid.

[0003] At present, some scholars have conducted research on the application of distributed power sources such as photovoltaics, wind turbines, electric vehicles, and mobile energy storage in emergency scenarios. In terms of the application of electric vehicles, some scholars have comprehensively considered the characteristics of distributed resources such as electric vehicles, mobile energy storage, and repair teams, giving full play to their complementary advantages, and establishing a power supply restoration strategy for the distribution network before and after the disaster. The current research regards electric vehicles as controllable bidirectional loads, which can flexibly adjust their output when the power grid needs it, and plays a strong supporting role in emergency scenarios. As a business model that has grown rapidly in recent years, the advantage of electric vehicle swap stations is that the energy storage batteries in them are very easy to transport. At present, research on electric vehicle swap stations mainly focuses on site optimization, operation control, and economic dispatch, and their application in emergency power supply scenarios has not yet been deeply explored.

[0004] The energy storage batteries in electric vehicle battery swap stations are easy to transport. When the distribution network is decoupled, the energy storage batteries in different regions can be dispatched by electric vehicles. Compared with mobile power sources such as emergency power generation vehicles and mobile energy storage devices, swappable electric vehicles have the advantages of large number, high flexibility, and low power generation cost in emergency scenarios. Its scheduling problem has certain similarities with the control of mobile energy storage. In the analysis, the road network and the power grid need to be jointly modeled. However, unlike mobile energy storage, the use of electric vehicles to transport energy storage batteries in battery swap stations needs to consider the operating constraints in the battery swap stations. At the same time, energy storage batteries are large in number and small in capacity. Relying solely on the analysis of mobile energy storage equipment in existing studies cannot solve practical problems. Summary of the invention

[0005] The purpose of the present invention is to overcome the shortcomings of the prior art and provide a distribution network recovery control system and method based on an electric vehicle battery swap station. Starting from the application scenario of distribution network disaster disconnection, combined with the operating constraints in the battery swap station and the characteristics of large number and small capacity of energy storage batteries, the cross-regional circulation of energy storage batteries in the battery swap station is realized by dispatching swappable electric vehicles, which effectively reduces the operating cost of the power system in the event of disaster disconnection, improves the rapid recovery capability after the disaster, and maximizes the safe operation of important loads.

[0006] To achieve the above object, the present invention is implemented by adopting the following technical solutions:

[0007] In a first aspect, the present invention provides a distribution network recovery control method based on an electric vehicle battery swap station, comprising:

[0008] Obtain the operating information of all energy storage batteries and battery swap stations in the area;

[0009] Establish an optimal scheduling model that considers the energy storage batteries in the transport swap station based on the operation information;

[0010] Taking the system operation cost in the region as the objective function, combined with the constraints of the energy storage battery and the battery swap station, the energy storage battery location information and output power information with the lowest system operation cost are obtained through the optimization scheduling model;

[0011] The energy storage batteries in the battery swap station are dispatched based on the location information and output power information of the energy storage batteries.

[0012] Furthermore, the operation information includes the load of each area, the remaining power of the energy storage battery, the location of the energy storage battery, the maximum output power of the voltage-type power supply, the load loss cost and the power generation cost of each power supply.

[0013] Furthermore, the objective function is expressed as:

[0014] min J=C lost +C tran +C ge

[0015] Where J is the objective function; C lost is the loss cost of the load in each decoupling operation area, C tran is the cost of transporting energy storage batteries using electric vehicles, C ge is the generation cost of all power sources in the system;

[0016]

[0017] Where ΔT is an optimization time interval; W q is the unit load loss cost of the power supply area q; M p It is the collection of power supply areas; and are the load power and actual output power at time t in region q respectively;

[0018]

[0019]

[0020] Where M i is the set of all batteries in position i, and Respectively indicate whether the mth battery is at position i at time t and t-1; M bat represents the set of all energy storage batteries; β is the set of all transportation modes covering all locations; C i,j Represents the unit transportation cost coefficient of transporting energy storage batteries from point i to point j;

[0021]

[0022] In the formula, Cbat q and Cvol q are the unit power generation costs of energy storage batteries and voltage source power supplies in the power supply area q respectively; and They are respectively the total output power of the energy storage battery and the total output power of the voltage source power supply at time t in area q.

[0023] Furthermore, the constraint condition includes the number of energy storage batteries, which is expressed as:

[0024]

[0025]

[0026]

[0027] Where M p is the collection of power supply areas; M bat Represents the collection of all energy storage batteries; N represents the 0-1 type optimization variable that determines the battery location; bat is the number of batteries in all battery swap stations in the entire system, N sat The maximum number of available battery bays in a battery swap station.

[0028] Furthermore, the constraint condition includes the energy storage battery power constraint, which is expressed as:

[0029]

[0030] In the formula, and are the percentage remaining power state of the mth battery at time t+1 and t respectively; SOC max and SOC min It is the upper and lower limits of allowed charge and discharge; P m , and η are the output power, discharge capacity and discharge efficiency of the mth battery respectively; ΔT is an optimization time interval; M bat Represents the collection of all energy storage batteries.

[0031] Furthermore, the constraint condition includes a power constraint, which is expressed as:

[0032]

[0033]

[0034]

[0035]

[0036] Where M p is the collection of power supply areas; M bat Represents the collection of all energy storage batteries; is the total output power of the energy storage battery; P m is the output power of the mth battery; represents the 0-1 type optimization variable that determines the battery location; ΔT is an optimization time interval; t ij is the transportation time from location i to location j; and Respectively indicate whether the mth battery is at position j and position i at time t and t-1; Indicates the maximum output power of the energy storage battery; represents the total output power of the voltage source power supply at time t in region q, It represents the maximum total output power of the voltage source power supply at time t in region q; Represents the total output power of the energy storage batteries in area q at time t; k is a constant.

[0037] Furthermore, the constraint condition includes a power flow constraint, which is expressed as:

[0038]

[0039]

[0040]

[0041]

[0042] Umin ≤U t,k ≤U max

[0043] -P kv,max ≤P kv ≤P kv,max

[0044] -Q kv,max ≤Q kv ≤Q kv,max

[0045] M E represents the set of all nodes (k, v) in the distribution network; P kv , Q kv 、r kv 、x kv , P kv,max and Q kv,max represents the active power, reactive power, line resistance, line reactance, maximum active power flow and maximum reactive power flow between nodes (k, v); P bk represents the active power between nodes (b, k); represents the total reactive power output of the energy storage power source at node k at time t; represents the total reactive power output of the voltage source power supply at node k at time t; represents the total reactive power output of the local power source of node k at time t; Q bk represents the reactive power between nodes (b, k); α kv Indicates whether a failure has occurred between nodes; and is the injected energy storage battery power, voltage source power injection power and actual load power of node k; U t,k is the voltage value of node k at time t; U t,v is the voltage value of node v at time t; U max and U min are the upper and lower limits of voltage operation; U0 is the rated value of node voltage operation, and H is a constant.

[0046] In a second aspect, the present invention provides a distribution network recovery control system based on an electric vehicle battery swap station, comprising:

[0047] Receiving module: used to obtain the operating information of all energy storage batteries and battery swap stations in the area;

[0048] Modeling module: used to establish an optimization scheduling model that considers the energy storage batteries in the transportation swap station based on operation information;

[0049] Optimization module: It is used to take the system operation cost in the region as the objective function, combine the constraints of the energy storage battery and the battery swap station, and obtain the energy storage battery location information and output power information with the lowest system operation cost through the optimization scheduling model;

[0050] Scheduling module: used to schedule the energy storage batteries in the battery swap station based on the location information and output power information of the energy storage batteries.

[0051] In a third aspect, the present invention provides a distribution network recovery control device based on an electric vehicle battery swap station, including a processor and a storage medium;

[0052] The storage medium is used to store instructions;

[0053] The processor is used to operate according to the instructions to execute the steps of any of the above methods.

[0054] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of any of the above methods when executed by a processor.

[0055] Compared with the prior art, the present invention has the following beneficial effects:

[0056] 1. The method provided by the present invention utilizes replaceable electric vehicle transportation energy storage batteries to improve the operational flexibility of the distribution network in the application scenario of distribution network disaster decoupling. The battery replacement mode of electric vehicles refers to the centralized storage and charging of a large number of batteries through centralized charging stations, and the battery replacement service for electric vehicles is provided at the battery distribution station, or the battery charging, logistics deployment, and battery replacement services are integrated into one. This mode can solve the problem of long charging time.

[0057] Second, after obtaining basic information in the distribution system, an optimization scheduling model considering the transportation of energy storage batteries in the battery swap station is established, and the loss cost of the load in each decoupling operation area, the energy storage transportation cost and the power generation cost are used as the objective function, so as to minimize the system operation cost through optimization scheduling. At the same time, considering the operation status of the network, constraints including the number of energy storage batteries, the power of energy storage batteries, the output power of the power supply and the flow control are established, so that the control variables are within a reasonable range.

[0058] 3. Starting from the application scenario of the distribution network disaster disconnection, the present invention explores the strategy of improving the elasticity of electric vehicle battery swap stations, making full use of the growing resources of electric vehicle battery swap stations, and realizing the cross-regional circulation of battery storage batteries in battery swap stations by dispatching swappable electric vehicles. This technology takes into account the loss cost, power generation cost and transportation cost of the load under the distribution network disaster disconnection, and establishes a mixed integer programming model including the transportation network and the power grid. The proposed method can effectively reduce the operating cost of the power system under the disaster disconnection, improve the rapid recovery capability after the disaster, and maximize the safe operation of important loads. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] The accompanying drawings constituting a part of the present invention are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the accompanying drawings:

[0060] Figure 1 A control strategy architecture for a distribution network recovery control method based on an electric vehicle battery swap station provided in the first embodiment of the present invention;

[0061] Figure 2 The disaster decoupling simulation example structure provided in the first embodiment of the present invention;

[0062] Figure 3 The output power composition of each area of ​​the distribution network under the simulation example provided in the first embodiment of the present invention;

[0063] Figure 4 A diagram showing the position conversion of each battery in a simulation example provided in the first embodiment of the present invention;

[0064] Figure 5 The output power of each battery in the simulation example provided in the first embodiment of the present invention. DETAILED DESCRIPTION

[0065] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that the embodiments and features in the embodiments of the present invention can be combined with each other without conflict.

[0066] The following detailed description is an exemplary description, which is intended to provide further detailed description of the present invention. Unless otherwise specified, all technical terms used in the present invention have the same meaning as those generally understood by those skilled in the art to which the present invention belongs. The terms used in the present invention are only for describing specific embodiments, and are not intended to limit the exemplary embodiments according to the present invention.

[0067] Embodiment 1:

[0068] Please refer to Figure 1, a distribution network recovery control method based on an electric vehicle battery swap station, the method comprising the following steps:

[0069] S101. Obtaining operation information of all energy storage batteries and battery swap stations in the area;

[0070] Electric vehicle battery swap stations have great potential in emergency power supply scenarios. Figure 1 The proposed control strategy architecture is demonstrated. When the distribution network is decomposed into several independently operated areas due to a fault, the degree of energy shortage in different areas may be different according to the information stored in the power big data cloud database. At this time, the grid structure is damaged, and the energy flow cannot be realized through the grid, which in turn causes the loss of part of the power load. The energy storage batteries in the battery swap stations in different regions can be mutually dispatched by electric vehicles between the road networks, and the energy storage batteries in the energy-sufficient areas can be transported to the shortage areas to reduce the load loss. The specific operation information includes: the load in each area, the remaining power of the energy storage battery, the location of the energy storage battery, the maximum output power of the voltage-type power supply, the load loss cost, the power generation cost of each power supply, etc.

[0071] S102, establishing an optimization scheduling model considering the energy storage batteries in the transport battery swap station;

[0072] After obtaining the operating information within the distribution system, an optimization scheduling model is established that takes into account the transportation of energy storage batteries in the battery swap station. The load loss cost, energy storage transportation cost and power generation cost in each decoupled operating area are used as the objective function. The system operating cost is minimized through optimized scheduling.

[0073] Specifically, the operation steps of the optimization scheduling model include:

[0074] Step 1: Comprehensively consider the loss cost, energy storage and transportation cost, and power generation cost to establish the objective function of the optimization model;

[0075] Step 2: Consider the upper and lower operating limits and physical limitations of the equipment and set equality and inequality constraints;

[0076] Step 3: Linearize the nonlinear part of the optimization model to make it easier to solve;

[0077] Step 4: Use optimization tools to solve the optimization problem.

[0078] For ease of description, the above four steps are combined for explanation below.

[0079] 1. Objective Function

[0080] In order to improve the resilience of the distribution network in emergency scenarios, as shown in formula (1), the proposed strategy takes the load loss cost, energy storage transportation cost and power generation cost in each decoupling operation area as the objective function, and minimizes the system operation cost by optimizing the scheduling.

[0081] min J=C lost +C tran +C ge (1)

[0082] Where J is the objective function; C lost is the loss cost of the load in each decoupling operation area, C tran is the cost of transporting energy storage batteries using electric vehicles, C ge It is the power generation cost of all power sources in the system. The optimization algorithm needs to determine whether the cost of transporting batteries under current conditions can balance the load loss cost and power generation cost, and then decide on the transportation strategy of energy storage batteries.

[0083] 1. Load loss cost

[0084] As shown in formula (2), the load loss cost C lost It is composed of the difference between the load and the power output power in each de-energized operating area, representing the power output shortfall in the system at that time.

[0085]

[0086] Where ΔT is an optimization time interval; W q is the unit load loss cost of the power supply area q; M p It is the collection of power supply areas; and They are the load power and actual output power at time t in area q. When the energy is sufficient, the difference between the two is zero. However, when the energy in the area is insufficient, the system will lose part of the load, resulting in load loss costs, which in turn increases the system operation costs.

[0087] (II) Transportation costs

[0088] Since the distribution grid has been decoupled in disaster scenarios, and the energy storage batteries in the battery swap station can be transported by electric vehicles, when there is insufficient power in a certain area, the surplus energy storage batteries in the battery swap station in the adjacent area can be transported to supply power. The transportation transfer always occurs when the battery has sufficient remaining power and the transportation cost is less than the load loss cost and power generation cost.

[0089] In order to facilitate the modeling of the transportation process of energy storage batteries, this section defines two 0-1 variables and It is used to describe the location of the energy storage battery in the battery swap station and is defined as shown in formula (3).

[0090]

[0091] Where M iis the set of all batteries in position i, and Indicates whether the mth battery is at position i at time t and t-1. The latter is determined by the result of the previous operation in the calculation, and is a constant in the optimization process. In case of special scenarios such as traffic congestion on the road and failure to arrive on time due to an accident, all position variables of the battery will be All can be set to 0, indicating that the battery is not located at any battery swap station.

[0092] If the two 0-1 variables in formula (3) change during a certain optimization process, it means that the energy storage battery has been transferred through the electric vehicle, and the total cost C of the transportation process is tran The definition of is shown in formula (4).

[0093]

[0094] Where M bat represents the set of all energy storage batteries; β is the set of all transportation modes covering all locations; C i,j Represents the unit transportation cost coefficient of transporting the energy storage battery from point i to point j. The purpose of constructing the transportation cost is to set the energy storage transportation cost part that has not been transported to 0, while the energy storage battery that has been transported will increase the transportation cost in the objective function. It is worth noting that in practice, the cost of round-trip transportation between two battery swap stations may not be the same. If a certain transportation route is not available at this time, the transportation cost of this transportation route can be adjusted to a value close to infinity, so that the optimization strategy abandons transportation on this route.

[0095] (III) Power generation cost

[0096] In emergency scenarios, the distribution network is divided into independent operating parts. It is assumed that in each part, there are a certain number of emergency power sources (such as energy storage power stations, diesel generators, emergency power supply vehicles, etc.) in voltage source control mode to establish voltage for the system, while most energy storage batteries in the battery swap station are in current source control mode, and their output power is controlled and dispatched. Formula (5) shows the total power generation cost of the system. The scarcity of the two types of power sources can be characterized by adjusting the size of the unit power generation cost coefficient. The more scarce an energy source is, the higher its power generation cost is.

[0097]

[0098] In the formula, Cbat q and Cvol q are the unit power generation costs of energy storage batteries and voltage source power supplies in the power supply area q respectively; and They are respectively the total output power of the energy storage battery and the total output power of the voltage source power supply at time t in area q.

[0099] 2. Constraints

[0100] (I) Constraints on the number of energy storage batteries

[0101] During operation and transportation, since the same battery can only appear at a certain location, it is impossible for it to appear at multiple swap stations at the same time. Therefore, as shown in formula (6), it is necessary to constrain the location of the 0-1 variable battery.

[0102]

[0103] Formulas (7) and (8) are constraints on the total number of batteries, keeping the total number of batteries constant and the maximum available battery compartment space at each battery swap station is limited. bat is the number of batteries in all battery swap stations in the entire system, N sat The maximum number of available battery compartments in the battery swap station. Represents a 0-1 type optimization variable that determines the battery location.

[0104]

[0105]

[0106] (II) Energy storage battery capacity

[0107] The swappable energy storage batteries of electric vehicles are all lithium-ion batteries. Overcharging or over-discharging will cause irreversible damage to the batteries. Therefore, equations (9) and (10) establish the constraints to describe the power of the energy storage batteries.

[0108]

[0109]

[0110] In the formula, and are the percentage remaining power state of the mth battery at time t+1 and t respectively; SOC max and SOC min It is the upper and lower limits of allowed charge and discharge; P m , and η are the output power, discharge capacity and discharge efficiency of the mth battery respectively, and the latter two are constants in the optimized operation.

[0111] (III) Power Constraint

[0112] Formula (11) establishes the total output power of the energy storage battery The relationship between the optimization variables is determined by the 0-1 type optimization variables that determine the battery location The output power P of each energy storage battery m It is composed of the product of continuous optimization variables.

[0113]

[0114] During operation, each power source cannot exceed the rated operating power limit, so equations (12) and (13) specify the upper and lower limits of each power source in the optimization calculation. Since the transportation process of the energy storage battery consumes a certain amount of time and causes a certain amount of power loss, equation (12) constrains the upper limit of the energy storage battery discharge, where t ij is the transportation time from location i to location j. Equation (14) specifies the penetration rate of the energy storage battery, ensuring that a certain amount of spinning reserve power is left in the system, where k is a constant. and Respectively represent ***; and Respectively indicate whether the mth battery is at position j and position i at time t and t-1; represents the total output power of the voltage source power supply at time t in region q, It represents the maximum total output power of the voltage source power supply at time t in region q; represents the total output power of the energy storage batteries in area q at time t. However, when there is no spinning reserve power in the system, the load will be borne entirely by the energy storage batteries, and constraint (14) will be cancelled.

[0115]

[0116]

[0117]

[0118] (IV) Flow Constraints

[0119] This technology uses a linearized power flow model to ensure that the power flows of the distribution network can be solved and the system does not exceed the operating limit. Equations (15) to (16), (20) to (21) constrain the balance of the power flow and are combined with equation (2) to calculate the load loss cost; equations (17) to (19) calculate the voltage of each node so that it operates within the rated range.

[0120]

[0121]

[0122]

[0123]

[0124] U min ≤U t,k ≤U max (19)

[0125] -P kv,max ≤P kv ≤P kv,max (20)

[0126] -Q kv,max ≤Q kv ≤Q kv,max (twenty one)

[0127] Where M E represents the set of all nodes (k, v) in the distribution network, P kv , Q kv 、r kv 、x kv , P kv,max and Q kv,max represents the active power, reactive power, line resistance, line reactance, maximum active power flow and maximum reactive power flow between nodes (k, v); P bk represents the active power between nodes (b, k); represents the total reactive power output of the energy storage power source at node k at time t; represents the total reactive power output of the voltage source power supply at node k at time t; represents the total reactive power output of the local power source of node k at time t; Q bk represents the reactive power between nodes (b, k); α kv Indicates whether a failure has occurred between nodes; and is the injected energy storage battery power, voltage source power injection power and actual load power of node k; U t,k is the voltage value of node k at time t; U t,v is the voltage value of node v at time t; U max and U min are the upper and lower limits of voltage operation; U0 is the rated value of node voltage operation, and H is a constant with a large value.

[0128] 3. Model Linearization Conversion

[0129] In the process of establishing the equality constraint, since equation (11) introduces the product of 0-1 type optimization variables and continuous optimization variables, it brings nonlinearity to the optimization problem. The convergence of the solution of nonlinear mixed integer optimization problems is usually not guaranteed, so it is necessary to perform an equivalent linear transformation. As shown in equation (22), the linearized intermediate variable is introduced:

[0130]

[0131] Linear intermediate variable Z q,m All inequality constraints shown in formula (23) need to be satisfied:

[0132]

[0133] In the formula, Z q,m is a linear intermediate variable; P m is the output power of the mth battery; represents the 0-1 type optimization variable that determines the battery location; M p is the collection of power supply areas; M bat Represents the collection of all energy storage batteries; is the maximum output power of the mth battery.

[0134] This embodiment takes into account the introduction of the product of 0-1 type optimization variables and continuous optimization variables in the constraint conditions, which brings nonlinearity to the optimization problem. Therefore, the nonlinear part of the model is analyzed later, and the optimization problem is converted into a linear mixed integer optimization problem that is easy to solve, and finally the optimal output power and transportation scheduling instructions of each energy storage battery are obtained.

[0135] 4. Solving Optimization Problems

[0136] The optimization problem is solved by optimization, where the objective function is (1) and the constraints are (2) to (21). Finally, the optimization variable energy storage battery location information and the optimal output power information are returned.

[0137] S103, using the communication network to send instructions to the electric vehicle to guide the battery-swappable electric vehicle to travel to an area with power shortage;

[0138] After obtaining the optimized output power and transportation scheduling instructions, the instruction values ​​are further sent to electric vehicles to guide the swappable electric vehicles to travel to areas with power shortages.

[0139] This embodiment builds Figure 2 In the simulation example shown, the distribution network in the example is divided into three independent areas due to a fault. Each area has a battery swap station and a certain number of generator sets. The three areas can be connected through the transportation network, and the parameters related to the transportation cost can be seen in Table 1. The distance between areas 2 and 3 is longer, while the distance between areas 1 and 2 is shorter, and the round-trip transportation time between the areas is also different.

[0140]

[0141]

[0142] Table 1 Transportation cost related parameters

[0143] At the initial moment, there are 7, 6, and 8 nearly fully charged energy storage battery packs in areas 1, 2, and 3 respectively. There are also a certain number of electric vehicles near the battery swap station, which are allowed to respond to dispatch commands in emergencies, replace the energy storage batteries in the battery swap station, and obtain certain profits.

[0144] The load conditions of each area in the example are derived from the actual operating data of a certain area, which is divided into 17 power consumption periods. The initial load of area 1 is large, the overall load of area 2 is small and the power consumption is relatively stable, and the load of area 3 has large periodic fluctuations. Figure 3 The composition of output power in each area is shown, showing the power of battery, voltage source power supply and loss load; Figure 4 The different colors on the horizontal axis show the location of all batteries, and the specific location will move with the electric vehicle according to the actual situation; Figure 5 The output power of each battery in each time period is displayed in three dimensions. The following will analyze each time period:

[0145] (1) Periods 1 to 3: At the initial moment, the remaining power of the energy storage batteries in each battery swap station is relatively large. Since the power generation cost of energy storage batteries is lower, the optimization algorithm will give priority to using energy storage batteries for power generation. However, due to the restrictions of the spinning reserve constraint (13), the system will retain some spinning reserve units. As time goes by, each energy storage battery is discharged in turn until it reaches the lower limit of SOC.

[0146] (2) Period 4-5: At time 4, due to the heavy load in area 3, the energy storage batteries are almost exhausted. At the same time, the overall load in area 1 is small, and the battery packs in area 1 still have sufficient power. Considering that the transportation cost of the battery packs in area 1 is less than the power generation cost of the additional rotating backup power supply in area 3, the optimization algorithm issues an order to transport battery packs No. 15, 16, 17, and 20 to area 3, effectively reducing the operating cost of area 1.

[0147] (3) Periods 6 to 9: As time goes by, the energy storage batteries are exhausted. The battery swap station issues a command to attract energy storage batteries. It is assumed that the energy storage batteries in areas 1 to 5 and 16 to 21 are replaced with new battery packs with a SOC of 95% to simulate the scenario of replacing energy storage batteries. Since the addition of new batteries will effectively reduce operating costs, the optimization algorithm will give priority to the use of energy storage battery power while satisfying the spinning reserve constraint (13). At the same time, considering that the power in area 1 is relatively abundant at this time, and there are no available energy storage batteries in area 2, the optimization strategy will transport the battery packs in area 1 to area 2, reducing the power generation cost in area 2; subsequently, some batteries in area 1 will be transported to area 3 where the remaining battery power is relatively small.

[0148] (4) Time period 10-13: At time 10, the battery packs 1-11 and 14-20 in the three areas were replaced with new battery packs with a SOC of 95%. At the same time, the load in area 2 rose to an important level, and its load loss cost suddenly rose to 300 yuan / (kW·h). In addition, the rotating standby power supply was in short supply, and the power generation cost rose to 100 yuan / (kW·h). At this time, all resources needed to be mobilized to ensure the load in area 2, which was used to simulate the scenario of energy shortage in a certain area and sufficient energy in other areas. The optimization algorithm took into account that the load in area 2 was more important at this time. Therefore, when the energy storage battery in area 2 was exhausted, the battery packs 6, 15, 16, and 17 in the remaining areas were dispatched to area 2. After dispatching the energy storage batteries in the battery swap station, the power shortage in area 2 was alleviated to a certain extent, reducing the operating cost.

[0149] (5) Periods 14-17: At this time, the power shortage in area 2 has been alleviated, and area 1 has no available rotating reserve power supply. At the same time, its load has risen to a more important level, and its load loss cost has risen to 300 yuan / (kW·h). In addition, the rotating reserve power supply is relatively scarce, and the power generation cost has risen to 100 yuan / (kW·h). Battery packs 1, 4, 5, 11-21 are replaced with new batteries with a SOC of 95% to simulate a scenario where there is no available power supply in a certain period of time. Analyzing the simulation results, batteries 16, 17, 18, and 20 are transferred to area 1, and then the battery pack 16 will transfer the last remaining energy to area 2 after supporting area 1. The optimization strategy gives priority to the shortage of area 1, and guarantees the load power consumption of area 1 with the maximum capacity of the entire system, significantly reducing the load loss cost of area 1 and enhancing the system resilience.

[0150] Embodiment 2:

[0151] A distribution network recovery control system based on an electric vehicle battery swap station can implement a distribution network recovery control method based on an electric vehicle battery swap station described in Embodiment 1, including:

[0152] Receiving module: used to obtain the operating information of all energy storage batteries and battery swap stations in the area;

[0153] Modeling module: used to establish an optimization scheduling model that considers the energy storage batteries in the transportation swap station based on operation information;

[0154] Optimization module: It is used to take the system operation cost in the region as the objective function, combine the constraints of the energy storage battery and the battery swap station, and obtain the energy storage battery location information and output power information with the lowest system operation cost through the optimization scheduling model;

[0155] Scheduling module: used to schedule the energy storage batteries in the battery swap station based on the location information and output power information of the energy storage batteries.

[0156] Embodiment three:

[0157] The embodiment of the present invention further provides a device, which can implement the distribution network recovery control method based on the electric vehicle battery replacement station described in the first embodiment, including a processor and a storage medium;

[0158] The storage medium is used to store instructions;

[0159] The processor is used to operate according to the instructions to perform the steps of the following method:

[0160] Obtain the operating information of all energy storage batteries and battery swap stations in the area;

[0161] Establish an optimal scheduling model that considers the energy storage batteries in the transport swap station based on the operation information;

[0162] Taking the system operation cost in the region as the objective function, combined with the constraints of the energy storage battery and the battery swap station, the energy storage battery location information and output power information with the lowest system operation cost are obtained through the optimization scheduling model;

[0163] The energy storage batteries in the battery swap station are dispatched based on the location information and output power information of the energy storage batteries.

[0164] Embodiment 4:

[0165] The embodiment of the present invention further provides a computer-readable storage medium, which can implement the distribution network recovery control method based on the electric vehicle battery replacement station described in the first embodiment, and stores a computer program thereon, which implements the steps of the following method when the program is executed by a processor:

[0166] Obtain the operating information of all energy storage batteries and battery swap stations in the area;

[0167] Establish an optimal scheduling model that considers the energy storage batteries in the transport swap station based on the operation information;

[0168] Taking the system operation cost in the region as the objective function, combined with the constraints of the energy storage battery and the battery swap station, the energy storage battery location information and output power information with the lowest system operation cost are obtained through the optimization scheduling model;

[0169] The energy storage batteries in the battery swap station are dispatched based on the location information and output power information of the energy storage batteries.

[0170] It is known from common technical knowledge that the present invention can be implemented by other embodiments that do not deviate from its spirit or essential features. Therefore, the above disclosed embodiments are only illustrative in all respects and are not exclusive. All changes within the scope of the present invention or within the scope equivalent to the present invention are included in the present invention.

[0171] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0172] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0173] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0174] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0175] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A distribution network recovery control method based on an electric vehicle battery swap station, characterized in that: include: Obtain the operating information of all energy storage batteries and battery swap stations in the area; Establish an optimal scheduling model that considers the energy storage batteries in the transport swap station based on the operation information; Taking the system operation cost in the region as the objective function, combined with the constraints of the energy storage battery and the battery swap station, the energy storage battery location information and output power information with the lowest system operation cost are obtained through the optimization scheduling model; The energy storage batteries in the battery swap station are dispatched based on the location information and output power information of the energy storage batteries.

2. The distribution network recovery control method based on electric vehicle battery swap station according to claim 1 is characterized in that: The operation information includes the load in each area, the remaining power of the energy storage battery, the location of the energy storage battery, the maximum output power of the voltage-type power supply, the load loss cost and the power generation cost of each power supply.

3. The distribution network recovery control method based on electric vehicle battery swap station according to claim 1 is characterized in that: The expression of the objective function is: my J=C lost +C tran +C ge Where J is the objective function; C lost is the loss cost of the load in each decoupling operation area, C tran is the cost of transporting energy storage batteries using electric vehicles, C ge is the generation cost of all power sources in the system; Where ΔT is an optimization time interval; W q is the unit load loss cost of the power supply area q; M p It is the collection of power supply areas; and are the load power and actual output power at time t in region q respectively; Where M i is the set of all batteries in position i, and Respectively indicate whether the mth battery is at position i at time t and t-1; M bat Represents the collection of all energy storage batteries; β is the set of all transportation modes for all locations; C i,j Represents the unit transportation cost coefficient of transporting energy storage batteries from point i to point j; In the formula, Cbat q and Cvol q are the unit power generation costs of energy storage batteries and voltage source power supplies in the power supply area q respectively; and They are respectively the total output power of the energy storage battery and the total output power of the voltage source power supply at time t in area q.

4. The distribution network recovery control method based on electric vehicle battery swap station according to claim 1 is characterized in that: The constraint condition includes the number of energy storage batteries, which is expressed as: Where M p is the collection of power supply areas; M bat Represents the collection of all energy storage batteries; N represents the 0-1 type optimization variable that determines the battery location; bat is the number of batteries in all battery swap stations in the entire system, N sat The maximum number of available battery bays in a battery swap station.

5. The distribution network recovery control method based on electric vehicle battery swap station according to claim 1 is characterized in that: The constraint condition includes the energy storage battery power constraint, which is expressed as: In the formula, and are the percentage remaining power state of the mth battery at time t+1 and t respectively; SOC max and SOC min It is the upper and lower limits of allowed charging and discharging; P m , and η are the output power, discharge capacity and discharge efficiency of the mth battery respectively; ΔT is an optimization time interval; M bat Represents the collection of all energy storage batteries.

6. The distribution network recovery control method based on electric vehicle battery swap station according to claim 1 is characterized in that: The constraints include power constraints, expressed as: Where M p is the collection of power supply areas; M bat Represents the collection of all energy storage batteries; is the total output power of the energy storage battery; P m is the output power of the mth battery; represents the 0-1 type optimization variable that determines the battery location; ΔT is an optimization time interval; t ij is the transportation time from location i to location j; and Respectively indicate whether the mth battery is at position j and position i at time t and t-1; Indicates the maximum output power of the energy storage battery; represents the total output power of the voltage source power supply at time t in region q, It represents the maximum total output power of the voltage source power supply at time t in region q; Represents the total output power of the energy storage batteries in area q at time t; k is a constant.

7. The distribution network recovery control method based on electric vehicle battery swap station according to claim 1 is characterized in that: The constraints include power flow constraints, which are expressed as: IN min ≤U t,k ≤U max -P kv,max ≤P kv ≤P kv,max -Q kv,max ≤Q kv ≤Q kv,max M E represents the set of all nodes (k, v) in the distribution network; P kv , Q kv 、r kv 、x kv , P kv,max and Q kv,max represents the active power, reactive power, line resistance, line reactance, maximum active power flow and maximum reactive power flow between nodes (k, v); P bk represents the active power between nodes (b, k); represents the total reactive power output of the energy storage power source at node k at time t; represents the total reactive power output of the voltage source power supply at node k at time t; represents the total reactive power output of the local power source of node k at time t; Q bk represents the reactive power between nodes (b, k); α kv Indicates whether a failure has occurred between nodes; and is the injected energy storage battery power, voltage source power injection power and actual load power of node k; U t,k is the voltage value of node k at time t; U t,v is the voltage value of node v at time t; U max and U min are the upper and lower limits of voltage operation; U0 is the rated value of node voltage operation, and H is a constant.

8. A distribution network recovery control system based on an electric vehicle battery swap station, characterized in that: include: Receiving module: used to obtain the operating information of all energy storage batteries and battery swap stations in the area; Modeling module: used to establish an optimization scheduling model that considers the energy storage batteries in the transportation swap station based on operation information; Optimization module: It is used to take the system operation cost in the region as the objective function, combine the constraints of the energy storage battery and the battery swap station, and obtain the energy storage battery location information and output power information with the lowest system operation cost through the optimization scheduling model; Scheduling module: used to schedule the energy storage batteries in the battery swap station based on the location information and output power information of the energy storage batteries.

9. A distribution network recovery control device based on an electric vehicle battery replacement station, characterized in that: including processor and storage medium; The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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