Method and system for light storage charging station to participate in load recovery service of power distribution network

Abstract

The invention discloses a method and system for a light storage charging station to participate in a power distribution network load recovery service, and belongs to the field of electrical engineering. The method comprises the following steps: modeling a photovoltaic system, an energy storage system and an electric vehicle charging pile in the optical storage charging station, and constructing an optical storage charging station integrated model in combination with power balance constraints of multiple devices in the optical storage charging station; constructing a power distribution network load recovery model; integrating the optical storage charging station integrated model into the power distribution network load recovery model to obtain an optimization model of the optical storage charging station participating in the power distribution network load recovery service; and performing convexity processing on non-convex constraints in the optimization model, and solving the optimization model by using a commercial solver to obtain a recovery strategy. An optical storage charging station multi-element equipment mathematical model and a power distribution network load recovery model are respectively established and integrated together, and an optimal energy scheduling strategy of available resources in a power distribution network under an extreme power failure accident is solved by taking maximization of weighted load power supply time as a main optimization target.

Description

Technical Field

[0001] The present invention belongs to the field of electrical engineering, and in particular relates to a method and system for a photovoltaic storage charging station to participate in a load recovery service of a distribution network. Background Art

[0002] As a flexible and adjustable resource on the user side, electric vehicles have both storage and load attributes. The photovoltaic charging station, which integrates electric vehicle charging facilities, energy storage equipment, and photovoltaic power generation equipment, has great development potential for intelligent regulation as a flexible resource. However, as a producer and seller of energy, the photovoltaic charging station has the ability to transmit energy bidirectionally with the power grid, so it has the potential to regulate the operating state of the power system. In the extreme events of frequent power system failures and power outages, photovoltaic charging stations with multiple power supply capabilities will become an important part of improving the resilience of the power grid in the future new power system. By feeding back electric energy to the power grid, it can restore the support of important power loads in extreme cases, thereby reducing the harm caused by power outages and giving full play to its flexible resource characteristics. However, the coupling of its internal multiple devices makes the integrated modeling process of photovoltaic charging stations more complicated, and it is difficult to determine the output scheduling of each device in the process of participating in the load recovery service. Therefore, it is necessary to consider the multiple heterogeneous characteristics and flexible power supply capabilities of photovoltaic charging stations, and study the scheduling method of photovoltaic charging stations participating in the distribution network load recovery service under extreme power outages.

[0003] In the load recovery problem under extreme events, the available energy of the system is always limited. Therefore, it is necessary not only to weigh the importance of the load to be restored and the required power, but also to reasonably apply the deployable power supply resources. The three core multi-devices in the photovoltaic storage charging station all have the ability to supply power to the power grid during a power outage, but how to coordinate the operation of the equipment in the photovoltaic storage charging station to maximize the load-weighted power supply time of the distribution network is the key to the load recovery problem. The literature [Distribution network load recovery strategy considering electric vehicle guidance and network reconstruction] considers the preferences of electric vehicle users, establishes a user guidance decision model, and combines the distribution network reconstruction recovery method, considers the power grid constraints and electric vehicle discharge constraints, etc., to construct a distribution network fault recovery model, but it only considers the recovery role of conventional charging stations, and there is currently little discussion on the method of photovoltaic storage charging stations with higher flexibility, stronger power supply capacity and more complex structure to participate in the distribution network load recovery. Summary of the invention

[0004] In view of the deficiencies in the prior art, the purpose of the present invention is to provide a method and system for a photovoltaic storage charging station to participate in the load recovery service of a distribution network, thereby solving the problems in the prior art.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] A method for a photovoltaic storage charging station to participate in a distribution network load recovery service, comprising the following steps:

[0007] Model the photovoltaic, energy storage system and electric vehicle charging piles in the photovoltaic and energy storage charging station, and build an integrated model of the photovoltaic and energy storage charging station by combining the power balance constraints of multiple devices in the photovoltaic and energy storage charging station;

[0008] Construct a load restoration model for distribution networks;

[0009] The integrated model of photovoltaic and storage charging stations is integrated into the load recovery model of the distribution network to obtain the optimization model of photovoltaic and storage charging stations participating in the load recovery service of the distribution network.

[0010] The non-convex constraints in the optimization model of photovoltaic charging stations participating in the load restoration service of the distribution network are convexified, and the optimization model is solved using a commercial solver to obtain the restoration strategy.

[0011] Furthermore, the photovoltaic model in the photovoltaic charging station is:

[0012]

[0013] In the formula, r is the light intensity, Γ is the Gamma function, r max is the maximum light intensity, α and β are the parameters of Beta distribution; P pv is the photovoltaic output, S is the total area of ​​the photovoltaic module, η pv is the photoelectric conversion rate;

[0014] The energy storage system in the photovoltaic charging station is modeled as;

[0015]

[0016] In the formula, the subscript t represents the time period; The charging state of the energy storage system of the photovoltaic charging station; and are the power and efficiency of charging the energy storage system, respectively; and are the power and efficiency of the energy storage system discharge respectively; and are the maximum values ​​of charging power and discharging power of the energy storage system respectively; and are the minimum values ​​of charging power and discharging power of the energy storage system respectively; and They are the upper and lower limits of the state of charge of the energy storage system respectively; It is a Boolean variable introduced to linearize the mutual exclusion constraint of the energy storage system's charging and discharging states. When it is 1, it indicates that the energy storage system is in the charging state, and when it is 0, it indicates that it is in the discharging state.

[0017] The electric vehicle charging pile in the photovoltaic storage charging station is modeled as:

[0018]

[0019] In the formula, The charge state of electric vehicles in the photovoltaic charging station; and The power and efficiency of charging electric vehicles, respectively; and are the power and efficiency of electric vehicle discharge respectively; and are the maximum values ​​of the charging power and discharging power of the electric vehicle respectively; and are the minimum values ​​of the charging power and discharging power of electric vehicles respectively; and They are respectively the upper limit of the state of charge of electric vehicles and the state of charge reserved to meet the necessary travel needs of electric vehicle users; It is a Boolean variable introduced to linearize the mutual exclusion constraint of the charging and discharging states of electric vehicles. When it is 1, it means that the electric vehicle is in the charging state, and when it is 0, it means that it is in the discharging state.

[0020] Furthermore, the power balance constraints of the multiple devices in the photovoltaic charging station are:

[0021]

[0022] Where P t in and P t out They are the power flowing from the distribution network into the photovoltaic storage charging station and the power fed back from the photovoltaic storage charging station to the distribution network; and is the corresponding interaction power upper limit; It is a Boolean variable introduced to linearize the mutual exclusion constraint of the bidirectional energy interaction between the photovoltaic storage charging station and the distribution network. When it is 1, it means that the photovoltaic storage charging station feeds back electric energy to the distribution network, and when it is 0, it means that the photovoltaic storage charging station obtains electric energy from the distribution network.

[0023] Furthermore, the constraints of the distribution network load recovery model are:

[0024]

[0025] β ji ·S ij,t =y ij,t (twenty four)

[0026] S ij,t -S max (1-βji )≤y ij,t ≤S ij,t (25)

[0027] S min β ji ≤y ij,t ≤S max β ji (26)

[0028]

[0029] Where E is the set of distribution network branches, N is the set of nodes, and branch (i, j) represents the branch with nodes i and j as endpoints; α ij is a Boolean variable indicating whether branch ij is connected; β ij is a Boolean variable of the parent-child relationship between nodes. When it is 1, it means that node j is the parent node of node i, otherwise it means that node j is the child node of node i; T is the set of power outage periods, L is the set of nodes whose loads are to be restored; v i,t is the square of the voltage amplitude at the node; r i,j and x ij is the resistance and reactance of the branch; P ij,t and Q ij,t is the active and reactive power of the branch; i ij,t is the square of the branch current amplitude; M is a large positive real number; S ij,t is the complex power of the branch; s i,t is the net injected complex power of the node; z hi is the impedance of the branch, and are the generator output power and load power of the node respectively; γ i,t is a load status Boolean variable. If it is 1, it means the load of the node has been restored, otherwise it is 0. loss,t is the total network loss of the distribution network; S max and S min S ij,t The upper and lower limits of is the output of the generator, ΔT is the duration of each period, E i,total is the total energy that the generator can provide; T1 is the set of time periods excluding the first time period; V i,max and V i,min are the upper and lower limits of the node voltage amplitude respectively; I ij,max is the upper limit of current amplitude; S max is the upper limit of complex power; and It is the upper limit of the active and reactive power output of the node.

[0030] Furthermore, the objective function of the distribution network load recovery model is:

[0031]

[0032] In the formula, ω i is the load weight coefficient; N EV is the number of electric vehicles; C cost,v is the subsidy that electric vehicle users can obtain for participating in load recovery services; σ1, σ2, and σ3 are the coefficients of the three objectives respectively; and It is the discharge subsidy and charging price for electric vehicles in the solar-storage charging station.

[0033] Furthermore, the optimization model of the photovoltaic storage charging station participating in the distribution network load recovery service is:

[0034]

[0035] A system for a photovoltaic storage charging station to participate in a distribution network load recovery service, comprising:

[0036] Photovoltaic energy storage charging station model building module: Modeling the photovoltaic, energy storage system and electric vehicle charging piles in the photovoltaic energy storage charging station, and combining the power balance constraints of multiple devices in the photovoltaic energy storage charging station to build an integrated model of the photovoltaic energy storage charging station;

[0037] Distribution network model building module: building a distribution network load recovery model;

[0038] Model integration module: Integrate the integrated model of the photovoltaic storage charging station into the distribution network load recovery model to obtain the optimization model of the photovoltaic storage charging station participating in the distribution network load recovery service;

[0039] And, the model solving module: convexifies the non-convex constraints in the optimization model of photovoltaic storage charging stations participating in the load recovery service of the distribution network, and uses a commercial solver to solve the optimization model to obtain the recovery strategy.

[0040] A computer storage medium stores a readable program, which, when running, can execute the above-mentioned method of a photovoltaic storage charging station participating in the load recovery service of a distribution network.

[0041] An electronic device, comprising: a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other through the communication bus;

[0042] The memory is used to store at least one executable instruction, and the executable instruction enables the processor to execute operations corresponding to the above-mentioned method for a photovoltaic storage charging station to participate in the load recovery service of the distribution network.

[0043] A computer program product includes computer instructions, wherein the computer instructions instruct a computing device to execute operations corresponding to the above-mentioned method for a photovoltaic energy storage charging station to participate in distribution network load recovery service.

[0044] Beneficial effects of the present invention:

[0045] 1. The present invention establishes a multi-device mathematical model of a photovoltaic storage charging station and a load recovery model of a distribution network, and integrates them together, taking maximizing the weighted load power supply time as the main optimization goal, and solving the optimal energy scheduling strategy for available resources in the distribution network under extreme power outage accidents.

[0046] 2. The present invention not only describes the coupling relationship of multiple devices in the photovoltaic storage charging station, but also connects it with the distribution network, takes into account the two-way energy interaction between the photovoltaic storage charging station and the distribution network, and further uses this feature to realize the load power supply recovery of the distribution network in the event of a power outage; this method can give full play to the flexible advantages and energy supply potential of the photovoltaic storage charging station, and is a way to make full use of local resources in the distribution network.

[0047] 3. The method of the present invention also takes into account the interests of system network losses and electric vehicle users while ensuring the load recovery effect, has a certain degree of comprehensiveness and comprehensiveness, and also improves the practicability of the method. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0049] Figure 1 It is a flow chart of the method for the photovoltaic storage charging station of the present invention to participate in the load recovery service of the distribution network. DETAILED DESCRIPTION

[0050] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0051] Example 1

[0052] like Figure 1 As shown, a method for a photovoltaic storage charging station to participate in a distribution network load recovery service includes the following steps:

[0053] S1, modeling the photovoltaic, energy storage system and electric vehicle charging piles in the photovoltaic and energy storage charging station, and building an integrated model of the photovoltaic and energy storage charging station in combination with the power balance constraints of multiple devices in the photovoltaic and energy storage charging station;

[0054] 1) Modeling of photovoltaics in the photovoltaic charging station;

[0055] The Beta distribution is used to fit the measured light intensity data, and then the photovoltaic output data is calculated based on the actual physical parameters of the photovoltaic modules. The light intensity obeys the probability density function shown in formula (1):

[0056]

[0057] In the formula, r is the light intensity, Γ is the Gamma function, r max is the maximum light intensity, α and β are the parameters of Beta distribution, and the parameters are determined by fitting the actual data.

[0058] The light intensity can be used to further calculate the photovoltaic output based on the parameters of the photovoltaic modules installed in the photovoltaic charging station:

[0059]

[0060] Where P pv is the photovoltaic output, S is the total area of ​​the photovoltaic module, η pv is the photoelectric conversion rate. In the actual operation of the photovoltaic storage charging station, the photovoltaic output can also be predicted through a large amount of historical data, thus providing a data basis for the energy scheduling of the photovoltaic storage charging station.

[0061] 2) Modeling the energy storage system in the photovoltaic charging station;

[0062]

[0063]

[0064] In the formula, the subscript t represents the time period; The charging state of the energy storage system of the photovoltaic charging station; and are the power and efficiency of charging the energy storage system, respectively; and are the power and efficiency of the energy storage system discharge respectively; and are the maximum values ​​of charging power and discharging power of the energy storage system respectively; and are the minimum values ​​of charging power and discharging power of the energy storage system respectively; and They are the upper and lower limits of the state of charge of the energy storage system respectively; It is a Boolean variable introduced to linearize the mutual exclusion constraint of the energy storage system's charge and discharge states. When it is 1, it indicates that the energy storage system is in a charging state, and when it is 0, it indicates that it is in a discharging state. Formula (3) reveals the change in the state of charge of the energy storage system caused by the charging and discharging behavior, and formulas (4) to (6) limit the upper and lower limits of the energy storage system's operation.

[0065] 3) Modeling of electric vehicle charging piles in the photovoltaic charging station:

[0066]

[0067] In the formula, The charge state of electric vehicles in the photovoltaic charging station; and The power and efficiency of charging electric vehicles, respectively; and are the power and efficiency of electric vehicle discharge respectively; and are the maximum values ​​of the charging power and discharging power of the electric vehicle respectively; and are the minimum values ​​of the charging power and discharging power of electric vehicles respectively; and They are respectively the upper limit of the state of charge of electric vehicles and the state of charge reserved to meet the necessary travel needs of electric vehicle users; It is a Boolean variable introduced to linearize the mutual exclusion constraint of the charging and discharging state of electric vehicles. When it is 1, it means that the electric vehicle is in the charging state, and when it is 0, it means that it is in the discharging state. Formula (7) reveals the change of the charge state of electric vehicles caused by the charging and discharging behavior. Formulas (8) and (9) limit the upper and lower limits of the charging and discharging power of electric vehicles. Formula (10) ensures that electric vehicle users participating in the load recovery of the distribution network can still retain the charge state required for necessary travel.

[0068] The multiple devices in the photovoltaic charging station and their bidirectional power interaction with the distribution network should maintain a balance:

[0069]

[0070] Where P t in and P t out They are the power flowing from the distribution network into the photovoltaic storage charging station and the power fed back from the photovoltaic storage charging station to the distribution network; and is the corresponding interaction power upper limit; It is a Boolean variable introduced to linearize the mutual exclusion constraint of the bidirectional energy interaction between the photovoltaic storage charging station and the distribution network. When it is 1, it means that the photovoltaic storage charging station feeds back electric energy to the distribution network, and when it is 0, it means that the photovoltaic storage charging station obtains electric energy from the distribution network.

[0071] Through the power balance constraints of multiple devices in the photovoltaic and energy storage charging station, photovoltaics, electric vehicles, and energy storage systems can be effectively linked to achieve coordinated output scheduling of multiple devices in the photovoltaic and energy storage charging station.

[0072] S2, construct the distribution network load recovery model;

[0073] 1) In order to achieve load recovery of the distribution network under extreme power outage accidents, it is first necessary to mathematically model the distribution network based on the Distflow model.

[0074] First, considering that extreme events may cause damage to the internal branches of the distribution network, it is necessary to maintain the radial topology of the distribution network through preset tie switch actions. The specific constraints are as follows:

[0075]

[0076] Where E is the set of distribution network branches, N is the set of nodes, and branch (i, j) represents the branch with nodes i and j as endpoints; α ij is a Boolean variable indicating whether branch ij is connected; β ij is a Boolean variable of the parent-child relationship between nodes. When it is 1, it means that node j is the parent node of node i, otherwise it means that node j is the child node of node i. Formula (14) describes the connectivity of the line, formula (15) shows that any node except the root node has only one parent node, and formula (16) shows that the root node has no parent node.

[0077] Secondly, establish the power flow constraints of the distribution network:

[0078]

[0079] Where T is the set of power outage periods, L is the set of nodes to be restored; v i,t is the square of the voltage amplitude at the node; r i,j and x ij is the resistance and reactance of the branch; P ij,t and Q ij,t is the active and reactive power of the branch; i ij,t is the square of the branch current amplitude; M is a large positive real number; S ij,t is the complex power of the branch; s i,t is the net injected complex power of the node; z hi is the impedance of the branch, and are the generator output power and load power of the node respectively; γ i,t is a load status Boolean variable. If it is 1, it means the load of the node has been restored, otherwise it is 0. loss,t is the total network loss of the distribution network. Equations (17) and (18) describe the changes in node voltage in the distribution network, Equation (19) is the complex power balance constraint of the node, Equations (20) and (21) are used to calculate the net injected complex power of the load node and the photovoltaic charging station node, Equation (22) represents the variable relationship between current, voltage, active power, and reactive power, and Equation (23) calculates the total network loss of the distribution network.

[0080] Since Equation (19) is a nonlinear constraint, a specific method needs to be used to linearize it to ensure good solvability. The nonlinear term β can be ji S ij,t With the linear term y ij,t Make a substitution.

[0081] β ji ·S ij,t =y ij,t (twenty four)

[0082] S ij,t -S max (1-β ji )≤y ij,t ≤S ij,t (25)

[0083] S min β ji ≤y ij,t ≤S max β ji (26)

[0084] In the formula, S max and S min S ij,t The upper and lower limits of .

[0085] Formula (22) can be convexified by the second-order cone relaxation method and replaced by formula (27):

[0086]

[0087] Then, constraints are established to describe the limited power supply resources in the distribution network:

[0088]

[0089] In the formula, is the output of the generator, ΔT is the duration of each period, E i,total is the total energy that the generator can provide. This constraint describes the limited power supply capacity of the generator in the power outage state.

[0090] 2) In order to ensure the quality of load restoration, it is also necessary to ensure that the restored loads will not lose power again. The specific constraints are as follows:

[0091]

[0092] Wherein, T1 is the set of time periods excluding the first time period.

[0093] 3) In the load restoration service of the distribution network, the primary goal of the dispatch objective function should be to maximize the effect of load restoration. Considering the differences in the importance of the load to be restored, the restoration effect needs to be measured by the weighted load power supply time. At the same time, reducing system network losses and reducing the participation subsidies paid to electric vehicle users are secondary goals. The specific objective function is as follows:

[0094]

[0095] In the formula, ω i is the load weight coefficient; N EV is the number of electric vehicles; C cost,v is the subsidy that electric vehicle users can obtain for participating in load recovery services; σ1, σ2, and σ3 are the coefficients of the three objectives. The three terms of the objective function represent the load recovery effect, the total network loss of the system, and the total subsidy cost paid to electric vehicle users.

[0096] The subsidy paid to electric vehicle users is calculated as follows:

[0097]

[0098] In the formula, and It is the discharge subsidy and charging price for electric vehicles in the solar-storage charging station.

[0099] In addition, the load recovery problem of the distribution network still needs to ensure that the system operates within a reasonable range:

[0100]

[0101] Where V i,max and V i,min are the upper and lower limits of the node voltage amplitude respectively; I ij,max is the upper limit of current amplitude; S max is the upper limit of complex power; and It is the upper limit of the active and reactive power output of the node.

[0102] S3, integrating the photovoltaic storage charging station integrated model into the distribution network load recovery model to obtain the optimization model of the photovoltaic storage charging station participating in the distribution network load recovery service;

[0103] The optimization model of the photovoltaic storage charging station participating in the distribution network load recovery service is:

[0104]

[0105] S4, convexify the non-convex constraints in the optimization model, and use a commercial solver to solve the optimization model to obtain the recovery strategy.

[0106] Among them, the commercial solvers are Gurobi or Cplex.

[0107] The present invention can participate in the load recovery work of the distribution network after an extreme power outage by rationally utilizing the power supply capacity of multiple devices in the photovoltaic storage charging station. The method takes maximizing the weighted load power supply time as the main optimization goal, while also taking into account the system network loss and the interests of electric vehicle users, and realizes the optimal energy scheduling of the internal resources of the photovoltaic storage charging station and the resources in the distribution network, effectively improving the load recovery capacity of the distribution network and its resilience to extreme events.

[0108] Based on similar inventive concepts, an embodiment of the present invention also provides a computer storage medium storing a readable program, which, when the program is running, can execute the above-mentioned method of a photovoltaic charging station participating in the distribution network load recovery service.

[0109] Based on similar inventive concepts, an embodiment of the present invention provides an electronic device, comprising: a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other through the communication bus;

[0110] The memory is used to store at least one executable instruction, and the executable instruction enables the processor to execute operations corresponding to the above-mentioned method for a photovoltaic storage charging station to participate in the load recovery service of the distribution network.

[0111] Based on similar inventive concepts, an embodiment of the present invention also provides a computer program product, including computer instructions, which instruct a computing device to execute operations corresponding to the above-mentioned method for a photovoltaic energy storage charging station to participate in distribution network load recovery service.

[0112] Example 2

[0113] In this embodiment, a system for a photovoltaic storage charging station to participate in a distribution network load recovery service is proposed, specifically comprising:

[0114] Photovoltaic energy storage charging station model building module: Modeling the photovoltaic, energy storage system and electric vehicle charging piles in the photovoltaic energy storage charging station, and combining the power balance constraints of multiple devices in the photovoltaic energy storage charging station to build an integrated model of the photovoltaic energy storage charging station;

[0115] Distribution network model building module: building a distribution network load recovery model;

[0116] Model integration module: Integrate the integrated model of the photovoltaic storage charging station into the distribution network load recovery model to obtain the optimization model of the photovoltaic storage charging station participating in the distribution network load recovery service;

[0117] And, the model solving module: convexifies the non-convex constraints in the optimization model, and uses a commercial solver to solve the optimization model to obtain a recovery strategy.

[0118] The method of the present invention may be implemented in hardware, firmware, or as software or computer code that may be stored in a recording medium (such as a CDROM, RAM, floppy disk, hard disk, or magneto-optical disk), or as computer code that is originally stored in a remote recording medium or a non-temporary machine-readable medium downloaded over a network and will be stored in a local recording medium, so that the method described herein may be stored in such software processing on a recording medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware (such as an ASIC or FPGA). It is understood that a computer, processor, microprocessor controller, or programmable hardware includes a storage component (e.g., RAM, ROM, flash memory, etc.) that can store or receive software or computer code, and when the software or computer code is accessed and executed by a computer, processor, or hardware, the method described herein is implemented. In addition, when a general-purpose computer accesses the code for implementing the method shown herein, the execution of the code converts the general-purpose computer into a dedicated computer for executing the method shown herein.

[0119] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements all fall within the scope of the present invention to be protected.

Claims

1. A method for a photovoltaic storage charging station to participate in a distribution network load recovery service, characterized in that: The following steps are involved: Model the photovoltaic, energy storage system and electric vehicle charging piles in the photovoltaic and energy storage charging station, and build an integrated model of the photovoltaic and energy storage charging station by combining the power balance constraints of multiple devices in the photovoltaic and energy storage charging station; Construct a load restoration model for distribution networks; The integrated model of photovoltaic and storage charging stations is integrated into the load recovery model of the distribution network to obtain the optimization model of photovoltaic and storage charging stations participating in the load recovery service of the distribution network. The non-convex constraints in the optimization model of photovoltaic charging stations participating in the load restoration service of the distribution network are convexified, and the optimization model is solved using a commercial solver to obtain the restoration strategy.

2. A method for a photovoltaic storage charging station to participate in distribution network load recovery service according to claim 1, characterized in that: The photovoltaic model in the photovoltaic storage charging station is: In the formula, r is the light intensity, Γ is the Gamma function, r max is the maximum light intensity, α and β are the parameters of Beta distribution; P pv is the photovoltaic output, S is the total area of ​​the photovoltaic module, η pv is the photoelectric conversion rate; The energy storage system in the photovoltaic charging station is modeled as; In the formula, the subscript t represents the time period; The charging state of the energy storage system of the photovoltaic charging station; and are the power and efficiency of charging the energy storage system, respectively; and are the power and efficiency of the energy storage system discharge respectively; and are the maximum values ​​of charging power and discharging power of the energy storage system respectively; and are the minimum values ​​of charging power and discharging power of the energy storage system respectively; and They are the upper and lower limits of the state of charge of the energy storage system respectively; It is a Boolean variable introduced to linearize the mutual exclusion constraint of the energy storage system's charging and discharging states. When it is 1, it indicates that the energy storage system is in the charging state, and when it is 0, it indicates that it is in the discharging state. The electric vehicle charging pile in the photovoltaic storage charging station is modeled as: In the formula, The charge state of electric vehicles in the photovoltaic charging station; and The power and efficiency of charging electric vehicles, respectively; and are the power and efficiency of electric vehicle discharge respectively; and are the maximum values ​​of the charging power and discharging power of the electric vehicle respectively; and are the minimum values ​​of the charging power and discharging power of electric vehicles respectively; and They are the upper limit of the state of charge of electric vehicles and the state of charge reserved to meet the necessary travel needs of electric vehicle users; It is a Boolean variable introduced to linearize the mutual exclusion constraints of the charging and discharging states of electric vehicles. When it is 1, it means that the electric vehicle is in the charging state, and when it is 0, it means that it is in the discharging state.

3. A method for a photovoltaic charging station to participate in distribution network load recovery service according to claim 2, characterized in that: The power balance constraint of multiple devices in the photovoltaic charging station is: In the formula, and They are the power flowing from the distribution network into the photovoltaic storage charging station and the power fed back from the photovoltaic storage charging station to the distribution network; and is the corresponding interaction power upper limit; It is a Boolean variable introduced to linearize the mutual exclusion constraint of the bidirectional energy interaction between the photovoltaic storage charging station and the distribution network. When it is 1, it means that the photovoltaic storage charging station feeds back electric energy to the distribution network, and when it is 0, it means that the photovoltaic storage charging station obtains electric energy from the distribution network.

4. A method for a photovoltaic storage charging station to participate in distribution network load recovery service according to claim 3, characterized in that: The constraints of the distribution network load recovery model are: β ji ·S ij,t =y ij,t (24) S ij,t -S max (1-b) ji )≤y ij,t ≤S ij,t (25) S min ·b ji ≤y ij,t ≤S max ·b ji (26) Where E is the set of distribution network branches, N is the set of nodes, and branch (i, j) represents the branch with nodes i and j as endpoints; α ij is a Boolean variable indicating whether branch ij is connected; β ij is a Boolean variable of the parent-child relationship between nodes. When it is 1, it means that node j is the parent node of node i, otherwise it means that node j is the child node of node i; T is the set of power outage periods, L is the set of nodes whose loads are to be restored; v i,t is the square of the voltage amplitude at the node; r i,j and x ij is the resistance and reactance of the branch; P ij,t and Q ij,t is the active and reactive power of the branch; i ij,t is the square of the branch current amplitude; M is a large positive real number; S ij,t is the complex power of the branch; s i,t is the net injected complex power of the node; z hi is the impedance of the branch, and are the generator output power and load power of the node respectively; γ i,t is a load status Boolean variable. If it is 1, it means the load of the node has been restored, otherwise it is 0. loss,t is the total network loss of the distribution network; S max and S min S ij,t The upper and lower limits of is the output of the generator, ΔT is the duration of each period, E i,total is the total energy that the generator can provide; T1 is the set of time periods excluding the first time period; V i,max and V i,min are the upper and lower limits of the node voltage amplitude respectively; I ij,max is the upper limit of the current amplitude; S max is the upper limit of complex power; and It is the upper limit of the active and reactive power output of the node.

5. A method for a photovoltaic storage charging station to participate in distribution network load recovery service according to claim 4, characterized in that: The objective function of the distribution network load recovery model is: In the formula, ω i is the load weight coefficient; N EV is the number of electric vehicles; C cost,v is the subsidy that electric vehicle users can obtain for participating in load recovery services; σ1, σ2, and σ3 are the coefficients of the three objectives respectively; and It is the discharge subsidy and charging price for electric vehicles in the solar-storage charging station.

6. A method for a photovoltaic storage charging station to participate in distribution network load recovery service according to claim 5, characterized in that: The optimization model of the photovoltaic storage charging station participating in the distribution network load recovery service is:

7. A system for a photovoltaic storage charging station to participate in the load recovery service of a distribution network, characterized in that: include: Photovoltaic energy storage charging station model building module: Modeling the photovoltaic, energy storage system and electric vehicle charging piles in the photovoltaic energy storage charging station, and combining the power balance constraints of multiple devices in the photovoltaic energy storage charging station to build an integrated model of the photovoltaic energy storage charging station; Distribution network model building module: building a distribution network load recovery model; Model integration module: Integrate the integrated model of the photovoltaic storage charging station into the distribution network load recovery model to obtain the optimization model of the photovoltaic storage charging station participating in the distribution network load recovery service; And, the model solving module: convexifies the non-convex constraints in the optimization model of photovoltaic storage charging stations participating in the load recovery service of the distribution network, and uses a commercial solver to solve the optimization model to obtain the recovery strategy.

8. A computer storage medium storing a readable program, characterized in that: When the program is running, it can execute the method of a photovoltaic storage charging station participating in the distribution network load recovery service as described in any one of claims 1-6.

9. An electronic device, characterized in that: include: A processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus; The memory is used to store at least one executable instruction, and the executable instruction enables the processor to perform operations corresponding to a method for a photovoltaic energy storage charging station to participate in a distribution network load recovery service as described in any one of claims 1-6.

10. A computer program product comprising computer instructions, characterized in that: The computer instructions instruct the computing device to perform operations corresponding to the method for a photovoltaic energy storage charging station to participate in distribution network load recovery service as described in any one of claims 1-6.

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