Electric vehicle cluster coordination control method and device of three-phase unbalanced power flow system

Abstract

The embodiment of the invention provides an electric vehicle cluster coordination control method and device for a three-phase unbalanced power flow system. The method comprises the steps that power grid operation parameters of the three-phase unbalanced power flow system are acquired; generating system key parameters of the three-phase unbalanced power flow system according to the power grid operation parameters through a pre-constructed three-phase unbalanced power flow linearization equation; through a pre-constructed generalized energy storage model of the electric vehicle clusters, according to the key parameters of the system, generating energy storage residual energy of each electric vehicle cluster; through a pre-constructed real-time demand response optimization model, calculation is carried out according to system key parameters and stored energy residual energy, and coordination control parameters of each electric vehicle cluster are generated, so that the sensitivity, real-time performance and voltage stability of the system can be improved, the operation cost is reduced, and the overall efficiency is improved; and powerful support is provided for popularization and sustainable development of the electric automobile.

Description

Technical Field

[0001] The present invention relates to the technical field of power system dispatching control, and in particular to a method and device for coordinated control of electric vehicle clusters in a three-phase unbalanced power flow system. Background Art

[0002] Three-phase imbalance refers to the situation where the amplitude or phase difference between the three-phase current or voltage is not equal, which may be caused by uneven distribution of different loads, equipment failure or operation error. This problem may lead to unevenness in the power grid, which in turn causes uneven distribution of loads between different phases, further exacerbating the imbalance. With the rapid growth of the number of electric vehicles, the voltage control of the distribution network faces unprecedented challenges. The instability of charging of large-scale electric vehicle clusters may cause voltage fluctuations, posing a threat to the stable operation of the power system. In response to the coordination problem of charging of electric vehicle clusters, strategies such as reactive power control and voltage reactive power regulation have been proposed in related technologies. These strategies rely on smart grid systems to coordinate the charging behavior of electric vehicles by real-time monitoring and responding to the state of the power grid to maintain voltage stability. However, in large-scale electric vehicle clusters, the system's demand for real-time response and dynamic scheduling may cause calculation and communication delays, complex calculations, and slow response speeds, thereby reducing the sensitivity and real-time performance of the system and affecting voltage stability. Summary of the invention

[0003] One object of the present invention is to provide a method for coordinated control of electric vehicle clusters in a three-phase unbalanced power flow system, which can improve the sensitivity, real-time performance and voltage stability of the system, reduce operating costs and improve overall efficiency, and provide strong support for the popularization and sustainable development of electric vehicles. Another object of the present invention is to provide a device for coordinated control of electric vehicle clusters in a three-phase unbalanced power flow system. Another object of the present invention is to provide a computer-readable medium. Another object of the present invention is to provide a computer device.

[0004] In order to achieve the above objectives, the present invention discloses, on one hand, a coordinated control method for an electric vehicle cluster of a three-phase unbalanced power flow system, comprising:

[0005] Obtaining grid operation parameters of three-phase unbalanced power flow system;

[0006] Through the pre-built three-phase unbalanced power flow linearization equation, the key system parameters of the three-phase unbalanced power flow system are generated according to the power grid operation parameters;

[0007] Through the pre-built generalized energy storage model of electric vehicle clusters, the energy storage surplus energy of each electric vehicle cluster is generated according to the key system parameters;

[0008] Through the pre-built real-time demand response optimization model, calculations are performed based on key system parameters and remaining energy storage to generate coordinated control parameters for each electric vehicle cluster.

[0009] Preferably, before generating the key system parameters of the three-phase unbalanced power flow system according to the power grid operation parameters by using the pre-constructed three-phase unbalanced power flow linearization equation, the method further includes:

[0010] Construct the basic equations of three-phase balanced power flow system;

[0011] Linearize the nonlinear terms of the basic equations of the three-phase balanced power flow system to generate linearized equations;

[0012] The three-phase unbalanced factor is introduced into the basic equation of the three-phase balanced power flow system to construct the three-phase unbalanced power flow system equation;

[0013] According to the linearized equation, the three-phase unbalanced power flow system equation is linearized to generate the three-phase unbalanced power flow linearized equation.

[0014] Preferably, before generating the energy storage surplus energy of each electric vehicle cluster according to the key system parameters through the pre-built generalized energy storage model of the electric vehicle cluster, the method further includes:

[0015] Construct an electric vehicle model based on the interaction mode between electric vehicles and the power grid;

[0016] Based on the Minkowski sum, the electric vehicle model is mapped to the Minkowski space and a generalized energy storage model of the electric vehicle cluster is constructed.

[0017] Preferably, before generating the coordinated control parameters of each electric vehicle cluster by calculating according to the key system parameters and the remaining energy of the energy storage through the pre-built real-time demand response optimization model, it also includes:

[0018] Construct a two-layer demand response model, the upper model is a model with the goal of minimizing energy management costs, and the lower model is a model with the goal of maximizing the total social energy efficiency;

[0019] The double-layer demand response model is transformed into a single-layer model to construct an intraday demand response model;

[0020] Based on the rolling optimization modeling strategy, the intraday demand response model is optimized and a real-time demand response optimization model is constructed.

[0021] Preferably, the method further comprises:

[0022] Obtain the charging pile power deviation time series data and current power deviation data within the historical time period;

[0023] Generate a power deviation prediction value according to the preset power deviation sensitivity parameter, the charging pile power deviation time series data and the current power deviation data;

[0024] The charging strategy control quantity is generated through the preset charging control sensitivity parameters and the electric energy deviation prediction value.

[0025] The present invention also discloses an electric vehicle cluster coordination control device for a three-phase unbalanced power flow system, comprising:

[0026] A power grid operation parameter acquisition unit, used to acquire power grid operation parameters of a three-phase unbalanced power flow system;

[0027] A system key parameter generating unit, used for generating system key parameters of a three-phase unbalanced power flow system according to a pre-built three-phase unbalanced power flow linearization equation and power grid operation parameters;

[0028] An energy storage surplus energy generation unit is used to generate energy storage surplus energy of each electric vehicle cluster according to key system parameters through a pre-built generalized energy storage model of the electric vehicle cluster;

[0029] The coordination control unit is used to generate coordination control parameters for each electric vehicle cluster based on calculations made according to key system parameters and remaining energy storage through a pre-built real-time demand response optimization model.

[0030] Preferably, the device further comprises:

[0031] Basic equation building unit, used to build the basic equation of three-phase balanced power flow system;

[0032] A linearization processing unit is used to perform linearization processing on the nonlinear terms of the basic equation of the three-phase balanced power flow system to generate a linearized equation;

[0033] A three-phase unbalanced power flow system equation construction unit, which is used to introduce three-phase unbalanced factors into the basic equation of the three-phase balanced power flow system and construct the three-phase unbalanced power flow system equation;

[0034] The three-phase unbalanced power flow linearization equation construction unit is used to perform linearization processing on the three-phase unbalanced power flow system equation according to the linearization equation to generate the three-phase unbalanced power flow linearization equation.

[0035] The present invention also discloses a computer-readable medium on which a computer program is stored. When the program is executed by a processor, the method described above is implemented.

[0036] The present invention also discloses a computer device, including a memory and a processor, wherein the memory is used to store information including program instructions, the processor is used to control the execution of program instructions, and the processor implements the above method when executing the program.

[0037] The present invention also discloses a computer program product, including a computer program / instruction, and the method described above is implemented when the computer program / instruction is executed by a processor.

[0038] The present invention obtains the grid operation parameters of the three-phase unbalanced power flow system; generates the key system parameters of the three-phase unbalanced power flow system according to the grid operation parameters through a pre-constructed three-phase unbalanced power flow linearization equation; generates the energy storage surplus energy of each electric vehicle cluster according to the system key parameters through a pre-constructed generalized energy storage model of the electric vehicle cluster; generates the coordinated control parameters of each electric vehicle cluster by calculating according to the system key parameters and the energy storage surplus energy through a pre-constructed real-time demand response optimization model, which can improve the sensitivity, real-time performance and voltage stability of the system, reduce operating costs and improve overall efficiency, and provide strong support for the popularization and sustainable development of electric vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] 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, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0040] Figure 1 A flow chart of a method for coordinated control of an electric vehicle cluster in a three-phase unbalanced power flow system provided by an embodiment of the present invention;

[0041] Figure 2 A flow chart of another electric vehicle cluster coordination control method for a three-phase unbalanced power flow system provided by an embodiment of the present invention;

[0042] Figure 3 A schematic diagram of the structure of an electric vehicle cluster coordination control device for a three-phase unbalanced power flow system provided by an embodiment of the present invention;

[0043] Figure 4 A schematic diagram of the structure of a computer device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0044] 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.

[0045] It should be noted that the electric vehicle cluster coordinated control method and device for a three-phase unbalanced power flow system disclosed in the present application can be used in the field of artificial intelligence technology, and can also be used in any field outside the field of artificial intelligence technology. The application field of the electric vehicle cluster coordinated control method and device for a three-phase unbalanced power flow system disclosed in the present application is not limited.

[0046] In order to facilitate the understanding of the technical solution provided by the present application, the relevant contents of the technical solution of the present application are first explained below. With the increasingly severe environmental pollution problems and energy situation, electric vehicles (EVs) have received widespread attention and support due to their energy-saving, environmental protection and clean characteristics. Therefore, how to effectively control the charging and discharging process of electric vehicles has become a research focus in academia and industry. This involves the intelligent management of electric vehicle charging facilities, the load balancing of the power system, and the interaction between electric vehicles and the power grid. The orderly control of the charging process of electric vehicles not only helps to reduce the load pressure of the power grid, but also improves the efficiency of electric energy utilization, promotes the application of renewable energy, and thus promotes the energy structure to a cleaner and more sustainable direction. With the sharp increase in the number of electric vehicles and the increasing complexity of the power system, the charging coordination scheme needs to respond to the real-time requirements of the system more efficiently and quickly. In large-scale electric vehicle clusters, the system's demand for real-time response and dynamic scheduling may cause computing and communication delays, thereby reducing the sensitivity and real-time performance of the system. Therefore, it is necessary to further optimize the charging coordination algorithm, improve computing efficiency, and reduce communication delays to ensure the real-time performance and stability of the system. Electric vehicle charging coordination involves technologies in multiple fields, such as electric vehicles, energy management systems, and intelligent network connection technologies. The collaborative work and information exchange between these technologies are essential to achieve voltage stability control. Therefore, it is necessary to establish unified technical standards and interoperability specifications to promote collaborative work and information exchange between different devices and systems. By formulating unified standards and specifications, operating costs can be reduced, overall efficiency can be improved, and the sustainable development of the electric vehicle industry can be promoted.

[0047] In summary, in order to cope with the voltage stability challenges brought by electric vehicle cluster charging, it is necessary to optimize the charging coordination scheme and achieve unified technical standards and interoperability. These measures will help improve the real-time and stability of the system, reduce operating costs and improve overall efficiency, and provide strong support for the popularization and sustainable development of electric vehicles.

[0048] With the widespread popularity of electric vehicles, a large number of DC charging piles are connected to the power grid, resulting in increasingly serious three-phase imbalance in the distribution network. Three-phase imbalance may cause voltage fluctuations, current overload, increased losses, and shortened equipment life, which will have a negative impact on the reliability, stability, and economy of the power system. With the rapid increase in the number of electric vehicles, the impact of cluster charging behavior on the distribution network is becoming increasingly significant. The key issue facing Electric Vehicles Charging Coordination (EVCC) is how to meet the charging needs of a large number of electric vehicles while ensuring the stable operation of the power grid.

[0049] This application develops a corresponding demand response strategy by real-time monitoring of the operating status of the power grid and the charging needs of electric vehicles. This strategy comprehensively considers factors such as the three-phase imbalance of the power grid, the charging power and charging time of electric vehicles, and calculates the best charging coordination plan through an optimization algorithm. This plan can maximize the charging needs of electric vehicles while ensuring the stable operation of the power grid. This application also provides an electric vehicle model, a generalized energy storage model based on electric vehicle clusters, and a demand response model. The electric vehicle model is responsible for the operating status of the power grid and the charging needs of electric vehicles; the generalized energy storage model based on the electric vehicle cluster regards the electric vehicle cluster connected by multiple electric vehicle aggregators (EVAs) as an overall energy storage system, realizing the integration and management of distributed energy; the demand response model uses the approximate linearization method to calculate based on the collected data, formulates the best demand response strategy, and outputs the strategy results to the charging pile and the power grid dispatching system to achieve charging coordination and power grid stability.

[0050] The core architecture of the intraday electricity market is composed of electric vehicle aggregators (EVAs), power trading centers, and distribution system operators (DSOs), forming a complex and sophisticated operating system. As an important part of this system, electric vehicle aggregators (EVAs) play a role in predicting and managing the charging demand of electric vehicles. Through in-depth analysis and prediction of key parameters of electric vehicles connected in their respective regions in the next day, including initial SOC (state of charge), target SOC, expected charging start time and expected end time, EVA can build an accurate generalized energy storage model. These models not only reflect the charging demand of electric vehicles, but also reveal their potential energy storage capacity, providing an important basis for the balance of supply and demand in the electricity market.

[0051] In the process of pursuing the maximization of its own interests, each EVA will adopt a non-cooperative game strategy to trade electricity with the upper-level power grid. Since the upper-level power grid is large in scale and has sufficient power supply capacity, there is an obvious order of priority between the power trading center and the charging station in the decision-making process. Under this game relationship, EVA needs to accurately formulate demand response strategies and power purchase plans to ensure that energy management costs are minimized while meeting the charging needs of electric vehicles.

[0052] The present invention has the advantages of high computing efficiency, fast response speed, strong adaptability, etc. Through real-time monitoring and rapid response, it can effectively solve the voltage stability problem caused by electric vehicle cluster charging, improve the operation efficiency and reliability of the power grid, and provide strong support for the popularization and sustainable development of electric vehicles.

[0053] The following takes the electric vehicle cluster coordination control device of the three-phase unbalanced power flow system as an example of the execution subject to illustrate the implementation process of the electric vehicle cluster coordination control method of the three-phase unbalanced power flow system provided by the embodiment of the present invention. It can be understood that the execution subject of the electric vehicle cluster coordination control method of the three-phase unbalanced power flow system provided by the embodiment of the present invention includes but is not limited to the electric vehicle cluster coordination control device of the three-phase unbalanced power flow system.

[0054] Figure 1 A flow chart of a method for coordinated control of an electric vehicle cluster in a three-phase unbalanced power flow system provided by an embodiment of the present invention is shown in FIG. Figure 1 As shown, the method includes:

[0055] Step 101: Obtain power grid operation parameters of a three-phase unbalanced power flow system.

[0056] Step 102: Generate key system parameters of the three-phase unbalanced power flow system according to the pre-constructed three-phase unbalanced power flow linearization equation and the power grid operation parameters.

[0057] Step 103 : Generate the energy storage surplus energy of each electric vehicle cluster according to the key system parameters through the pre-built generalized energy storage model of the electric vehicle cluster.

[0058] Step 104: Generate coordinated control parameters for each electric vehicle cluster by calculating based on key system parameters and energy storage surplus energy using a pre-built real-time demand response optimization model.

[0059] In the technical solution provided by the embodiment of the present invention, the grid operation parameters of the three-phase unbalanced power flow system are obtained; through the pre-constructed three-phase unbalanced power flow linearization equation, the system key parameters of the three-phase unbalanced power flow system are generated according to the grid operation parameters; through the pre-constructed generalized energy storage model of the electric vehicle cluster, the energy storage surplus energy of each electric vehicle cluster is generated according to the system key parameters; through the pre-constructed real-time demand response optimization model, calculation is performed according to the system key parameters and the energy storage surplus energy to generate the coordination control parameters of each electric vehicle cluster, which can improve the sensitivity, real-time performance and voltage stability of the system, reduce operating costs and improve overall efficiency, and provide strong support for the popularization and sustainable development of electric vehicles.

[0060] Figure 2 A flow chart of another electric vehicle cluster coordinated control method for a three-phase unbalanced power flow system provided by an embodiment of the present invention is as follows: Figure 2 As shown, the method includes:

[0061] Step 201: construct a basic equation of a three-phase balanced power flow system.

[0062] In the embodiment of the present invention, each step is executed by an electric vehicle cluster coordination control device of a three-phase unbalanced power flow system.

[0063] In the embodiment of the present invention, according to the characteristics of the distribution network, a branch active power (P ik ), reactive power (Q ik ) and the node voltage square (U), which reflects the operating characteristics and balance conditions of the distribution network:

[0064] U0=|V s | 2 =U s

[0065]

[0066] Where U0 is the square value of the voltage at the initial node (usually the reference node), which is related to the power supply voltage; U s is the square of the power supply voltage (equivalent to U0); V s is the complex value of the power supply voltage; U k is the square value of the voltage at the equilibrium node k, which represents the square of the voltage amplitude; U i is the square value of the voltage at node i; r ik and x ik are the line resistance and reactance between nodes i and k respectively; P ik is the active power of the branch; Q ik is the reactive power of the branch; and are the active load demand and reactive load demand of node k respectively; and are the fixed active load and reactive load of node k under nominal voltage conditions, respectively; and are the linear coefficients of active load and reactive load and voltage at node k respectively; and All are nonlinear terms.

[0067] Step 202: Linearize the nonlinear terms of the basic equation of the three-phase balanced power flow system to generate a linearized equation.

[0068] In the embodiment of the present invention, the nonlinear term is identified and In order to simplify the calculation and improve the practicability of the model, linearization technology is used to process the nonlinear terms and transform them into a form that is easier to solve.

[0069] Specifically, the nonlinear terms are linearized to generate linearized equations. and It is expressed as the following linearized equation:

[0070]

[0071] Among them, h ik (P,Q) is the power flow density or transmission intensity between nodes i and k, which represents the power load density transmitted under unit voltage; S ik is the complex power between nodes i and k; V i is the voltage amplitude of node i; |z ik | represents the impedance modulus of the line between nodes i and k.

[0072] Furthermore, for h ik (P,Q) is linearized:

[0073]

[0074] Among them, h ik (P,Q) is the power flow density or transmission intensity between nodes i and k; P ik0 and Q ik0 are respectively the active power and reactive power flowing from node i to node k in the initial state; S ik0 is the complex power between node i and node k in the initial state; V i0 represents the voltage amplitude of node i in the initial state; P ik is the active power of the branch; Q ik is the reactive power of the branch.

[0075] Step 203: Introduce a three-phase unbalanced factor into the basic equation of the three-phase balanced power flow system to construct a three-phase unbalanced power flow system equation.

[0076] In the embodiment of the present invention, on the basis of the linearization of the three-phase balanced power flow, the three-phase unbalanced factors that may exist in the distribution network are further considered, including but not limited to uneven load distribution, line parameter differences, etc. These factors will have an important impact on the calculation of power flow and the formulation of control strategies. The three-phase unbalanced power flow system equation is constructed as follows:

[0077] U N+1 =|V s |⊙|V s |

[0078]

[0079] Among them, U N+1 is the square value of the voltage at the source node in the three-phase system; V s is the complex value of the power supply voltage; V k and V i Respectively represent the three-phase voltage vectors of node k and node i; z ik represents the three-phase impedance matrix between nodes i and k, which is a 3×3 symmetric matrix; U k is the square value of the voltage at the equilibrium node k, which represents the square of the voltage amplitude; P ik is the active power of the branch; Q ik is the reactive power of the branch; and are the active load demand and reactive load demand of node k respectively; and are the fixed active load and reactive load of node k under nominal voltage conditions, respectively; and are the linear coefficients of active load and reactive load and voltage at node k respectively; and All are nonlinear terms.

[0080] It is worth noting that z ik represents the three-phase impedance matrix between nodes i and k, which is a 3×3 symmetric matrix, z ik Including line impedance z ik,ab 、z ik,ac and z ik,bc ,

[0081] It is worth noting that V k =[V ka ,V kb ,V kc ] Tis the three-phase voltage vector of node k, which is a 3×1 dimensional vector; V i =[V ia ,V ib ,V ic ] T is the three-phase voltage vector of node i, which is a 3×1-dimensional vector; is the active power of the branch, which is a 3×1 dimensional vector; is the reactive power of the branch, which is a 3×1 dimensional vector.

[0082] Step 204: According to the linearized equation, the three-phase unbalanced power flow system equation is linearized to generate a three-phase unbalanced power flow linearized equation.

[0083] In the embodiment of the present invention, the approximate linearization is obtained by referring to the three-phase balanced linearized power flow, and the following is obtained: and The approximate linear representation of is:

[0084]

[0085] Among them, u pik and u qik They are respectively the voltage loss to active power P ik and reactive power Q ik Sensitivity coefficient of deviation; f pik 、f qik They are respectively active power loss and active power P ik and reactive power Q ik Sensitivity coefficient of deviation; g pik , g qik Respectively, the reactive power loss is proportional to the active power P ik and reactive power Q ik The sensitivity coefficient of the deviation; P ik0 , Q ik0 are the initial state active power and initial state reactive power respectively.

[0086] In the embodiment of the present invention, a distribution network power flow linearization method is introduced to transform a complex non-convex nonlinear model into a linear model that is easier to solve.

[0087] Step 205: Obtain power grid operation parameters of the three-phase unbalanced power flow system.

[0088] In an embodiment of the present invention, the power grid operation parameters include but are not limited to the initial sensitivity coefficient of voltage loss to the deviation of active power and reactive power, the initial sensitivity coefficient of active power loss to the deviation of active power and reactive power, the sensitivity coefficient of reactive power loss to the deviation of active power and reactive power, the initial state active power, the initial state reactive power, the square value of the voltage of the source node in the three-phase system, the complex value of the power supply voltage, the three-phase voltage vector of the node, the three-phase impedance matrix of the node, the square value of the node voltage, the active load demand of the node, the reactive load demand of the node, the fixed active load and reactive load of the node under nominal voltage conditions, and the linear coefficient of the active load and reactive load of the node with the voltage.

[0089] Step 206: Generate key system parameters of the three-phase unbalanced power flow system according to the pre-constructed three-phase unbalanced power flow linearization equation and the power grid operation parameters.

[0090] Specifically, the three-phase unbalanced power flow linearization equation is calculated according to the power grid operation parameters, and the key system parameters of the three-phase unbalanced power flow system are obtained by solving the equation. The key system parameters of the three-phase unbalanced power flow system include but are not limited to the corrected loss parameters, voltage amplitude, power flow, power deviation and sensitivity coefficient.

[0091] Among them, the corrected loss parameters include: voltage loss correction item Active power loss correction and reactive power loss correction

[0092] The power flow includes: the active power of the branch and the reactive power of the branch.

[0093] Among them, the power deviation is the active power P of the branch ik The initial active power P ik0 The difference between ΔP ik , and the branch reactive power Q ik With the initial state reactive power Q ik0 The difference between ΔQ ik .

[0094] Among them, the sensitivity coefficients include: the sensitivity coefficient u of voltage loss to active power deviation pik , the sensitivity coefficient u of voltage loss to reactive power deviation qik , the sensitivity coefficient of active power loss to active power deviation f pik , the sensitivity coefficient f of active power loss to reactive power deviation qik , the sensitivity coefficient of reactive power loss to active power deviation g pik , the sensitivity coefficient of reactive power loss to reactive power deviation g qik .

[0095] Furthermore, the operation of the power grid system is controlled according to the key system parameters, and the unbalanced current and voltage fluctuations that may occur in the power grid are processed by approximate linearization, which effectively improves the stability and reliability of the power grid. This innovative method not only simplifies the complex power flow calculation, but also improves the calculation efficiency and accuracy, providing strong support for the intelligent charging of electric vehicle clusters.

[0096] The present invention helps to extend the service life of key equipment (such as transformers, generators, etc.) in the power system by reducing the equipment load imbalance caused by three-phase imbalance, reduces the negative impact of unbalanced current and voltage on the equipment, further improves the reliability and stability of the equipment, and provides a strong guarantee for the long-term operation and maintenance of the power system.

[0097] Step 207: construct an electric vehicle model according to the interaction mode between the electric vehicle and the power grid.

[0098] In the embodiment of the present invention, when an electric vehicle interacts with the power grid, there are two models: vehicle-to-grid (V2G) and grid-to-vehicle (G2V). The present invention considers both models to construct an electric vehicle model. For an individual electric vehicle, based on the charging and discharging constraints and the battery state of charge constraints, its model can be expressed as follows:

[0099] P min ≤P ch ≤P max

[0100] P min ≤P dis ≤P max

[0101]

[0102] SOC min ≤SOC t ≤SOC max

[0103] SOC need ≤SOC out ≤1

[0104] P h =0, if t<t in or t>t out

[0105] Among them, P ch , P dis Respectively represent the charging power and discharging power of the electric vehicle battery; P maxIndicates the upper limit of charging power or discharging power, P min Indicates the lower limit of charging power or discharging power, that is, the power range of charging and discharging equipment; SOC t , SOC t+1 Respectively represent the charging state of the battery at time t and t+1; SOC need Indicates the minimum required state of the battery when completing a task or traveling; SOC out Indicates the minimum battery state of charge when the vehicle leaves the charging station; SOC max Indicates the upper limit of battery state of charge, SOC min Represents the lower limit of battery state of charge, that is, battery state of charge constraint; η c , η f Respectively represent the charging efficiency and discharging efficiency; P h Indicates charging state, non-charging state P h =0, indicating that EV can only be charged and discharged when connected to a charging pile, t in Indicates that before entering the charging station, t out Indicates immediately after the charging station; Δt indicates the time interval; C N Indicates the rated capacity of the battery.

[0106] In the embodiment of the present invention, the electric vehicle model makes full use of the energy storage characteristics of the electric vehicle and integrates the two interaction modes of V2G and G2V, so that the electric vehicle can interact with the power grid in a two-way manner, enhance the flexibility and responsiveness of the power grid, and is responsible for managing and controlling the charging and discharging process of the electric vehicle to ensure efficient and safe charging.

[0107] Step 208: Based on the Minkowski sum, the electric vehicle model is mapped to the Minkowski space to construct a generalized energy storage model of the electric vehicle cluster.

[0108] In the embodiment of the present invention, in order to simplify the model and reduce the number of variables, a generalized energy storage model based on the Minkowski sum is adopted. The Minkowski sum is the sum of two point sets, which is used to describe their expansion or extension. The Minkowski sum is the sum of the point sets of two Euclidean spaces, also known as the expansion set of these two spaces. The Minkowski sum of point sets A and B is defined as:

[0109] A⊕B={a+b|a∈A,b∈B}

[0110] Among them, A and B represent two variable spaces; a and b are elements in them respectively; A⊕B represents their Minkowski sum.

[0111] In the embodiment of the present invention, the Minkowski sum is applied to construct the energy storage model of the electric vehicle cluster. Specifically, the energy storage space of the individual electric vehicles is regarded as a point set, and the generalized energy storage model of the entire cluster is obtained through the Minkowski sum operation. Specifically, based on the Minkowski sum, the electric vehicle model is mapped to the Minkowski space, and the generalized energy storage model of the electric vehicle cluster is obtained as follows:

[0112]

[0113] Among them, j is the serial number of each electric vehicle cluster; is the charging power of electric vehicle cluster j at time t; is the upper limit of the charging power of electric vehicle cluster j at time t; is the discharge power of electric vehicle cluster j at time t; is the lower limit of the charging power of electric vehicle cluster j at time t; S j,t is the residual energy of electric vehicle cluster j at time t; S j,t is the residual energy of electric vehicle cluster j at time t-1; η ch , η dis They represent charging efficiency and discharging efficiency respectively; Δt represents the time interval; ΔS j,t It represents the change in generalized energy storage capacity caused by individual plugging and unplugging of electric vehicles, which has nothing to do with charging and discharging, but only depends on whether the EV is connected to the grid; is the upper limit of the remaining energy of electric vehicle cluster j at time t; is the lower limit of the remaining energy of electric vehicle cluster j at time t.

[0114] In the embodiment of the present invention, the generalized energy storage model based on the electric vehicle cluster aims to alleviate the three-phase imbalance problem of the power grid and improve the stability of the power system by intelligently scheduling the energy storage resources of the electric vehicle cluster; it can comprehensively consider the energy storage capacity of all electric vehicles in the cluster and provide a more accurate and efficient decision-making basis for demand response.

[0115] In the embodiment of the present invention, by converting the electric vehicle cluster into a generalized energy storage system, the flexibility of energy is significantly improved. The energy storage characteristics of the electric vehicle battery are maximized, allowing the system to store and release electrical energy more flexibly. This not only helps to balance the load demand of the power system, but also promotes the integration and utilization of renewable energy. Based on the electric vehicle cluster, by adopting the generalized energy storage model of Minkowski and, the electric vehicle cluster connected by multiple electric vehicle aggregators is regarded as a whole, which effectively reduces the complexity of modeling, improves the calculation efficiency, and realizes the centralized management and optimal utilization of distributed energy.

[0116] Step 209: Generate the energy storage surplus energy of each electric vehicle cluster based on the pre-built generalized energy storage model of the electric vehicle cluster and the key system parameters.

[0117] Specifically, a generalized energy storage model of the electric vehicle cluster is inputted according to key system parameters to perform calculations, and the energy storage surplus energy of each electric vehicle cluster is obtained.

[0118] Step 210: construct a two-layer demand response model, wherein the upper layer model is a target model for minimizing energy management costs, and the lower layer model is a target model for maximizing total social energy efficiency.

[0119] It is worth noting that the demand response model is built based on the following assumptions:

[0120] (1) Complete information assumption, no information gap. That is, each EVA can accurately obtain the intraday prediction results of other EVAs, including but not limited to the initial SOC, target SOC, charging start time and end time, etc.

[0121] (2) The upstream power grid is large enough, and the bids of each EVA will not affect the power supply curve of the power generation.

[0122] (3) Assume that the three EVAs A, B, and C given in the embodiment of the present invention are respectively connected to the a, b, and c phases of the same node of the micro-electric network.

[0123] In the intraday stage, a two-layer demand response model is constructed to ensure that the energy management cost is minimized while meeting the charging demand of electric vehicles. In the upper model, each electric vehicle aggregator (EVA) carefully formulates demand response strategies and power purchase plans based on the actual intraday data of its own generalized energy storage, with the goal of minimizing energy management costs. These strategies and plans not only take into account the charging needs of electric vehicles, but also take into account factors such as the power supply capacity of the power grid and electricity price fluctuations, ensuring the maximization of the economic benefits of EVA.

[0124] In order to further optimize the upper model, a dynamic prediction model for electric vehicle charging demand is introduced. Based on historical data and real-time monitoring, the model uses machine learning and artificial intelligence technology to accurately predict the charging demand of electric vehicles in the future. In this way, EVA can more accurately formulate charging strategies and power purchase plans to cope with demand changes in different time periods and improve energy efficiency. Its dynamic prediction model is:

[0125]

[0126] in, is the expected value of charging demand at time t+1; D avgis the historical average charging demand value; D(t) is the actual charging demand value at time t; α is an adjustable parameter used to adjust the relationship between the expected value and the actual value.

[0127] It is worth noting that by making comprehensive use of historical data and the latest observations, future charging demand can be predicted more accurately. The choice of parameter α can be adjusted according to actual conditions to adapt to the predicted demand in different time periods and scenarios. This prediction model provides an important reference for the management and scheduling of electric vehicle charging demand, which helps to optimize charging strategies and improve the operating efficiency of the power system.

[0128] The lower model is led by the power trading center, which balances the market based on the charging and discharging data reported by each EVA with the goal of maximizing the total social energy efficiency. In this process, the power trading center needs to comprehensively consider factors such as the power supply capacity of the power grid, electricity price fluctuations, market demand, and the power purchase plans of each EVA, and achieve supply and demand balance and efficient operation of the power market by optimizing the allocation and scheduling of power resources. At the same time, the distribution system operator (DSO) plays a vital role in this process, responsible for verifying and ensuring the security of the distribution network. By using advanced monitoring technology and analysis tools, it is ensured that the power grid always maintains a safe and stable operating state while bearing the charging load of electric vehicles.

[0129] At the same time, the distribution system operator (DSO) has strengthened the security measures for the distribution network. In addition to the use of advanced monitoring technology and analysis tools, intelligent sensing devices have been introduced to monitor the operating status of the distribution network in real time, and to respond and adjust quickly through the automatic control system to ensure that the power grid always maintains a safe and stable operating state while bearing the charging load of electric vehicles. In order to evaluate the load condition of the power grid, a power grid security verification model of DSO is proposed: To ensure that the power grid remains safe and stable when carrying the charging load of electric vehicles. It judges the safety of the power grid by comparing the ratio of the actual load of the power grid to the rated load. If the actual load exceeds the rated load, there may be a risk of power grid overload, and measures need to be taken to reduce the load pressure.

[0130] Step 211: convert the double-layer demand response model into a single-layer model to construct an intraday demand response model.

[0131] In order to simplify the solution process of the two-layer model and improve the optimization efficiency, the KKT (Karush-Kuhn-Tucker) condition is used to transform the two-layer problem into a single-layer problem for solution, and the intraday demand response model is obtained:

[0132]

[0133] in, The cost of electric vehicles in the intraday market needs to be minimized; is the electricity price at time t in the intraday market; is the charging power of electric vehicle cluster j at time t; is the discharge power of electric vehicle cluster j at time t; S j,t is the residual energy of electric vehicle cluster j at time t; S j,t is the residual energy of electric vehicle cluster j at time t-1; They represent charging efficiency and discharging efficiency respectively; Δt represents the time interval; ΔS j,t It represents the change of generalized energy storage capacity caused by plugging and unplugging individual electric vehicles, which has nothing to do with charging and discharging, but only depends on whether the EV is connected to the grid; P ij,t represents the power flow from node i to node j; represents the total power flowing out of node j; represents the load demand of node j at time t; U A , U B , U C Indicates the actual value of the three-phase voltage; U arg Indicates the target reference value of the three-phase voltage to ensure that the voltage deviation in the system does not exceed the specified range.

[0134] In the embodiment of the present invention, through this conversion, the optimal demand response strategy and power purchase plan can be solved more efficiently, and the intelligent charging and demand response of electric vehicle clusters in the power system can be realized. This not only helps to improve the user experience of electric vehicles, but also provides strong support for the sustainable development of the power market.

[0135] Step 212: Based on the rolling optimization modeling strategy, the intraday demand response model is optimized to construct a real-time demand response optimization model.

[0136] In the embodiment of the present invention, the real-time power demand response system uses a rolling optimization modeling strategy to enable the electric vehicle aggregator (EVA) to quickly adjust the spot market trading strategy with the power grid based on the real-time collected charging pile power deviation data, thereby ensuring the real-time balance of its own power. In this process, the upper objective function focuses on minimizing the operating cost of EVA, while the lower objective function is committed to maximizing social welfare. Through rolling optimization, the model can not only respond to the demand fluctuations of the power grid in real time, but also fully consider the operational benefits of the electric vehicle cluster, thereby improving the overall energy utilization efficiency.

[0137] Such a design not only provides a flexible and efficient solution for the intelligent charging and demand response of electric vehicles in the power system, but also helps promote the stable operation and sustainable development of the power system. With the continuous expansion of the electric vehicle market and the increasing complexity of the power system, the importance of the real-time power demand response system will become more and more prominent, providing strong support for future energy management. The real-time demand response optimization model is as follows:

[0138]

[0139] in, The cost of electric vehicles in the intraday market needs to be minimized; is the electricity price at time t in the intraday market; is the electricity price at time t in the real-time market; is the daily charging power of electric vehicle cluster j at time t; is the daily discharge power of electric vehicle cluster j at time t; is the real-time charging power of electric vehicle cluster j at time t; is the real-time discharge power of electric vehicle cluster j at time t; S j,t is the residual energy of electric vehicle cluster j at time t; S j,t is the residual energy of electric vehicle cluster j at time t-1; They represent charging efficiency and discharging efficiency respectively; Δt represents the time interval; ΔS j,t It represents the change of generalized energy storage capacity caused by plugging and unplugging individual electric vehicles, which has nothing to do with charging and discharging, but only depends on whether the EV is connected to the grid; P ij,t represents the power flow from node i to node j; represents the total power flowing out of node j; represents the load demand of node j at time t; U A , U B , U C Indicates the actual value of the three-phase voltage; U arg Indicates the target reference value of the three-phase voltage to ensure that the voltage deviation in the system does not exceed the specified range.

[0140] In the embodiment of the present invention, the real-time demand response optimization model is an intelligent charging and demand response (DR) optimization model of an electric vehicle cluster, which optimizes the charging and discharging behavior of electric vehicles by considering the day-ahead (DA) and real-time (RT) power demand response strategies to promote the stability of the power system and improve the economy of electric vehicles. The model goal is to minimize the cost of charging and discharging electric vehicles, and ensure that electric vehicles can be charged and discharged intelligently while meeting power demand and voltage constraints, thereby achieving better power management effects.

[0141] Step 213: Generate coordinated control parameters for each electric vehicle cluster by calculating based on key system parameters and energy storage surplus energy using a pre-built real-time demand response optimization model.

[0142] In the embodiment of the present invention, the real-time demand response optimization model is calculated based on the key system parameters and the energy storage surplus energy input to obtain the coordinated control parameters of each electric vehicle cluster. The coordinated control parameters include the daily charging power of the electric vehicle cluster, the real-time charging power of the electric vehicle cluster, the daily discharge power of the electric vehicle cluster, the real-time discharge power of the electric vehicle cluster, and the energy storage state of the electric vehicle cluster.

[0143] In the embodiment of the present invention, a real-time demand response optimization model is used to ensure that at each time t, the charging and discharging of electric vehicles maintains a balance in the load of the power system; and ensure that the charging / discharging strategy of the electric vehicle does not cause the voltage deviation to exceed the specified range.

[0144] It is worth noting that in the real-time demand response optimization model, three-phase power flow linearization is used to simplify the power flow calculation problem of the power system and link it with the charging scheduling of electric vehicles, which is mainly reflected in the following aspects:

[0145] Relationship between voltage state and power flow balance: Voltage changes will affect power flow and load balance. Through three-phase power flow linearization, the voltage changes caused by electric vehicle charging / discharging can be directly calculated during the optimization process, which in turn affects the stability of the power system.

[0146] Load balance constraints: The relationship between the charging and discharging power of electric vehicles and the load demand and power flow in the power system is established through the power flow equation. Three-phase balanced power flow linearization helps to quickly calculate the impact of electric vehicle charging and discharging on the system load balance, especially when three-phase imbalance is involved.

[0147] Interaction between demand response and electricity market: By optimizing the charging / discharging strategy of electric vehicles, the demand and supply of the electricity market can be better balanced. Especially when performing demand response under different electricity prices and load conditions, three-phase power flow linearization provides efficient calculation of power system voltage and power flow.

[0148] Step 214: Obtain charging pile power deviation time series data and current power deviation data within a historical time period.

[0149] In the embodiment of the present invention, the historical time period may be determined according to actual needs, and the embodiment of the present invention does not limit this.

[0150] In the embodiment of the present invention, the charging pile power deviation time series data includes historical moments in the historical time period and corresponding historical power deviation data; the current power deviation data may be the most recent power deviation data obtained by measurement.

[0151] Step 215: Generate a power deviation prediction value according to a preset power deviation sensitivity parameter, the charging pile power deviation time series data and the current power deviation data.

[0152] Specifically, according to the charging pile power deviation time series data, the average value of the charging pile power deviation in the historical time period is generated; E =D a +α×(D o -D a ), calculate the power deviation sensitivity parameter, the average value of the charging pile power deviation in the historical time period and the current power deviation data to generate the power deviation prediction value.

[0153] Among them, D E is the predicted value of electric energy deviation; D a is the average value of the charging pile power deviation in the historical period; D o is the current power deviation data; α is the power deviation sensitivity parameter.

[0154] It is worth noting that the power deviation sensitivity parameter α is an adjustable parameter used to adjust the relationship between the expected value and the latest observed value, and determines the sensitivity of the expected value to the change of the latest observed value. A larger α value will make the expected value follow the change of the latest observed value faster, while a smaller α value will make the expected value relatively stable and will not be affected by the large fluctuations of the latest observed value.

[0155] In the embodiment of the present invention, by generating the power deviation prediction value, EVA can more accurately predict the trend of the power deviation of the charging pile, so as to formulate a more effective charging strategy to ensure the real-time balance of power and optimize its operating cost.

[0156] Step 216: Generate a charging strategy control value using a preset charging control sensitivity parameter and an electric energy deviation prediction value.

[0157] Specifically, through A t =β×D E , the charging control sensitivity parameters and the predicted value of electric energy deviation are calculated to generate the charging strategy control quantity.

[0158] Among them, A t represents the adjustment amount of the charging strategy; β is the charging control sensitivity parameter, which represents the sensitivity of the adjustment amount to the deviation and determines the strength of the relationship between the size of the adjustment amount and the deviation; D E is the predicted value of electric energy deviation.

[0159] In the embodiment of the present invention, by generating a charging strategy control amount, an electric vehicle aggregator (EVA) can be helped to adjust its charging strategy according to the real-time charging pile power deviation. When the deviation is large, the adjustment amount will also increase accordingly to adapt to the change in power demand. By adjusting the charging control sensitivity parameter β, the sensitivity of the adjustment amount to the deviation can be flexibly controlled, thereby achieving accurate adjustment of the charging strategy.

[0160] The embodiment of the present invention is implemented on a computer. An IEEE33 node test system is used; through the demand response strategy and power purchase plan in the upper model, EVA can reasonably arrange the charging time and charging amount of electric vehicles according to the power grid load and electricity price fluctuations. During the peak period of power grid load, EVA can postpone the charging time of some electric vehicles or reduce the charging power through flexible scheduling to reduce the load pressure of the substation. Secondly, the lower model is dominated by the power trading center, and its goal is to maximize the total energy efficiency of society. In this process, the power trading center comprehensively considers factors such as power grid power supply capacity, electricity price fluctuations, demand, and power purchase plans of each EVA, and optimizes the allocation and scheduling of power resources to achieve supply and demand balance and efficient operation of the power system. Through such a mechanism, EVA can be encouraged to take more load adjustment measures during peak periods to further smooth the substation load. In summary, the cluster control strategy can effectively smooth the substation load and improve the operation stability and efficiency of the power system by optimizing the charging behavior of electric vehicles to make it more intelligent and flexible. This not only helps to reduce the load peak of the power system and reduce the pressure of the power system, but also improves the reliability and sustainability of the power grid, and provides strong support for future energy management and operation. Orderly charging and discharging can effectively smooth out the load in the substation and improve the stability and reliability of the power grid, while disordered charging and discharging will increase the volatility of the power grid load and have an adverse effect on the power system. By reasonably guiding and managing the charging behavior of electric vehicles, the potential of electric vehicles in smoothing the load in the substation can be maximized to provide support for the sustainable development and operation of the power system. It can be seen from the actual test results that the technical solution provided by this application can maintain the three-phase imbalance of the test system at a good level. In practical applications, this will significantly improve the three-phase imbalance problem. Through the verification of IEEE33 node examples and actual data, the intelligent charging system of the present invention can derive the optimal charging and discharging strategy, and verify its correctness and effectiveness through the program. This invention provides a comprehensive and efficient solution for the intelligent charging of electric vehicle clusters in the power system, and provides strong support for the stable operation of the power system and the popularization and promotion of electric vehicles.

[0161] The uniqueness of the solution of the present invention lies in its multi-faceted innovation, which provides a comprehensive, efficient and feasible solution to the intelligent charging and demand response problems of electric vehicle clusters in power systems.

[0162] 1. The present invention adopts a generalized energy storage concept based on Minkowski addition, and regards the electric vehicle cluster as a flexible energy storage system. This innovative idea makes full use of the energy storage characteristics of electric vehicle batteries, integrates the electric vehicle cluster into a distributed energy storage device, and injects new vitality into the power system. Through this concept, electric vehicles not only become energy consumers, but also stabilizers and regulators of the power grid.

[0163] 2. In order to solve the three-phase imbalance problem that is common in power systems, the present invention proposes a method for approximate linearization of the three-phase unbalanced power flow in the distribution network. This method effectively improves the stability and reliability of the power grid by approximate linearization of the unbalanced current and voltage fluctuations that may occur in the power grid. This innovative method not only simplifies the complex power flow calculation, but also improves the calculation efficiency and accuracy, providing strong support for the intelligent charging of electric vehicle clusters.

[0164] 3. The present invention also constructs a demand response model for multiple electric vehicle aggregators in the power system. This model provides a flexible strategy for the energy management of electric vehicle clusters, and encourages electric vehicles to perform intelligent charging and discharging during market peak and trough periods by optimizing demand response strategies. This not only helps to balance the load of the power system and improve energy efficiency, but also promotes the interaction between electric vehicles and the power grid, and enhances the flexibility and responsiveness of the power system.

[0165] In summary, the uniqueness of the solution of the present invention lies in its innovative generalized energy storage concept, the approximate linearization method of the three-phase unbalanced power flow of the distribution network, and the multi-electric vehicle aggregator demand response model. These innovations together constitute a comprehensive, efficient and feasible solution, providing strong support for the intelligent charging and demand response problems of electric vehicle clusters in the power system.

[0166] It is worth noting that the acquisition, storage, use, and processing of data in the technical solution of this application are in compliance with the relevant provisions of laws and regulations. The user information in the embodiments of this application is obtained through legal and compliant channels, and the acquisition, storage, use, and processing of user information are authorized and agreed by the customer.

[0167] It is worth noting that the information collected in this application is information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of relevant data comply with the relevant laws, regulations and standards of relevant countries and regions, take necessary confidentiality measures, do not violate public order and good customs, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0168] It is worth noting that the technical solution provided in this application provides users with corresponding operation entrances for them to choose to agree or reject the automated decision-making results; if the user chooses to reject, the expert decision-making process will be entered.

[0169] In the technical solution of the electric vehicle cluster coordinated control method of the three-phase unbalanced power flow system provided by the embodiment of the present invention, the power grid operating parameters of the three-phase unbalanced power flow system are obtained; through the pre-constructed three-phase unbalanced power flow linearization equation, the system key parameters of the three-phase unbalanced power flow system are generated according to the power grid operating parameters; through the pre-constructed generalized energy storage model of the electric vehicle cluster, the energy storage surplus energy of each electric vehicle cluster is generated according to the system key parameters; through the pre-constructed real-time demand response optimization model, calculation is performed according to the system key parameters and the energy storage surplus energy to generate the coordinated control parameters of each electric vehicle cluster, which can improve the sensitivity, real-time performance and voltage stability of the system, reduce operating costs and improve overall efficiency, and provide strong support for the popularization and sustainable development of electric vehicles.

[0170] Figure 3 A schematic diagram of the structure of an electric vehicle cluster coordination control device for a three-phase unbalanced power flow system provided by an embodiment of the present invention, the device is used to execute the above-mentioned electric vehicle cluster coordination control method for a three-phase unbalanced power flow system, such as Figure 3 As shown, the device includes: a power grid operation parameter acquisition unit 11, a system key parameter generation unit 12, an energy storage surplus energy generation unit 13 and a coordination control unit 14.

[0171] The power grid operation parameter acquisition unit 11 is used to acquire the power grid operation parameters of the three-phase unbalanced power flow system.

[0172] The system key parameter generating unit 12 is used to generate the system key parameters of the three-phase unbalanced power flow system according to the power grid operation parameters through the pre-constructed three-phase unbalanced power flow linearization equation.

[0173] The energy storage surplus energy generating unit 13 is used to generate the energy storage surplus energy of each electric vehicle cluster according to the key parameters of the system through the pre-constructed generalized energy storage model of the electric vehicle cluster.

[0174] The coordination control unit 14 is used to generate coordination control parameters of each electric vehicle cluster by calculating according to key system parameters and energy storage surplus energy through a pre-built real-time demand response optimization model.

[0175] In the embodiment of the present invention, the device further includes: a basic equation construction unit 15, a linearization processing unit 16, a three-phase unbalanced power flow system equation construction unit 17 and a three-phase unbalanced power flow linearization equation construction unit 18.

[0176] The basic equation construction unit 15 is used to construct the basic equation of the three-phase balanced power flow system.

[0177] The linearization processing unit 16 is used to perform linearization processing on the nonlinear terms of the basic equation of the three-phase balanced power flow system to generate a linearized equation.

[0178] The three-phase unbalanced power flow system equation construction unit 17 is used to introduce three-phase unbalanced factors into the three-phase balanced power flow system basic equation to construct the three-phase unbalanced power flow system equation.

[0179] The three-phase unbalanced power flow linearization equation construction unit 18 is used to perform linearization processing on the three-phase unbalanced power flow system equation according to the linearization equation to generate the three-phase unbalanced power flow linearization equation.

[0180] In the embodiment of the present invention, the device further includes: an electric vehicle model building unit 19 and a generalized energy storage model building unit 20 .

[0181] The electric vehicle model building unit 19 is used to build an electric vehicle model according to the interaction mode between the electric vehicle and the power grid.

[0182] The generalized energy storage model building unit 20 is used to map the electric vehicle model to the Minkowski space based on the Minkowski sum, and to build a generalized energy storage model of the electric vehicle cluster.

[0183] In the embodiment of the present invention, the device further includes: a double-layer demand response model construction unit 21, a single-layer conversion unit 22 and a real-time demand response optimization model construction unit 23.

[0184] The double-layer demand response model construction unit 21 is used to construct a double-layer demand response model, wherein the upper layer model is a target model for minimizing energy management costs, and the lower layer model is a target model for maximizing total social energy efficiency.

[0185] The single-layer conversion unit 22 is used to perform single-layer conversion on the double-layer demand response model to construct an intra-day demand response model.

[0186] The real-time demand response optimization model building unit 23 is used to optimize the intraday demand response model based on the rolling optimization modeling strategy to build a real-time demand response optimization model.

[0187] In the embodiment of the present invention, the device further includes: a deviation data acquisition unit 24 , an electric energy deviation prediction unit 25 and a charging strategy control amount generation unit 26 .

[0188] The deviation data acquisition unit 24 is used to acquire the charging pile power deviation time series data and the current power deviation data within the historical time period.

[0189] The power deviation prediction unit 25 is used to generate a power deviation prediction value according to a preset power deviation sensitivity parameter, the charging pile power deviation time series data and the current power deviation data.

[0190] The charging strategy control amount generating unit 26 is used to generate the charging strategy control amount according to the preset charging control sensitivity parameter and the electric energy deviation prediction value.

[0191] In the scheme of the embodiment of the present invention, the grid operation parameters of the three-phase unbalanced power flow system are obtained; through the pre-constructed three-phase unbalanced power flow linearization equation, the system key parameters of the three-phase unbalanced power flow system are generated according to the grid operation parameters; through the pre-constructed generalized energy storage model of the electric vehicle cluster, the energy storage surplus energy of each electric vehicle cluster is generated according to the system key parameters; through the pre-constructed real-time demand response optimization model, calculations are performed based on the system key parameters and the energy storage surplus energy to generate the coordinated control parameters of each electric vehicle cluster, which can improve the system's sensitivity, real-time performance and voltage stability, reduce operating costs and improve overall efficiency, and provide strong support for the popularization and sustainable development of electric vehicles.

[0192] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer device, and specifically, the computer device may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0193] An embodiment of the present invention provides a computer device, including a memory and a processor, the memory is used to store information including program instructions, the processor is used to control the execution of the program instructions, and when the program instructions are loaded and executed by the processor, the steps of the embodiment of the electric vehicle cluster coordinated control method for the above-mentioned three-phase unbalanced power flow system are implemented. For a specific description, please refer to the embodiment of the electric vehicle cluster coordinated control method for the above-mentioned three-phase unbalanced power flow system.

[0194] Reference below Figure 4 , which shows a schematic diagram of the structure of a computer device 600 suitable for implementing an embodiment of the present application.

[0195] like Figure 4 As shown, the computer device 600 includes a central processing unit (CPU) 601, which can perform various appropriate operations and processes according to a program stored in a read-only memory (ROM) 602 or a program loaded from a storage part 608 into a random access memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the computer device 600 are also stored. The CPU 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0196] The following components are connected to the I / O interface 605: an input section 606 including a keyboard, a mouse, etc.; an output section 607 including a cathode ray tube (CRT), a liquid crystal feedback device (LCD), etc., and a speaker, etc.; a storage section 608 including a hard disk, etc.; and a communication section 609 including a network interface card such as a LAN card, a modem, etc. The communication section 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to the I / O interface 605 as needed. A removable medium 611, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 610 as needed, so that a computer program read therefrom is installed as needed as the storage section 608.

[0197] In particular, according to an embodiment of the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product, which includes a computer program tangibly contained on a machine-readable medium, and the computer program includes program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication part 609, and / or installed from the removable medium 611.

[0198] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0199] For the convenience of description, the above device is described in various units according to their functions. Of course, when implementing the present application, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0200] 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.

[0201] 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 including 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.

[0202] 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 The steps for the functions specified in one or more boxes.

[0203] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0204] The acquisition, storage, use, and processing of data in the technical solution of this application comply with the relevant provisions of national laws and regulations.

[0205] It should be noted that in the embodiments of the present application, certain software, components, models and other existing solutions in the industry may be mentioned, and they should be regarded as exemplary. Their purpose is only to illustrate the feasibility of implementing the technical solution of the present application, but it does not mean that the applicant has or will necessarily use the solution.

[0206] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment or an embodiment in combination with software and hardware. Moreover, the present application may adopt 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.) that contain computer-usable program code.

[0207] The present application may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0208] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0209] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A coordinated control method for electric vehicle clusters in a three-phase unbalanced power flow system, characterized in that: The method comprises: Obtain the power grid operation parameters of the three-phase unbalanced power flow system; Generate key system parameters of the three-phase unbalanced power flow system according to the power grid operation parameters by using the pre-constructed three-phase unbalanced power flow linearization equation; Generate the energy storage surplus energy of each electric vehicle cluster according to the key parameters of the system through a pre-built generalized energy storage model of the electric vehicle cluster; Through the pre-built real-time demand response optimization model, calculations are performed based on the key parameters of the system and the remaining energy of the energy storage to generate the coordinated control parameters of each electric vehicle cluster.

2. The electric vehicle cluster coordinated control method for a three-phase unbalanced power flow system according to claim 1, characterized in that: Before generating the key system parameters of the three-phase unbalanced power flow system according to the power grid operation parameters by using the pre-constructed three-phase unbalanced power flow linearization equation, the method further includes: Construct the basic equations of three-phase balanced power flow system; Linearizing the nonlinear terms of the basic equation of the three-phase balanced power flow system to generate a linearized equation; Introducing a three-phase unbalanced factor into the three-phase balanced power flow system basic equation to construct a three-phase unbalanced power flow system equation; According to the linearized equation, the three-phase unbalanced power flow system equation is linearized to generate a three-phase unbalanced power flow linearized equation.

3. The electric vehicle cluster coordinated control method for a three-phase unbalanced power flow system according to claim 1, characterized in that: Before generating the energy storage surplus energy of each electric vehicle cluster according to the key system parameters through the pre-built generalized energy storage model of the electric vehicle cluster, the method further includes: Construct an electric vehicle model based on the interaction mode between electric vehicles and the power grid; Based on the Minkowski sum, the electric vehicle model is mapped to the Minkowski space to construct a generalized energy storage model of the electric vehicle cluster.

4. The electric vehicle cluster coordinated control method for a three-phase unbalanced power flow system according to claim 1, characterized in that: Before the coordinated control parameters of each electric vehicle cluster are generated by calculating according to the key system parameters and the remaining energy of the energy storage through the pre-built real-time demand response optimization model, the method further includes: Construct a two-layer demand response model, the upper model is a model with the goal of minimizing energy management costs, and the lower model is a model with the goal of maximizing the total social energy efficiency; Performing single-layer conversion on the double-layer demand response model to construct an intra-day demand response model; Based on the rolling optimization modeling strategy, the intraday demand response model is optimized to construct the real-time demand response optimization model.

5. The electric vehicle cluster coordinated control method for a three-phase unbalanced power flow system according to claim 1, characterized in that: The method further comprises: Obtain the charging pile power deviation time series data and current power deviation data within the historical time period; Generate a power deviation prediction value according to the preset power deviation sensitivity parameter, the charging pile power deviation time series data and the current power deviation data; The charging strategy control amount is generated by using the preset charging control sensitivity parameter and the electric energy deviation prediction value.

6. An electric vehicle cluster coordination control device for a three-phase unbalanced power flow system, characterized in that: The device comprises: A power grid operation parameter acquisition unit, used to acquire power grid operation parameters of a three-phase unbalanced power flow system; A system key parameter generating unit, used for generating system key parameters of a three-phase unbalanced power flow system according to the power grid operation parameters by using a pre-constructed three-phase unbalanced power flow linearization equation; An energy storage surplus energy generation unit is used to generate energy storage surplus energy of each electric vehicle cluster according to the key parameters of the system through a pre-built generalized energy storage model of the electric vehicle cluster; The coordination control unit is used to generate coordination control parameters of each electric vehicle cluster by calculating according to the key parameters of the system and the remaining energy of the energy storage through a pre-built real-time demand response optimization model.

7. The electric vehicle cluster coordination control device for a three-phase unbalanced power flow system according to claim 6, characterized in that: The device also includes: Basic equation building unit, used to build the basic equation of three-phase balanced power flow system; A linearization processing unit, used for performing linearization processing on the nonlinear terms of the basic equation of the three-phase balanced power flow system to generate a linearized equation; A three-phase unbalanced power flow system equation construction unit, used to introduce a three-phase unbalanced factor into the three-phase balanced power flow system basic equation to construct a three-phase unbalanced power flow system equation; The three-phase unbalanced power flow linearization equation construction unit is used to perform linearization processing on the three-phase unbalanced power flow system equation according to the linearization equation to generate the three-phase unbalanced power flow linearization equation.

8. A computer readable medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the electric vehicle cluster coordinated control method of the three-phase unbalanced power flow system described in any one of claims 1 to 5 is implemented.

9. A computer device comprising a memory and a processor, wherein the memory is used to store information including program instructions, and the processor is used to control the execution of the program instructions, characterized in that: When the program instructions are loaded and executed by the processor, the electric vehicle cluster coordinated control method of the three-phase unbalanced power flow system according to any one of claims 1 to 5 is implemented.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the electric vehicle cluster coordinated control method for a three-phase unbalanced power flow system according to any one of claims 1 to 5 is implemented.

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