Voltage regulation method, device, equipment and medium for distribution market in which electric vehicle clusters participate

Abstract

The present invention relates to the technical field of electric vehicle energy management, and in particular to a voltage regulation method, device, equipment and medium for a distribution market in which electric vehicle clusters participate. The method comprises: based on the characteristics of electric vehicle clusters, constructing a distribution network model including electric vehicle clusters and constructing an energy trading market model; based on the distribution network model and the energy trading market model, constructing a voltage regulation problem of a non-cooperative game; based on the voltage regulation problem, designing a distributed regulation algorithm for online parameter estimation; verifying the actual application effect of the distributed regulation algorithm for online parameter estimation through simulation; the voltage regulation problem of the present invention takes into account the energy trading between electric vehicle clusters and has guiding significance; the present invention also proposes a distributed algorithm containing online parameter estimation, which realizes dynamic adjustment based on real-time information, and the algorithm reduces the dependence on precise model information and has stronger adaptability and robustness.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicle energy management, and in particular to a voltage regulation method, device, equipment and medium for a power distribution market in which electric vehicle clusters participate. Background Art

[0002] In recent years, under favorable conditions such as technological innovation and policy support, the sales and ownership of electric vehicles in my country have grown rapidly. At present, my country has become the world's largest electric vehicle market. The rapid popularization of electric vehicles has brought new challenges to the stable operation of distribution networks. In order to improve economic benefits and promote energy consumption, electric vehicle clusters will spontaneously trade energy. Energy trading between electric vehicle clusters will affect the power flow in the distribution network, further causing changes in the node voltage of the distributed distribution network. Voltage fluctuations in the distribution network will weaken the performance of the distribution network and even cause potential dangers. How to coordinate and control the power generation behavior and energy trading behavior of each electric vehicle cluster is crucial for the voltage control of the distribution network.

[0003] However, the existing distributed distribution network voltage regulation problem only considers electrical factors. However, in a distributed distribution network dominated by electric vehicle clusters, the operation of the distribution network cannot ignore the economic benefits of electric vehicle clusters. In addition, power transactions between electric vehicle clusters will affect the power flow in the distribution network, thereby causing node voltage fluctuations. How to control the node voltage of the distribution network while taking into account the economic benefits of each electric vehicle cluster is a major challenge currently faced. Therefore, it is necessary to establish a suitable distribution network voltage regulation model for electric vehicles to participate in the energy trading market.

[0004] The information disclosed in this background technology section is only intended to deepen the understanding of the overall background technology of the present invention, and should not be regarded as acknowledging or suggesting in any form that the information constitutes the prior art already known to those skilled in the art. Summary of the invention

[0005] The present invention provides a method, device, equipment and medium for regulating voltage in a power distribution market in which electric vehicle clusters participate, thereby effectively solving the problems in the background technology.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is: a voltage regulation method for a distribution market in which electric vehicle clusters participate, comprising the following steps:

[0007] S10: Based on the characteristics of the electric vehicle cluster, a distribution network model including the electric vehicle cluster is constructed; the distribution network model uses a linear Distflow model to obtain a power flow equation, and expresses a linear relationship between voltage and electric vehicle cluster power generation through an association matrix;

[0008] S20: Based on the distribution network model, an energy trading market model is constructed; the energy trading market model maintains the power balance of the system through power generation and power trading, ensures that the power generation power of the electric vehicle cluster meets the upper and lower bound constraints, and uses the trading model to determine the transaction price, ensures that the total amount of power trading of all electric vehicle clusters is zero, and calculates the transaction cost of the electric vehicle cluster;

[0009] S30: Based on the distribution network model and the energy trading market model, a non-cooperative game voltage regulation problem is constructed; the voltage regulation problem balances the economic benefits of the electric vehicle cluster and the voltage regulation demand, and guides the electric vehicle cluster to minimize the economic cost while achieving the effect of controlling the voltage of the distribution network node by increasing the penalty function of the voltage deviation;

[0010] S40: Based on the voltage regulation problem, design a distributed regulation algorithm for online parameter estimation; the distributed regulation algorithm for online parameter estimation includes variable iteration and parameter estimation, updating decision variables and Lagrange multipliers through information exchange and projected gradient method, performing online parameter estimation using Kalman filter, and updating control strategy according to parameter estimation values;

[0011] S50: verifying the actual application effect of the online parameter estimation distributed regulation algorithm through simulation, and checking the voltage regulation capability of the electric vehicle cluster in different scenarios as well as the stability and economy of the system.

[0012] Furthermore, in step S10, the distribution network model uses a linear Distflow model to obtain a power flow equation, including:

[0013]

[0014] Where V i represents the complex voltage at node i; r i,j and x i,j Respectively represent the resistance and reactance on the circuit (i, j); P i,j and Q i,j They represent the active power and reactive power flowing through line (i, j) respectively; v j represents the voltage at node j; P j，k and Q j，k Respectively represent the active power and reactive power flowing through line (j, k); represents the set of downstream nodes that are physically connected to node j; p j represents the active power injection of node j; q j represents the reactive power injection of node j; v i represents half of the square of the complex voltage at node i; E f Represents the set of all lines in the distribution network.

[0015] Furthermore, in step S10, the linear relationship between the voltage and the electric vehicle cluster power generation is represented by an association matrix, and the model includes:

[0016] v=Rg p +Xg q -Rd p -Xd q -M -T m 0 v s ;

[0017] Where R = M -T diag(r)M -1 With X = M -T diag(x)M -1 are all real symmetric positive definite matrices, and -M -T m 0 =1, 1 is a column vector whose elements are all 1; g p and g q They are defined as and A vector composed in order; d p and d q They are defined as and A vector composed in order; v s represents the fixed voltage of node 0; v represents the voltage of node 0. i Column vector composed of .

[0018] Furthermore, in step S20, the power generation of the electric vehicle cluster is ensured to meet the upper and lower bound constraints, and the model includes:

[0019]

[0020] In the formula, and Represents the minimum power and maximum power of active power generation and reactive power generation respectively; and They represent the active power generation and reactive power generation of the electric vehicle cluster respectively; N represents the set of all nodes except node 0.

[0021] Furthermore, in step S20, the transaction cost of the electric vehicle cluster is calculated, and the model includes:

[0022]

[0023] In the formula, c tr Represents the transaction unit price in the energy market; tr i represents the active power traded by electric vehicle cluster i; si <0 indicates the price sensitivity of electric vehicle cluster i; w i Indicates willingness to purchase; i tr (w i ,w -i ) represents the transaction cost function; W -i To remove w i The column vector composed of the purchase intention of the remaining electric vehicle clusters; 1 T Represents a column vector whose elements are all 1.

[0024] Furthermore, in step S30, a non-cooperative game voltage regulation problem is constructed, and the model includes:

[0025]

[0026] Au-b≥0;

[0027] v i =π i (g i ,g -i );

[0028] In the formula, J i (u i ,u -i ) represents the utility function; f i c (g i ) represents the cost of electricity generation; f i tr (w i ,w -i ) represents transaction costs; V(v i ) represents the voltage penalty function; represents local constraint; Au-b≥0 represents coupling constraint; v i =π i (g i ,g -i ) represents the relationship between node voltage and generated power.

[0029] Furthermore, in step S40, the control strategy is updated according to the parameter estimation value, and each electric vehicle cluster can calculate the partial derivative using the following model, which includes:

[0030]

[0031] In the formula, v 0 is the voltage regulation target; Denotes the utility function for u i The partial derivative of Indicates that the parameter estimates are used Reconstruct the coefficient matrix; represents the element in the i-th row and i-th column of the matrix, represents the element in the i+Nth row and i+Nth column of the matrix; w i represents the purchase intention of electric vehicle cluster i; v i is half of the square of the complex voltage amplitude at node i; and is a constant; and Respectively represents the active power generation and reactive power generation of the electric vehicle cluster.

[0032] Furthermore, in step S40, the steps of designing a distributed adjustment algorithm for online parameter estimation include:

[0033] S41: Initializing relevant variables in the electric vehicle cluster, including decision variables, information variables, Lagrange multipliers and parameter estimates of the electric vehicle cluster;

[0034] S42: performing the kth iteration, each electric vehicle cluster needs to exchange information with other electric vehicle clusters, wherein the exchanged information includes updating the decision variables and the information variables of the electric vehicle cluster, and adjusting the Lagrange multiplier;

[0035] S43: Execute the online parameter estimation value update, including:

[0036] Online updating of the parameter estimation values ​​of the electric vehicle cluster according to the coefficient matrix;

[0037] Calculating the estimated partial derivatives required for the next iteration using the updated estimated parameter values;

[0038] S44: In each iteration, coordination between electric vehicle clusters is achieved through the information exchange and the parameter estimation values ​​to achieve a globally optimized voltage regulation strategy.

[0039] The present invention also includes a voltage regulating device for a power distribution market in which electric vehicle clusters participate, using the above method, including:

[0040] Constructing a distribution network model unit, based on the characteristics of the electric vehicle cluster, constructing a distribution network model including the electric vehicle cluster, wherein the distribution network model uses a linear Distflow model to obtain a power flow equation, and uses an association matrix to represent a linear relationship between voltage and electric vehicle cluster power generation;

[0041] Constructing an energy trading market model unit, and constructing an energy trading market model based on the distribution network model; the energy trading market model maintains the power balance of the system through power generation and power trading, ensures that the power generation power of the electric vehicle cluster meets the upper and lower bound constraints, and uses the trading model to determine the transaction price, ensures that the total amount of power trading of all electric vehicle clusters is zero, and calculates the transaction cost of the electric vehicle cluster;

[0042] Constructing a non-cooperative game voltage regulation problem unit, based on the distribution network model and the energy trading market model, constructing a non-cooperative game voltage regulation problem; the voltage regulation problem balances the economic benefits of the electric vehicle cluster and the voltage regulation demand, and guides the electric vehicle cluster to minimize the economic cost while achieving the effect of controlling the voltage of the distribution network node by increasing the penalty function of the voltage deviation;

[0043] A distributed regulation algorithm unit for online parameter estimation is used to design a distributed regulation algorithm for online parameter estimation based on the voltage regulation problem; the distributed regulation algorithm for online parameter estimation includes variable iteration and parameter estimation, updates decision variables and Lagrange multipliers through information exchange and projected gradient method, performs online parameter estimation using Kalman filter, and updates control strategy according to parameter estimation value;

[0044] The algorithm effect verification unit verifies the actual application effect of the distributed regulation algorithm of online parameter estimation through simulation, and examines the voltage regulation capability of the electric vehicle cluster in different scenarios as well as the stability and economy of the system.

[0045] The present invention also includes a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method described above is implemented.

[0046] The present invention also includes a storage medium on which a computer program is stored. When the computer program is executed by a processor, the method described above is implemented.

[0047] The beneficial effects of the present invention are:

[0048] The present invention uses a non-cooperative game model to construct the distribution network voltage control problem of electric vehicle clusters participating in the energy trading market, and proposes a distributed algorithm with online parameter estimation to obtain the variational generalized Nash equilibrium of the game problem, thereby obtaining the optimal voltage regulation strategy. The voltage regulation problem of the present invention takes into account the energy trading between electric vehicle clusters, which is consistent with the background of the increasing popularity of electric vehicles today and has guiding significance; the present invention proposes a distributed algorithm with online parameter estimation, which reduces the dependence on precise model information and has stronger adaptability and robustness. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] 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 recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0050] Figure 1 A flow chart of a voltage regulation method for a distribution market in which electric vehicle clusters participate;

[0051] Figure 2 It is a schematic diagram of IEEE 33-node power distribution network;

[0052] Figure 3 It is a schematic diagram of the convergence process of the relative error of the parameter estimation value;

[0053] Figure 4 It is a schematic diagram of the voltage changes of four nodes in the distribution network;

[0054] Figure 5 Schematic diagram of node voltage values ​​under equilibrium achieved by different algorithms;

[0055] Figure 6 It is a schematic diagram of the power generated by the electric vehicle cluster and the power involved in the transaction (active power);

[0056] Figure 7 Schematic diagram of voltage loss at each time from 8:00 to 21:00 under the two algorithms;

[0057] Figure 8 A schematic diagram of the structure of a voltage regulator for a distribution market in which electric vehicle clusters participate;

[0058] Fig. 9 A schematic diagram of the structure of a computer device. DETAILED DESCRIPTION

[0059] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0060] like Figure 1 As shown: A method for regulating voltage in a power distribution market in which electric vehicle clusters participate, comprising the following steps:

[0061] S10: Based on the characteristics of electric vehicle clusters, a distribution network model including electric vehicle clusters is constructed; the distribution network model uses the linear Distflow model to obtain the power flow equation, and expresses the linear relationship between voltage and electric vehicle cluster power generation through the correlation matrix;

[0062] S20: Based on the distribution network model, an energy trading market model is constructed; the energy trading market model maintains the power balance of the system through power generation and power trading, ensuring that the power generation of the electric vehicle cluster meets the upper and lower bound constraints, and uses the trading model to determine the transaction price, ensuring that the total amount of power trading of all electric vehicle clusters is zero, and calculating the transaction cost of the electric vehicle cluster; it helps to evaluate the economic benefits of its market participation and make corresponding adjustments;

[0063] S30: Based on the distribution network model and energy trading market model, a non-cooperative game voltage regulation problem is constructed; the voltage regulation problem balances the economic benefits of the electric vehicle cluster and the voltage regulation requirements, and by increasing the penalty function of voltage deviation, guides the electric vehicle cluster to minimize the economic cost while achieving the effect of controlling the voltage of the distribution network node;

[0064] S40: Based on the voltage regulation problem, a distributed regulation algorithm for online parameter estimation is designed; the distributed regulation algorithm for online parameter estimation includes variable iteration and parameter estimation, updating decision variables and Lagrange multipliers through information exchange and projected gradient method, using Kalman filter for online parameter estimation, and updating the control strategy according to the parameter estimation value;

[0065] S50: Verify the actual application effect of the distributed regulation algorithm with online parameter estimation through simulation, and examine the voltage regulation capability of the electric vehicle cluster in different scenarios as well as the stability and economy of the system.

[0066] The present invention uses a non-cooperative game model to construct a distribution network voltage control problem in which electric vehicle clusters participate in the energy trading market, and proposes a distributed algorithm with online parameter estimation to obtain the variational generalized Nash equilibrium of the game problem, thereby obtaining the best voltage regulation strategy. The present invention has the following advantages: First, the voltage regulation problem of the present invention takes into account the energy trading between electric vehicle clusters, which is consistent with the background of the increasing popularity of electric vehicles today and has guiding significance; second, the present invention proposes a distributed algorithm with online parameter estimation, which reduces the dependence on precise model information and has stronger adaptability and robustness.

[0067] By introducing a non-cooperative game model into the voltage regulation problem, taking into account the economic benefits and voltage regulation needs of the electric vehicle cluster, the electric vehicle cluster minimizes its own economic costs while controlling the voltage of the distribution network nodes, which not only meets the technical requirements of the power grid operation, but also ensures the economic interests of the participants, and encourages the cluster to actively participate in the power grid voltage regulation.

[0068] By designing a distributed regulation algorithm for online parameter estimation, dynamic adjustment based on real-time information is achieved. The algorithm performs online parameter estimation through a Kalman filter, enabling the electric vehicle cluster to update the control strategy in a timely manner according to real-time voltage deviation, power demand and other factors, ensuring the flexibility and robustness of the system operation.

[0069] The participation of electric vehicle clusters effectively shares the pressure of traditional regulation equipment in voltage regulation. By optimizing the use of distributed resources, it reduces dependence on expensive, centralized power grid equipment and improves the resilience and scalability of the power grid. As a mobile distributed power source, electric vehicle clusters can provide flexible power generation and regulation capabilities in the distribution market, help alleviate the instability of renewable energy generation, and promote the consumption and utilization of more renewable energy.

[0070] Step S10: Construct a distribution network model including electric vehicle clusters:

[0071] Consider a radial active distribution network with (N+1) nodes, using the set Represents all nodes in the distribution network. Node 0 is a substation with a fixed voltage. is the set of all nodes except node 0. For electrical lines in the distribution network, we use the set Therefore, the entire distribution network can be represented by Give a description.

[0072] We believe that, except for node 0, each node in the distribution network is connected to an electric vehicle cluster, and the electric vehicle cluster number corresponds to the distribution network node number. In addition, each node is connected to an active load With reactive load The active power generation and reactive power generation of the electric vehicle cluster are defined as and Then the power injection model of nodes other than node 0 in the distribution network is:

[0073]

[0074] Define the set of adjacent nodes of node i as

[0075] As a preferred embodiment of the above, in step S10, the distribution network model uses a linear Distflow model to obtain a power flow equation, including:

[0076]

[0077] Where V i represents the complex voltage at node i; r i,j and x i,j Respectively represent the resistance and reactance on the circuit (i, j); P i,j and Q i,j They represent the active power and reactive power flowing through line (i, j) respectively; v j represents the voltage at node j; P j，k and Q j，k Respectively represent the active power and reactive power flowing through line (j, k); represents the set of downstream nodes that are physically connected to node j; p j represents the active power injection of node j; q j represents the reactive power injection of node j; v i represents half of the square of the complex voltage at node i; E f Represents the set of all lines in the distribution network.

[0078] In order to further simplify the form, we introduce The correlation matrix

[0079] Where m 0 T for The first row of , M is the remaining matrix, and the compact form of the power flow equation is obtained:

[0080] MP = g p -d p ;

[0081] MQ=g q -d q ;

[0082] [m 0 M T ]·[v s v T ] T =diag(r)P+diag(x)Q;

[0083] where g p and g q They are defined as and A vector composed in order, d p d qThe definitions of , P, Q and v are similar.

[0084] Because of the figure is a connected graph, then the matrix Full rank. Multiply both sides of equations (3a) and (3b) by M -1 , substitute into (3c)$ and eliminate P and Q;

[0085] In this embodiment, in step S10, the linear relationship between voltage and electric vehicle cluster power generation is represented by an association matrix, and the model includes:

[0086] v=Rg p +Xg q -Rd p -Xd q -M -T m 0 v s ;

[0087] Where R = M -T diag(r)M -1 With X = M -T diag(x)M -1 are all real symmetric positive definite matrices, and -M -T m 0 =1, 1 is a column vector whose elements are all 1; g p and g q They are defined as and A vector composed in order; d p and d q They are defined as and A vector composed in order; v s represents the fixed voltage of node 0; v represents the voltage of node 0. i Column vector composed of .

[0088] By using the correlation matrix to linearize the relationship between voltage and the power generation of electric vehicle clusters, the voltage changes in the distribution network can be described more accurately. This precise modeling method can help better understand the impact of electric vehicle clusters on the voltage of the distribution network, thereby achieving more effective voltage regulation; the linearized model makes the calculation process simpler and reduces the computational complexity, which is especially important for real-time online calculations. It can improve the operating efficiency of the algorithm and ensure that the algorithm responds quickly in a dynamic environment. The use of real symmetric positive definite matrices can ensure the stability and convergence of the solution, improve the algorithm's adaptability to various operating environments, and enhance the robustness of the entire system.

[0089] Step S20: Constructing an energy trading market model:

[0090] This patent considers a scenario in which electric vehicle clusters trade active power through an aggregator. Each electric vehicle cluster can control its power generation, and the active load and reactive load are uncontrollable. Each electric vehicle cluster can maintain power balance by generating electricity and participating in energy trading.

[0091] Step S21: controllable power generation;

[0092] In step S20, it is ensured that the power generation of the electric vehicle cluster meets the upper and lower bound constraints. The model includes:

[0093]

[0094] In the formula, and Represents the minimum power and maximum power of active power generation and reactive power generation respectively; and They represent the active power generation and reactive power generation of the electric vehicle cluster respectively; N represents the set of all nodes except node 0.

[0095] By clearly defining the upper and lower bounds, we can ensure that the active and reactive power generation of the electric vehicle cluster is always within a reasonable range, preventing the power output from being too large or too small. This not only ensures the stable operation of the power grid, but also protects the power generation equipment from being overloaded or operating in an unreasonable state. By setting the upper and lower bounds of the power generation, we can avoid the problem of power overload or underload in the electric vehicle cluster during peak load or low load, thereby improving the operating safety of the entire distribution network and the reliability of the system.

[0096] The power generation cost of electric vehicle cluster i is:

[0097]

[0098] in and is a constant.

[0099] Step S22: transaction model;

[0100] Define the active power tr traded by electric vehicle cluster i i for:

[0101] tr i =s i c tr +w i ;

[0102] In the formula, s i <0 indicates the price sensitivity of electric vehicle cluster i, c tr is the transaction cost, w i Indicates willingness to buy. i<0, the electric vehicle cluster sells electricity; otherwise, if tr i >0, the electric vehicle cluster purchases electricity. To ensure the overall power balance in the region, it is necessary to ensure that the total amount of electricity traded by all electric vehicle clusters is 0, that is, the following conditions must be met:

[0103]

[0104] Under this condition, the transaction unit price of the energy market can be obtained as:

[0105]

[0106] where w:=col(w 1 ,w 2 ,…,w N ). Each electric vehicle cluster meets the active load demand through power generation and power trading, which is expressed as:

[0107]

[0108] As a preferred embodiment of the above, in step S20, the transaction cost of the electric vehicle cluster is calculated, and the model includes:

[0109]

[0110] In the formula, c tr Represents the transaction unit price in the energy market; tr i represents the active power traded by electric vehicle cluster i; s i <0 indicates the price sensitivity of electric vehicle cluster i; w i Indicates willingness to purchase; i tr (w i ,w -i ) represents the transaction cost function; W -i To remove w i The column vector composed of the purchase intention of the remaining electric vehicle clusters; 1 T Represents a column vector whose elements are all 1.

[0111] By taking into account the transaction costs of electric vehicle clusters in the energy market through a clear transaction cost model, the power generation and transaction behavior of each cluster can be optimized, the cost expenditure in the power transaction process can be reduced, and the overall economic benefits can be improved; the concept of price sensitivity is introduced in the model, which can adjust the power transaction behavior according to the different responses of electric vehicle clusters to energy market prices, which helps the cluster make the best decision under different price conditions and maximize economic benefits; by introducing the willingness to purchase, the model can dynamically reflect market demand and adapt to market price changes, so that electric vehicle clusters can respond according to current market conditions. This flexibility helps to cope with fluctuations in the energy market and improve the efficiency of market participation.

[0112] Step S30: Constructing the voltage regulation problem using non-cooperative game

[0113] This patent uses non-cooperative game problems to construct the distribution network voltage regulation problem in the energy trading market. This problem takes into account both the voltage control effect and the economic benefits of electric vehicle clusters.

[0114] First, the decision variables of electric vehicle cluster i are defined as:

[0115]

[0116] In the energy trading market, each electric vehicle cluster hopes to minimize the total cost of the individual under the constraint of maintaining power balance. At the same time, in order to achieve voltage regulation, each electric vehicle cluster needs to assume corresponding responsibilities. Therefore, this patent considers adding a voltage penalty function to the cost of the electric vehicle cluster through measurement feedback. The greater the deviation between the node voltage and the target voltage, the greater the value of the penalty function, so that the electric vehicle cluster can control the voltage of the distribution network node while minimizing the economic cost. The optimization problem of each electric vehicle cluster is expressed as:

[0117]

[0118] v i =π i (g i ,g -i );

[0119] In the formula, Any v i =π i (g i ,g -i ) can be obtained from the above linear relationship model between voltage and electric vehicle cluster power generation. is the voltage penalty function, the node voltage v where the electric vehicle cluster is located i Can be obtained through measurement.

[0120] The domain set of decision variables that satisfies the local constraint (5) is defined as The domain set of decision variables that satisfy the coupling constraints is The domain set of decision variables that satisfies the global constraints is For the purpose of subsequent algorithm design, we consider expressing the coupling constraint (8) in the form of Au-b ≥ 0. and i :=col(0 (i-1)×1 ,1,0 (N-i)×1 ). Define the matrix A i and vector b is as follows:

[0121]

[0122] Since all electric vehicle clusters have equal status, The final linear inequality form of the coupling constraint is:

[0123]

[0124] In this embodiment, in step S30, a voltage regulation problem of a non-cooperative game is constructed, and the model includes:

[0125]

[0126] Au-b≥0;

[0127] v i =π i (g i ,g -i );

[0128] In the formula, J i (u i ,u -i ) represents the utility function; f i c (g i ) represents the cost of electricity generation; f i tr (w i ,w -i ) represents transaction costs; V(v i ) represents the voltage penalty function; represents local constraint; Au-b≥0 represents coupling constraint; v i =π i (g i ,g -i ) represents the relationship between node voltage and generated power.

[0129] This game model takes into account the interaction and competition between electric vehicle clusters. By combining the utility function, power generation cost, transaction cost and voltage penalty function, it reaches the optimal solution for global voltage regulation and improves the overall stability of the power grid. By constructing a non-cooperative game model, it can simulate the independent decision-making behavior of each electric vehicle cluster in voltage regulation, improve the flexibility and autonomy of the system, adapt to the distributed structure between electric vehicle clusters, and reduce dependence on centralized control.

[0130] Its generalized Nash equilibrium is defined as follows:

[0131]

[0132] Disutility function J i (u i ,u -i ) is:

[0133]

[0134] Where s(v i )=v i -v 0 ,

[0135] Then the pseudo gradient is defined as:

[0136]

[0137] The Lagrangian function of each electric vehicle cluster is defined as follows:

[0138]

[0139] On the basis of generalized Nash equilibrium, all electric vehicle clusters have the same Lagrange multiplier The equilibrium is called variational generalized Nash equilibrium. Variational generalized equilibrium is economically "non-discriminatory". The marginal loss caused by the coupling constraints suffered by each participant is approximately the same. In the end, the benefits obtained by each participant are relatively fair and closer to the actual situation.

[0140] Step S40: Design a distributed adjustment algorithm for online parameter estimation:

[0141] In order to solve the voltage regulation problem of the above non-cooperative game, this patent designs a distributed regulation algorithm with online parameter estimation. The algorithm is divided into two steps: variable iteration and parameter estimation, including variable iteration and online estimation of parameters.

[0142] Step 1 is variable iteration. In order to achieve the consensus of Lagrange multipliers among all electric vehicle clusters, the algorithm introduces auxiliary variables z iIn addition, the iteration of the algorithm requires information exchange between electric vehicle clusters. We introduce a connected undirected graph To represent the information exchange between electric vehicle clusters. In the example, the set of electric vehicle clusters that can exchange information with electric vehicle cluster i is The decision variables are updated by projected gradient descent, the auxiliary variables are updated by consensus error discrete integration, and the Lagrange multipliers are updated by a combination of projected gradient ascent and consensus error proportional integration.

[0143] Step 2 is the online estimation of parameters. This patent estimates the coefficient matrices R and X in equation (4) online. Substituting equation v = Rg p +Xg q -Rd p -Xd q -M -T m 0 v s Rewrite it to get:

[0144]

[0145] in v 0 =-Rd p -Xd q -M -T m 0 v s . Split the coefficient matrix H into columns and form a column vector Establish a dynamic model for h:

[0146] h k =h k-1 +ω p,k ;

[0147]

[0148] in is the process noise, To measure noise. represents the Kronecker product.

[0149] By designing the following Kalman filter, we can get the recursive estimate of the parameter h:

[0150]

[0151] Σ k =(IK k-1 G △,k-1 )Σ k-1 +Σp,k ;

[0152] in, is the Kalman gain. Using the parameter estimate Reconstruction coefficient matrix

[0153] In step S40, the control strategy is updated according to the parameter estimation value, and each electric vehicle cluster can calculate the partial derivative using the following model, which includes:

[0154]

[0155] In the formula, s(v i )=v i -v 0 , v 0 is the voltage regulation target; Denotes the utility function for u i The partial derivative of Indicates that the parameter estimates are used Reconstruct the coefficient matrix; represents the element in the i-th row and i-th column of the matrix, represents the element in the i+Nth row and i+Nth column of the matrix; w i represents the purchase intention of electric vehicle cluster i; v i is half of the square of the complex voltage amplitude at node i; and is a constant; and Respectively represents the active power generation and reactive power generation of the electric vehicle cluster.

[0156] The calculation of partial derivatives provides an optimization direction for the power generation adjustment of electric vehicle clusters. Based on this mathematical model, the optimal solution for power regulation can be obtained, thereby achieving precise control of grid voltage regulation and improving the overall operating efficiency of the grid. The calculation of partial derivatives depends on the update of parameter estimates, which enables each iteration to be calculated based on the latest system information, improving the robustness and accuracy of the optimization process and adapting to the complex and dynamically changing grid environment.

[0157] As a preference for the above embodiment, in step S40, a distributed adjustment algorithm for online parameter estimation is designed, the steps comprising:

[0158] S41: Initialize relevant variables in the electric vehicle cluster, including decision variables, information variables, Lagrange multipliers and parameter estimates of the electric vehicle cluster; provide necessary initial conditions for subsequent iterations;

[0159] S42: Performing the kth iteration, each electric vehicle cluster needs to exchange information with other electric vehicle clusters, and the information exchange includes updating the decision variables and information variables of the electric vehicle cluster, and adjusting the Lagrange multiplier; this step ensures that each cluster can transmit information to each other and update its state in each iteration, so as to promote the system to develop towards an equilibrium state;

[0160] S43: Execute online parameter estimation value update, including:

[0161] The parameter estimates of the electric vehicle cluster are updated online according to the coefficient matrix;

[0162] Use the updated parameter estimates to calculate the partial derivative estimates required for the next iteration; this step is the key to ensuring the accuracy of parameter estimates and the convergence of the algorithm;

[0163] S44: In each iteration, coordination between electric vehicle clusters is achieved through information exchange and parameter estimation to achieve a globally optimized voltage regulation strategy. This step brings together the results of the previous steps and optimizes the voltage regulation effect by coordinating the behaviors of each electric vehicle cluster.

[0164] By designing a distributed regulation algorithm for online parameter estimation, each electric vehicle cluster can independently perform calculations and exchange information with other clusters without the need for a centralized controller, greatly improving the scalability and adaptability of the system; the distributed algorithm for online parameter estimation enables the system to dynamically adjust parameter estimates according to real-time changes, and gradually optimize decision variables and control strategies, ensuring the accuracy and stability of grid voltage regulation; the adjustment of Lagrange multipliers helps to solve the constraint problems between clusters and ensure the convergence of the distributed algorithm, thereby driving the system towards the global optimal solution in each iteration; by updating parameter estimates online, the system can flexibly respond to changes in the power grid, avoiding the limitations brought by static parameters and improving the resilience of electric vehicle clusters in complex power markets; through the calculation of partial derivatives, the system can provide optimization directions for each iteration, enabling electric vehicle clusters to make optimal decisions based on the latest parameter estimates, further ensuring the improvement of global optimization capabilities.

[0165] The iteration process of each variable is shown in Table 1.

[0166] Table 1 Distributed voltage regulation algorithm with online parameter estimation

[0167]

[0168]

[0169] Step 5: Verify the actual application effect of the distributed adjustment algorithm for online parameter estimation:

[0170] Two examples are used to illustrate the effects of the present invention.

[0171] We use the IEEE 33-node model to simulate radial distribution networks, such as Figure 2 As shown in the figure. In the distribution network, solid dots represent nodes and straight lines represent power lines between two nodes. Node 0 is used as a substation and its voltage is a constant. In addition, the remaining nodes are connected to the corresponding numbered electric vehicle clusters, and the number of electrical lines is the same as the number of electric vehicle clusters. For the information exchange between different electric vehicle clusters, in order to minimize the information exchange, we make each electric vehicle cluster communicate only with the electric vehicle clusters adjacent to its serial number (electric vehicle cluster No. 1 communicates with electric vehicle clusters No. 2 and No. 32).

[0172] Node 0 is used as the reference voltage, and its per-unit value is v s = 1p.u.. For electric vehicle clusters, the upper limit of active power production Lower limit Reactive power production limit Lower limit The power generation cost coefficients of each electric vehicle cluster are set to be the same, which are The target voltage is set to v 0 =1p.u.

[0173] A. Example 1: From one equilibrium to another

[0174] After each electric vehicle cluster reaches equilibrium, we increase the active load of each electric vehicle cluster by 1%. At this time, the voltage of each node drops, the algorithm starts to work, and the electric vehicle cluster searches for a new equilibrium.

[0175] Figure 3 Relative error of parameter estimates The following figure shows the convergence process of the relative error of the parameter estimation after the equilibrium is broken. It can be seen that in the initial 20 iterations, the relative error decreased rapidly from 0.25 to less than 0.01, and as the iterations proceeded, the relative error continued to decrease, indicating that the design of the online parameter estimation algorithm is reasonable and effective.

[0176] Figure 4 The voltage changes of the four nodes in the distribution network after the balance is broken are shown. During the entire iteration process, the node voltage fluctuates slightly. After about 1,500 iterations, the voltage of each node is stable again, and the difference from the control target is no more than 5%, indicating that the algorithm has a good voltage stabilization effect.

[0177] Figure 5The node voltage values ​​under equilibrium are shown for different algorithms. By comparing the present invention with an algorithm with known parameters, it can be found that the results of the two groups of experiments are very close, which shows the effectiveness of online parameter estimation. It is obvious that the algorithm that only uses fixed parameter estimation without updating has poor performance, and nearly half of the node voltages are ultimately less than 0.9 pu, which further illustrates the necessity of online parameter estimation.

[0178] Figure 6 The power generation and trading power of each electric vehicle cluster are shown when a new equilibrium is reached. Each electric vehicle cluster meets its own needs through power generation and trading, and it has been verified that the sum of the power involved in the transaction is 0, which meets the overall power balance.

[0179] B. Example 2: Real-world scenario simulation

[0180] In this case, we refer to the load change trend from 8 am to 9 pm in real life, and let the active load demand of each electric vehicle cluster change every 10 minutes. Before the next change, the sum of the voltage losses of each node in the distribution network is recorded. .

[0181] Figure 7 The sum of voltage losses at each moment under the two algorithms is shown. It can be found that before 11 a.m., the sum of voltage losses of the algorithm of the present invention is slightly higher than that of the algorithm with known parameters. This is because the online parameter estimation algorithm does not obtain sufficiently accurate parameter estimation values ​​at this time, which leads to poor control effect. Subsequently, the online parameter estimation algorithm uses sufficient knowledge to obtain sufficiently accurate parameter estimation values. At this time, the control effect of the algorithm of the present patent is almost the same as that of the algorithm with known parameters. This simulation experiment further illustrates the effectiveness of the present invention in practical applications.

[0182] The present invention also includes a voltage regulating device for a power distribution market in which electric vehicle clusters participate, such as Figure 8 As shown, using the method as described above, including:

[0183] Construct a distribution network model unit. Based on the characteristics of electric vehicle clusters, a distribution network model including electric vehicle clusters is constructed. The distribution network model uses the linear Distflow model to obtain the power flow equation, and expresses the linear relationship between voltage and electric vehicle cluster power generation through the correlation matrix.

[0184] Construct an energy trading market model unit. Based on the distribution network model, construct an energy trading market model. The energy trading market model maintains the power balance of the system through power generation and power trading, ensuring that the power generation of the electric vehicle cluster meets the upper and lower bound constraints. At the same time, the trading model is used to determine the transaction price, ensuring that the total amount of power trading of all electric vehicle clusters is zero, and calculating the transaction cost of the electric vehicle cluster.

[0185] Construct a non-cooperative game voltage regulation problem unit. Based on the distribution network model and energy trading market model, construct a non-cooperative game voltage regulation problem. The voltage regulation problem balances the economic benefits of the electric vehicle cluster and the voltage regulation demand. By increasing the penalty function of voltage deviation, the electric vehicle cluster is guided to minimize the economic cost while achieving the effect of controlling the voltage of the distribution network node.

[0186] The distributed regulation algorithm unit of online parameter estimation designs a distributed regulation algorithm of online parameter estimation based on the voltage regulation problem. The distributed regulation algorithm of online parameter estimation includes variable iteration and parameter estimation. The decision variables and Lagrange multipliers are updated through information exchange and projected gradient method. The Kalman filter is used for online parameter estimation, and the control strategy is updated according to the parameter estimation value.

[0187] The algorithm effect verification unit verifies the actual application effect of the distributed regulation algorithm with online parameter estimation through simulation, and examines the voltage regulation capability of the electric vehicle cluster in different scenarios as well as the stability and economy of the system.

[0188] See also Fig. 9 A computer device 400 provided in an embodiment of the present application includes: a processor 410 and a memory 420, wherein the memory 420 stores a computer program executable by the processor 410, and when the computer program is executed by the processor 410, the above method is executed.

[0189] The embodiment of the present application further provides a storage medium 430 on which a computer program is stored. When the computer program is run by the processor 410, the above method is executed.

[0190] Among them, the storage medium 430 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable red-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, disk or optical disk.

[0191] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. "Multiple" means two or more, unless otherwise clearly and specifically defined.

[0192] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0193] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, unless they are contradictory.

[0194] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention belong.

[0195] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0196] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0197] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0198] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present invention. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A voltage regulation method for a power distribution market in which electric vehicle clusters participate, characterized in that: The steps include: S10: Based on the characteristics of the electric vehicle cluster, a distribution network model including the electric vehicle cluster is constructed; the distribution network model uses a linear Distflow model to obtain a power flow equation, and expresses a linear relationship between voltage and electric vehicle cluster power generation through an association matrix; S20: Based on the distribution network model, an energy trading market model is constructed; the energy trading market model maintains the power balance of the system through power generation and power trading, ensures that the power generation power of the electric vehicle cluster meets the upper and lower bound constraints, and uses the trading model to determine the transaction price, ensures that the total amount of power trading of all electric vehicle clusters is zero, and calculates the transaction cost of the electric vehicle cluster; S30: Based on the distribution network model and the energy trading market model, a non-cooperative game voltage regulation problem is constructed; the voltage regulation problem balances the economic benefits of the electric vehicle cluster and the voltage regulation demand, and guides the electric vehicle cluster to minimize the economic cost while achieving the effect of controlling the voltage of the distribution network node by increasing the penalty function of the voltage deviation; S40: Based on the voltage regulation problem, design a distributed regulation algorithm for online parameter estimation; The distributed regulation algorithm for online parameter estimation includes variable iteration and parameter estimation, updating decision variables and Lagrange multipliers through information exchange and projected gradient method, performing online parameter estimation using Kalman filter, and updating control strategy according to parameter estimation value; S50: verifying the actual application effect of the online parameter estimation distributed regulation algorithm through simulation, and checking the voltage regulation capability of the electric vehicle cluster in different scenarios as well as the stability and economy of the system.

2. The method for regulating the voltage in the power distribution market in which electric vehicle clusters participate according to claim 1, characterized in that: In step S10, the distribution network model uses a linear Distflow model to obtain a power flow equation, including: Where V i represents the complex voltage at node i; r i,j and x i,j Respectively represent the resistance and reactance on the circuit (i, j); P i,j and Q i,j They represent the active power and reactive power flowing through line (i, j) respectively; v j represents the voltage at node j; P j,k and Q j,k Respectively represent the active power and reactive power flowing through line (j, k); represents the set of downstream nodes that are physically connected to node j; p j represents the active power injection of node j; q j represents the reactive power injection of node j; v i represents half of the square of the complex voltage at node i; E f Represents the set of all lines in the distribution network.

3. The voltage regulation method for the power distribution market in which electric vehicle clusters participate according to claim 1 is characterized in that: In step S10, the linear relationship between the voltage and the electric vehicle cluster power generation is represented by an association matrix, and the model includes: v=Rg p +Xg q -Rd p -Xd q -M -T m0v s ; Where R = M -T diag(r)M -1 With X = M -T diag(x)M -1 are all real symmetric positive definite matrices, and -M -T m0=1, 1 is a column vector whose elements are all 1; g p and g q They are defined as and A vector composed in order; d p and d q They are defined as and A vector composed in order; v s represents the fixed voltage of node 0; v represents the voltage of node 0. i Column vector composed of .

4. The method for regulating voltage in a power distribution market in which electric vehicle clusters participate according to claim 1, characterized in that: In step S20, the power generation of the electric vehicle cluster is ensured to meet the upper and lower bound constraints, and the model includes: In the formula, and Represents the minimum power and maximum power of active power generation and reactive power generation respectively; and They represent the active power generation and reactive power generation of the electric vehicle cluster respectively; N represents the set of all nodes except node 0.

5. The method for regulating voltage in a power distribution market in which electric vehicle clusters participate according to claim 1, characterized in that: In step S20, the transaction cost of the electric vehicle cluster is calculated, and the model includes: In the formula, c tr Represents the transaction unit price in the energy market; tr i represents the active power traded by electric vehicle cluster i; s i <0 indicates the price sensitivity of electric vehicle cluster i; w i Indicates willingness to purchase; i tr (w i ,w -i ) represents the transaction cost function; W -i To remove w i The column vector composed of the purchase intention of the remaining electric vehicle clusters; 1 T Represents a column vector whose elements are all 1.

6. The method for regulating voltage in a power distribution market in which electric vehicle clusters participate according to claim 1, characterized in that: In step S30, the voltage regulation problem of the non-cooperative game is constructed, and the model includes: Au-b≥0; v i =π i (g i ,g -i ); In the formula, J i (u i ,u -i ) represents the utility function; f i c (g i ) represents the cost of electricity generation; f i tr (w i ,w -i ) represents transaction costs; V(v i ) represents the voltage penalty function; represents local constraint; Au-b≥0 represents coupling constraint; v i =π i (g i ,g -i ) represents the relationship between node voltage and generated power.

7. The method for regulating voltage in a power distribution market in which electric vehicle clusters participate according to claim 1, characterized in that: In step S40, the control strategy is updated according to the parameter estimation value, and each electric vehicle cluster can calculate the partial derivative using the following model, which includes: In the formula, s(v i )=v i -v0, v0 is the voltage regulation target; Denotes the utility function for u i The partial derivative of Indicates that the parameter estimates are used Reconstruct the coefficient matrix; represents the element in the i-th row and i-th column of the matrix, represents the element in the i+Nth row and i+Nth column of the matrix; w i represents the purchase intention of electric vehicle cluster i; v i is half of the square of the complex voltage amplitude at node i; and is a constant; and Respectively represents the active power generation and reactive power generation of the electric vehicle cluster.

8. The method for regulating voltage in a power distribution market in which electric vehicle clusters participate according to claim 1, characterized in that: In step S40, the design of a distributed adjustment algorithm for online parameter estimation comprises the following steps: S41: Initializing relevant variables in the electric vehicle cluster, including decision variables, information variables, Lagrange multipliers and parameter estimates of the electric vehicle cluster; S42: performing the kth iteration, each electric vehicle cluster needs to exchange information with other electric vehicle clusters, wherein the exchanged information includes updating the decision variables and the information variables of the electric vehicle cluster, and adjusting the Lagrange multiplier; S43: Execute the online parameter estimation value update, including: Online updating of the parameter estimation values ​​of the electric vehicle cluster according to the coefficient matrix; Calculating the estimated partial derivatives required for the next iteration using the updated estimated parameter values; S44: In each iteration, coordination between electric vehicle clusters is achieved through the information exchange and the parameter estimation values ​​to achieve a globally optimized voltage regulation strategy.

9. A voltage regulating device for a power distribution market in which electric vehicle clusters participate, characterized in that: Use of the method according to any one of claims 1 to 8, comprising: Constructing a distribution network model unit, based on the characteristics of the electric vehicle cluster, constructing a distribution network model including the electric vehicle cluster, wherein the distribution network model uses a linear Distflow model to obtain a power flow equation, and uses an association matrix to represent a linear relationship between voltage and electric vehicle cluster power generation; Constructing an energy trading market model unit, and constructing an energy trading market model based on the distribution network model; the energy trading market model maintains the power balance of the system through power generation and power trading, ensures that the power generation power of the electric vehicle cluster meets the upper and lower bound constraints, and uses the trading model to determine the transaction price, ensures that the total amount of power trading of all electric vehicle clusters is zero, and calculates the transaction cost of the electric vehicle cluster; Constructing a non-cooperative game voltage regulation problem unit, based on the distribution network model and the energy trading market model, constructing a non-cooperative game voltage regulation problem; the voltage regulation problem balances the economic benefits of the electric vehicle cluster and the voltage regulation demand, and guides the electric vehicle cluster to minimize the economic cost while achieving the effect of controlling the voltage of the distribution network node by increasing the penalty function of the voltage deviation; A distributed regulation algorithm unit for online parameter estimation is used to design a distributed regulation algorithm for online parameter estimation based on the voltage regulation problem; the distributed regulation algorithm for online parameter estimation includes variable iteration and parameter estimation, updates decision variables and Lagrange multipliers through information exchange and projected gradient method, performs online parameter estimation using Kalman filter, and updates control strategy according to parameter estimation value; The algorithm effect verification unit verifies the actual application effect of the distributed regulation algorithm of online parameter estimation through simulation, and examines the voltage regulation capability of the electric vehicle cluster in different scenarios as well as the stability and economy of the system.

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

11. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

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