Energy management method and system based on demand response and electric vehicle charging and discharging

Abstract

The invention discloses an energy management method and system based on demand response and electric vehicle charging and discharging, and relates to the technical field of demand side response and optimized operation of a power system. The method comprises the following steps: constructing a user load shedding willingness curve model, and determining acceptable load shedding proportions of industrial / commercial / residential users under different incentive prices based on the user load shedding willingness curve model; and an electric vehicle charging and discharging schedulable model is established, and the charging and discharging power, the battery capacity and the energy balance constraint of the electric vehicle in different time periods are determined based on the electric vehicle charging and discharging schedulable model. According to the invention, dual objectives of VPP profit maximization and system supply and demand balance can be realized; when the VPP optimal day operation strategy is executed, the monitoring assistant can conveniently switch the target control point and the standby control point in time according to the real-time state information of the target control point; the transmission load of a communication channel can be reduced, and the situation that an execution instruction is mistakenly received or a control instruction is mistakenly sent can be reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of demand side response and optimal operation of power systems, and in particular to an energy management method and system based on demand response and charging and discharging of electric vehicles. BACKGROUND

[0002] Under the promotion of the "double carbon" goal, distributed renewable energy and electric vehicles are growing explosively, and the traditional "large power grid-large power plant" mode is accelerating towards a distributed and intelligent direction. As an advanced carrier integrating distributed energy (DER), energy storage and controllable load, virtual power plant (VPP) can significantly improve the flexibility of the power grid and the level of new energy consumption.

[0003] However, there are still some problems in the existing VPP energy management strategy: for example, the demand response mechanism is "one size fits all", and the differentiated load shedding willingness of industrial, commercial, residential and other types of users under time-of-use, two-stage and critical peak prices is not well described; the modeling of the dispatchable potential of electric vehicles is too simplified, and lacks a detailed quantification of their "spatial and temporal" charging and discharging capabilities; under high-dimensional nonlinear constraints, conventional optimization algorithms are prone to local optimization, making it difficult to achieve global maximum VPP profit; at the same time, the coupling effects of seasonal load fluctuations, incentive price elasticity and DR execution period are not systematically modeled, resulting in poor robustness of the dispatching scheme.

[0004] Furthermore, when electric vehicles execute the charging and discharging plan in the VPP energy management, it is inconvenient to timely schedule the charging and discharging of electric vehicles with abnormal faults, so that the optimal daily operation strategy of the VPP can be better executed. SUMMARY

[0005] The purpose of the present application is to provide an energy management method and system based on demand response and charging and discharging of electric vehicles to solve the problems raised in the background.

[0006] In order to achieve the above-mentioned purpose, the present application provides the following technical solution: an energy management method based on demand response and charging and discharging of electric vehicles, comprising the following steps: S1, constructing a user load shedding willingness curve model, determining the acceptable load shedding proportion of industrial / commercial / residential users under different incentive prices based on the user load shedding willingness curve model; S2, establishing an electric vehicle charging and discharging dispatchable model, determining the charging and discharging power, battery capacity and energy balance constraints of electric vehicles in different time periods based on the electric vehicle charging and discharging dispatchable model; S3, establishing a three-stage profit calculation formula before / after DR execution, constructing a VPP profit maximization model based on the three-stage profit calculation formula; S4. Based on the acceptable load reduction ratios for industrial / commercial / residential users under different incentive prices, as well as the charging and discharging power, battery capacity, and energy balance constraints at different times, an improved wolf pack search algorithm is used to solve the VPP profit maximization model, outputting the optimal daily operating strategy and maximum profit for the VPP, and then executing the optimal daily operating strategy for the VPP.

[0007] In a preferred embodiment, the step of constructing a user load reduction intention curve model and determining the acceptable load reduction ratio for industrial / commercial / residential users under different incentive prices based on the user load reduction intention curve model includes: In the formula, Let be the acceptable load reduction ratio for the i-th user at time t. ; Let be the incentive price for the i-th user at time t; , , For the i-th user The parameters of the curve; The agreed load reduction capacity for the i-th user at time t; Let be the maximum load reduction capacity of the i-th user at time t; This represents the rebate amount for the i-th user at time t. L represents the total rebate amount received by users at time t; L represents the number of users at time t.

[0008] In a preferred embodiment, establishing a schedulable charging and discharging model for electric vehicles, and determining the charging and discharging power, battery capacity, and energy balance constraints of electric vehicles at different time periods based on the schedulable charging and discharging model, includes: The total power model of the electric vehicle is set as follows: In the formula, Let be the total power of the electric vehicle at time t. The power of a single electric vehicle, Let t be the number of electric vehicles charging at the charging station. Battery energy storage dynamics include charging and discharging scenarios, and the output power of electric vehicles is the energy storage difference between the two consecutive stages. Wherein: the power constraint for charging is: , The energy storage renewal formula for charging is: , The power constraint for discharge is: , The formula for energy renewal during discharge is: , In the formula, The charging coefficient; The discharge coefficient; Let t be the total energy storage capacity of the battery at time t. This represents the total energy storage capacity of the battery at time t+1. This represents the battery's maximum energy storage capacity.

[0009] In a preferred embodiment, the step of establishing a three-stage profit calculation formula before DR execution / during DR execution / after DR execution, and constructing a VPP profit maximization model based on the three-stage profit calculation formula, includes: Before DR is executed, the formula for calculating the profit F1 from t=1 to n-1 is as follows: , In the formula, n is the time when the demand response begins; The load provided by VPP at time t; The load provided to the utility at time t; Let VPP be the selling price at time t; Let VPP be the utility at time t; Let be the selling price of renewable energy at time t; Let be the selling price of the electric vehicle at time t; Let be the amount of electricity generated by renewable energy at time t; Let t be the amount of electricity generated by the electric vehicle at time t; In DR execution, the profit F2 from t=n to m is calculated using the following formula: , In the formula, m is the moment when the demand response ends; The user load reduction status at time t; Let VPP be the incentive price for the user at time t; Let VPP be the utility incentive price at time t; The historical electricity purchase information of VPP for the 5 working days prior to time t; After DR is executed, the profit F3 from t=m+1 to 24 is calculated using the following formula: , Based on the three-stage profit calculation formula before, during, and after DR execution, the VPP profit maximization model is obtained. The calculation formula is as follows: ; The VPP energy balance constraint is as follows: ; In the formula, This refers to the amount of electricity generated by photovoltaics. Rebate restrictions: ; Battery capacity constraints: , In the formula, Let be the capacity of the battery at time t; This represents the initial capacity of the battery. This represents the battery's maximum energy storage capacity.

[0010] In a preferred embodiment, the VPP profit maximization model is solved using an improved wolf pack search algorithm, based on the acceptable load reduction ratios for industrial / commercial / residential users under different incentive prices, as well as the charging / discharging power, battery capacity, and energy balance constraints at different times, to output the optimal daily operating strategy and maximum profit for the VPP. This includes: Determine the operating scenario, which includes one of the following: summer TOU period and non-summer TOU period, summer two-stage period and non-summer two-stage period, summer critical peak period and non-summer critical peak period; For the operational scenario, the objective function is to maximize the daily profit of VPP, and the range of values ​​of VPP energy balance constraint, rebate constraint and battery capacity constraint is used as the range of the solution space; the wolf pack size, iteration number threshold and convergence accuracy are determined; the wolf pack position is initialized and the wolf king is set as the optimal solution: each wolf is a set of variables to be optimized for the corresponding operational scenario. The variables to be optimized include: DR incentive price for each time period, electric vehicle charging and discharging plan and load reduction ratio of each user; Set step size parameters: initial exploration step size, pursuit step size, attack step size; Through iterative cycles of searching, roaming, calling and chasing, and attacking and capturing, the global optimal solution is gradually approached until the iteration threshold is reached or the convergence accuracy is met. The optimal solution output at this point is taken as the optimal solution for the corresponding running scenario. The optimal solution is used as the optimal daily operating strategy for the corresponding operating scenario of VPP, and the maximum profit is calculated based on the optimal daily operating strategy of VPP. The optimal daily operation strategy for VPP is executed, which includes sending the DR incentive price for each time period, electric vehicle charging and discharging plan, and load reduction ratio for each user to the industrial / commercial / residential users of DR in the corresponding operation scenario.

[0011] In a preferred embodiment, the search roaming, calling and chasing, and attack capture include: Search Roaming: The individual represented by the wolf king is assigned to the optimal solution in the current solution space, and other exploring wolves continue to search for better solutions in the solution space, updating the wolf king's position; Calling and chasing: The members of the wolf pack who respond to the call to chase are called the wolves; when the alpha wolf initiates a calling behavior by howling, it will summon the surrounding wolves to move quickly to its position; the wolves that hear the alpha wolf's howl will move quickly to the alpha wolf's position with relatively large chasing strides; when iterating k+1 in the d-th dimension, the position of the wolves and the distance between the position of the wolves and the position of the alpha wolf are calculated. Attack and capture: When the wolf approaches the area where the prey is located, it enters attack mode, and its position update method introduces random perturbation to enhance local search capabilities.

[0012] In a preferred embodiment, the electric vehicle charging and discharging plan includes: Determine the scale and parameters of electric vehicles in the electric vehicle charging and discharging plan; Configure an execution point for the communication port of each electric vehicle connected to the charging pile, associate multiple execution receiving points with each execution point, and associate two execution instruction identifiers with each execution receiving point. The execution point is a virtual machine, and the execution receiving point is a communication port. VPP is configured with a control center, which is associated with multiple control points. These control points are mapped one-to-one with multiple execution points. Each control point is associated with multiple control sending points, and each control sending point is associated with two identifiers for control instructions. The control center is a cloud server, the control points are virtual machines, and the control sending points are communication ports. Establish a transmission channel between the one-to-one corresponding control point and execution point; The control center breaks down the overall task corresponding to the electric vehicle charging and discharging plan into multiple sub-tasks through monitoring points, distributes the multiple sub-tasks to multiple control points and activates the control sending points. The control sending points activate the corresponding execution receiving points, and after the execution receiving points are activated, the electric vehicles execute the corresponding sub-tasks.

[0013] In a preferred embodiment, establishing a transmission channel between the one-to-one corresponding control point and execution point includes: Control points with the same control commands are associated to obtain an association group; Configure a monitoring assistant for the associated group. The monitoring assistant is connected to multiple control points in the associated group. The monitoring assistant is used to carry two identifiers and a time window of the subtask and to monitor multiple control points in the associated group. The control point for receiving and distributing subtasks is used as the target control point. Trigger conditions are set for the monitoring assistant. The monitoring assistant switches the target control point based on the trigger conditions. The trigger conditions are that the target control point in the associated group meets preset conditions, including that the target control point has an abnormal condition. Communication channels are established between multiple control sending points and multiple execution receiving points within the corresponding control points, and these multiple communication channels form a transmission channel.

[0014] In a preferred embodiment, the control sending point activates the corresponding execution receiving point. After the execution receiving point is activated, the electric vehicle performs the corresponding sub-task, including: The control center sends the subtask identifier and time window to the target control point. The monitoring assistant receives the subtask identifier and performs a consistency match with the identifier of the control command associated with the control sending point. If they match, the monitoring assistant activates the corresponding control sending point. If they do not match, the monitoring assistant does not activate the corresponding control sending point. Once activated, the control sending point sends its associated identifier to the corresponding execution receiving point through the communication channel, and activates the execution receiving point. The electric vehicle corresponding to the execution receiving point then executes the corresponding subtask. Among them, a one-to-one connection line of the same length is established between the monitoring assistant and multiple control points in the associated group; Each traction line is configured with multiple traction points and one moving point. The multiple traction points are evenly distributed along the traction line. The moving point is used to carry the control point to move along the traction line following the traction points. The initial position of the moving point is located in the middle of the multiple traction points. Each traction point is bound to its position information on the traction line. The difference between the real-time status information of the control point and the preset standard threshold is calculated. If the difference is positive, the moving point carries the control point and moves it towards the monitoring assistant by an offset corresponding to the difference. One traction point corresponds to one offset. If the difference is negative, the moving point carries the control point and moves it away from the monitoring assistant by an offset corresponding to the difference. If the difference is zero, the moving point does not move. The moving point with the minimum number of traction points between it and the monitoring assistant is used as the backup control point. The monitoring assistant pre-matches the subtask identifier, time window and control sending point within the backup control point. The pre-matched control sending point is in an active state. An association line is set between the backup control point and the target control point. The association line is associated with a trigger condition. The monitoring assistant monitors the target control point through the connection line. When the target control point meets the trigger condition, the monitoring assistant activates the pre-matched control sending point through the connection line. The activated control sending point is then matched with the corresponding execution receiving point and the communication channel is enabled.

[0015] This invention also provides an energy management system based on demand response and electric vehicle charging / discharging, comprising: The first construction module is used to build a user load reduction intention curve model, and based on the user load reduction intention curve model, determine the acceptable load reduction ratio of industrial / commercial / residential users under different incentive prices; The second construction module is used to establish a schedulable charging and discharging model for electric vehicles, and to determine the charging and discharging power, battery capacity and energy balance constraints of electric vehicles at different times based on the schedulable charging and discharging model. The third building module is used to establish the three-stage profit calculation formulas before DR execution, during DR execution, and after DR execution, and to build the VPP profit maximization model based on the three-stage profit calculation formulas. The strategy output module is used to solve the VPP profit maximization model based on the acceptable load reduction ratio of industrial / commercial / residential users under different incentive prices, as well as the charging and discharging power, battery capacity and energy balance constraints at different times. It adopts an improved wolf pack search algorithm to output the optimal daily operation strategy and maximum profit of VPP, and executes the optimal daily operation strategy of VPP.

[0016] The technical effects and advantages provided by the present invention in the above technical solution are as follows: 1. This invention overcomes the shortcomings of traditional VPP scheduling in ignoring user-side response potential and EV flexibility by introducing a VPP energy management method based on demand response and electric vehicle charging and discharging co-optimization. It achieves refined modeling and joint optimization of distributed energy resources (DER) and user load, significantly improving the operating efficiency and economy of VPP. 2. The present invention, through the constructed VPP energy management model, comprehensively considers the user load reduction intention curve, EV charging and discharging behavior, renewable energy output and electricity pricing mechanism. It can dynamically adjust the dispatch strategy under various mechanisms such as time-of-use pricing, two-stage pricing and critical peak pricing, so as to achieve the dual goals of maximizing VPP profits and balancing system supply and demand. 3. The improved wolf pack search algorithm proposed in this invention outperforms traditional algorithms in terms of global search capability and convergence stability. It can quickly find the optimal solution in complex high-dimensional solution space and effectively improve the scheduling decision accuracy and computational efficiency of VPP under different seasons, different incentive prices and different DR cycles. 4. By incorporating EVs as dispatchable resources into the VPP operation framework, not only is the grid's adaptability to renewable energy fluctuations improved, but user participation under electricity price incentives is also enhanced, promoting the consumption of clean energy and the achievement of low-carbon goals. 5. By using the monitoring assistant to pre-match the identifier and time window of the subtask corresponding to the target control point with the control sending point of the backup control point, the monitoring assistant can promptly switch between the target control point and the backup control point based on the real-time status information of the target control point when the triggering conditions are met. This ensures that the subtasks of the target control point can be executed without interruption. By setting the association group, control sending point, and execution receiving point, the transmission load of the communication channel can be reduced, and the occurrence of execution command receiving errors or control command sending errors can be reduced, so that the VPP optimal daily operation strategy can be executed better. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0018] Figure 1 This is a flowchart of the method of the present invention.

[0019] Figure 2 This is a schematic diagram of the transmission channel structure in this invention.

[0020] Figure 3 This is a schematic diagram of the monitoring assistant and the target control point in this invention.

[0021] Figure 4 This is a schematic diagram of the system structure in this invention; Figure 5 This is a schematic diagram of the load reduction intention curve of industrial users in this invention; Figure 6 This is a schematic diagram of the load reduction intention curve of residential / commercial users in this invention; Figure 7 This is a schematic diagram illustrating the load reduction amount for each user under the DR incentive price in this invention. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] Example 1, please refer to Figures 1 to 3 , Figures 5 to 7As shown in this embodiment, the energy management method based on demand response and electric vehicle charging / discharging includes the following steps: S1. Construct a user load reduction intention curve model, and determine the acceptable load reduction ratio for industrial / commercial / residential users under different incentive prices based on the user load reduction intention curve model; S2. Establish a schedulable charging and discharging model for electric vehicles, and determine the charging and discharging power, battery capacity, and energy balance constraints of electric vehicles at different times based on the schedulable charging and discharging model. S3. Establish a three-stage profit calculation formula for DR execution: before DR execution, during DR execution, and after DR execution. Based on the three-stage profit calculation formula, construct a VPP profit maximization model. S4. Based on the acceptable load reduction ratios for industrial / commercial / residential users under different incentive prices, as well as the charging and discharging power, battery capacity, and energy balance constraints at different times, an improved wolf pack search algorithm is used to solve the VPP profit maximization model, outputting the optimal daily operating strategy and maximum profit for the VPP, and then executing the optimal daily operating strategy for the VPP.

[0024] In one embodiment, constructing the user load reduction intention curve model includes: , In the formula, Let i be the load reduction intention of the i-th user at time t. (Proportion, such as a 20% load reduction), used to reflect the amount of load reduction that the user is willing to make; The incentive price (such as electricity price subsidy) for the i-th user at time t is the core factor influencing the willingness to reduce load. , , For the i-th user The parameters of the curve (obtained by fitting historical data) are used to characterize the differences in load reduction intentions among different user types (e.g., industrial users may be more sensitive to incentive prices). The agreed load reduction capacity for the i-th user at time t; Let be the maximum load reduction capacity of the i-th user at time t; The rebate for the i-th user at time t (i.e., the subsidy the user receives for participating in demand response). Let L be the total rebate amount for users at time t; L be the number of users at time t. Specifically, in In the calculation process, a quadratic function form (αλ) is used. 2 +βλ+c) represents the nonlinear growth of the willingness to reduce load with the incentive price; in As a weight, the user Transform into ;exist = × This means paying subsidies based on the agreed-upon load reduction capacity; by aggregating the rebates from L users, the amount is obtained. (Total rebate); Furthermore, by accurately characterizing user behavior (the willingness to reduce load changes with price) through nonlinear functions, it is possible to provide a flexible quantitative basis for user-side resources for VPP optimized scheduling.

[0025] In one embodiment, establishing a schedulable charging and discharging model for electric vehicles includes: The total power model of the electric vehicle is set as follows: ; In the formula, Let be the total power of the electric vehicle at time t. The power of a single electric vehicle, Let t be the number of electric vehicles charging at the charging station. Battery energy storage dynamics include charging and discharging scenarios; the output power of an electric vehicle is the energy storage difference between the two consecutive stages. The power constraint for charging is: , The energy storage renewal formula for charging is: , The power constraint for discharge is: , The formula for energy renewal during discharge is: , In the formula, The charging coefficient; The discharge coefficient; Let t be the total energy storage capacity of the battery at time t; This represents the total energy storage capacity of the battery at time t+1. This refers to the battery's rated maximum energy storage capacity. Specifically, the energy driving the electric motor of the car is directly provided by the car battery. The electric vehicle charge and discharge dispatchable model is used to describe the physical rules of the charging and discharging behavior of electric vehicles in the VPP: that is, the total power of the electric vehicle is obtained by aggregating "the power of a single electric vehicle × the number of vehicles". Battery energy storage must meet the safety constraints of "charging does not exceed the rated maximum energy storage capacity and discharging does not exceed the total energy storage capacity at the current moment". Among them, energy storage "accumulates (charging) or consumes (discharging)" over time to reflect the dynamic changes of energy. Through the electric vehicle charge and discharge dispatchable model, the VPP can coordinate the charging and discharging of electric vehicles and participate in grid demand response (such as peak and valley regulation, frequency support, etc.).

[0026] In one embodiment, constructing the VPP profit maximization model includes: The VPP profit maximization model is divided into three phases in daily scheduling: before DR starts, during DR execution, and after DR ends. The DR cycle includes: Phase 1: Before DR begins (t=1 to n-1), there are no user load reduction incentive expenditures, and the profit calculation formula is as follows: , In the formula, n is the time when the demand response begins; The load provided by VPP at time t; The load provided to the utility at time t; Let VPP be the selling price at time t; Let VPP be the utility at time t; Let be the selling price of renewable energy at time t; Let be the selling price of the electric vehicle at time t; Let be the amount of electricity generated by renewable energy at time t; Let t be the amount of electricity generated by the electric vehicle at time t; Specifically, before the DR (Diverterless Electricity Response) begins, this phase involves normal electricity consumption by users. The VPP (Vehicle Power Provider) purchases electricity from the grid, renewable energy generation meets the load, and the EV (Electric Vehicle) only performs charging and energy storage (i.e., charging and energy storage). (positive) Phase 2: DR Execution Period (t=n to m), during which user load reduction rebates need to be paid. The profit calculation formula is as follows: , In the formula, m is the moment when the demand response ends; The user load reduction status at time t; Let VPP be the incentive price for the user at time t; Let VPP be the utility incentive price at time t; The historical electricity purchase information of VPP for the 5 working days prior to time t; Specifically, the user reduces the load according to the agreement ( >0), VPP reduces electricity purchase expenses, EV can discharge to supplement load (i.e. (If it is negative, it generates discharge revenue), and at the same time, it needs to pay user rebates ( ); Phase 3: After the DR (t=m+1 to 24) ends, the system reverts to normal operation without DR incentives. Profit calculation is the same as in Phase 1, using the following formula: , Specifically, once users resume normal power supply, the EV will choose to charge and store energy or discharge to replenish energy based on the grid load gap, balancing supply and demand during the remaining time period; Therefore, the formula for calculating the profit of VPP during the DR cycle is as follows: ; The VPP profit maximization model is presupposed as follows: ; In the formula, This refers to the amount of electricity generated by photovoltaics. Specifically, the VPP energy balance constraint means that the net power demand of the VPP at any time t must be zero, that is, all power sources = all loads. The VPP energy balance constraint can also be expressed as: Ensuring real-time power balance between internal resources of the VPP and the main grid is the physical constraint of the optimization model; enabling the VPP to participate in the electricity market legally and safely, avoiding power shortages or surpluses. Rebate restrictions: ; Specifically, the incentive price at time t cannot be lower than the selling price of VPP at time t, nor can it be higher than the selling price of VPP at time t; Battery capacity constraints: , In the formula, Let be the capacity of the battery at time t; This represents the initial capacity of the battery. This represents the battery's maximum energy storage capacity. Specifically, battery capacity constraints can prevent overcharging (exceeding Q_s,max) or over-discharging (below 0), thus extending battery life.

[0027] In one embodiment, solving the VPP profit maximization model using an improved wolf pack search algorithm includes: Determine the operating scenario, which includes one of the following: summer TOU period and non-summer TOU period, summer two-stage period and non-summer two-stage period, summer critical peak period and non-summer critical peak period; For the operational scenario, the objective function is to maximize the daily profit of VPP, and the range of values ​​of VPP energy balance constraint, rebate constraint and battery capacity constraint is used as the range of the solution space. Determine the wolf pack size, the threshold for the number of iterations (e.g., 100 times, to avoid excessive iteration and time consumption), and the convergence accuracy (e.g., if the difference between two iterations of Fmax is <0.05 million yuan, it is considered converged). Initialize the wolf pack positions, setting the wolf alpha as the optimal solution: Each wolf represents a set of variables to be optimized for a specific operational scenario. These variables include: the DR incentive price for each time period. Electric vehicle charging and discharging plan The load reduction ratio for each user; Set step size parameters: Initial exploration step size , gradually growing Attack stride (Subsequent adjustments will be made according to adaptive rules); Through iterative cycles of searching, roaming, calling and chasing, and attacking and capturing, the global optimal solution is gradually approached until the iteration threshold is reached or the convergence accuracy is met. The optimal solution output at this point is taken as the optimal solution for the corresponding running scenario. The optimal solution is used as the optimal daily operating strategy for the corresponding operating scenario of VPP, and the maximum profit is calculated based on the optimal daily operating strategy of VPP. The optimal daily operation strategy for VPP is executed, which includes sending the DR incentive price for each time period, electric vehicle charging and discharging plan, and load reduction ratio for each user to the industrial / commercial / residential users of DR in the corresponding operation scenario.

[0028] In one embodiment, the search roaming, calling and chasing, and attack capture include: Search and Roaming: The individual represented by the wolf king is assigned to the optimal solution in the current solution space (denoted as Ylead). Other exploring wolves continuously search for better solutions in the solution space, updating the wolf king's position as follows: , In the formula, The updated position of the exploratory wolf is represented by the position of the i-th exploratory wolf after exploring in the d-th dimension and the ρ-th direction, where ρ is the index of the exploration direction. The initial position of the exploratory wolf is represented by the initial value of the i-th exploratory wolf in the d-th dimension (corresponding to a certain variable to be optimized, such as DR incentive price or EV charging and discharging power), which belongs to the random feasible solution in the solution space. The dimension is randomly identified: the value is 1, 2, ..., N (N is the size of the wolf pack), which identifies the variable dimension (the index of the value of d) corresponding to the current exploration. h represents the number of exploration directions: the value is explicitly 5~20 (randomly selected), which is used to balance search accuracy and efficiency, that is, each exploring wolf needs to traverse h different directions to search. Specifically, in this invention, the number of subgroups N is set to 50 (i.e., the wolf pack size is 50, including 1 alpha wolf, 30% predatory wolves, and 70% exploratory wolves). The number of exploration directions h is randomly selected between 5 and 20 to balance search accuracy and efficiency. After each exploration, if the objective function value of the new position is better than that of the current alpha wolf, the alpha wolf position is updated; otherwise, the original position is retained. By introducing directional exploration and an adaptive step size mechanism, the improved wolf pack algorithm effectively enhances the ability to identify the optimal solution region. Calling and Chase: Members of the wolf pack that respond to a chase call are called "fierce wolves." When the alpha wolf initiates a call by howling, it summons the surrounding fierce wolves to move rapidly to its location. Wolves that hear the alpha wolf's howl will move quickly to the alpha wolf's location with relatively large chasing strides. During the k+1 iteration in the d-th dimension, the positions of the fierce wolves and the distance between their positions and the alpha wolf's position are calculated, as shown in the following formula: , In the formula, This indicates the optimal position of the Wolf King in the i-th dimension variable during the k-th iteration (corresponding to the variable value of the current optimal VPP profit). This represents the current position of the ferocious wolf in the i-th dimension and the d-th dimension at the k-th iteration; This represents the update position of the ferocious wolf in the i-th dimension and the d-th dimension at the k-th iteration; The average proximity of a wolf pack to its alpha wolf is expressed in the formula. Let be the length of the value range of the i-th dimension variable. Dnear is used to determine whether the wolf is close to the optimal solution region. The maximum value of the i-th dimension variable is the upper limit of the variable to be optimized (such as DR incentive price, EV charging and discharging power) in the i-th dimension (determined by the solution space constraints). The minimum value of the i-th dimension variable is represented by the lower limit of the variable to be optimized in the i-th dimension (determined by the solution space constraints). This indicates the number of wolves involved in the calculation. For example, the value is the total number of wolves M (15 wolves), which is the total number of wolves used for distance statistics. Distance weighting coefficient: value range [0.8, 1.2] (adaptive adjustment), used to balance the influence of the dispersion of the wolf pack's location; The average distance between a single wolf and the location of the wolf king is used to evaluate the search effectiveness of a single wolf. Attack and Capture: When the wolf approaches the prey's location, it enters attack mode. Its position update method introduces random perturbation to enhance local search capabilities, as shown in the following formula: in, The wolf's position before the attack: At the kth iteration, the initial position of the wolf in the i-th dimension and the d-th dimension during the attack phase (i.e., the final position after being called and chased). The position of the wolf after the attack is represented by: At the (k+1)th iteration, after updating with random perturbation, the final position of the wolf in the i-th dimension and the d-th dimension; Rand represents the attack random number: its value ranges [-1, 1], follows a uniform distribution, and is used to introduce random perturbation to avoid the algorithm getting trapped in local optima. The attack step size is adjusted accordingly; this strategy enables the wolf pack to have a stronger ability to search locally and escape local optima when approaching the optimal solution; by introducing randomness and adaptive step size adjustment, the improved wolf pack algorithm outperforms the traditional wolf pack algorithm in terms of convergence accuracy and robustness.

[0029] It should be noted that the traditional Wolf Pack Algorithm (WPSA) has two main drawbacks: the lack of necessary information exchange among wolves may lead to overly dispersed scouting behavior, causing the algorithm to deviate from the global optimum; and the step size parameter of the attack movement is constant. If set improperly, the convergence performance of the algorithm may decrease. The improved wolf pack algorithm in this invention, however, enhances the scouting behavior of the scouting wolves and the encirclement strategy of the wolf pack in solving the VPP profit maximization model.

[0030] Furthermore, to verify the effectiveness of the present invention, the optimal profit of VPP was simulated under different electricity price periods in summer and non-summer (including TOU period, two-period period and critical peak period); Figure 5 and Figure 6 Load reduction intention curves are presented for industrial users and residential / commercial users respectively. These curves are used to calculate the DR incentive price for users to determine the load reduction amount (pucur,t).

[0031] The time intervals for the electricity price cycle in the example are described as follows: (1) TOU period: 10:00-16:00; (2) Two-stage period: 10:00-12:00 and 13:00-16:00; (3) Key peak period: 13:00-15:00.

[0032] For example, this invention provides power purchase plans for different users of a VPP on non-summer working days, wherein the load reduction amount for each user is determined based on the DR incentive price, such as... Figure 7 As shown: The incentive price for utility DR is set to 2 times λDR,t and 4 times λDR,t. User DR implementation methods include TOU, two-stage periods, and critical peak periods. Each user adjusts the load reduction price based on the execution period and incentive, as shown in Table 1. As shown in Table 1, regardless of whether it's the TOU period, two-stage period, or critical peak period, the higher the incentive price, the greater the load reduction.

[0033] Table 1

[0034] As shown in Table 1, when the incentive price is twice λDR,t, the VPP provides users with a DR incentive price of TWD4, meeting the minimum load shedding requirement. Industrial users reduce their load by approximately 5% of their electricity consumption, while residential / commercial users reduce their load by approximately 10%, indicating a higher curtailment rate among residential / commercial users. When the incentive price is set to four times λDR,t, the DR incentive price increases, leading to a greater willingness to shed load. Industrial users reduce their load by 19.81% of their electricity consumption, while residential / commercial users reduce their load by 25.47%. Nevertheless, the higher base electricity consumption of industrial users results in a relatively higher curtailment amount.

[0035] Table 2 shows the maximum daily profit of the VPP outside of summer. The VPP achieves the highest daily profit during key peak periods when the DR incentive price is set to 2 times λDR,t. The maximum daily profit for the VPP is RMB 59,824.86. Similarly, the highest profit during the TOU period is achieved when the DR incentive price is set to 4 times, reaching RMB 61,705.01.

[0036] Table 2

[0037] Furthermore, higher incentive prices for utility companies (DRs) can impact VPP operations. This study uses λDR,t of 2x, 4x, and 6x to simulate the impact of TOU, two-stage, and critical peak periods on VPP profitability. Table 3 shows the impact of incentive prices for each period. As shown in the table below, higher incentive multipliers lead to higher VPP profits and higher load factors. During TOU periods, VPP profits are higher than in other periods. Additionally, VPPs achieve greater profits when electric vehicles and solar vehicles are considered to have higher incentive prices for utility DRs, thus impacting VPP operations.

[0038] Table 3

[0039] Furthermore, this invention compares algorithms, testing EP, GA, PSO, the original WPSA, and the IWPSA used in this invention side-by-side, all with the objective function of 4×λDR,t during the summer TOU period. Table 4 shows a comparison of EP, GA, PSO, WPSA, and IWPSA used in the simulation. This study uses four times λDR,t to simulate the profit of VPP during the TOU period. The table shows that IWPSA achieves better results than EP, GA, PSO, and WSA; it also shows that IWPSA improves search performance and explores a more likely global optimum.

[0040] Table 4

[0041] In one embodiment, the electric vehicle charging and discharging program includes: Determine the scale and parameters of electric vehicles in the electric vehicle charging and discharging plan. The electric vehicle parameters include the battery capacity and maximum charging and discharging power of the electric vehicle. Configure an execution point for the communication port of each electric vehicle connected to the charging pile, associate multiple execution receiving points with each execution point, and associate two execution instruction identifiers with each execution receiving point. The execution point is a virtual machine, and the execution receiving point is a communication port. VPP is configured with a control center, which is associated with multiple control points. These control points are mapped one-to-one with multiple execution points. Each control point is associated with multiple control sending points, and each control sending point is associated with two identifiers for control instructions. The control center is a cloud server, the control points are virtual machines, and the control sending points are communication ports. Establish a transmission channel between the one-to-one corresponding control point and execution point; The control center breaks down the overall task corresponding to the electric vehicle charging and discharging plan into multiple sub-tasks through monitoring points, distributes the multiple sub-tasks to multiple control points and activates the control sending points. The control sending points activate the corresponding execution receiving points, and after the execution receiving points are activated, the electric vehicles execute the corresponding sub-tasks.

[0042] In one embodiment, establishing a transmission channel between a one-to-one corresponding control point and execution point includes: Control points with the same control commands are associated to obtain an association group; Configure a monitoring assistant for the associated group. The monitoring assistant is connected to multiple control points in the associated group. The monitoring assistant is used to carry two identifiers and a time window of the subtask and to monitor multiple control points in the associated group. The control point for receiving and distributing subtasks is used as the target control point. Trigger conditions are set for the monitoring assistant. The monitoring assistant switches the target control point based on the trigger conditions. The trigger conditions are that the target control point in the associated group meets preset conditions, including that the target control point has an abnormal condition. Communication channels are established between multiple control sending points and multiple execution receiving points within the corresponding control points, and these multiple communication channels form a transmission channel.

[0043] In one embodiment, the control sending point activates the corresponding execution receiving point. After the execution receiving point is activated, the electric vehicle executes the corresponding sub-task, including: The control center sends the subtask identifier and time window to the target control point. The monitoring assistant receives the subtask identifier and performs a consistency match with the identifier of the control command associated with the control sending point. If they match, the monitoring assistant activates the corresponding control sending point. If they do not match, the monitoring assistant does not activate the corresponding control sending point. Once activated, the control sending point sends its associated identifier to the corresponding execution receiving point through the communication channel, and activates the execution receiving point. The electric vehicle corresponding to the execution receiving point then executes the corresponding subtask. Among them, a one-to-one connection line of the same length is established between the monitoring assistant and multiple control points in the associated group; Each traction line is configured with multiple traction points and one moving point. The multiple traction points are evenly distributed along the traction line. The moving point is used to carry the control point to move along the traction line following the traction points. The initial position of the moving point is located in the middle of the multiple traction points. Each traction point is bound to its position information on the traction line. The difference between the real-time status information of the control point and the preset standard threshold is calculated. If the difference is positive, the moving point carries the control point and moves it towards the monitoring assistant by an offset corresponding to the difference. One traction point corresponds to one offset. If the difference is negative, the moving point carries the control point and moves it away from the monitoring assistant by an offset corresponding to the difference. If the difference is zero, the moving point does not move. The moving point with the minimum number of traction points between it and the monitoring assistant is used as the backup control point. The monitoring assistant pre-matches the subtask identifier, time window and control sending point within the backup control point. The pre-matched control sending point is in an active state. An association line is set between the backup control point and the target control point. The association line is associated with a trigger condition. The monitoring assistant monitors the target control point through the connection line. When the target control point meets the trigger condition, the monitoring assistant activates the pre-matched control sending point through the connection line. The activated control sending point is then matched with the corresponding execution receiving point and the communication channel is enabled.

[0044] It should be noted that by setting execution points on the communication ports of each EV connected to the charging pile, and associating multiple execution receiving points within each execution point; control points are obtained by mapping corresponding execution orders, and multiple control sending points associated with each control point are connected to corresponding execution receiving points. The control center breaks down the total task of the VPP optimal daily operation strategy to the corresponding control points. Breakdown refers to dividing the total task according to the maximum charging and discharging power and the minimum time window of each EV. For example, the control center breaks down the total task of the VPP optimal daily operation strategy to the corresponding control points: clearly defining the total task requirements (18:00-19:00). Based on the maximum charging and discharging power of each EV (30kW discharge at 00:00, minimum time window of 1 hour), first select EV combinations whose sum of nominal power matches the total task, then select EVs whose schedulable time period includes the target window and whose continuous duration is not less than the minimum time window, and determine the optimal combination by combining the real-time status information of each EV; for example, when 10 EVs (each with charging and discharging power of 10kW, 10kW, 10kW, 10kW, 5kW, 5kW, 5kW, 8kW, 8kW, and 7kW) need to complete a 30kW charging and discharging task, the final selection is four EVs with power of 10kW, 5kW, 8kW, and 7kW. Taking a 10kW EV as an example, four control points corresponding to 10kW EVs are associated to form an association group. It is known that the control sending points of the four EVs corresponding to these four control points are the same (i.e., the control commands and identifiers corresponding to the control sending points are the same). A monitoring assistant is configured on each of the four control points within the association group. The monitoring assistant monitors the real-time status information of the four control points. A connecting line is established between the monitoring assistant and the four control points, and traction points are set on the connecting line. By obtaining the position of the control point carried by the mobile point at the traction point, the distance between the control point and the monitoring assistant can be shortened based on the real-time status parameters of the control point. The control point with the fewest traction points between the monitoring assistant and the mobile point is designated as the backup control point. A connection line is set between the target control point and the monitoring assistant. Triggering conditions are set on the connection line. The monitoring assistant pre-matches the identifier and time window of the subtask corresponding to the target control point with the control sending point of the backup control point. When the target control point meets the triggering conditions, the monitoring assistant activates the pre-matched control sending point through the connection line. The activated control sending point is then matched with the corresponding execution receiving point and the communication channel is enabled. At the same time, the monitoring assistant retracts the identifier and time window of the subtask within the target control point (the time window is used to control the activation time of the activated control sending point). This allows the monitoring assistant to switch between the target control point and the backup control point in a timely manner based on the real-time status information of the target control point, enabling the subtasks of the target control point to be executed without interruption. By setting up association groups, subtasks can always be executed within control points that have the same control commands (the same control sending points), ensuring that the switching between the target control point and the backup control point meets the needs of the subtasks. Multiple control sending points within a control point are set based on the corresponding EV's rated charging and discharging power range (taking an EV with a maximum charging and discharging power of 10kW as an example, the multiple execution receiving points for this EV's corresponding execution point can be: identifiers corresponding to +1kW and -1kW, identifiers corresponding to +2kW and -2kW, ..., identifiers corresponding to +10kW and -10kW). The multiple control sending points for its corresponding control point are +1kW... The identifiers corresponding to -1kW, +2kW and -2kW, ..., +10kW and -10kW can activate the control sending point and execution receiving point when the corresponding control sending point is activated, thereby enabling the corresponding communication channel. By matching the identifiers of the control sending point and execution receiving point when the communication channel is enabled, the corresponding EV can directly execute the execution instructions associated with the activated execution receiving point through the execution receiving point. This reduces the transmission load of the communication channel and reduces the occurrence of execution instruction receiving errors or control instruction sending errors, so that the VPP optimal daily operation strategy can be executed better.

[0045] Specifically, there are two identifiers corresponding to the execution receiving point and the control sending point. One identifier contains a "+" hash value, and the other contains a "-" hash value. The identifiers corresponding to subtasks also contain corresponding "+" or "-" hash values, with "+" and "-" representing charging and discharging of the electric vehicle, respectively. The identifiers of subtasks can be obtained from a preset hash mapping table, which can be set within the control center. The identifiers corresponding to the control sending point and the execution receiving point are both obtained from the preset hash mapping table. The triggering condition is that the target control point in the associated group meets preset conditions, which include... This includes situations where the target control point experiences abnormal conditions, such as: the real-time status parameters of the target control point being lower than a preset standard threshold; real-time status information is obtained through a heartbeat mechanism: for example, by establishing a communication channel between the execution receiving point and the control sending point, the execution receiving point sends one heartbeat packet to the control sending point every 1 second, and the control sending point receives and counts the heartbeat packets every 10 seconds. The system uses the actual number of heartbeat packets received in this period as the real-time status information, and the preset standard threshold is 8 heartbeat packets received per period. The specific heartbeat packet sending time, statistical period, and preset standard threshold can also be set according to the actual situation.

[0046] Example 2, please refer to Figure 4 As shown in the figure, the energy management system based on demand response and electric vehicle charging and discharging described in this embodiment includes: a first construction module, a second construction module, a third construction module, and a strategy output module; The first construction module is used to construct a user load reduction intention curve model, and to determine the acceptable load reduction ratio for industrial / commercial / residential users under different incentive prices based on the user load reduction intention curve model. The second building module is used to establish a schedulable charging and discharging model for electric vehicles, and to determine the charging and discharging power, battery capacity and energy balance constraints of electric vehicles at different times based on the schedulable charging and discharging model. The third building module is used to establish a three-stage profit calculation formula before DR execution, during DR execution, and after DR execution, and to build a VPP profit maximization model based on the three-stage profit calculation formula. The strategy output module is used to solve the VPP profit maximization model based on the acceptable load reduction ratio of industrial / commercial / residential users under different incentive prices, as well as the charging and discharging power, battery capacity and energy balance constraints at different times. It uses an improved wolf pack search algorithm to output the optimal daily operation strategy and maximum profit of the VPP, and then executes the optimal daily operation strategy of the VPP.

[0047] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. An energy management method based on demand response and electric vehicle charging / discharging, characterized in that, Includes the following steps: S1. Construct a user load reduction intention curve model, and determine the acceptable load reduction ratio for industrial / commercial / residential users under different incentive prices based on the user load reduction intention curve model; S2. Establish a schedulable charging and discharging model for electric vehicles, and determine the charging and discharging power, battery capacity, and energy balance constraints of electric vehicles at different times based on the schedulable charging and discharging model. S3. Establish a three-stage profit calculation formula for DR execution: before DR execution, during DR execution, and after DR execution. Based on the three-stage profit calculation formula, construct a VPP profit maximization model. S4. Based on the acceptable load reduction ratios for industrial / commercial / residential users under different incentive prices, as well as the charging and discharging power, battery capacity, and energy balance constraints at different times, an improved wolf pack search algorithm is used to solve the VPP profit maximization model, outputting the optimal daily operating strategy and maximum profit for the VPP, and then executing the optimal daily operating strategy for the VPP.

2. The energy management method based on demand response and electric vehicle charging / discharging according to claim 1, characterized in that, The construction of the user load reduction intention curve model, and the determination of the acceptable load reduction ratio for industrial / commercial / residential users under different incentive prices based on the user load reduction intention curve model, includes: ， In the formula, Let be the acceptable load reduction ratio for the i-th user at time t. ; Let be the incentive price for the i-th user at time t; , , For the i-th user The parameters of the curve; The agreed load reduction capacity for the i-th user at time t; Let be the maximum load reduction capacity of the i-th user at time t; This represents the rebate amount for the i-th user at time t. L represents the total rebate amount received by users at time t; L represents the number of users at time t.

3. The energy management method based on demand response and electric vehicle charging / discharging according to claim 2, characterized in that, The establishment of a schedulable charging and discharging model for electric vehicles, and the determination of charging and discharging power, battery capacity, and energy balance constraints of electric vehicles at different time periods based on the schedulable charging and discharging model, include: The total power model for electric vehicles is set as follows: ; In the formula, Let be the total power of the electric vehicle at time t. The power of a single electric vehicle, Let t be the number of electric vehicles charging at the charging station. Battery energy storage dynamics include charging and discharging scenarios, and the output power of electric vehicles is the energy storage difference between the two consecutive stages. Wherein: the power constraint for charging is: , The energy storage renewal formula for charging is: , The power constraint for discharge is: , The formula for energy renewal during discharge is: , In the formula, The charging coefficient; The discharge coefficient; Let t be the total energy storage capacity of the battery at time t. This represents the total energy storage capacity of the battery at time t+1. This represents the battery's maximum energy storage capacity.

4. The energy management method based on demand response and electric vehicle charging / discharging according to claim 3, characterized in that, The establishment of a three-stage profit calculation formula before, during, and after DR execution, and the construction of a VPP profit maximization model based on the three-stage profit calculation formula, includes: Before DR is executed, the formula for calculating the profit F1 from t=1 to n-1 is as follows: ， In the formula, n is the time when the demand response begins; The load provided by VPP at time t; The load provided to the utility at time t; Let VPP be the selling price at time t; Let VPP be the utility at time t; Let be the selling price of renewable energy at time t; Let be the selling price of the electric vehicle at time t; Let be the amount of electricity generated by renewable energy at time t; Let t be the amount of electricity generated by the electric vehicle at time t; In DR execution, the profit F2 from t=n to m is calculated using the following formula: ， In the formula, m is the time when the demand response ends; The user load reduction status at time t; Let VPP be the incentive price for the user at time t; Let VPP be the utility incentive price at time t; The historical electricity purchase information of VPP for the 5 working days prior to time t; After DR is executed, the profit F3 from t=m+1 to 24 is calculated using the following formula: ， Based on the three-stage profit calculation formula before, during, and after DR execution, the VPP profit maximization model is obtained. The calculation formula is as follows: ; The VPP energy balance constraint is as follows: ； In the formula, This refers to the amount of electricity generated by photovoltaics. Rebate restrictions: ; Battery capacity constraints: , In the formula, Let be the capacity of the battery at time t; This represents the initial capacity of the battery. This represents the battery's maximum energy storage capacity.

5. The energy management method based on demand response and electric vehicle charging / discharging according to claim 4, characterized in that, Based on the acceptable load reduction ratios for industrial / commercial / residential users under different incentive prices, as well as the charging / discharging power, battery capacity, and energy balance constraints at different times, an improved wolf pack search algorithm is used to solve the VPP profit maximization model, outputting the optimal daily operating strategy and maximum profit for the VPP, including: Determine the operating scenario, which includes one of the following: summer TOU period and non-summer TOU period, summer two-stage period and non-summer two-stage period, summer critical peak period and non-summer critical peak period; For the operational scenario, the objective function is to maximize the daily profit of VPP, and the range of values ​​of VPP energy balance constraint, rebate constraint and battery capacity constraint is used as the range of the solution space. Determine the wolf pack size, iteration threshold, and convergence accuracy; Initialize the wolf pack positions and set the wolf king as the optimal solution: each wolf is a set of variables to be optimized for the corresponding operating scenario. The variables to be optimized include: DR incentive price for each time period, electric vehicle charging and discharging plan, and load reduction ratio for each user. Set step size parameters: initial exploration step size, pursuit step size, attack step size; Through iterative cycles of searching, roaming, calling and chasing, and attacking and capturing, the global optimal solution is gradually approached until the iteration threshold is reached or the convergence accuracy is met. The optimal solution output at this point is taken as the optimal solution for the corresponding running scenario. The optimal solution is used as the optimal daily operating strategy for the corresponding operating scenario of VPP, and the maximum profit is calculated based on the optimal daily operating strategy of VPP. The optimal daily operation strategy for VPP is executed, which includes sending the DR incentive price for each time period, electric vehicle charging and discharging plan, and load reduction ratio for each user to the industrial / commercial / residential users of DR in the corresponding operation scenario.

6. The energy management method based on demand response and electric vehicle charging / discharging according to claim 5, characterized in that, The search roaming, calling and chasing, attack and capture include: Search Roaming: The individual represented by the wolf king is assigned to the optimal solution in the current solution space, and other exploring wolves continue to search for better solutions in the solution space, updating the wolf king's position; Calling and chasing: The members of the wolf pack who respond to the call to chase are called the wolves; when the alpha wolf initiates a calling behavior by howling, it will summon the surrounding wolves to move quickly to its position; the wolves that hear the alpha wolf's howl will move quickly to the alpha wolf's position with relatively large chasing strides; when iterating k+1 in the d-th dimension, the position of the wolves and the distance between the position of the wolves and the position of the alpha wolf are calculated. Attack and capture: When the wolf approaches the area where the prey is located, it enters attack mode, and its position update method introduces random perturbation to enhance local search capabilities.

7. The energy management method based on demand response and electric vehicle charging / discharging according to claim 5, characterized in that, The electric vehicle charging and discharging plan includes: Determine the scale and parameters of electric vehicles in the electric vehicle charging and discharging plan; Configure an execution point for the communication port of each electric vehicle connected to the charging pile, associate multiple execution receiving points with each execution point, and associate two execution instruction identifiers with each execution receiving point. The execution point is a virtual machine, and the execution receiving point is a communication port. VPP is configured with a control center, which is associated with multiple control points. These control points are mapped one-to-one with multiple execution points. Each control point is associated with multiple control sending points, and each control sending point is associated with two identifiers for control instructions. The control center is a cloud server, the control points are virtual machines, and the control sending points are communication ports. Establish a transmission channel between the one-to-one corresponding control point and execution point; The control center breaks down the overall task corresponding to the electric vehicle charging and discharging plan into multiple sub-tasks through monitoring points, distributes the multiple sub-tasks to multiple control points and activates the control sending points. The control sending points activate the corresponding execution receiving points, and after the execution receiving points are activated, the electric vehicles execute the corresponding sub-tasks.

8. The energy management system based on demand response and electric vehicle charging / discharging according to claim 7, characterized in that, Establishing a transmission channel between a one-to-one corresponding control point and execution point includes: Control points with the same control commands are associated to obtain an association group; Configure a monitoring assistant for the associated group. The monitoring assistant is connected to multiple control points in the associated group. The monitoring assistant is used to carry two identifiers and a time window of the subtask and to monitor multiple control points in the associated group. The control point for receiving and distributing subtasks is used as the target control point. Trigger conditions are set for the monitoring assistant. The monitoring assistant switches the target control point based on the trigger conditions. The trigger conditions are that the target control point in the associated group meets preset conditions, including that the target control point has an abnormal condition. Communication channels are established between multiple control sending points and multiple execution receiving points within the corresponding control points, and these multiple communication channels form a transmission channel.

9. The energy management system based on demand response and electric vehicle charging / discharging according to claim 8, characterized in that, The control sending point activates the corresponding execution receiving point. After the execution receiving point is activated, the electric vehicle executes the corresponding sub-tasks, including: The control center sends the subtask identifier and time window to the target control point. The monitoring assistant receives the subtask identifier and performs a consistency match with the identifier of the control command associated with the control sending point. If they match, the monitoring assistant activates the corresponding control sending point. If they do not match, the monitoring assistant does not activate the corresponding control sending point. Once activated, the control sending point sends its associated identifier to the corresponding execution receiving point through the communication channel, and activates the execution receiving point. The electric vehicle corresponding to the execution receiving point then executes the corresponding subtask. Among them, a one-to-one connection line of the same length is established between the monitoring assistant and multiple control points in the associated group; Each traction line is configured with multiple traction points and one moving point. The multiple traction points are evenly distributed along the traction line. The moving point is used to carry the control point to move along the traction line following the traction points. The initial position of the moving point is located in the middle of the multiple traction points. Each traction point is bound to its position information on the traction line. The difference between the real-time status information of the control point and the preset standard threshold is calculated. If the difference is positive, the moving point carries the control point and moves it towards the monitoring assistant by an offset corresponding to the difference. One traction point corresponds to one offset. If the difference is negative, the moving point carries the control point and moves it away from the monitoring assistant by an offset corresponding to the difference. If the difference is zero, the moving point does not move. The moving point with the minimum number of traction points between it and the monitoring assistant is used as the backup control point. The monitoring assistant pre-matches the subtask identifier, time window and control sending point within the backup control point. The pre-matched control sending point is in an active state. An association line is set between the backup control point and the target control point. The association line is associated with a trigger condition. The monitoring assistant monitors the target control point through the connection line. When the target control point meets the trigger condition, the monitoring assistant activates the pre-matched control sending point through the connection line. The activated control sending point is then matched with the corresponding execution receiving point and the communication channel is enabled.

10. An energy management system based on demand response and electric vehicle charging / discharging, used to implement the energy management method based on demand response and electric vehicle charging / discharging as described in any one of claims 1-9, characterized in that, include: The first construction module is used to build a user load reduction intention curve model, and based on the user load reduction intention curve model, determine the acceptable load reduction ratio of industrial / commercial / residential users under different incentive prices; The second construction module is used to establish a schedulable charging and discharging model for electric vehicles, and to determine the charging and discharging power, battery capacity and energy balance constraints of electric vehicles at different times based on the schedulable charging and discharging model. The third building module is used to establish the three-stage profit calculation formulas before DR execution, during DR execution, and after DR execution, and to build the VPP profit maximization model based on the three-stage profit calculation formulas. The strategy output module is used to solve the VPP profit maximization model based on the acceptable load reduction ratio of industrial / commercial / residential users under different incentive prices, as well as the charging and discharging power, battery capacity and energy balance constraints at different times. It adopts an improved wolf pack search algorithm to output the optimal daily operation strategy and maximum profit of VPP, and executes the optimal daily operation strategy of VPP.

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