Hybrid optimization method for energy management of electric vehicle charging and discharging station

Abstract

The invention discloses a hybrid optimization method for energy management of an electric vehicle charging and discharging station, and the method comprises the steps: collecting the static information of the electric vehicle charging and discharging station, and the dynamic information of a to-be-charged electric vehicle and an energy storage system; taking the static information and the dynamic information as input parameters of a mixed meta-heuristic optimization algorithm; wherein the mixed meta-heuristic optimization algorithm comprises a greedy algorithm and a particle swarm optimization algorithm; solving a charging and discharging scheme of the electric vehicle through a greedy algorithm; solving a charging and discharging scheme of an energy storage system of the electric vehicle charging and discharging station through a particle swarm algorithm; based on the charging and discharging scheme, setting charging and discharging power of the electric vehicle and the energy storage system in the charging and discharging station at the current time period; and finally, outputting the actual charging and discharging power in each time period as a final charging and discharging scheme, and outputting the maximum capacity of the energy storage system as the planned capacity of the energy storage system. The energy management efficiency of the electric vehicle charging and discharging station can be further improved, and the charging requirement of a user is better met.

Description

Technical Field

[0001] The present invention relates to the field of scheduling of electric vehicle charging and discharging stations, and in particular to a hybrid optimization method for energy management of electric vehicle charging and discharging stations. Background Art

[0002] With the rapid growth of new energy vehicles, the demand for electric vehicle charging and discharging stations has increased dramatically. At the same time, the promotion of time-of-use electricity pricing has also brought new challenges to the operation and management of charging and discharging stations. How to rationally schedule the charging process of electric vehicles amidst fluctuating electricity prices, improve charging efficiency, and reduce operating costs has become a key issue that needs to be addressed.

[0003] Energy management for electric vehicle charging and discharging stations involves rationally and effectively managing the charging and discharging processes of the energy storage system and each electric vehicle within the station to ensure efficient operation and meet the charging needs of different electric vehicles, while also reducing operating costs and alleviating grid load. This includes determining the maximum capacity of the energy storage system and arranging the charging and discharging times and powers of the electric vehicles and energy storage system.

[0004] As a critical infrastructure, electric vehicle charging and discharging stations are gradually achieving deep integration with the power grid through the application of technologies such as intelligent scheduling and big data analytics. This not only improves energy efficiency but also significantly reduces operating costs and environmental impact. However, existing technologies still have some problems and shortcomings, particularly in optimizing the operational efficiency of charging and discharging stations, coping with electricity price fluctuations, and utilizing energy storage systems.

[0005] In the existing technology, Desheng Li et al. proposed a charging scheduling scheme for large electric vehicle charging and discharging stations; Viegas et al. developed a method for optimizing charging and discharging scheduling through a fuzzy logic controller; Qiongjie Dai et al. proposed an optimization model to determine the optimal capacity of solar generators and energy storage systems in electric vehicle charging stations, and studied the charging and discharging modes of energy storage systems and their interaction strategies with the power grid; Masoud Honarmand et al. proposed an optimal scheduling model for electric vehicles in smart parking lots; Soodeh Negarestani et al. studied the capacity and energy flow optimization problems of energy storage systems in fast charging stations.

[0006] However, the common problems existing in the above prior art are:

[0007] (1) Charging power regulation is not flexible.

[0008] Current charging and discharging stations mostly use a fixed or simple power distribution scheme during the charging process. Electric vehicles are often charged at maximum or average power immediately after being connected. This approach lacks flexibility and cannot adjust to changes in grid load and the actual charging needs of electric vehicles. Consequently, it fails to fully utilize the difference in peak and off-peak electricity prices to reduce charging costs. Furthermore, during peak periods, the simultaneous charging of large numbers of electric vehicles can cause a sharp increase in grid load, even creating a "peak upon peak" phenomenon, further increasing pressure on the grid.

[0009] (2) Insufficient utilization of the two-way interactive capabilities of the vehicle network.

[0010] Currently, many charging stations only support one-way charging, meaning electric vehicles draw power from the grid to charge, failing to fully utilize the potential of electric vehicles as energy storage units. With the advancement of electric vehicle technology, V2G (Vehicle to Grid) technology has enabled electric vehicles to feed power back to the grid, a process known as reverse charging. This technology not only alleviates grid load but also provides additional financial benefits for vehicle owners. However, V2G technology is not widely used in existing charging stations, failing to effectively utilize the energy storage capabilities of electric vehicles to balance grid fluctuations.

[0011] (3) The capacity of the energy storage system is not fully optimized.

[0012] In many existing charging and discharging stations, the capacity configuration and management of energy storage systems are inadequate. Energy storage systems are supposed to store energy during periods of low grid load and release it during peak periods, helping charging and discharging stations take advantage of off-peak electricity prices to reduce costs and mitigate grid fluctuations. However, the energy storage systems in many charging and discharging stations are not properly optimized, failing to provide sufficient energy during peak periods, impacting operational efficiency and increasing costs. Summary of the Invention

[0013] The purpose of the present invention is to provide a hybrid optimization method for energy management of electric vehicle charging and discharging stations, so as to further improve the energy management efficiency of electric vehicle charging and discharging stations, thereby reducing various costs of charging and discharging stations and better meeting the charging needs of electric vehicle users.

[0014] In order to achieve the above tasks, the present invention adopts the following technical solutions:

[0015] A hybrid optimization method for energy management of electric vehicle charging and discharging stations, comprising:

[0016] Collect static information of electric vehicle charging and discharging stations, as well as dynamic information of electric vehicles to be charged and energy storage systems; use the static information and dynamic information as input parameters of a hybrid meta-heuristic optimization algorithm; wherein the hybrid meta-heuristic optimization algorithm includes a greedy algorithm and a particle swarm algorithm;

[0017] A greedy algorithm is used to solve the charging and discharging plan for electric vehicles. In the greedy algorithm, each electric vehicle is first charged at the maximum charging power as the initial charging plan. Then, the user-set charging mode contained in the collected dynamic information is determined. If the cost-priority mode is used, the energy of each electric vehicle at the end of each time period is used to allocate the electric vehicle discharge power, thereby obtaining a charging and discharging plan for each electric vehicle.

[0018] The particle swarm algorithm is used to solve the charging and discharging scheme of the energy storage system of the electric vehicle charging and discharging station solution. In the particle swarm algorithm, the charging and discharging scheme of the energy storage system, the movement speed and step size of the particles are characterized by assigning position and velocity attributes to particles. Artificially generated particles are added to the initialized particle swarm to improve the quality of the initial population. The fitness function is constructed based on the electricity cost, energy storage system investment cost, and user compensation cost to calculate the fitness of the particles. At the same time, based on the preset optimization rules, the dominated solutions of the particles are eliminated, and finally the global optimal position of the particles is obtained as the optimal charging and discharging scheme of the energy storage system.

[0019] Furthermore, the method further comprises:

[0020] According to the charging and discharging plan of the electric vehicle and the energy storage system, the charging and discharging power of the electric vehicle and the energy storage system in the charging and discharging station is set in the current period. After a fixed time interval, if the set stop time is not reached, it is determined whether there is a new electric vehicle arriving. If so, the greedy algorithm is re-executed; otherwise, the existing charging and discharging plan is continued. If the set stop time is reached, the actual charging and discharging power of the electric vehicle and the energy storage system in each period is output as the final charging and discharging plan, and the maximum capacity of the energy storage system is output as its planned capacity.

[0021] Furthermore, static information includes electricity prices in each time period, the power and capacity cost coefficients of the energy storage system, the profit coefficient when discharging to the grid, and the maximum power of the connection point between the charging and discharging station and the grid;

[0022] Dynamic information includes the electric vehicle’s charging mode, current power, required power, expected stay time, and maximum charge and discharge power, as well as the current power of the energy storage system;

[0023] The charging mode includes a time priority mode and a cost priority mode.

[0024] Furthermore, the charging and discharging scheme of electric vehicles is solved by a greedy algorithm. The specific process includes:

[0025] (2.1) Obtaining dynamic information and static information as input parameters;

[0026] (2.2) Sort the time periods according to the electricity price from low to high. For the time periods with the same electricity price, arrange them in chronological order to generate a sorted time period set.

[0027] (2.3) generating an initial charging plan for the electric vehicle based on the set of time periods, allowing the electric vehicle to charge at a maximum charging power until demand is met or the electric vehicle leaves;

[0028] (2.4) Determine whether the current charging mode of the electric vehicle is the time priority mode. If so, complete the plan formulation and go to step (2.15); if not, continue to step (2.5);

[0029] (2.5) Calculate the battery energy of the electric vehicle at the end of each period;

[0030] (2.6) Enter the discharge power allocation process. This process first determines the high electricity price period ts i and low electricity price period ts j , electric vehicles are used in some high electricity price periods i The discharge power is increased, and at the same time part of the low electricity price period ts j The charging power increases;

[0031] (2.7) Determine the high electricity price period ts based on the power transfer step size i Can more power be added? If yes, then continue with step (2.8), otherwise go to step (2.12);

[0032] (2.8) Determine the low electricity price period ts based on the power transfer step j Can more power be added? If yes, continue with step (2.9), otherwise go to step (2.13);

[0033] (2.9) Using the power transfer step size, the charging and discharging power of electric vehicles during high and low electricity price periods, update the charging and discharging plan of electric vehicles;

[0034] (2.10) Update the battery energy of each time period and determine whether the battery energy of the electric vehicle in each time period after adjustment is within the allowable range; if it is within the allowable range, go to step (2.7) and continue iteration; otherwise, go to step (2.11);

[0035] (2.11) If the adjustment causes the battery energy in a certain period to exceed the allowable range, the battery energy is restored to the state before the adjustment; then, go to step (2.13);

[0036] (2.12) Update period ts i The subscript i=i-1, continue to optimize the power allocation of other time periods; then, go to step (2.7) to continue iteration;

[0037] (2.13) Update period ts j Subscript j = j + 1, continue to optimize the power allocation of other time periods; then, go to step (2.8) to continue iteration;

[0038] (2.14) Determine whether there is room for further optimization of power allocation based on the electricity price of the time period. If so, go to step (2.7) and continue iteration; otherwise, go to step (2.15);

[0039] (2.15) Determine whether there are electric vehicles to be charged. If so, return to step (2.1) to continue formulating a charging and discharging plan for them; if not, the algorithm ends.

[0040] Furthermore, in the particle swarm algorithm, a uniform random method is used to generate the position and velocity of each particle in the initial population when initializing the particle swarm; the position x of the kth particle is k The position component of each dimension in Randomly from Select from Indicates the maximum charge and discharge power of the energy storage system. Positive numbers indicate charging, and negative numbers indicate discharging. Speed ​​v j The components of each dimension are randomly selected from The minimum and maximum values ​​of the velocity are set according to a certain proportion of the position component value range.

[0041] Furthermore, the step of adding artificially generated particles to the initialized particle swarm is as follows:

[0042] For the artificial particle position x s Each position component of If the electricity price in the current period t is higher than that in all subsequent periods, Set to Otherwise, if the electricity price in the current period t is the lowest among all periods, Set to Otherwise Set to 0; then, randomly select Select speed v s The component of each dimension.

[0043] Furthermore, the fitness function formula of the particle is as follows:

[0044] F=C e +C i +C r

[0045] Among them, C eThe electricity cost is calculated as the cost of purchasing electricity from the grid minus the revenue earned from discharging electricity to the grid.

[0046] C i The investment cost of the energy storage system includes capacity cost and power cost. The capacity cost is used to purchase batteries of a specified capacity, while the power cost is used to build converters and transformers. During the calculation, the investment cost of the energy storage system is converted into a daily cost based on its attenuation throughout the day.

[0047] C r Represents the user compensation cost, which is the compensation fee given to users when the charging and discharging station fails to meet their needs. It is equal to the unit penalty cost multiplied by the sum of the unmet needs of all users.

[0048] Furthermore, the method of eliminating the dominated particle solution based on the preset optimization rule includes:

[0049] The position of each particle is checked at each time interval to see if the following conditions are met:

[0050] Condition 1: The current period is charging, and there is no subsequent period with a higher electricity price than the current period; Condition 2: The current period is discharging, and this period has the lowest global electricity price;

[0051] If any of the above conditions is met during the t-th period, the corresponding position component is modified to 0.

[0052] A terminal device includes a processor, a memory, and a computer program stored in the memory; when the processor executes the computer program, the hybrid optimization method for energy management of an electric vehicle charging and discharging station is implemented.

[0053] A computer-readable storage medium stores a computer program; when the computer program is executed by a processor, the hybrid optimization method for energy management of an electric vehicle charging and discharging station is implemented.

[0054] Compared with the prior art, the present invention has the following technical features:

[0055] 1.Flexibly adjust charging power.

[0056] This invention proposes a refined charging power control method that can flexibly adjust the charging power of each electric vehicle based on the grid load and the charging needs of the electric vehicle. During off-peak hours, the charging power can be appropriately increased to allow users to charge when electricity prices are low; during peak hours, the charging power is automatically reduced to reduce the burden on the grid. This reduces charging costs for users and effectively mitigates grid load fluctuations.

[0057] 2. Optimize the use of vehicle-to-grid (V2G) technology.

[0058] This invention also incorporates V2G technology into charging and discharging stations, enabling electric vehicles to function as distributed energy storage devices, actively releasing electricity to the grid during peak load periods, helping to balance the load and reduce grid fluctuations. Furthermore, vehicle owners can earn financial rewards by participating in V2G technology, further reducing charging costs.

[0059] 3. Optimize the capacity configuration of the energy storage system.

[0060] This invention incorporates an energy storage system into the charging and discharging station and optimizes its capacity. Specifically, the energy storage system stores electricity during off-peak hours and releases it during peak hours, helping to balance the grid load and reduce grid fluctuations. By optimizing the capacity configuration and management of the energy storage system, this invention ensures that the energy storage system provides sufficient power support during critical periods, thereby improving the operational efficiency of the charging and discharging station, reducing grid load, and lowering overall operating costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 This is a flow chart of the application process of the method of the present invention in an electric vehicle charging and discharging station;

[0062] Figure 2 An image of the membership function of the battery capacity of an electric vehicle in one embodiment of the present invention;

[0063] Figure 3 This is the overall research roadmap of the method of the present invention. DETAILED DESCRIPTION

[0064] The present invention provides a hybrid optimization method for energy management of electric vehicle charging and discharging stations. Figure 1 , including the following steps:

[0065] Step 1: Collect static information of electric vehicle charging and discharging stations, as well as dynamic information of electric vehicles to be charged and energy storage systems; use the static information and dynamic information as input parameters of a hybrid meta-heuristic optimization algorithm; wherein the hybrid meta-heuristic optimization algorithm includes a greedy algorithm and a particle swarm algorithm.

[0066] The static information of an electric vehicle charging and discharging station refers to information that does not change within the optimization cycle, including the electricity price in each period, the power and capacity cost coefficient of the energy storage system, the profit coefficient when discharging to the grid, and the maximum power of the connection point between the charging and discharging station and the grid.

[0067] Dynamic information refers to information that changes over time during the optimization cycle, including data related to electric vehicles to be charged and energy storage systems.

[0068] Specifically, electric vehicles waiting to be charged are those that have been connected to a charging station but have not yet decided on a charging plan and therefore have not yet started charging. The information that needs to be collected includes the charging mode (time priority or cost priority), current power, required power, expected stay time, and maximum charge and discharge power.

[0069] The charging mode is selected by the vehicle owner. If the "time priority" mode is selected, the electric vehicle will charge at maximum power until the required power level is reached, and no active discharge will be performed. If the "cost priority" mode is selected, the charging and discharging plan of the electric vehicle will be determined by the charging and discharging station through an algorithm. For the energy storage system, the information required is mainly the current power level; all collected information will serve as input parameters for the hybrid metaheuristic optimization algorithm in steps 2 and 3.

[0070] Step 2: Solve the charging and discharging plan of the electric vehicles through a greedy algorithm. In the greedy algorithm, each electric vehicle is first charged at the maximum charging power as the initial charging plan. Then, the user-set charging mode contained in the collected dynamic information is determined. If the cost priority mode is adopted, the energy of each electric vehicle at the end of each time period is used to distribute the electric vehicle discharge power, thereby obtaining the charging and discharging plan for each electric vehicle.

[0071] The hybrid metaheuristic algorithm proposed in this invention includes a greedy algorithm and a particle swarm optimization algorithm. Step 2 will describe the greedy algorithm used to formulate an electric vehicle charging and discharging plan. The final output plan includes the charging and discharging power values ​​of each electric vehicle in each time period. The execution process of the greedy algorithm is as follows:

[0072] (2.1) Dynamic information and static information are obtained as input parameters, including: the charging mode of the electric vehicle to be charged, the current power, the required power, the expected stay time, the maximum charge and discharge power, and the electricity price in each time period.

[0073] (2.2) Sort the time periods from low to high according to the electricity price. For the time periods with the same electricity price, arrange them in chronological order to generate a sorted time period set.

[0074] (2.3) Based on the time period set, an initial charging plan is generated for the electric vehicle, allowing the electric vehicle to charge at the maximum charging power until the demand is met or the electric vehicle leaves, as shown in the following formula:

[0075]

[0076] in, It represents the charging and discharging power of the electric vehicle in the tth period. A positive number indicates charging, and a negative number indicates discharging. It represents the maximum charging power of the electric vehicle, and ST represents the set of time periods during which the electric vehicle stays in the charging and discharging station.

[0077] (2.4) Determine whether the current charging mode of the electric vehicle is the time priority mode. If so, complete the plan formulation and go to step (2.15); if not, continue with step (2.5).

[0078] (2.5) The battery energy of the electric vehicle at the end of each period is calculated by the following formula to facilitate constraint judgment during subsequent power transfer. They represent the battery energy of the electric vehicle at the tth period and the t-1th period respectively; Δt represents the preset time interval, IE EV Indicates the initial battery energy when the electric vehicle starts charging. It represents the charge and discharge power of the electric vehicle in the tth period. A positive number indicates charging, and a negative number indicates discharging. Indicates whether the electric vehicle is charging. If it is charging, its value is 1, otherwise it is 0; Indicates whether the electric vehicle is discharging. When discharging, its value is 1, otherwise it is 0. EV Indicates the charging and discharging efficiency of electric vehicles.

[0079]

[0080]

[0081] (2.6) Enter the discharge power allocation process. This process first determines the high electricity price period ts i and low electricity price period ts j , electric vehicles are used in some high electricity price periods i The discharge power is increased, and at the same time part of the low electricity price period ts j The charging power is increased to ensure that the electric vehicle battery energy meets the charging demand; initially, the last period n is set as the initial period for increasing the discharge power, and the first period is the initial period for increasing the charging power, that is, i=n,j=1.

[0082] (2.7) Check the period ts according to the following formula i Can more power be added, Indicates time period ts i The charging and discharging power of the electric vehicle, p is the power transfer step, which indicates how much the power value should change each time the charging and discharging plan is updated. is the maximum charge and discharge power of the electric vehicle; if the formula is satisfied, continue with step (2.8), otherwise go to step (2.12).

[0083]

[0084] (2.8) Check the period ts according to the following formula j Can more power be added, Indicates time period ts j The charging and discharging power of the electric vehicle, p is the power transfer step, which indicates how much the power value should change each time the charging and discharging plan is updated. is the maximum charge and discharge power of the electric vehicle; if the formula is satisfied, continue with step (2.9), otherwise go to step (2.13).

[0085]

[0086] (2.9) Update the charge and discharge plan according to the following formula; where, Indicates time period ts i 、ts j The charging and discharging power of the electric vehicle; p is the power transfer step, which indicates how much the power value should change each time the charging and discharging plan is updated.

[0087]

[0088]

[0089] (2.10) Update the energy state of each period according to the formula described in step (2.5) The following formula is used to determine whether the battery energy of the electric vehicle in each period after adjustment is within the allowable range; where DOD is the maximum depth of discharge of the electric vehicle battery, ME EV is the maximum battery energy of the electric vehicle; if the following formula is satisfied, go to step (2.7) and continue iterating; otherwise, go to step (2.11).

[0090]

[0091] (2.11) If the adjustment causes the battery energy in a certain period to exceed the allowable range, the algorithm will fall back to the state before the adjustment, as shown in the following formula; where, Indicates time period ts i 、ts j The charging and discharging power of the electric vehicle; p is the power transfer step, which indicates how much the power value should change each time the charging and discharging plan is updated; then, go to step (2.13).

[0092]

[0093]

[0094] (2.12) Update period ts iThe subscript i=i-1, continue to optimize the power allocation of other time periods; then, go to step (2.7) to continue iteration.

[0095] (2.13) Update period ts j The subscript j=j+1, continue to optimize the power allocation of other time periods; then, go to step (2.8) to continue iteration.

[0096] (2.14) Determine whether there is room for further optimization of power allocation, that is, whether EP(ts i )>EP(ts j ), if the formula is satisfied, go to step (2.7) and continue iterating; otherwise, go to step (2.15); where EP(ts i )、EP(ts j ) represent time period ts i 、ts j The electricity price; if the period ts i The electricity price is greater than ts j , it means that it is still possible to gain benefits by increasing the discharge power.

[0097] (2.15) Determine whether there are electric vehicles to be charged. If so, return to step (2.1) to continue formulating a charging and discharging plan for them; if not, the algorithm ends.

[0098] After the greedy algorithm completes, it generates a charge and discharge plan for each electric vehicle, including the charge and discharge power for each time period. For example, electric vehicle 1's charge and discharge power from 8:01 to 8:05 is 50 kW, and from 8:06 to 8:10 it is -35 kW; electric vehicle 2's charge and discharge power from 10:07 to 10:11 is 40 kW, and from 10:12 to 10:16 it is 0 kW, and so on. Positive charge and discharge power indicates charging, while negative values ​​indicate discharging.

[0099] Step 3: Use the particle swarm algorithm to find the charging and discharging plan of the energy storage system for the electric vehicle charging and discharging station solution. In the particle swarm algorithm, the charging and discharging plan of the energy storage system, the movement speed and step size of the particles are characterized by assigning position and velocity attributes to particles. Artificially generated particles are added to the initialized particle swarm to improve the quality of the initial population. A fitness function is constructed based on electricity cost, energy storage system investment cost, and user compensation cost to calculate the fitness of the particles. At the same time, based on the preset optimization rules, the dominated solutions of the particles are eliminated, and finally the global optimal position of the particles is obtained as the optimal charging and discharging plan of the energy storage system.

[0100] In the particle swarm algorithm, each particle has two properties: position and velocity. Position is a vector that represents a charge and discharge scheme of the energy storage system; velocity represents the direction and step size of the particle's movement in the search space. Specifically, the position of the kth particle can be defined as Among them, n represents the dimension of the search space, corresponding to the time period in the problem being solved, and the value of each dimension represents the power of the corresponding time period, for example, represents the charging and discharging power of the energy storage system in the tth period, and so on; similarly, the speed can be defined as The specific steps of the particle swarm algorithm are as follows:

[0101] (3.1) Obtain the algorithm input parameters, including: the number of remaining time periods, the charging and discharging plans of all electric vehicles, the electricity price in each time period, the power and capacity cost coefficient of the energy storage system, the profit coefficient when discharging to the grid, and the maximum power of the connection point between the charging and discharging station and the grid.

[0102] (3.2) Initialize the particle swarm. Use a uniform random method to generate the position and velocity of each particle in the initial swarm. Specifically, the position x of the kth particle is k The position component of each dimension in Randomly from Select from Indicates the maximum charge and discharge power of the energy storage system. A positive number indicates charging, and a negative number indicates discharging. Speed ​​v j The components of each dimension are randomly selected from The minimum and maximum values ​​of the velocity are set according to a certain ratio of the position component value range so that the velocity can match the search space scale.

[0103] (3.3) Add an artificially generated particle to the initialized particle swarm. The specific generation process is as follows: For the artificial particle position x s Each position component of If the electricity price in the current period t is higher than that in all subsequent periods, Set to Otherwise, if the electricity price in the current period t is the lowest among all periods, Set to Otherwise Set to 0. Then, according to the method of initializing the speed in step (3.2), randomly select Select speed v sThe particle is generated based on analysis and experience. Compared with randomly generated particles, its quality is relatively high. Adding such particles to the initial population can improve the quality of the initial population. The artificially generated particles participate in the iteration process in the subsequent particle swarm algorithm just like ordinary particles.

[0104] (3.4) Start iteration and initialize the number of iterations j = 1.

[0105] (3.5) Calculate the particle fitness. The particle fitness value is calculated according to the following formula:

[0106] F=C e +C i +C r (11)

[0107] Among them, C e represents the electricity cost, C i represents the investment cost of the energy storage system, C r Represents the user compensation cost. The calculation formula for each cost is as follows.

[0108] Specifically, the electricity cost C e The calculation method is to subtract the income from discharging electricity to the grid from the expenditure of the charging and discharging station on purchasing electricity from the grid. The specific formula is as follows:

[0109]

[0110] Where t is the subscript representing the time period, and i is the subscript representing the electric vehicle. is the power input from the grid to the charging and discharging station during period t. is the charge and discharge power of the i-th electric vehicle in the t-th period. If it is charging, it is a positive number, and if it is discharging, it is a negative number. is a binary variable indicating whether the ith electric vehicle is discharged during the tth period, 1 if it is discharged and 0 otherwise. is the power discharged by the energy storage system to the grid during the tth period. α is a proportional coefficient between 0 and 1, indicating that when the charging and discharging station discharges to the grid, the grid pays a profit at α times the electricity price. Therefore, when electric vehicles and energy storage systems discharge to the grid to obtain profits, they need to multiply by α. Δt is the length of a period. Multiplying by Δt means that all the aforementioned power is converted into energy. EP t The electricity price in period t, multiplied by EP t Indicates that energy is calculated as a cost. The calculation formula is as follows:

[0111]

[0112] in, represents the charging and discharging power of the energy storage system in the tth period, is a binary variable indicating whether the energy storage system is discharged during the tth period, which is 1 if it is discharged and 0 otherwise. is the power discharged by the energy storage system to the electric vehicle during the tth period. The specific calculation formula is as follows:

[0113]

[0114] The power input from the grid to the charging and discharging station in period t The calculation formula is as follows:

[0115]

[0116] In addition, the power input from the grid to the charging station, or the power discharged from the charging station to the grid, must not exceed the maximum power limit of the connection point. The specific formula is as follows:

[0117]

[0118]

[0119] in, represents the maximum power that can be accepted for power transmission between the charging and discharging station and the power grid. If the constraint of formula (16) is violated during the calculation process, Set to Substitute into formulas (15) and (14) and recalculate If the constraint of formula (17) is violated, let And according to and Calculation of the value Then substitute the calculated results into formula (13) and (14) to recalculate

[0120] Next is the energy storage system investment cost C i Considering that energy storage systems experience capacity decay during operation, their lifespan ends when their capacity drops to 80% of their original value. Therefore, this solution converts the investment cost of the energy storage system into a daily cost based on its degree of decay over the course of a day. Specifically, the investment cost includes capacity cost and power cost. The capacity cost is used to purchase batteries of a specified capacity, while the power cost is used to build equipment such as converters and transformers to ensure that their power output meets demand. The specific formula is as follows:

[0121]

[0122] Among them, BCP is the capacity cost coefficient, which represents the cost of increasing the upper limit of energy storage system by one unit. is the capacity of the energy storage system at the end of period t, It is used to calculate the maximum capacity of the energy storage system throughout the day. It represents the total capacity cost of the energy storage system. BPP is the power cost coefficient, which represents the cost of increasing the maximum charge and discharge power of the energy storage system by one unit. is the charge and discharge power of the energy storage system during the tth period. It is a positive number during charging and a negative number during discharging. Therefore, the absolute value sign needs to be used to ensure that it is always positive. It is used to calculate the maximum charge and discharge power of the energy storage system throughout the day. It also represents the total power cost of the energy storage system. is the inverse of the expected life of the energy storage system, which is used to convert the investment cost into daily cost. represents the cycle degradation of the energy storage system in the tth period, is the cycle capacity decay of the energy storage system throughout the day, The maximum capacity decay allowed during the entire life cycle of the energy storage system. The cycle degradation of the energy storage system at the tth period The calculation formula is as follows:

[0123]

[0124] in, Indicates whether the energy storage system is discharged in the tth period. If yes, the value is 1, otherwise it is 0. Indicates the maximum energy of the energy storage system during operation. It represents the cycle life of the energy storage system from full discharge to the end of time period t. This value is calculated using an empirical formula based on the actual capacity decay data of the battery. The formula is as follows:

[0125]

[0126] Where e represents a natural constant (e≈2.71828). The maximum energy of the energy storage system It is determined based on the energy value of each period during the operation of the energy storage system. The specific formula is as follows:

[0127]

[0128] In addition, the initial energy and final energy of the energy storage system must be equal to prevent the energy accumulation effect. The specific formula is as follows:

[0129]

[0130] in, It is the minimum energy value of the energy storage system. In order to prevent over-discharge, it is usually a constant greater than 0. During the operation of the energy storage system, it must also be ensured that its energy is always greater than The specific formula is as follows:

[0131]

[0132] Starting from the first period, the energy state of the energy storage system in each period is calculated in turn according to the following energy update formula:

[0133]

[0134] in, is the charge and discharge power of the energy storage system in the tth period, and Δt is the length of the period. and 0-1 variables representing whether the energy storage system is charging or discharging in time period t. If the discharge Indicates the charging and discharging efficiency of the energy storage system.

[0135] Finally, the cost C of compensating users whose charging needs are not met r , represents the compensation paid to users when a charging / discharging station fails to meet their needs. In effect, the compensation cost transforms the hard constraint of meeting user charging needs into a soft constraint within the objective function. This is done to avoid excessive narrowing of the solution space during algorithm execution, thereby increasing solution diversity. The result is equal to the unit penalty cost multiplied by the sum of all users' unmet needs.

[0136]

[0137] Among them, URC represents the unit penalty cost, DE i represents the energy demand of the i-th electric vehicle, is the energy of the i-th electric vehicle at the end of charging. It is used to calculate the difference between the energy of the i-th electric vehicle at the end of charging and the required energy. Because sometimes the electric vehicle is charged with more electricity, the calculation result is negative, so it is used To eliminate these negative numbers, ensure that the final cumulative result can correctly represent the sum of unmet needs.

[0138] (3.6) Update the global optimal position g and the historical optimal position p of each particle k For each particle, if the fitness of the current particle is better than the previous historical optimal position, the individual optimal position is updated; similarly, if the fitness of the current particle is better than the global optimal position, the global optimal position is updated.

[0139] (3.7) Update the inertia weight ω. The specific update formula is as follows. Where ω is the inertia weight, which determines the influence of the velocity of the previous generation of particles on the velocity of the current generation. ω s 、ω e Represent the starting value and ending value of ω respectively. The present invention adopts the method of linearly changing inertia weight to update ω, so that ω changes from the starting value ω to the ending value ω as the iteration proceeds. s Decrease linearly to the final value ω e This is to facilitate extensive search in the early stages of iteration and concentrated search in the later stages, thereby improving the overall effect of the algorithm. max is the maximum number of iterations.

[0140]

[0141] (3.8) Update the particle velocity. The specific formula is as follows:

[0142] v j+1,k =ω·v j,k +c1·r1·(p k -x j,k )+c2·r2·(gx j,k ) (27)

[0143] Among them, v j,k is the velocity of the kth particle in the jth generation. c1 and c2 are acceleration constants, which control the speed of the particle moving towards the individual optimal and global optimal positions. r1 and r2 are random numbers in the interval [0,1]. k is the individual optimal position of the kth particle, and g is the global optimal position.

[0144] (3.9) Update the position of the particle. The specific formula is as follows.

[0145] x j+1,k =x j,k +v j+1,k (28)

[0146] Among them, x j,k 、x j+1,k represents the position of the kth particle in the jth and j+1th generations, respectively, v j+1,k represents the velocity of the kth particle in the j+1th generation.

[0147] (3.10) Determine each component of the updated particle position Is it within the specified range? Inside. like Then set like Then set otherwise No modification is made within the specified range.

[0148] (3.11) According to the optimization rule based on the combination of experience and analysis, the dominated solutions are eliminated to improve the quality of the solution. Specifically, the rule will adjust the position x of each particle. j+1,k Check whether the following conditions are met in each period: Condition 1, the current period is charging, and there is no subsequent period with a higher electricity price than the current period; Condition 2, the current period is discharging, and this period is the period with the lowest global electricity price. If the tth period meets any of the above conditions, modify the corresponding position component This operation is beneficial because: if condition one is met, the additional energy generated by the current charge cannot profit from the price difference, as there will be no subsequent periods with higher electricity prices, and instead will increase costs. If condition two is met, discharging during the period with the lowest electricity prices cannot profit from the price difference, making the discharge uneconomical. The core of this optimization rule is to eliminate dominated solutions, avoiding ineffective or even harmful operations, thereby improving the rationality and efficiency of the solution. By correcting poor-quality solutions, the overall algorithm performance is improved.

[0149] (3.12) Determine whether the current number of iterations j reaches the maximum number of iterations j max If it is not reached, return to step (3.5) to continue the iteration; otherwise, end the algorithm iteration.

[0150] (3.13) The algorithm iteration ends and the global optimal position g is output, indicating that the optimal energy storage system charging and discharging plan is obtained during the algorithm operation. Specifically, g=(g 1 ,g 2 ,…,g n ), where each component g t Represents the charge and discharge power of the energy storage system during the tth time period. Positive numbers indicate charging, and negative numbers indicate discharging. For example, if the start time is 8:00 and the end time is 24:00, and each time period is 5 minutes, g = (30, 100, -60, ...). This means that the energy storage system's charging power from 8:00 to 8:04 is 30 kW, the charging power from 8:05 to 8:09 is 100 kW, the discharging power from 8:10 to 8:14 is 60 kW, and so on.

[0151] Step 4: Set the charge and discharge power of the electric vehicles and energy storage system in the charging and discharging station during the current period based on the charge and discharge plans of the electric vehicles and the energy storage system. After a fixed time interval Δt, if the set stop time has not been reached, determine whether a new electric vehicle has arrived. If so, return to step 2 and re-execute; otherwise, continue to execute the existing charge and discharge plan. If the set stop time is reached, output the actual charge and discharge power of the electric vehicles and energy storage system in each period as the final charge and discharge plan, and output the maximum capacity of the energy storage system as its planned capacity.

[0152] Specifically, the resulting charging and discharging plan includes the charging and discharging power of the electric vehicle and energy storage system in each time period. For example, the charging and discharging power of electric vehicle 1 from 8:01 to 8:05 is 50 kW, and the charging and discharging power from 8:06 to 8:10 is -35 kW; the charging and discharging power of the energy storage system from 8:01 to 8:05 is 150 kW, and so on. The planned capacity of the energy storage system, for example, 450 kW-h, is also obtained.

[0153] Example:

[0154] 1. Parameter settings.

[0155] 1.1 User Conduct.

[0156] In order to make the experimental case closer to the actual situation, the behaviors of two types of users, time-sensitive and price-sensitive, were taken into consideration. Time-sensitive users require that charging needs be completed in the shortest time, that is, charging at maximum power, and are not regulated by charging stations; price-sensitive users are sensitive to charging costs, require full charging within a specified time, and are willing to accept regulation of charging power to reduce costs. The charging demand of this type of users will also decrease as the price of electricity increases, and vice versa. The proportions of time-sensitive and price-sensitive users are 40% and 60% respectively. For the power of electric vehicles, use Figure 2 The membership function shown maps it to very low (VL), low (L), medium (M), high (H), and very high (VH). In addition, the changes in charging demand for price-sensitive users under different electricity prices are characterized by fuzzy logic control. The fuzzy rules are shown in Table 1.

[0157] Table 1 Fuzzy rules for charging demand of price-sensitive users changing with electricity prices

[0158]

[0159] Note: ±x% refers to a change of x% based on the original SOC that needs to be charged.

[0160] 1.2 Energy storage system.

[0161] Table 2 shows the relevant parameters of the energy storage system. The power cost coefficient represents the cost of increasing the maximum power by 1 kW, primarily the cost of constructing converters and transformers. The capacity cost coefficient represents the cost of increasing the maximum capacity by 1 kWh.

[0162] Table 2 Energy storage system parameter settings

[0163]

[0164] 1.3 Electric vehicle charging and discharging requirements.

[0165] Based on the literature, this scheme constructs a distribution of electric vehicle charging and discharging demand. The battery capacity of the electric vehicle is 50 kWh, and the maximum charging and discharging power is 60 kW. In the standard scenario, the arrival time follows a two-parameter Weibull distribution with a = 0.9831 and b = 16.8; the departure time follows a normal distribution with a mean of 18 and a standard deviation of 3; the initial state of charge (SOC) follows a normal distribution with a mean of 0.36 and a standard deviation of 0.15; and the demand state of charge (SOC) follows a normal distribution with a mean of 0.86 and a standard deviation of 0.14. The total number of electric vehicles throughout the day is approximately 50. In addition to the standard scenario, this scheme constructs four additional scenarios with varying numbers of electric vehicles and mean departure times to test the algorithm's applicability to different demand scenarios. These are shown in Table 3.

[0166] Table 3 Different scenarios of electric vehicle charging demand

[0167]

[0168] 1.4 Scale of electric vehicle charging and discharging stations.

[0169] The charging and discharging station has 25 charging piles, and the maximum power limit of the grid connection point is 1700kw.

[0170] 1.5 time-of-use electricity price.

[0171] The time-of-use electricity price regulations for Guangzhou in the Guangdong Province time-of-use electricity price policy document are adopted. 10-12 o'clock and 14-19 o'clock are peak hours, with an electricity price of 103.77 cents / kWh; 0-8 o'clock is the off-peak period, with an electricity price of 23.20 cents / kWh; the rest of the time is the flat period, with an electricity price of 61.04 cents / kWh.

[0172] 1.6 Particle Swarm Optimization Parameters.

[0173] The parameter settings of the particle swarm algorithm are shown in Table 4. The parameter values ​​are set with reference to existing literature, and each parameter is individually adjusted within a reasonable value range. The best performing parameter is selected as the final parameter value.

[0174] Table 4 Particle swarm algorithm parameter settings

[0175]

[0176] 2. Experimental results.

[0177] 2.1V2G and energy storage system effectiveness analysis experiment.

[0178] This experiment primarily analyzes the impact of introducing V2G and energy storage systems on the operating costs of charging and discharging stations. First, a V2G effectiveness experiment was conducted, comparing a heuristic algorithm from the literature (without V2G) with the greedy algorithm proposed in this solution (with V2G). The results are shown in Table 5. The experimental results show that in different scenarios, the introduction of V2G and the use of the greedy algorithm for optimization both resulted in varying degrees of operating cost reduction, with an average reduction of 22.91%. This demonstrates that V2G technology, optimized using the greedy algorithm, can significantly improve the efficiency of electric vehicle charging and discharging stations.

[0179] Table 5 V2G effectiveness analysis experimental results

[0180]

[0181]

[0182] Next, we conduct an experiment to test the effectiveness of the energy storage system. This scheme compares the greedy algorithm (without energy storage) with the proposed hybrid metaheuristic optimization algorithm (with energy storage). The experimental results are shown in Table 6. The experimental results show that the introduction of the energy storage system and the use of the particle swarm optimization algorithm further reduce the operating costs of the charging and discharging stations, with an average reduction of 17.71%, which is a significant effect.

[0183] Table 6 Experimental results of energy storage system effectiveness analysis

[0184]

[0185] 2.2 Performance comparison experiment of hybrid metaheuristic optimization algorithm and basic scheduling rules in multiple scenarios.

[0186] To validate the effectiveness of the proposed hybrid metaheuristic optimization algorithm, this scheme compared it with three commonly used basic scheduling rules: maximum power immediate charging, maximum power delayed charging, and average power charging. Maximum power immediate charging is a straightforward and simple strategy that charges electric vehicles to full capacity immediately upon arrival and is the most common charging method. Maximum power delayed charging adjusts the charging timing to complete charging at maximum power during an appropriate time period before the electric vehicle departs. This is suitable for electric vehicles that arrive in a concentrated time period but depart at different times. Average power charging charges electric vehicles at a constant average power throughout their journey, ensuring they are fully charged upon departure. This is a balanced charging strategy. Furthermore, this scheme designs simple energy storage system rules to operate in conjunction with the above three rules. Specifically, when there are no subsequent periods with higher electricity prices, the energy storage system discharges at maximum power; during periods with the lowest electricity prices, it charges at maximum power; and during other periods, no operation is performed. This rule allows the energy storage system to simply and effectively exploit peak and valley electricity price differences, thereby improving the overall performance of the rule.

[0187] In the specific experimental process, this solution generated 10 cases using a correlation distribution and repeated the experiment 10 times using the hybrid metaheuristic optimization algorithm on each case. The average objective function value was used as a comparison metric to reduce randomness. The experimental results are shown in Table 7. As can be seen from the table, the proposed hybrid metaheuristic optimization algorithm outperformed the basic scheduling rule in all scenarios, with a lead ranging from 28.60% to 68.72%, demonstrating its superior performance. From the perspective of the number of users, the lead of the hybrid metaheuristic optimization algorithm gradually narrowed as the number of users increased. This is because when the number of users is small, the total amount of user demand to be met is smaller, leaving more time for V2G discharge. At the same time, the energy storage system can also have more power available for discharge, resulting in greater optimization space, further reducing operating costs. Similarly, from the perspective of average user stay time, the hybrid metaheuristic optimization algorithm performed better in scenarios with longer stay times. This is also because a longer stay time provides more time for V2G discharge.

[0188] Table 7 Experimental results of performance comparison between hybrid metaheuristic optimization algorithm and basic scheduling rules in multiple scenarios

[0189]

[0190] Based on the above embodiments, the effects of the present invention are as follows:

[0191] The introduction of V2G and energy storage systems significantly reduces operating costs: Experimental results show that V2G technology, optimized using a greedy algorithm, significantly reduces the operating costs of charging and discharging stations, with an average reduction of 22.91%. Furthermore, the introduction of energy storage systems combined with a hybrid metaheuristic optimization algorithm further reduced operating costs, by an average of 17.71%. This demonstrates the significant advantages of effective application of V2G technology and energy storage systems in cost optimization.

[0192] A hybrid meta-heuristic optimization algorithm outperforms traditional scheduling rules: Compared with traditional charging scheduling strategies (including maximum power instant charging, maximum power delayed charging, and average power charging), the proposed hybrid meta-heuristic optimization algorithm demonstrates superior performance across multiple scenarios, with margins ranging from 28.60% to 68.72%. In particular, in scenarios with a small number of users and longer stays, the optimization algorithm can better utilize V2G discharge and energy storage systems, improving battery efficiency and further reducing operating costs.

[0193] The hybrid metaheuristic optimization algorithm has wide applicability: The optimization method proposed in this study demonstrates great flexibility and adaptability in different scenarios. The advantages of the hybrid metaheuristic optimization algorithm are particularly evident in scenarios with a small number of users and long durations of stay. This fully demonstrates the method's advantages in multi-scenario adaptability and operational efficiency, allowing for effective adjustment and optimization based on actual needs.

[0194] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. A hybrid optimization method for energy management of electric vehicle charging and discharging stations, characterized in that: include: Collect static information of electric vehicle charging and discharging stations, as well as dynamic information of electric vehicles to be charged and energy storage systems; Using static information and dynamic information as input parameters of a hybrid meta-heuristic optimization algorithm; wherein the hybrid meta-heuristic optimization algorithm includes a greedy algorithm and a particle swarm algorithm; A greedy algorithm is used to solve the charging and discharging plan for electric vehicles. In the greedy algorithm, each electric vehicle is first charged at the maximum charging power as the initial charging plan. Then, the user-set charging mode contained in the collected dynamic information is determined. If the cost-priority mode is used, the energy of each electric vehicle at the end of each time period is used to allocate the electric vehicle discharge power, thereby obtaining a charging and discharging plan for each electric vehicle. The particle swarm algorithm is used to determine the charging and discharging scheme of the energy storage system at the electric vehicle charging and discharging station. In the particle swarm algorithm, the charging and discharging scheme of the energy storage system, the movement speed and step size of the particles are characterized by assigning position and velocity attributes to the particles. Artificially generated particles are added to the initialized particle swarm to improve the quality of the initial population. A fitness function is constructed based on electricity cost, energy storage system investment cost, and user compensation cost to calculate the fitness of particles. At the same time, based on preset optimization rules, solutions where particles are dominated are eliminated, and finally the global optimal position of the particles is obtained as the optimal charging and discharging solution for the energy storage system.

2. The hybrid optimization method for energy management of electric vehicle charging and discharging stations according to claim 1, characterized in that: The method further comprises: According to the charging and discharging plan of the electric vehicle and the energy storage system, the charging and discharging power of the electric vehicle and the energy storage system in the charging and discharging station is set in the current period. After a fixed time interval, if the set stop time is not reached, it is determined whether there is a new electric vehicle arriving. If so, the greedy algorithm is re-executed; otherwise, the existing charging and discharging plan is continued. If the set stop time is reached, the actual charging and discharging power of the electric vehicle and the energy storage system in each period is output as the final charging and discharging plan, and the maximum capacity of the energy storage system is output as its planned capacity.

3. The hybrid optimization method for energy management of electric vehicle charging and discharging stations according to claim 1, characterized in that: Static information includes electricity prices for each period, the power and capacity cost coefficients of the energy storage system, the profit coefficient when discharging to the grid, and the maximum power of the connection point between the charging and discharging station and the grid; Dynamic information includes the electric vehicle’s charging mode, current power, required power, expected stay time, and maximum charge and discharge power, as well as the current power of the energy storage system; The charging mode includes a time priority mode and a cost priority mode.

4. The hybrid optimization method for energy management of electric vehicle charging and discharging stations according to claim 1, characterized in that: The charging and discharging plan of electric vehicles is solved by a greedy algorithm. The specific process includes: (2.1) Obtaining dynamic information and static information as input parameters; (2.2) Sort the time periods according to the electricity price from low to high. For the time periods with the same electricity price, arrange them in chronological order to generate a sorted time period set. (2.3) generating an initial charging plan for the electric vehicle based on the set of time periods, allowing the electric vehicle to charge at a maximum charging power until demand is met or the electric vehicle leaves; (2.4) Determine whether the current charging mode of the electric vehicle is the time priority mode. If so, complete the plan formulation and go to step (2.15); if not, continue to step (2.5); (2.5) Calculate the battery energy of the electric vehicle at the end of each period; (2.6) Enter the discharge power allocation process. This process first determines the high electricity price period ts i and low electricity price period ts j , electric vehicles are used in some high electricity price periods i The discharge power is increased, and at the same time part of the low electricity price period ts j The charging power increases; (2.7) Determine the high electricity price period ts based on the power transfer step size i Can more power be added? If yes, then continue with step (2.8), otherwise go to step (2.12); (2.8) Determine the low electricity price period ts based on the power transfer step j Can more power be added? If yes, continue with step (2.9), otherwise go to step (2.13); (2.9) Using the power transfer step size, the charging and discharging power of electric vehicles during high and low electricity price periods, update the charging and discharging plan of electric vehicles; (2.10) Update the battery energy of each time period and determine whether the battery energy of the electric vehicle in each time period after adjustment is within the allowable range; if it is within the allowable range, go to step (2.7) and continue iteration; otherwise, go to step (2.11); (2.11) If the adjustment causes the battery energy in a certain period to exceed the allowable range, the battery energy is restored to the state before the adjustment; then, go to step (2.13); (2.12) Update period ts i The subscript i=i-1, continue to optimize the power allocation of other time periods; then, go to step (2.7) to continue iteration; (2.13) Update period ts j Subscript j = j + 1, continue to optimize the power allocation of other time periods; then, go to step (2.8) to continue iteration; (2.14) Determine whether there is room for further optimization of power allocation based on the electricity price of the time period. If so, go to step (2.7) and continue iteration; otherwise, go to step (2.15); (2.15) Determine whether there are electric vehicles to be charged. If so, return to step (2.1) to continue formulating a charging and discharging plan for them; if not, the algorithm ends.

5. The hybrid optimization method for energy management of electric vehicle charging and discharging stations according to claim 1, characterized in that: In the particle swarm algorithm, a uniform random method is used to generate the position and velocity of each particle in the initial population when initializing the particle swarm; the position x of the kth particle is k The position component of each dimension in Randomly from Select from Indicates the maximum charge and discharge power of the energy storage system. Positive numbers indicate charging, and negative numbers indicate discharging. Speed ​​v j The components of each dimension are randomly selected from The minimum and maximum values ​​of the velocity are set according to a certain proportion of the position component value range.

6. The hybrid optimization method for energy management of electric vehicle charging and discharging stations according to claim 1, characterized in that: The step of adding artificially generated particles to the initialized particle swarm is as follows: For the artificial particle position x s Each position component of If the electricity price in the current period t is higher than that in all subsequent periods, Set to Otherwise, if the electricity price in the current period t is the lowest among all periods, Set to Otherwise Set to 0; then, randomly select Select speed v s The component of each dimension.

7. The hybrid optimization method for energy management of electric vehicle charging and discharging stations according to claim 1, characterized in that: The fitness function formula of the particle is as follows: F=C e +C i +C r Among them, C e The electricity cost is calculated as the cost of purchasing electricity from the grid minus the revenue earned from discharging electricity to the grid. C i The investment cost of the energy storage system includes capacity cost and power cost. The capacity cost is used to purchase batteries of a specified capacity, while the power cost is used to build converters and transformers. During the calculation, the investment cost of the energy storage system is converted into a daily cost based on its attenuation throughout the day. C r Represents the user compensation cost, which is the compensation fee given to users when the charging and discharging station fails to meet their needs. It is equal to the unit penalty cost multiplied by the sum of the unmet needs of all users.

8. The hybrid optimization method for energy management of electric vehicle charging and discharging stations according to claim 1, characterized in that: The method of eliminating the dominated particle solution based on the preset optimization rules includes: The position of each particle is checked at each time interval to see if the following conditions are met: Condition 1: The current period is charging, and there is no subsequent period with a higher electricity price than the current period; Condition 2: The current period is discharging, and this period has the lowest global electricity price; If any of the above conditions is met during the t-th period, the corresponding position component is modified to 0.

9. A terminal device comprising a processor, a memory, and a computer program stored in the memory; characterized in that: When the processor executes the computer program, it implements the hybrid optimization method for energy management of an electric vehicle charging and discharging station according to any one of claims 1 to 8.

10. A computer-readable storage medium storing a computer program; wherein: When the computer program is executed by a processor, the hybrid optimization method for energy management of an electric vehicle charging and discharging station according to any one of claims 1 to 8 is implemented.

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