Charging scheduling method, medium and equipment for "vehicle-station" bilateral benefit game considering distribution network safety

Abstract

The present invention proposes a "vehicle-station" bilateral benefit game charging scheduling method, medium and equipment that takes into account the safety of the distribution network, including: constructing a road network structure model; analyzing the travel charging needs of EV users within the road network; establishing a comprehensive travel cost-utility value calculation model that takes into account the factors affecting user station selection; establishing a charging station-side economic benefit calculation model that takes into account the safety of distribution network operation; and constructing an electric vehicle master-slave game optimization charging scheduling strategy with the goal of "vehicle-station" bilateral benefit balance and win-win. The "vehicle-station" master-slave game charging scheduling strategy constructed by the present invention can effectively reduce the charging cost-utility value of electric vehicle users, while ensuring the safety of distribution network operation, improving the economic benefits of the charging station side, and helping to optimize resource allocation and achieve a bilateral benefit balance and win-win situation between the user side and the charging station side.

Description

Technical Field

[0001] The present invention belongs to the field of dynamic charging scheduling of electric vehicles, and specifically relates to a "vehicle-station" bilateral benefit game charging scheduling method, medium and equipment taking into account the safety of distribution network. Background Art

[0002] With the continuous advancement of new energy vehicles, electric vehicles (EVs) have ushered in large-scale growth. However, the time-varying characteristics of road conditions and disordered charging behavior during the travel of a large number of electric vehicles have brought about problems such as traffic congestion and high charging costs, and also brought great operating pressure to the charging station-distribution network coupling system. In order to improve user charging satisfaction while improving the operating safety of the coupling system, it is necessary to study an effective EV orderly charging guidance scheduling strategy. In terms of the charging scheduling strategy of electric vehicles coupled with road and electricity, existing research is difficult to take into account the bilateral subject benefits of reducing the cost of charging travel on the user side and improving the operating safety of charging stations connected to the distribution network. Summary of the invention

[0003] In view of the deficiencies in the prior art, the present invention provides a "vehicle-station" bilateral benefit game charging scheduling method, medium and device taking into account the safety of distribution network.

[0004] To achieve the above object, the present invention adopts the following technical solutions:

[0005] A "vehicle-station" bilateral benefit game charging scheduling method taking distribution network safety into account, characterized by comprising:

[0006] S1: Construct a traffic network structure model;

[0007] S2: Based on the travel time probability distribution, travel destination probability distribution and charging demand judgment constraints of EV users within the traffic network, the spatiotemporal distribution characteristics of EV users' charging demand are obtained, and EV users with charging demand are screened out accordingly;

[0008] S3: Based on the charging distance, the waiting time in the station and the charging cost, a calculation function of the charging utility value of the EV user side is constructed;

[0009] S4: Considering the operational safety of the charging station connected to the distribution network, taking into account the charging load of the charging station and the equipment service balance rate, a charging revenue calculation function on the charging station side is constructed;

[0010] S5: Construct a master-slave game model between charging stations and EV users. The charging station, as the upper-level leader, adjusts the electricity price according to the charging status in the station to maximize the charging benefits. The EV user, as the follower, formulates the optimal charging guidance strategy to minimize the charging utility value. Through game equilibrium, the electricity price of each charging station and the optimal station solution for each EV user are finally output.

[0011] To optimize the above technical solutions, the specific measures taken also include:

[0012] Furthermore, in step S1, the traffic network structure model includes: road network traffic node coordinates, road section length, adjacency matrix based on road network topology structure and road network node flow time series.

[0013] Further, step S2 is specifically as follows:

[0014] S21: Count the travel frequencies of EV users in different time periods within the traffic network, and characterize the probability distribution t of EV users’ travel times based on the statistical data;

[0015] S22: Divide the traffic network into functional areas, predict the travel destinations of EV users through the Markov chain method, analyze the travel probability p between different functional areas, and build a daily travel demand model for EV users based on the user travel time probability distribution t;

[0016] S23: Based on the daily travel demand model of EV users, combined with EV driving state parameters and charging demand judgment constraints, the spatiotemporal distribution characteristics of EV users' charging demand are obtained, and EV users with charging demand are screened out accordingly.

[0017] Furthermore, in step S33, the EV driving state parameters include the battery capacity C totol 、The remaining power Soc when EV sends a charging request sta and unit power consumption μ; the charging demand judgment constraint is expressed as follows:

[0018]

[0019] In the formula, represents the sum of the lengths of the road sections to the destination, d ij represents the length of the road segment ij, x ij The decision variable indicating whether to drive on road section ij, i and j are different nodes in the node set N; if the judgment condition is not met, it means that the EV user needs to perform charging operation.

[0020] Further, step S3 is specifically as follows:

[0021] S31: EV at time t is calculated based on the adaptive Dijkstra algorithm sta Go to the available charging stations d The optimal path is obtained, and then the distance between EV and each charging station is obtained. And the journey time

[0022] S32: Describing time periods based on M / M / c / ∞ queuing theory Waiting time for car owners Using exponential form Indicates the waiting time in queue, and the calculation formula is as follows:

[0023]

[0024] S33: Take every hour as the vehicle-station charging scheduling period, and the period interval is Period EV in charging station c d Charging cost It is expressed as follows:

[0025]

[0026] In the formula, Indicates time period Is the EV at the charging station? d The decision variables for charging, the vehicle in the time period Located in the charging station c d 1 when charging, otherwise 0; For the period EV in charging station c d Charging costs; Indicates that EV is at charging station c d In the period Charging pricing; For the period EV arrives at charging station c d The amount of electricity after charging is finished; Reaching a charging station for EV d The remaining power when For EV at charging station c d Charging time; C totol is the battery capacity; For EV at charging station c d Charging efficiency;

[0027] S34: Calculate the time to leave the charging station for:

[0028]

[0029] Obtaining the departure time based on the adaptive Dijkstra algorithm Distance from start to destination The distance that EV users have to travel to the charging station is for:

[0030]

[0031] S35: Use Indicates time period Internal charging station d For the attractiveness utility of individual charging EV users, the linear weighted summation method is used to normalize it, and the calculation function of the charging utility value on the EV user side is obtained as shown in the following formula:

[0032]

[0033] In the formula, F1, F2, and F3 are the weight coefficients of the three utility value indicators, and they satisfy F1+F2+F3=1;

[0034] S36: Establish decision variables and waiting time in queues constraints.

[0035] Furthermore, in step S36, the time period The decision variables for the selection of charging stations by EVs are constrained as follows:

[0036]

[0037] When EV is in the period Go to the charging station d When charging, you need to wait in line for the EV in front to finish charging. Set the maximum charging endurance time constraint. The constraint is expressed as follows:

[0038]

[0039] In the formula, For charging station c d The maximum charging waiting time.

[0040] Further, step S4 is specifically as follows:

[0041] S41: Calculation period Charging station within d Comprehensive income as follows:

[0042]

[0043] In the formula, Indicates time period Internal charging station d Total operating income, Indicates time period Internal charging station d The cost of purchasing electricity, Indicates time period Internal charging station dPenalty charges due to station overload, uneven charging services, and node voltage excursions;

[0044] S42: For the current period There are f EVs in the cluster that have issued charging requests. Therefore, the charging station c in this period d The cost of purchasing electricity is:

[0045]

[0046] In the formula, For the period Internal charging station d Charging load; For the period Internal charging station d The cost of purchasing electricity; For charging station c d The electricity purchase price at time t; For the period Starting charging station c d The base load; For the period The i-th EV is at charging station c d Charging power; Indicates during the period Is the i-th EV at charging station c? d The decision variables for charging;

[0047] Then charging station c d Charging cost for:

[0048]

[0049] In the formula, For charging station c d In the period Adjusted charging pricing;

[0050] S43: Calculate the penalty fee for the charging station, specifically:

[0051] When the charging station load is greater than the accessible load threshold During operation, the charging station The distribution network needs to pay penalty fees for:

[0052]

[0053] Where φ is the overload penalty price coefficient. When , φ takes the value of 1, otherwise it takes the value of 0;

[0054] Charging stations with large voltage deviations during the period The distribution network needs to pay penalty fees for:

[0055]

[0056] Where v is the voltage deviation penalty price coefficient, For the period Internal charging station d The node voltage per unit value;

[0057] When an EV goes to a charging station with a higher designated balancing rate to charge, it will be charged an additional electricity price compensation, and the charging station will need to pay a penalty fee to the distribution network.

[0058]

[0059] In the formula, For the period The total load of each charging station connected to the distribution network is allocated to the charging station c according to the number of charging piles at each station. d The average charging load; For charging station c d The adjustment factor α is used to control the sensitivity of charging electricity price to changes in load balancing rate;

[0060] Finally got punitive fees for charging stations for:

[0061]

[0062] S44: Establish charging pricing and load constraints within the station, specifically:

[0063] The charging station pricing meets the corresponding boundary range constraints of each time period, as shown below:

[0064]

[0065] In the formula, γ2 and γ1 are the upper and lower limit ratios of charging electricity price fluctuation respectively;

[0066] When EVs choose charging stations to go to, they must ensure that each charging station is within the capacity range of the distribution transformer, as shown in the following formula:

[0067]

[0068] In the formula, For charging station c d Maximum capacity of transformer.

[0069] Further, step S5 is specifically as follows:

[0070] S51: There are f EV users and g charging stations in the traffic network model. Charging guidance strategy model Described as:

[0071]

[0072] In the formula, {c d1 , c d2 , …, c dg , EV1, EV2, …EV f} is the time period The set of participants in the game, c d1 , c d2 , …, c dg Indicates charging stations, EV1, EV2, ...EV f represents EV users; Indicates the electricity price information of the charging station cd, For charging station c d In the period The purchase price of electricity and the adjustment of the selling price of electricity; Indicates the information after the electric vehicle is dispatched. For EV to go to each charging station c d The economic cost, waiting time and total journey distance required; is the output set of each participant, Indicates time period The gth charging station c within dg The comprehensive income Indicates time period The fth electric vehicle EV f Select each charging station c d The charging utility value of

[0073] S52: Time period Internal EV user side objective function is the minimum EV cluster charging utility value, calculated as follows:

[0074]

[0075] In the formula, Indicates time period Electric Vehicle EV r Select charging stationc du The charging utility value after Indicates time period Electric Vehicle EV r After the game scheduling strategy, whether to choose charging station c du As a decision variable for charging stations;

[0076] Time Internal charging station side objective function is to maximize the charging benefit, and the calculation formula is as follows:

[0077]

[0078] In the formula, Indicates time period Internal charging station dj Charging income;

[0079] S53: By using the game strategy, the average charging utility value of the EV user side is minimized while the charging revenue of the charging station is maximized, so that the game reaches an equilibrium state. The equilibrium conditions are as follows:

[0080]

[0081] In the formula, and are the charging benefits at the w+1th and wth cycles respectively; and are the charging utility values ​​at the w+1th and wth cycles respectively; λ1 and λ2 are the set change thresholds;

[0082] S54: The particle swarm algorithm is used to solve the charging guidance strategy model, and the electricity price of each charging station and the optimal station selection plan for EV users are finally output after the particle swarm algorithm game.

[0083] Correspondingly, the present invention also proposes a computer-readable storage medium storing a computer program, characterized in that the computer program enables the computer to execute the "vehicle-station" bilateral benefit game charging scheduling method taking into account the safety of the distribution network as described above.

[0084] Correspondingly, the present invention also proposes an electronic device, characterized in that it includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, it implements the "vehicle-station" bilateral benefit game charging scheduling method that takes distribution network safety into account as described above.

[0085] The beneficial effects of the present invention are as follows: the present invention proposes a "vehicle-station" bilateral benefit game charging scheduling strategy that takes into account the safety of the distribution network, considers the factors affecting the user's station selection and quantifies the comprehensive travel cost, takes into account the economic benefits and operational safety of the charging station side, and constructs a master-slave game optimization scheduling strategy based on dynamic pricing with the goal of "vehicle-station" bilateral benefit balance and win-win. The charging guidance optimization model proposed by the present invention can effectively reduce the charging utility value of electric vehicle users while improving the economic benefits of the charging station side. BRIEF DESCRIPTION OF THE DRAWINGS

[0086] Figure 1 It is a flow chart of a "one-car-one-station" bilateral benefit game charging scheduling method taking distribution network safety into account in an embodiment.

[0087] Figure 2 It is a flow chart of selecting an electric vehicle charging station based on a "vehicle-station" master-slave game in an embodiment.

[0088] Figure 3 is a flow chart of a particle swarm optimization algorithm of an embodiment.

[0089] Figure 4 It is a schematic diagram of a traffic network model of an embodiment.

[0090] Figure 5 2 is a comparison chart of the number of vehicles in the station of the embodiment.

[0091] Figure 6 2 is a comparison diagram of charging loads of two schemes of the embodiment.

[0092] Figure 7 Schematic diagram of game output in the embodiment. DETAILED DESCRIPTION

[0093] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0094] In one embodiment, the present invention proposes a "vehicle-station" bilateral benefit game charging scheduling method taking into account the safety of the distribution network, such as Figure 1 As shown, the following steps are included.

[0095] S1: Construct a traffic network structure model, including the construction of traffic nodes and road section sets, the formation of the road network adjacency matrix, and the acquisition of road section traffic time series.

[0096] S2: According to the probability distribution t of EV users' travel time within the road network, the probability distribution p of travel destination based on the Markov chain (MCMC) method, and the Monte Carlo Markov chain, a daily travel demand model of EV users is established. On this basis, combined with the individual state parameters of EV, including battery capacity and power consumption per unit mileage, as well as the charging demand judgment constraints, the spatiotemporal distribution characteristics of EV users' charging demand are obtained.

[0097] S21: Study and record the travel frequency of EV users in different time periods within the road network, characterize the probability distribution t of EV users' travel time based on statistical data, and identify the peak and trough periods of users' travel.

[0098] S22: Divide the road network into functional areas, predict the travel destinations of electric vehicle users through the Markov chain (MCMC) method, and analyze the travel probability p between different functional areas. Combined with the user travel time probability distribution t, the EV cluster behavior decision distribution in the road network is constructed in one day.

[0099] S23: Analyze EV driving state parameters, namely battery capacity C total 、The remaining power Soc when EV sends a charging request sta And unit power consumption μ. Constraints are determined based on EV travel demand, battery status, and charging demand, that is, the power of the vehicle to reach the destination must meet the minimum requirement, and the remaining power Soc at the destination end，r It cannot fall below the safety threshold of 10%. The constraint is expressed as follows:

[0100]

[0101] In the formula, represents the sum of the lengths of the road sections to the destination, d ij represents the length of the road segment ij, x ij The decision variable indicating whether to drive on road section ij. If the judgment condition is not met, it means that the EV needs to perform charging operation. Based on this, the spatiotemporal distribution characteristics of EV user charging demand within the road network are determined.

[0102] S3: Based on the trade-off indicators of EV charging distance, queuing time in the station and charging cost, a user charging guidance utility value calculation function is constructed to characterize user charging satisfaction and allocate the optimal charging station to the user.

[0103] S31: Based on the adaptive Dijkstra algorithm and the charging demand judgment constraint calculation, the EV at time t sta Go to the available charging stations d The optimal path is obtained, and then the distance between EV and each charging station is obtained. And the journey time

[0104] S32: In the queuing system for electric vehicles to charge at charging stations, the rule of electric vehicles arriving at charging stations can be expressed by Poisson distribution; generally, the first-come-first-served principle is followed, and the queuing rule is a waiting system for multiple service stations; the service agencies are generally serial services, so the present invention describes the time period based on the M / M / c / ∞ queuing theory Waiting time for car owners

[0105] In the actual process of selecting a charging station, users want to enjoy the charging service immediately after arriving at the charging station, so they have a high waiting time. The tolerance is much smaller than the travel time, so the present invention uses the exponential form Indicates the waiting time in queue, and the calculation formula is as follows:

[0106]

[0107] S33: Assume that every hour is used as the vehicle-station charging scheduling period, and the period interval is Period EV in charging station c d Charging cost It is expressed as follows:

[0108]

[0109] In the formula, Indicates time period Is the EV at the charging station? d The decision variables for charging, the vehicle in the time period Located in the charging station c d 1 when charging, otherwise 0; For the period EV in charging station c d Charging costs; Indicates that EV is at charging station c d In the period Charging pricing; For the period EV arrives at charging station c d The amount of electricity after charging is finished; Reaching a charging station for EV d The remaining power when For EV at charging station c d Charging time; C total is the battery capacity; For EV at charging station c d charging efficiency.

[0110] S34: In order to fully consider the travel convenience between the charging station and the destination, the present invention further considers the distance cost from the charging station to the destination on the basis of the distance cost from the starting point to the charging station. for:

[0111]

[0112] Departure time based on improved adaptive Dijkstra algorithm Distance from start to destination The distance that EV charging users need to travel to the charging station is for:

[0113]

[0114] S35: When the EV cluster obtained by the travel decision distribution analysis participates in the charging station selection game, it is necessary to consider various factors affecting the experience of going to the charging station. In order to effectively quantify the cost mechanism for users to reach the charging station, the present invention proposes a charging station selection decision utility value function, which uses a weighted decision model to evaluate the charging user EV in the time period Charging costs at charging stations User waiting time and the travel distance from vehicle to station to destination The utility values ​​of the three factors are used to recommend the best charging station for electric vehicles.

[0115] use Indicates time period Internal charging station d For the attractiveness utility of individual EV charging users, the linear weighted summation method is used to normalize it, and the obtained comprehensive utility value objective function is shown in the formula:

[0116]

[0117] In the formula, F1, F2, and F3 are the weight coefficients of the three utility value indicators, and they satisfy F1+F2+F3=1.

[0118] S36: In the decision model for optimizing charging route guidance using dynamic road network information, binary variable constraints and remaining battery capacity constraints should be met. The decision variables for selecting charging stations The specific constraints are as follows:

[0119] (1) Assume that the charging owner has only one charging station as the final charging destination, and assume that there are g charging stations within the road network. Therefore, the time period The decision variables for the selection of charging stations by electric vehicles are constrained as follows:

[0120]

[0121] (2) Charging waiting time constraint: When the user is in the period Go to the charging station d When charging, you need to wait in line for the EV in front to finish charging. You need to set a maximum charging endurance time constraint. The constraint is expressed as follows:

[0122]

[0123] In the formula, For charging station c dThe maximum charging waiting time. When the EV waiting time at a charging station is too long, users will choose other charging stations to charge.

[0124] S4: Considering the operational safety of the charging station connected to the distribution network, taking into account the charging load of the charging station and the equipment service balance rate, a charging revenue calculation model for the charging station is constructed.

[0125] S41: Charging station operators are at the top of the master-slave game, and they use their own maximum profit as the optimization goal to dynamically adjust electricity prices and guide users to make charging decisions. Charging stations consider the demand response of EV clusters to conduct macro-control of real-time electricity prices. The main influencing factors are concentrated on the real-time load in the station and the equipment balance rate. Charging station within d The comprehensive income is expressed as follows:

[0126]

[0127] In the formula, Indicates time period Internal charging station d Total operating income; Indicates time period Internal charging station d The cost of purchasing electricity; Indicates time period Internal charging station d Penalty charges due to station overload, uneven charging service, and node voltage deviation.

[0128] S42: Considering the different electricity prices purchased by the charging station from the distribution network at different times, the current time period There are f EVs in the cluster that have issued charging requests, so the charging station c is d The cost of purchasing electricity is:

[0129]

[0130] In the formula, For the period Internal charging station d Charging load; For the period Internal charging station d The cost of purchasing electricity; For charging station c d In the period The electricity purchase price; For the period Starting charging station c d The base load; For the period The i-th EV is at charging station c dCharging power; Indicates during the period Is the i-th EV at charging station c? d The decision variable for charging. Then the charging station c d Charging cost for:

[0131]

[0132] In the formula, For charging station c d In the period Adjusted charging pricing.

[0133] S43: To ensure the safe and stable operation of the distribution network, the distribution network sends a boundary signal to each charging station, that is, the accessible load threshold set by the charging station under the constraints of the distribution network is met. When the load of the charging station is greater than the threshold, it is considered to be overloaded and a penalty electricity price is required. The distribution network needs to pay penalty fees for:

[0134]

[0135] Where φ is the overload penalty price coefficient. , φ takes the value of 1, otherwise it takes the value of 0.

[0136] In order to prevent electric vehicles from charging at charging stations with lower voltages and causing the voltage drop at the node to increase further, charging stations with larger voltage deviations are required to be located in the time period. The distribution network needs to pay penalty fees for:

[0137]

[0138] Where y is the voltage deviation penalty price coefficient, For the period Internal charging station d The node voltage per unit value.

[0139] The closer the charging load of each charging station is to the ideal situation, the more the total charging load is allocated to the charging load of the charging station according to the scale of the station, which means that the equipment service rate between charging stations is more balanced. In order to improve the overall service balance rate of charging stations in the road network, when EVs go to charging stations with lower designated balance rates to charge, the charging service fee will be reduced. On the contrary, if EVs go to charging stations with higher designated balance rates to charge, additional electricity price compensation will be charged, and the charging station will need to pay a penalty fee to the distribution network. The calculation steps are as follows:

[0140]

[0141] In the formula, For the period The total load of each charging station connected to the distribution network is allocated to the charging station c according to the number of charging piles at each station. d The average charging load; For charging station c d The adjustment factor α is used to control the sensitivity of charging electricity price to changes in load balancing rate.

[0142] Taking into account the above factors affecting charging costs, the penalty fee for charging stations is:

[0143]

[0144] S44: During the operation of the charging station, the charging pricing and load constraints within the station should be met at the same time, as follows:

[0145] Charging station pricing should meet the corresponding boundary range constraints for each time period, as follows:

[0146]

[0147] Wherein, γ2 and γ1 are the upper and lower limit ratios of charging electricity price fluctuation, respectively, and the present invention takes 4 / 3 and 2 / 3.

[0148] When choosing a charging station, EVs should ensure that each charging station is within the capacity range of the distribution transformer where it is located, as shown in the following formula:

[0149]

[0150] In the formula, For charging station c d Maximum capacity of transformer.

[0151] S5: Figure 2 and Figure 3 As shown in the figure, the particle swarm optimization algorithm is used to construct a master-slave game model between charging station operators and charging users. The charging station operator dynamically adjusts the electricity price according to the real-time charging status in the station to maximize the benefits. As the follower of the master-slave game, the charging user formulates the optimal charging guidance strategy to minimize the charging utility value. Through the game equilibrium, the electricity price of each charging station and the charging station selection path guidance plan for each EV are finally output.

[0152] S51: Assuming that there are f EVs and g charging stations in the "vehicle-station" road network model of the present invention, then the time period The vehicle-station charging scheduling strategy model is described as follows:

[0153]

[0154] In the formula, {c d1 , c d2 , …, c dg , EV1, EV2, …EV f} is the time period The set of participants in the game, c d1 , c d2 , …, c dg Indicates charging stations, EV1, EV2, ...EV f represents EV users; Indicates the electricity price information of the charging station cd, For charging station c d In the period The purchase price of electricity and the adjustment of the selling price of electricity; Indicates the information after the electric vehicle is dispatched. For EV to go to each charging station c d The economic cost, waiting time and total journey distance required; is the output set of each participant, Indicates time period The gth charging station c within dg The comprehensive income Indicates time period The fth electric vehicle EV f Select each charging station c d The charging utility value.

[0155] S52: Time period Internal user side objective function Is to minimize the EV cluster charging utility value, then the objective function is calculated as follows:

[0156]

[0157] In the formula, Indicates time period Electric Vehicle EV r Select charging stationc du The comprehensive travel utility value after Indicates time period Electric Vehicle EV r After the game scheduling strategy, whether to choose charging station c du As the decision variable of the charging station.

[0158] Time Internal charging station side objective function is to maximize the charging benefit, and the calculation formula is as follows:

[0159]

[0160] In the formula, Indicates time period Internal charging station dj comprehensive income.

[0161] S53: As a follower, the electric vehicle user selects the optimal charging station based on the dynamic pricing adjusted by the leader charging station. The present invention minimizes the average charging utility value on the user side and maximizes the profit of the charging station through a game strategy, thereby achieving the Stackelberg Nash equilibrium. When the game reaches equilibrium, the charging station side does not change the charging pricing, and the strategies of each participant do not change. The equilibrium condition is that the change in the utility value on the user side and the interest of the charging station operator during several consecutive iterations is less than a certain range, which is mathematically described as:

[0162]

[0163] In the formula, is the revenue of the charging station at the w+1th cycle; is the revenue of the charging station at the wth cycle; is the user-side utility value at the w+1th cycle; is the user-side utility value at the wth cycle; λ1 and λ2 are the set change thresholds.

[0164] S54: The particle swarm algorithm is used to solve the above "vehicle-station" dynamic pricing game optimization EV cluster charging scheduling strategy. First, the charging station electricity price particle boundary, population and parameters are initialized, the fitness value of each particle is calculated for non-dominated sorting, and the individual best particle is updated and selected. The particle position and speed are updated by judging whether the convergence criterion is met. After the particle swarm algorithm game, the dynamic electricity price of each charging station and the optimal station selection plan on the user side are finally output.

[0165] An example of a small road network using a "vehicle-station" bilateral benefit game charging scheduling method taking distribution network safety into account proposed by the present invention is as follows:

[0166] The experiment uses a time period of a certain area road network as the test network. The selected road network area consists of 53 nodes and 78 road sections, including 8 charging station nodes. The road network model is as follows: Figure 4 shown.

[0167] It is assumed that there are 25 pairs of electric vehicles with charging needs on the way to the destination within this time period, and two solutions, the game scheduling method designed by the present invention and guiding the vehicles to the nearest charging station for charging, are used for comparison and verification.

[0168] First, the charging electricity price of all charging stations in this period is set to 1.32 yuan. After the game dynamic pricing of the present invention, the charging electricity price is shown in Table 1.

[0169] Table 1 Dynamic pricing output obtained by the game optimization strategy of the present invention

[0170] CSI CS2 CS3 CS4 CS5 CS6 CS7 CS8 Original electricity price / yuan 1.32 1.32 1.32 1.32 1.32 1.32 1.32 1.32 Dynamic Pricing / Yuan 1.35 1.39 0.937 1.30 1.35 1.36 1.25 1.34

[0171] Figure 5 The numbers of electric vehicles in each charging station after scheduling are obtained for two different schemes. It can be clearly seen from the figure that the game optimization scheduling strategy of the present invention reduces the number of vehicles at the CS2 charging station compared with the closest distance scheme, that is, the proposed charging scheduling method can reduce the pressure on the use of charging station equipment.

[0172] Figure 6 The charging loads in each charging station after scheduling are obtained by two different schemes. It can be clearly seen from the figure that the game optimization scheduling strategy of the present invention can effectively balance the charging loads of each charging station compared with the charging scheduling method of the closest distance scheme, and ensure the safe operation of the charging station connected to the distribution network.

[0173] Figure 7 The utility value on the user side and the profit value of the charging station are obtained after several iterations of the game algorithm of the present invention. Among them, the overall utility value on the user side is 6.90, and the profit value on the charging station side is 1066.67 yuan. The overall utility value on the user side obtained by scheduling with the optimal distance charging station is 6.93, and the profit value on the charging station side is 885 yuan. Compared with the two schemes, it is obvious that the game algorithm of the present invention can reduce the overall utility value on the user side while increasing the profit on the charging station side.

[0174] In another embodiment, the present invention proposes a computer-readable storage medium storing a computer program, wherein the computer program enables a computer to execute the "vehicle-station" bilateral benefit game charging scheduling method taking distribution network safety into account as described in Example 1.

[0175] In another embodiment, the present invention proposes an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the "vehicle-station" bilateral benefit game charging scheduling method taking into account distribution network safety as described in Example 1 is implemented.

[0176] In the embodiments disclosed in the present application, the computer storage medium can be a tangible medium that can contain or store a program for use by or in conjunction with an instruction execution system, device or equipment. The computer storage medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the above. More specific examples of computer storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above.

[0177] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in this application can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0178] The above are only preferred embodiments of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, some improvements and modifications without departing from the principle of the present invention should be regarded as the protection scope of the present invention.

Claims

1. A "vehicle-station" bilateral benefit game charging scheduling method considering the safety of distribution network, characterized in that: include: S1: Construct a traffic network structure model; S2: Based on the travel time probability distribution, travel destination probability distribution and charging demand judgment constraints of EV users within the traffic network, the spatiotemporal distribution characteristics of EV users' charging demand are obtained, and EV users with charging demand are screened out accordingly; S3: Based on the charging distance, the queuing time in the station and the charging cost, a calculation function of the charging utility value at the EV user side is constructed; the specific steps of step S3 are as follows: S31: EV at time t is calculated based on the adaptive Dijkstra algorithm sta Go to the available charging stations d The optimal path is obtained, and then the distance between EV and each charging station is obtained. And the journey time S32: Describing time periods based on M / M / c / ∞ queuing theory Waiting time for car owners Using exponential form Indicates the waiting time in queue, and the calculation formula is as follows: S33: Take every hour as the vehicle-station charging scheduling period, and the period interval is Period Inside EV at charging station c d Charging cost It is expressed as follows: In the formula, Indicates time period Is the EV at the charging station? d The decision variables for charging, the vehicle in the time period Located in the charging station c d 1 when charging, otherwise 0; For the period EV in charging station c d Charging costs; Indicates that EV is at charging station c d In the period Charging pricing; For the period EV arrives at charging station c d The amount of electricity after charging is finished; Reaching a charging station for EV d The remaining power when For EV at charging station c d Charging time; C total is the battery capacity; For EV at charging station c d Charging efficiency; S34: Calculate the time to leave the charging station for: Obtaining the departure time based on the adaptive Dijkstra algorithm Distance from start to destination The distance that EV users have to travel to the charging station is for: S35: Use Indicates time period Internal charging station d For the attractiveness utility of individual charging EV users, the linear weighted summation method is used to normalize it, and the calculation function of the charging utility value on the EV user side is obtained as shown in the following formula: In the formula, F1, F2, and F3 are the weight coefficients of the three utility value indicators, and they satisfy F1+F2+F3=1; S36: Establish decision variables and waiting time in queues Constraints; S4: Considering the operational safety of the charging station connected to the distribution network, taking into account the charging load of the charging station and the equipment service balance rate, a charging station side charging revenue calculation function is constructed; step S4 is as follows: S41: Calculation period Charging station within d Comprehensive income as follows: In the formula, Indicates time period Internal charging station d Total operating income, Indicates time period Internal charging station d The cost of purchasing electricity, Indicates time period Internal charging station d Penalty charges due to station overload, uneven charging services, and node voltage excursions; S42: For the current period There are f EVs in the cluster that have issued charging requests. Therefore, the charging station c in this period d The cost of purchasing electricity is: In the formula, For the period Internal charging station d Charging load; For the period Internal charging station d The cost of purchasing electricity; For charging station c d The electricity purchase price at time t; For the period Starting charging station c d The base load; For the period The i-th EV is at charging station c d Charging power; Indicates during the period Is the i-th EV at charging station c? d The decision variables for charging; Then charging station c d Charging cost for: In the formula, For charging station c d In the period Adjusted charging pricing; S43: Calculate the penalty fee for the charging station, specifically: When the charging station load is greater than the accessible load threshold During operation, the charging station The distribution network needs to pay penalty fees for: Where φ is the overload penalty price coefficient. When , φ takes the value of 1, otherwise it takes the value of 0; Charging stations with large voltage deviations during the period The distribution network needs to pay penalty fees for: Where ν is the voltage deviation penalty price coefficient, For the period Internal charging station d The node voltage per unit value; When an EV goes to a charging station with a higher designated balancing rate to charge, it will be charged an additional electricity price compensation, and the charging station will need to pay a penalty fee to the distribution network. In the formula, For the period The total load of each charging station connected to the distribution network is allocated to the charging station c according to the number of charging piles at each station. d The average charging load; For charging station c d The adjustment factor α is used to control the sensitivity of charging electricity price to changes in load balancing rate; Finally got punitive fees for charging stations for: S44: Establish charging pricing and load constraints within the station, specifically: The charging station pricing meets the corresponding boundary range constraints of each time period, as shown below: In the formula, γ2 and γ1 are the upper and lower limit ratios of charging electricity price fluctuation respectively; When EVs choose charging stations to go to, they must ensure that each charging station is within the capacity range of the distribution transformer, as shown in the following formula: In the formula, For charging station c d Maximum capacity of transformer; S5: Construct a master-slave game model between charging stations and EV users. The charging station, as the upper leader, adjusts the electricity price according to the charging status in the station to maximize the charging benefits. The EV user, as the follower, formulates the optimal charging guidance strategy to minimize the charging utility value. The electricity price of each charging station and the optimal station plan for each EV user are finally output through game equilibrium. Step S5 is as follows: S51: There are f EV users and g charging stations in the traffic network model. Charging guidance strategy model Described as: In the formula, {c d1 ,c d2 ,…,c dg ,EV1,EV2,…EV f } is the time period The set of participants in the game, c d1 ,c d2 ,…,c dg Indicates charging stations, EV1, EV2, ... EV f represents EV users; Indicates the electricity price information of the charging station cd, For charging station c d In the period The purchase price of electricity and the adjustment of the selling price of electricity; Indicates the information after the electric vehicle is dispatched. For EV to go to each charging station c d The economic cost, waiting time and total journey distance required; is the output set of each participant, Indicates time period The gth charging station c within dg The comprehensive income Indicates time period The fth electric vehicle EV f Select each charging station c d The charging utility value of S52: Time period Internal EV user side objective function is the minimum EV cluster charging utility value, calculated as follows: In the formula, Indicates time period Electric Vehicle EV r Select charging stationc du The charging utility value after Indicates time period Electric Vehicle EV r After the game scheduling strategy, whether to choose charging station c du As a decision variable for charging stations; Time Internal charging station side objective function is to maximize the charging benefit, and the calculation formula is as follows: In the formula, Indicates time period Internal charging station dj Charging income; S53: By using the game strategy, the average charging utility value of the EV user side is minimized while the charging revenue of the charging station is maximized, so that the game reaches an equilibrium state. The equilibrium conditions are as follows: In the formula, and are the charging benefits at the w+1th and wth cycles respectively; and are the charging utility values ​​at the w+1th and wth cycles respectively; λ1 and λ2 are the set change thresholds; S54: The particle swarm algorithm is used to solve the charging guidance strategy model, and the electricity price of each charging station and the optimal station selection plan for EV users are finally output after the particle swarm algorithm game.

2. A "vehicle-station" bilateral benefit game charging scheduling method taking distribution network safety into account as claimed in claim 1, characterized in that: In step S1, the traffic network structure model includes: road network traffic node coordinates, road section length, adjacency matrix based on road network topology structure and road network node flow time series.

3. A "vehicle-station" bilateral benefit game charging scheduling method taking distribution network safety into account as claimed in claim 1, characterized in that: Step S2 is specifically as follows: S21: Count the travel frequencies of EV users in different time periods within the traffic network, and characterize the probability distribution t of EV users’ travel times based on the statistical data; S22: Divide the traffic network into functional areas, predict the travel destinations of EV users through the Markov chain method, analyze the travel probability p between different functional areas, and build a daily travel demand model for EV users based on the user travel time probability distribution t; S23: Based on the daily travel demand model of EV users, combined with EV driving state parameters and charging demand judgment constraints, the spatiotemporal distribution characteristics of EV users' charging demand are obtained, and EV users with charging demand are screened out accordingly.

4. A "vehicle-station" bilateral benefit game charging scheduling method taking distribution network safety into account as claimed in claim 3, characterized in that: In step S33, the EV driving state parameters include the battery capacity C total 、The remaining power Soc when EV sends a charging request sta and unit power consumption μ; the charging demand judgment constraint is expressed as follows: In the formula, represents the sum of the lengths of the road sections to the destination, d ij represents the length of the road segment ij, x ij The decision variable indicating whether to drive on road section ij, i and j are different nodes in the node set N; if the judgment condition is not met, it means that the EV user needs to perform charging operation.

5. A "vehicle-station" bilateral benefit game charging scheduling method taking distribution network safety into account as claimed in claim 1, characterized in that: In step S36, the time period The decision variables for the selection of charging stations by EVs are constrained as follows: When EV is in the period Go to the charging station d When charging, you need to wait in line for the EV in front to finish charging. Set the maximum charging endurance time constraint. The constraint is expressed as follows: In the formula, For charging station c d The maximum charging waiting time.

6. A computer-readable storage medium storing a computer program, characterized in that: The computer program enables the computer to execute the "vehicle-station" bilateral benefit game charging scheduling method taking into account the safety of the distribution network as described in any one of claims 1-5.

7. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the "vehicle-station" bilateral benefit game charging scheduling method taking into account the safety of the distribution network as described in any one of claims 1 to 5 is implemented.

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