Multi-type charging station collaborative planning method considering user comprehensive satisfaction

Abstract

The invention discloses a multi-type charging station collaborative planning method considering user comprehensive satisfaction, and relates to the field of power system planning and traffic energy infrastructure planning. Constructing a charging satisfaction model based on the fast charging waiting time and the idle slow charging pile remaining amount; based on the charging satisfaction model, establishing a superior and subordinate cascade optimization model of the multi-type charging stations; and solving the upper and lower cascade optimization model by an iteration capacity expansion method based on a greedy algorithm, converting a superior planning problem into a multi-round fast and slow charging pile configuration expansion problem, and adjusting configuration based on an operation result of a previous round in each round of iteration until a convergence condition is met. And outputting a charging station configuration scheme which meets the planning constraint and optimizes the system operation cost and the user satisfaction. And scientificity and high efficiency of charging facility layout are realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of power system planning and transportation energy infrastructure planning, and more particularly, to a multi-type charging station collaborative planning method considering user comprehensive satisfaction. BACKGROUND

[0002] The continuous growth of the number of electric vehicles has driven the urgent demand for charging infrastructure. As an important facility supporting the daily operation of electric vehicles, how to meet the charging needs of different users has become an urgent problem to be solved.

[0003] Electric vehicles usually provide users with two options: fast charging and slow charging. Fast charging can charge electric vehicles with sufficient power in a short time, which is convenient for users to continue driving, but it will cause certain damage to the battery. Slow charging is slower than fast charging, and the pressure on the battery is smaller, which can prolong the battery life, but it takes a long time to fully charge. At the same time, the prices of fast and slow charging equipment and their impact on the power grid are also different. From the perspective of the power system, when a large number of fast charging piles are built in the station, the new energy consumption rate of the power grid is improved, but it will bring pressure to the power lines around the charging station. When a large number of slow charging piles are built in the station, the power flow distribution of the power grid will be relatively balanced, but the new energy consumption level of the power grid will be correspondingly reduced.

[0004] Therefore, how to make the planning scheme of electric vehicle charging stations meet the needs of different types of users for fast charging piles and slow charging piles, optimize the operation of the power transportation coupled network while improving the charging satisfaction of users, and urgently need to put forward a new type of charging station collaborative planning method, which can be based on user comprehensive satisfaction, overall planning of the site selection and capacity configuration of different types of charging facilities, while meeting the requirements of power system constraints and investment economy, maximizing the user charging experience, and promoting the sustainable development of the electric vehicle industry In view of the above problems, the present application provides a solution. SUMMARY

[0005] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present application provide a multi-type charging station collaborative planning method considering user comprehensive satisfaction, which optimizes the site selection and capacity configuration of different types of charging stations, while maximizing the comprehensive satisfaction of electric vehicle users, and realizes the scientificity and efficiency of charging facility layout.

[0006] To achieve the above object, the present application provides the following technical scheme: A collaborative planning method for multiple types of charging stations considering overall user satisfaction includes the following steps: Based on the charging behavior characteristics of fast-charging and slow-charging electric vehicle users, a charging satisfaction model is constructed based on fast-charging waiting time and the remaining number of idle slow-charging piles; based on the charging satisfaction model, a cascaded optimization model for multiple types of charging stations is established; an iterative expansion method based on a greedy algorithm is used to solve the cascaded optimization model, transforming the upper-level planning problem into a multi-round fast and slow charging pile configuration expansion problem. In each iteration, the configuration is adjusted based on the results of the previous round until the convergence condition is met, outputting a charging station configuration scheme that satisfies planning constraints and optimizes system operating costs and user satisfaction.

[0007] In a preferred embodiment, the construction of the charging satisfaction model based on fast charging waiting time and the remaining number of available slow charging stations specifically involves: constructing a fast charging satisfaction function based on the queuing waiting time of fast charging users at the selected charging station; and constructing a slow charging satisfaction function based on whether there are available slow charging stations after slow charging users arrive at the charging station.

[0008] In a preferred embodiment, the optimization model for the selection of the upper-level site and the configuration of the charging pile capacity specifically involves: constructing an objective function for the upper-level model with the goal of minimizing the sum of the construction cost and operation and maintenance cost of the charging station; and establishing constraints on the upper-level model, including constraints on the number of charging stations, upper and lower limits on the number of charging piles, and constraints on the capacity of the charging station.

[0009] In a preferred embodiment, the lower-level charging scheduling optimization model specifically involves: constructing a lower-level model objective function with the goal of minimizing the sum of grid operating costs, transportation network operating costs, and user satisfaction costs; and establishing constraints for the lower-level model, including constraints on the spatiotemporal selection of fast-charging users, constraints on the spatiotemporal selection of slow-charging users, constraints on user charging status judgment, constraints on user satisfaction, constraints on charging prices, constraints on charging load, constraints on power flow balance, constraints on unit operation, constraints on renewable energy reduction, and constraints on transportation network saturation.

[0010] In a preferred embodiment, the power grid operating cost includes the operating cost of thermal power units, the cost of purchasing electricity, the cost of abandoning new energy sources, and the cost of network losses.

[0011] In a preferred embodiment, the operating cost of the transportation network is the cost of road congestion penalties.

[0012] In a preferred embodiment, the user satisfaction cost is the negative of the product of user satisfaction and the user satisfaction cost coefficient.

[0013] In a preferred embodiment, the iterative expansion method based on a greedy algorithm includes the following steps: The upper-level site selection and charging pile capacity configuration optimization model is transformed into a multi-round fast-charging and slow-charging pile configuration expansion problem. In each iteration, based on the optimal configuration scheme obtained in the previous round, a candidate expansion scheme set is generated. Each scheme, under the premise of satisfying the constraints on the number of charging piles and the charging station capacity, expands the configuration by adding fast-charging or slow-charging piles. The candidate expansion scheme set is input into the lower-level charging scheduling optimization model to solve for the optimal operating result of each scheme under the current scheduling scenario, obtaining its corresponding operating cost and planning cost. Based on the operating cost and planning cost of the optimal scheme selected in adjacent iterations, a sensitivity coefficient is calculated. The sensitivity coefficient is defined as the ratio of the difference in operating cost to the difference in planning cost, used to judge the convergence trend of the expansion iteration. If the sensitivity coefficient is lower than a preset threshold, or the current expansion scheme has met the planning constraints of the upper-level model, the expansion iteration is terminated, and the final charging station site selection and fast / slow charging pile configuration scheme is output.

[0014] The technical effects and advantages of the multi-type charging station collaborative planning method of the present invention, which considers overall user satisfaction, are as follows: 1. Based on the charging characteristics of fast-charging and slow-charging electric vehicle users, this invention establishes a charging satisfaction model based on fast-charging waiting time and the remaining availability of idle slow-charging stations. This model can more accurately reflect the charging needs and preferences of different user groups and improve the service matching and utilization rate of charging facilities.

[0015] 2. This invention considers the impact of charging station planning results on the operation and scheduling of the power transportation network and the charging satisfaction of different types of electric vehicle users. It establishes a multi-type charging station cascade optimization model that considers the overall user satisfaction. This model can effectively solve the problem of difficulty in planning the location of the charging station and the configuration quantity of different types of charging piles when the charging station contains different types of charging piles, thereby improving the feasibility and optimization effect of the charging station planning scheme.

[0016] 3. This invention proposes a greedy algorithm-based solution for a cascaded optimization model, transforming the upper-level charging pile planning model into a multi-round charging pile expansion problem. Expansion continues based on the results of each round's expansion plan until the convergence condition is met. This approach accelerates the model solution while ensuring the relative optimality of the planning results. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating a collaborative planning method for multiple types of charging stations that considers overall user satisfaction, according to the present invention. Figure 2 This is a flowchart of the solution process for the cascaded optimization model based on the greedy algorithm of this invention. Figure 3This is a typical daily wind power output diagram for this invention. Figure 4 This is a typical daily active power load diagram for this invention. Figure 5 This is a typical daily reactive load diagram for this invention. Figure 6 This is a graph showing the average charging price of charging stations at different times according to the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0019] Example 1, Figure 1 This invention presents a collaborative planning method for multiple types of charging stations that considers overall user satisfaction, comprising the following steps: S1. Based on the charging behavior characteristics of fast-charging and slow-charging electric vehicle users, a charging satisfaction model is constructed based on fast-charging waiting time and the remaining amount of available slow-charging stations.

[0020] In this embodiment, the construction of the charging satisfaction model based on fast charging waiting time and the remaining amount of available slow charging stations specifically involves: A fast charging satisfaction function is constructed based on the queuing time of fast charging users in the selected charging station, as shown in equation (1): (1) In the formula, The satisfaction level of the k-th fast-charging user choosing the n-th charging station. This represents the upper limit of user satisfaction for the k-th fast charging user. This represents the lower limit of user satisfaction for the k-th fast charging user. The critical queuing time corresponding to the upper limit of user satisfaction. This represents the critical queuing time corresponding to the lower limit of user satisfaction. Let be the queuing time at the nth charging station. The queuing time for fast charging users at the charging station is shown in equation (2): (2) In the formula: Let n be the expected interval time for the user to reach the nth charging station. The expected service time for the nth charging station is... Let Variance be the service time of the nth charging station. Let n be the number of fast charging piles at the nth charging station.

[0021] Based on whether there are available slow charging piles after arriving at the charging station, a slow charging satisfaction function is constructed. The charging behavior of slow charging users, represented by private cars, is significantly different from that of fast charging users. Slow charging users only go to the charging station closest to their company or residence during their commute, which is highly purposeful. The satisfaction of slow charging users with the charging station capacity planning is reflected in whether there are available slow charging piles after arriving at the charging station. If there are, the satisfaction level is high; otherwise, the satisfaction level is low, as shown in equation (3). (3) In the formula, The satisfaction level of the k-th slow-charging user when choosing the n-th charging station. This represents the upper limit of user satisfaction for the k-th slow charging user. This represents the lower limit of user satisfaction for the k-th slow charging user. A collection of slow charging stations. This indicates the occupancy status of the slow charging station. This indicates that the slow charging station is available. This indicates that the slow charging station is occupied.

[0022] This invention constructs a charging satisfaction model based on fast charging waiting time and the remaining availability of idle slow charging stations. This model accurately characterizes the differentiated charging behaviors of fast charging users who are sensitive to waiting time, and slow charging users who prioritize availability. It transforms user satisfaction from a qualitative description into a quantifiable and optimizable key indicator. This model not only improves the accuracy of expressing charging demand but also serves as the foundational input for cascaded optimization models, effectively guiding site selection and fast / slow charging station configuration strategies towards user preferences, achieving a synergistic improvement in user experience and system costs. Compared to traditional planning methods based primarily on electricity indicators or static demand, this invention more dynamically reflects changes in user behavior, significantly improving the rationality of charging infrastructure planning and the accuracy of user services.

[0023] S2. Based on the charging satisfaction model, establish a cascaded optimization model for multiple types of charging stations.

[0024] In this embodiment, the cascaded optimization model includes an upper-level site selection and charging pile capacity configuration optimization model and a lower-level charging scheduling optimization model; The optimization model for the selection of the upper-level site and the configuration of pile capacity is as follows: With the goal of minimizing the sum of construction costs and operation and maintenance costs of charging stations, a higher-level model objective function is constructed; Establish upper-level model constraints, including constraints on the number of charging stations, upper and lower limits on the number of charging piles, and charging station capacity.

[0025] The objective function of the higher-level model is shown in equation (4): (4) In the formula, This is the sum of the construction cost and the operation and maintenance cost of the charging station. For the construction cost of charging stations, For the operation and maintenance costs of charging stations, Let be the average discount rate for charging stations, and y be the operating life of the charging stations. The construction cost of charging stations includes fixed investment costs. Charging pile purchase cost and costs related to the total power of the charging pile Specifically, as shown in equation (5): (5) Charging pile purchase cost and costs related to the total power of the charging pile It can be expressed by equations (6)-(7): (6) (7) In equations (6)-(7): This refers to the number of slow charging piles constructed within charging station c. This refers to the number of fast charging piles built within charging station c. This represents the number of selectable website areas. The purchase cost of a single fast charging station. The purchase cost of a single slow charging station. The cost coefficient is related to the total power of the charging station. The power of the fast charging station. This refers to the power output of the slow charging station.

[0026] Operating and maintenance costs of charging stations and the construction cost of charging stations They show a positive correlation, as shown in equation (8): (8) In equation (8): This represents the correlation coefficient between the operation and maintenance costs and the construction costs of charging stations.

[0027] The specific constraints on the number of charging stations to be constructed are shown in equation (9): (9) In equation (9): It is a binary variable representing whether region c is selected as the construction site. This indicates that the site has been selected as the construction location. This indicates that the site was not selected as a construction site. The number of charging stations to be built.

[0028] The upper and lower limits of the number of charging piles are specifically shown in equations (10)-(12): (10) (11) (12) In equations (10)-(12): This represents the lower limit for the number of charging piles to be built at the c-th charging station. This represents the maximum number of charging piles that can be built at the c-th charging station. This represents the minimum number of fast-charging piles that can be built at the c-th charging station. This represents the maximum number of fast-charging piles that can be built at the c-th charging station. This represents the minimum number of slow-charging piles to be built at the c-th charging station. This represents the upper limit for the number of slow-charging piles that can be built at the c-th charging station.

[0029] The capacity constraint of the charging station is specifically shown in equation (13): (13) In equation (13), This represents the maximum total charging power of the c-th charging station.

[0030] Furthermore, the lower-level charging scheduling optimization model is specifically as follows: With the goal of minimizing the sum of power grid operating costs, transportation network operating costs, and user satisfaction costs, a lower-level model objective function is constructed. Establish lower-level model constraints, including spatiotemporal selection constraints for fast-charging users, spatiotemporal selection constraints for slow-charging users, user charging status judgment constraints, user satisfaction constraints, charging price constraints, charging load constraints, power flow balance constraints, unit operation constraints, renewable energy reduction constraints, and transportation network saturation constraints.

[0031] The objective function of the lower-level model is shown in Equation (14): (14) In the formula, It is the sum of power grid operating costs, transportation network operating costs, and user satisfaction costs. For power grid operating costs, For the operating costs of the transportation network, Cost of improving user satisfaction with charging.

[0032] The power grid operating cost includes the operating cost of thermal power units, the cost of purchasing electricity, the cost of abandoning new energy sources, and the cost of network losses, as shown in equation (15): (15) In equation (15), Typical number of days, The number of scheduling periods for a typical single day. The output cost coefficient of thermal power units. This refers to the number of thermal power units. For the first The actual output of the e-th thermal power unit at time t on a typical day. For the first The price of purchasing backup electricity at a typical time on a given day. For the first Electricity purchases at a typical time on a given day. For the number of wind turbines, This is the cost coefficient for wind curtailment. For the first The amount of wind curtailment at time t on a typical day. For the number of photovoltaic cells, This is the cost coefficient for abandoned light. For the first The amount of solar power wasted at time t on a typical day. For the collection of power grid branches, This is the network loss cost coefficient. Let x be the resistance value of branch xy. For the first The square of the branch current xy at a typical time t.

[0033] Transportation network operating costs The penalty cost for road congestion is shown in Equation (16): (16) In equation (16): A collection of roads in a transportation network. The coefficient representing the penalty cost for road congestion. For the first The saturation of road ij at time t on a typical day.

[0034] User satisfaction cost Defined as the negative product of user satisfaction and user satisfaction cost coefficient, as shown in equation (17): (17) In equation (17), User satisfaction cost coefficient For the first The charging satisfaction of the k-th user at time t on a typical day. This refers to the number of electric vehicles.

[0035] The fast charging user's spatiotemporal selection constraints include fast charging user charging time selection constraints (18)-(20) and fast charging user charging location selection constraints as shown in equations (21)-(22): (18) (19) (20) (twenty one) (twenty two) In equations (18)-(22): This represents the set of times that satisfy user i's charging desire. The state of charge limit when charging user i. The upper limit of the state of charge when charging user i. Let t be the state of charge of the i-th vehicle at time t, as shown in equation (23); for The average electricity price of all charging stations at any given time. The lowest of these average electricity prices. This is a binary variable representing whether user i selects charging during time period t, where 1 indicates selection and 0 indicates no selection. The charging time selected by the user, Let be the total cost of the i-th electric vehicle to reach the j-th charging station at time t. Let be the equivalent distance traveled by the i-th vehicle from its current position to the j-th charging station at time t. Let be the charging electricity price of the j-th charging station at time t. Let be the estimated waiting time at the j-th charging station at time t. The equivalent weighting coefficient for the charging electricity price at charging stations. The equivalent weighting coefficient represents the shortest equivalent distance from the electric vehicle to the charging station. This is the equivalent weighting coefficient for the estimated waiting time at charging stations. To achieve the lowest overall cost, The number of charging stations. It is a binary variable representing whether vehicle i chooses charging station j for charging. When =0, it means that vehicle i has not selected charging station j. =1, indicating that vehicle i selects charging station j. M is an arbitrarily large positive number used as an algebraic symbol in the calculation; (twenty three) In equation (23): Let be the battery capacity of the i-th vehicle. Let be the initial state of charge of the i-th vehicle. Let be the speed of the i-th vehicle. Let be the driving energy consumption of the i-th vehicle.

[0036] The spatiotemporal selection constraints for slow-charging users are specifically shown in equations (24)-(25): (twenty four) (25) In equations (24)-(25): Select the charging time for the k-th slow-charging user. Let k be the time it takes for the k-th slow-charging user to reach the nearest charging station to the company. Let k be the time it takes for the k-th slow-charging user to reach the nearest charging station. Let k be the distance from the home of the k-th slow-charging user to the nearest charging station. Let the distance from the company of the k-th slow-charging user to the nearest charging station be denoted as . The charging station selected for the k-th slow-charging user. The nearest charging station to the company that is closest to the k-th slow-charging user. The nearest charging station to the company of the k-th slow-charging user.

[0037] The user charging status determination constraint is specifically shown in equations (26)-(29): (26) (27) (28) (29) In equations (26)-(29): Let be the time required for the i-th vehicle to fully charge at time t. Let be the battery capacity of the i-th vehicle. The charging power for the i-th vehicle. For charging efficiency, Let t be the time required for the i-th electric vehicle to travel from its current location to the j-th charging station. Let be the distance the i-th electric vehicle travels from its current position to the j-th charging station. Let be the speed of the i-th electric vehicle. Let i be the initial charging time for the i-th electric vehicle. The time it takes for the i-th electric vehicle to develop the intention to charge. Let be the waiting time for the i-th electric vehicle at the charging station. Let be a binary variable indicating whether the i-th electric vehicle is in a charging state. =1 indicates that the electric vehicle is in a charging state. =0 indicates that it is not in a charging state, and t0 is any time of day.

[0038] The user satisfaction constraints are specifically shown in equations (1)-(3).

[0039] The charging price constraint is specifically shown in equations (30)-(31): (30) (31) In equations (30)-(31): For the first The average charging price at all charging stations at time t on a typical day. A reduction factor for charging prices at charging stations. Charging station charging price increase factor For the first The charging price at charging station c at a typical time on day t.

[0040] The charging load constraint is specifically shown in equations (32)-(33): (32) (33) In equations (32)-(33): For the first The active load of charging station c at a typical time t on a given day. For the first The charging power chosen by a typical daily electric vehicle user k. It means the first A binary variable representing whether user k chooses to charge at charging station c at time t on a typical day, where 1 indicates selection and 0 indicates no selection. For the first A typical daily user k's charging intention time set It means the first At a typical time t, does user k select...? A binary variable indicating when charging will take place, with 1 indicating selection and 0 indicating no selection. It means the first A typical daily user k chooses The variable represents whether the device is in a charging state at time t after charging at time period and charging station c. A value of 1 indicates that the device is in a charging state, and a value of 0 indicates that the device is not in a charging state. For the first The reactive load of charging station c at a typical time t on a given day. This refers to the power factor of the charging station.

[0041] The power flow balance constraints are specifically shown in equations (34)-(35): (34) (35) In equations (34)-(35): It is the collection of all branches of the power grid. For the first The active power of branch xy at a typical time t. For the first The current of branch xy at a typical time t. Let x be the resistance value of branch xy. For the first The active power of branch yz at a typical time t on a day. For the first The active power load of node y at a typical day t time. For the first The active power load of a charging station at node y at time t on a typical day. For the first The active power purchased by node y from the upper-level power grid at time t on a typical day. For the first The active power generated by a thermal power unit at node y at time t on a typical day. For the first The active power generated by wind power at node y at a typical day t. For the first The reduction in active power of wind power at node y on a typical day t. For the first The active power generated by the photovoltaic system at node y at a typical day t. For the first The reduction in photovoltaic active power at node y on a typical day t. For the set of power grid nodes, For the first The reactive power of branch xy at a typical time t on a day. Let xy be the reactance value of branch. For the first The reactive power of branch yz at a typical time t on a day. For the first The reactive load of node y at a typical time t on a given day. For the first The reactive load of a charging station at node y at time t on a typical day. For the first The reactive power injected by the upstream power grid into node y at time t on a typical day. For the first The reactive power generated by a thermal power unit at node y at time t on a typical day. For the first The reactive power generated by wind power at node y at a typical day t. For the first The amount of wind power reactive power reduction at node y at time t on a typical day. For the first The reactive power generated by the photovoltaic system at node y at a typical day t time. For the first The amount of photovoltaic reactive power reduction at node y at time t on a typical day. Let be the resistance value of branch yz. Let Y be the reactance value of branch YZ. Let y be the impedance magnitude of branch yz. For the first The current of branch yz at a typical time t. For the first The square of the voltage magnitude of node y at a typical time t. For the first The square of the voltage magnitude at node z at a typical day t.

[0042] The unit operation constraints are specifically shown in equations (36)-(37): (36) (37) In equations (36)-(37): This represents the minimum active power output of the eth thermal power unit. This represents the maximum active power output of the eth thermal power unit. For the first The active power output of the e-th thermal power unit at time t on a typical day. This represents the minimum reactive power output of the e-th thermal power unit. For the first The reactive power output of the e-th thermal power unit at time t on a typical day. This represents the maximum reactive power output of the eth thermal power unit.

[0043] The specific constraints on the reduction of new energy sources are shown in equations (38)-(41): (38) (39) (40) (41) In equations (38)-(41): For the first The active power generated by the photovoltaic system at time t on a typical day. For the first The reactive power generated by the r-th photovoltaic cell at time t on a typical day. For the first The reactive power reduction of the photovoltaic system at time t on a typical day. For the first The reduction in active power of photovoltaic power at time t on a typical day. For the first The active power generated by the f-th wind turbine at time t on a typical day. For the first The reactive power generated by the f-th wind turbine at time t on a typical day. For the first The reactive power reduction of the f-th wind power unit at time t on a typical day. For the first The active power reduction of the f-th wind power at time t on a typical day.

[0044] The traffic network saturation constraint is specifically shown in equations (42)-(43): (42) (43) In equations (42)-(43): To indicate the first A binary variable representing whether a user k who chooses the c-th charging station on a typical day passes through road ij at time t, where 1 indicates passing through and 0 indicates not passing through. For the first Let $j$ represent the set of roads that user $k$, who chooses to charge at the $c$-th charging station on a typical day, passes through at time $t$. Let $ij$ represent the roads between road nodes $i$ and $j$. For the first The saturation level of road ij at a typical day t time. For the first The traffic flow of road ij at time t on a typical day does not include scheduled electric vehicles. To indicate the first A binary variable representing whether a typical daily user k chooses to charge during time period t, where 1 indicates selection and 0 indicates no selection. Let be the road capacity of branch road ij.

[0045] The lower-level charging scheduling optimization model constructed in this invention aims to minimize the sum of grid operating costs, transportation network operating costs, and user satisfaction costs, comprehensively integrating dynamic response mechanisms across three dimensions: the power system, the transportation network, and user behavior. This model not only coordinates grid operating elements such as thermal power unit output, electricity purchase, renewable energy consumption, and power flow balance, but also fully considers the interconnected impact of traffic congestion and users' charging time and space choices. Thus, while ensuring the safe and efficient operation of the power-transport coupled network, it effectively guides electric vehicle users to choose more reasonable charging paths. Compared to traditional methods that only focus on optimizing a single system, this invention significantly enhances the adaptability of scheduling schemes and the overall economic efficiency of operation in complex multi-source environments, achieving a dual improvement in user satisfaction and system performance.

[0046] S3 uses an iterative expansion method based on a greedy algorithm to solve the cascaded optimization model. It transforms the upper-level planning problem into a multi-round fast and slow charging pile configuration expansion problem. In each iteration, the configuration is adjusted based on the results of the previous round until the convergence condition is met. The output is a charging station configuration scheme that satisfies planning constraints, optimizes system operating costs and user satisfaction.

[0047] In this embodiment, as Figure 2 As shown, the iterative expansion method based on the greedy algorithm includes the following steps: The optimization model of the site selection and charging pile capacity configuration of the upper-level site is transformed into a multi-round configuration expansion problem of fast charging piles and slow charging piles. In each iteration, a set of candidate expansion schemes is generated based on the optimal configuration scheme obtained in the previous round. Each scheme expands the configuration by adding fast charging piles or slow charging piles, under the premise of meeting the constraints of the number of charging piles and the capacity constraints of the charging station. The candidate expansion schemes are input into the lower-level charging scheduling optimization model to solve the optimal operating results of each scheme under the current scheduling scenario, and obtain their corresponding operating costs and planning costs. Based on the operating cost and planning cost of the optimal solution selected in adjacent iterations, a sensitivity coefficient is calculated. The sensitivity coefficient is defined as the ratio of the difference in operating cost to the difference in planning cost, and is used to judge the convergence trend of the expansion iteration. If the sensitivity coefficient is lower than the preset threshold, or if the current expansion scheme has met the planning constraints of the upper-level model, then the expansion iteration is terminated, and the final charging station site selection and fast / slow charging pile configuration scheme is output.

[0048] The specific formula for calculating the sensitivity coefficient is shown in equation (44): (44) In equation (44): Let m be the sensitivity coefficient of the m-th iteration. The optimal total running cost for the m-th iteration is... The optimal total running cost for the (m-1)th iteration is... The optimal total planning cost for the m-th iteration is... This represents the optimal total planning cost for the (m-1)th iteration.

[0049] Example 2 verifies the present invention "a collaborative planning method for multiple types of charging stations considering comprehensive user satisfaction", mainly including the following steps: 1) The test system consists of an IEEE 33-node system and a 27-node urban transportation network system.

[0050] The system originally had three charging stations, located at nodes 3, 7, and 10 of the transportation network and connected to nodes 20, 4, and 31 of the power grid. Each charging station had 60 fast-charging piles and 15 slow-charging piles. Three sites are currently under construction; two of these sites will be selected for charging station construction, located at nodes 14, 16, and 26 of the transportation network and connected to nodes 23, 33, and 11 of the power grid. Each charging station will have at least 16 fast-charging piles and 4 slow-charging piles, and at most 50 fast-charging piles and 25 slow-charging piles. Node 24 of the power grid has a 35MW wind farm, and nodes 17, 21, 32, and 33 each have a 500kW micro-turbine unit. Node 33 is connected to the main grid to purchase backup power. The urban transportation network system includes 2000 electric vehicles, of which 1800 are fast-charging users and 200 are slow-charging users, with a battery capacity of 82kWh and a driving speed of 40km / h. The charging station has a fast charging power of 45kW, a slow charging power of 7kW, and a power factor of 0.95.

[0051] Slow-charging users are divided into three categories based on their travel characteristics. Table 1 shows the charging locations of each category of slow-charging users in the transportation network during the morning and evening.

[0052] Table 1

[0053] Based on historical data of wind power output and load in a certain region, wind power output and active and reactive power loads for four typical days were sampled in each of the four seasons (spring, summer, autumn, and winter). The wind power output for the four typical days is as follows: Figure 3 As shown, the active power load for four typical days is as follows: Figure 4 As shown, the reactive load for four typical days is as follows: Figure 5 As shown, the average charging price at the charging station at different times is as follows: Figure 6 As shown in Table 2, the various power generation cost coefficients are as follows.

[0054] Table 2

[0055] 2) Validity Analysis of the Power-Transportation Coupled Network Planning Model This embodiment compares and analyzes five planning strategies: Scheme 1: Relying solely on existing charging stations to serve users; Scheme 2: Using an optimized model to obtain the planning scheme; Scheme 3: Selecting the same construction location as Scheme 2, but changing the fast and slow charging ratio within the charging station and increasing the number of charging piles within the station; Scheme 4: Having the same fast and slow charging ratio within the charging station as Scheme 2, but changing the construction location of the charging station; Scheme 5: Having the same fast and slow charging ratio within the charging station as Scheme 2, but changing the construction location of the charging station. The planning scheme program of this invention was written in MATLAB and solved using the GUROBI solver on an Intel Core i5-10400F mobile platform. This demonstrates the effectiveness and applicability of the invention.

[0056] The planning results of the five schemes are shown in Table 3, and the planning costs and annual operating costs of the five schemes are shown in Table 4.

[0057] Table 3

[0058] Table 4

[0059] 3) Analysis of the impact of the planning scheme on user satisfaction This embodiment compares and analyzes the differences in user satisfaction optimization between two planning schemes obtained by the planning method under different parameter settings: Scheme c(1) is the planning scheme obtained when the satisfaction cost coefficient is set to 0.3; Scheme c(2) is the planning scheme obtained when the satisfaction cost coefficient is set to 3; the results of the planning schemes are shown in Table 5.

[0060] Table 5

[0061] Meanwhile, this embodiment analyzes the impact of charging pile configuration in charging stations on the charging satisfaction of different types of users using three schemes: Scheme d(1) is the planning scheme obtained by the planning model of this invention; Scheme d(2) builds a large number of slow charging piles and a small number of fast charging piles in the same location; Scheme d(3) builds a large number of fast charging piles and slow charging piles in the same location; The planning and operation costs and user satisfaction results of the three planning schemes are shown in Table 6.

[0062] Table 6

[0063] In summary, the collaborative planning method for multiple types of charging stations proposed in this invention, which considers overall user satisfaction, maximizes overall user satisfaction while minimizing the overall cost of the solution. Different planning schemes can be obtained by adjusting the satisfaction-cost coefficient in the model. A larger coefficient results in a better overall user satisfaction in actual operation.

[0064] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0065] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0066] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0067] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0068] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0069] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A collaborative planning method for multiple types of charging stations considering overall user satisfaction, characterized in that, Includes the following steps: Based on the charging behavior characteristics of fast-charging and slow-charging electric vehicle users, a charging satisfaction model is constructed based on fast-charging waiting time and the remaining amount of available slow-charging stations. Based on the charging satisfaction model, a cascaded optimization model for multiple types of charging stations is established. The iterative expansion method based on the greedy algorithm solves the upper and lower cascade optimization model, transforming the upper planning problem into a multi-round fast and slow charging pile configuration expansion problem. In each iteration, the configuration is adjusted based on the running results of the previous round until the convergence condition is met, and the charging station configuration scheme that satisfies the planning constraints and optimizes the system operating cost and user satisfaction is output. The cascaded optimization model includes an upper-level site selection and charging pile capacity configuration optimization model and a lower-level charging scheduling optimization model.

2. The multi-type charging station collaborative planning method considering comprehensive user satisfaction as described in claim 1, characterized in that, The specific steps for constructing a charging satisfaction model based on fast charging waiting time and the remaining availability of idle slow charging stations are as follows: Construct a fast charging satisfaction function based on the queuing time of fast charging users at the selected charging station: In the formula, The satisfaction level of the k-th fast-charging user choosing the n-th charging station. This represents the upper limit of user satisfaction for the k-th fast charging user. This represents the lower limit of user satisfaction for the k-th fast charging user. The critical queuing time corresponding to the upper limit of user satisfaction. This represents the critical queuing time corresponding to the lower limit of user satisfaction. Let be the queuing time for the nth charging station; Construct a slow charging satisfaction function based on whether there are available slow charging stations after the slow charging user arrives at the charging station: In the formula, The satisfaction level of the k-th slow-charging user when choosing the n-th charging station. This represents the upper limit of user satisfaction for the k-th slow charging user. This represents the lower limit of user satisfaction for the k-th slow charging user. A collection of slow charging stations. This indicates the occupancy status of the slow charging station. This indicates that the slow charging station is available. This indicates that the slow charging station is occupied.

3. The multi-type charging station collaborative planning method considering comprehensive user satisfaction as described in claim 2, characterized in that, The optimization model for the selection of the upper-level site and the configuration of pile capacity is as follows: With the goal of minimizing the sum of charging station construction costs and operation and maintenance costs, the objective function of the higher-level model is constructed as follows: In the formula, This is the sum of the construction cost and the operation and maintenance cost of the charging station. For the construction cost of charging stations, For the operation and maintenance costs of charging stations, Let y be the average discount rate for the charging station, and y be the operating years of the charging station. Establish upper-level model constraints, including constraints on the number of charging stations, upper and lower limits on the number of charging piles, and charging station capacity.

4. The multi-type charging station collaborative planning method considering comprehensive user satisfaction as described in claim 3, characterized in that, The lower-level charging scheduling optimization model is specifically as follows: With the goal of minimizing the sum of power grid operating costs, transportation network operating costs, and user satisfaction costs, the objective function of the lower-level model is constructed as follows: In the formula, It is the sum of power grid operating costs, transportation network operating costs, and user satisfaction costs. For power grid operating costs, For the operating costs of the transportation network, Cost to improve user satisfaction with charging; Establish lower-level model constraints, including spatiotemporal selection constraints for fast-charging users, spatiotemporal selection constraints for slow-charging users, user charging status judgment constraints, user satisfaction constraints, charging price constraints, charging load constraints, power flow balance constraints, unit operation constraints, renewable energy reduction constraints, and transportation network saturation constraints.

5. The multi-type charging station collaborative planning method considering comprehensive user satisfaction as described in claim 4, characterized in that, The power grid operating costs include the operating costs of thermal power units, electricity purchase costs, costs of abandoning new energy sources, and network loss costs.

6. The multi-type charging station collaborative planning method considering comprehensive user satisfaction as described in claim 5, characterized in that, The operating cost of the transportation network The specific formula for calculating the penalty cost for road congestion is as follows: In the formula: A collection of roads in a transportation network. The coefficient representing the penalty cost for road congestion. For the first The saturation of road ij at time t on a typical day.

7. The multi-type charging station collaborative planning method considering comprehensive user satisfaction as described in claim 6, characterized in that, User satisfaction cost This is the negative product of user satisfaction and user satisfaction cost coefficient, as shown in the following formula: In the formula, User satisfaction cost coefficient For the first The charging satisfaction of the k-th user at time t on a typical day. This refers to the number of electric vehicles.

8. The collaborative planning method for multiple types of charging stations considering comprehensive user satisfaction as described in claim 7, characterized in that, The iterative expansion method based on the greedy algorithm includes the following steps: The optimization model of the site selection and charging pile capacity configuration of the upper-level site is transformed into a multi-round configuration expansion problem of fast charging piles and slow charging piles. In each iteration, a set of candidate expansion schemes is generated based on the optimal configuration scheme obtained in the previous round. Each scheme expands the configuration by adding fast charging piles or slow charging piles, under the premise of meeting the constraints of the number of charging piles and the capacity constraints of the charging station. The candidate expansion schemes are input into the lower-level charging scheduling optimization model to solve the optimal operating results of each scheme under the current scheduling scenario, and obtain their corresponding operating costs and planning costs. Based on the operating cost and planning cost of the optimal solution selected in adjacent iterations, a sensitivity coefficient is calculated. The sensitivity coefficient is defined as the ratio of the difference in operating cost to the difference in planning cost, and is used to judge the convergence trend of the expansion iteration. If the sensitivity coefficient is lower than the preset threshold, or if the current expansion scheme has met the planning constraints of the upper-level model, then the expansion iteration is terminated, and the final charging station site selection and fast / slow charging pile configuration scheme is output.

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