A power distribution network side dominated electric vehicle charging pile optimal utilization method

Abstract

The application relates to a power distribution network side dominated electric vehicle charging pile optimization utilization method, which can provide technical guidance for the optimization utilization of charging piles beneficial to power distribution network operation. The application comprises the following steps: step 1, taking the charging pile sharing charging time and power as decision variables, considering electric vehicle user charging power constraints and electric vehicle charging time continuous constraints, and establishing an upper sharing model of shared vehicle piles based on a peak-valley variance optimal target; step 2, taking the electric vehicle charging place as a decision variable, considering electric vehicle charging demand and power distribution network basic power load data, and establishing a lower sharing model of shared vehicle piles with the optimal target of reducing network loss; and step 3, selecting five indexes of system network loss, daily maximum peak-valley difference, node voltage offset extreme value, daily voltage offset cumulative value and transformer load rate to evaluate the influence of the charging pile sharing strategy on power grid operation.

Description

Technical Field

[0001] This invention relates to a method for optimizing the utilization of electric vehicle charging piles, which is dominated by the distribution network side and can provide technical guidance for optimizing the utilization of charging piles in the operation of the distribution network. Background Technology

[0002] To address the imbalance between supply and demand for charging infrastructure, one approach is to continue expanding its construction. However, this is difficult due to factors such as power grid upgrades, investment costs, and urban planning. Another approach is for private charging stations to be shared by applying for network access. Private charging facilities are generally idle for extended periods and have low utilization rates. Sharing private charging stations, by staggering charging times, can compensate for the supply gap caused by the imbalance in existing charging infrastructure. However, existing charging station sharing platforms built by charging station operators only provide charging station information inquiry and reservation services. Due to travel habits and traffic flow, charging users may concentrate on charging at certain stations, leading to both queuing and idle stations. This severely discourages some station owners from participating in sharing. In practice, participation in private charging station sharing is low, with only about 6% of private stations willing to share.

[0003] The sharing of private charging stations presents both challenges and opportunities for the operation and control of power distribution networks. Private charging facilities can be categorized as flexible resources, and the integration of a large number of flexible resources into the grid is beneficial for maintaining the stable operation of the distribution network. Directly or indirectly utilizing shared charging facilities to guide the orderly charging of electric vehicles can mitigate the negative impact of large-scale charging load integration on the distribution network. For example, coordinating the control voltage settings of charging stations can improve the overall voltage quality of the low-voltage distribution network. Developing charging power optimization strategies considering spatiotemporal characteristics can effectively reduce system load peak-valley differences, voltage deviations, and network losses. Utilizing transformer load changes to orderly control shared charging can achieve the goal of off-peak charging of electric vehicles while improving the transformer capacity of the distribution area to accommodate electric vehicle charging loads. Therefore, it is necessary to provide a new solution to meet actual operational needs. Summary of the Invention

[0004] From the perspectives of centralized control and multi-entity collaborative control on the distribution network side, this invention forms a systematic charging facility sharing strategy and its theory and method for coordinated operation with the distribution network. It proposes a multi-time period two-level decision model, in which the upper level decides the charging time and power of controllable electric vehicles, and the lower level decides the charging location of controllable and uncontrollable electric vehicles.

[0005] Meanwhile, the continuous nonlinear constraint of charging time is reconstructed into a linear constraint by introducing the difference concept and auxiliary array. The coupling constraint relaxation of power and time variables is achieved by using the principal subproblem of the generalized Benders decomposition algorithm. At the same time, the two-level model is solved by combining the Gurobi solver, and the impact of the proposed decision model on improving the voltage quality of distribution network operation is analyzed.

[0006] The technical solution adopted by the present invention, which is a method for optimizing the utilization of electric vehicle charging piles dominated by the distribution network side, includes the following steps:

[0007] Step 1: Using the shared charging time and power of charging piles as decision variables, taking into account the charging power constraints of electric vehicle users and the continuous charging time constraints of electric vehicles, a higher-level sharing model for shared vehicles and charging piles is established based on the peak-valley variance optimal objective.

[0008] Step 2: Using electric vehicle charging locations as decision variables, and considering data on electric vehicle charging demand and distribution network basic power load, establish a lower-level sharing model for shared vehicle charging stations with the optimal goal of reducing network losses.

[0009] Step 3: Select five indicators—system network loss, daily maximum peak-to-valley difference, extreme value of node voltage deviation, cumulative value of daily voltage deviation, and transformer load rate—to evaluate the impact of the charging pile sharing strategy on the power grid operation.

[0010] The present invention further includes the following specific solutions:

[0011] In step 1, the objective function of the upper-layer shared model is as follows:

[0012] The upper-level sharing model aims to reduce the peak-valley variance of load in different time periods, and the decision variables are the sharing time interval and power of the charging piles; the objective function is as follows:

[0013] (1);

[0014] In the formula: This represents the load variance after electric vehicle loads are connected to the distribution network; This represents the total load of the distribution network in time period t; This represents the average load over the entire scheduling cycle; T represents the time of the scheduling cycle.

[0015] (2);

[0016] In the formula: Indicates the base load; Let N represent the base load of node j during time period t; N represents the number of electric vehicle users participating in the sharing. Indicates the charging power of electric vehicles; This represents the charging power of the k-th electric vehicle at time t; This indicates the total load of the distribution network.

[0017] (3);

[0018] In step 1, the charging power constraint for the electric vehicle user is as follows:

[0019] Electric vehicle users need to meet charging power requirements The constraints on the relationship between battery SOC, charging time, and their value limits are as follows:

[0020] (4);

[0021] In the formula: , Charging power for the kth electric vehicle user Upper and lower bounds, 0-1 integer variables This represents the charging status of the k-th electric vehicle at time t. A status flag of 1 indicates a charging status, and a status flag of 0 indicates a non-charging status.

[0022] (5);

[0023] In the formula: , These are the arrival time and departure time of the k-th electric vehicle user at the shared charging station, respectively. For charging efficiency, Let be the state of charge of the electric vehicle at time t. This refers to the battery capacity.

[0024] (6);

[0025] (7);

[0026] In the formula: , for The upper and lower limits.

[0027] In step 1, the constraint on the continuity of charging time for electric vehicle users is as follows:

[0028] To meet the requirements of continuous charging status and flexible charging duration, the charging period [ , The charging status indicator within [ ] All values ​​must be 1, during non-charging periods. All are 0. Furthermore, unlike uncontrollable electric vehicles, the charging start time of controllable electric vehicles is... Both the end time (tend) and k can be within the shared time period. , The charging time state can be flexibly adjusted. However, to avoid frequent charging stops, the charging time state must meet the following continuous constraints:

[0029] (8);

[0030] (9);

[0031] (10);

[0032] (11);

[0033] In step 1, the constraint on the continuity of charging time for electric vehicle users is a non-convex nonlinear constraint. By introducing the difference concept to reconstruct the time constraint, it becomes a linear constraint, as follows:

[0034] Define difference variables Mark the charging status of the k-th electric vehicle user at two adjacent times t and t-1. , difference, For integer variables, all The difference array is constructed as follows:

[0035] (12);

[0036] In the formula: The value is equal to the first t items of the difference array. The sum, i.e., the charging state flags are equal to the prefix sum of the difference groups, is as follows:

[0037] (13);

[0038] In step 1, the charging status of the electric vehicle user under the differential charging time constraint can only take the values ​​0 and 1. Considering practical applications, this satisfies the continuous charging constraint. The value can only include two cases as follows:

[0039] Scenario 1: and ,satisfy , All other difference variables are equal to 0; at this time, the charging state flag is displayed. exist[ , The value is continuously equal to 1 within the time period. Scenario 1 meets the requirements for continuous charging of electric vehicles.

[0040] Scenario 2: ,satisfy =1, all other difference variables are set to 0; at this time, the charging status flag is... from The value remains constant at time T. Scenario two also meets the requirements for continuous charging of electric vehicles.

[0041] In addition, there is another scenario where the previous day's T-period was in a charging state, and charging continues during this scheduling cycle, i.e. The value is 1. It is also 1, which is the same as case 1.

[0042] according to Based on the possible values, the difference array constraints are derived as follows:

[0043] (14);

[0044] (15);

[0045] Introducing auxiliary arrays , The absolute value terms are linearized, and all variables are integers between 0 and 1. The auxiliary array is defined as follows:

[0046] (16);

[0047] , The mathematical relationship between the auxiliary array and the auxiliary array is as follows:

[0048] (17);

[0049] Difference array constraints are equivalent to the following linear constraints, where Z represents the set of integers:

[0050] (18);

[0051] (19);

[0052] (20);

[0053] (twenty one);

[0054] In summary, taking the shared charging time and power of charging piles as decision variables, and considering the charging power constraints of electric vehicle users and the continuity constraints of electric vehicle charging time, a Mixed Integer Quadratic Programming (MIQP) model is established based on the peak-valley variance optimal objective. The expression is as follows:

[0055] (twenty two);

[0056] In the formula: Charging state label for decision variables With continuous charging power Coupling constraints; for The feasible domain; for The feasible domain.

[0057] In step 1, the upper-layer shared model is solved using the generalized Benders decomposition algorithm to decompose the main problem and sub-problems, thereby relaxing the coupling constraints of power and time, as follows:

[0058] First, the upper bound space of the scheduling target is updated by solving the subproblems, forming new constraints, i.e., the cut plane backfills the main problem. Then, the lower bound space of the scheduling target is obtained by solving the main problem. Finally, the subproblems and the main problem are solved iteratively until the upper bound space and the lower bound space are consistent, at which point the iteration ends and the optimal solution is obtained. The specific algorithm flow is as follows.

[0059]

[0060] In step 2, the objective function of the lower-level shared model is as follows:

[0061] (twenty three);

[0062] In the formula: T is the total number of time periods in the scheduling cycle. The length of the scheduling period; This represents the distribution network loss; iij,t is the current flowing through branch lij in time period t, and L is the set of branches.

[0063] In step 2, the AC power flow constraints of the upper-layer shared model are as follows:

[0064] Branch flow model is used to describe AC ​​distribution network flow. All satisfy the following power flow constraints:

[0065] (twenty four);

[0066] (25);

[0067] In the formula: According to the branch power flow model, the active and reactive power balance equations for branch lij from node i to node j are: , For the active and reactive loads at node j during time period t, , The active and reactive power outputs of the generator unit at node j during time period t. , Let represent the active and reactive power flow of branch lij during time period t, rij and xij represent the resistance and reactance of branch lij, and iij,t represent the current flowing through branch lij during time period t. L is the set of terminal nodes of all branches whose starting point is j, and L is the set of branches.

[0068] (26);

[0069] The above equation is the voltage balance equation, where ui,t and uj,t are the voltages at nodes i and j respectively during time period t.

[0070] (27);

[0071] The above equation is the power equation at the beginning of the branch.

[0072] (28);

[0073] The above formula represents the voltage amplitude constraints for each node. , and are the upper and lower limits of the voltage amplitude at node j, respectively.

[0074] (29);

[0075] The above formula represents the active power output constraints of the units at each node. , These represent the upper and lower limits of the active power output of the unit at node j.

[0076] (30);

[0077] The above formula represents the reactive power constraints for each node. , These represent the upper and lower limits of reactive power output at node j, respectively.

[0078] (31);

[0079] The above formula is the amplitude constraint for the branch complex power Sij.

[0080] (32);

[0081] The above formula represents the composition of the connected load. Let t be the electric vehicle charging load at node j during time period t.

[0082] In step 2, the power flow model contains a large number of quadratic terms. , This makes solving the objective function difficult, so we replace it with linear terms Uj,t and Iij,t. , The revised version is as follows:

[0083] (33);

[0084] (34);

[0085] (35);

[0086] Furthermore, a second-order cone relaxation is applied to the equation, transforming the optimal power flow problem of the distribution network into a second-order cone programming (SOCP) problem, making it easier for commercial solvers to solve. See below for details:

[0087] (36);

[0088] In step 2, the constraint on the relationship between the number of shared charging piles and electric vehicles in the lower-level sharing model is as follows:

[0089] Electric vehicle load connected at distribution network node j The calculation is as follows:

[0090] (37);

[0091] In the formula: A collection of uncontrollable electric vehicles; Let be the decision variable, representing the shared charging pile at the distribution network node j where the k-th electric vehicle user is assigned to charge; 1{} is the indicator function, which takes the value 1 if the condition in {} is met, and 0 otherwise. , These are the charging power and charging period of the controllable electric vehicle, respectively, which are determined by the upper-level control model.

[0092] The charging end time, temp,k, of an uncontrollable electric vehicle to fully charge its required power is calculated by the following formula:

[0093] (38);

[0094] In the formula: ⌈⌉ is the floor function, and Pch represents the rated charging power. The charging information uploaded by the k-th electric vehicle user includes the charging start time. Initial state of charge Desired state of charge Battery capacity Charging efficiency .

[0095] Each electric vehicle user can be matched with a maximum of one shared charging station, and one charging station can only serve one electric vehicle user, forming a one-to-one matching relationship. The number of shared electric vehicles that each distribution network node can connect to is constrained as follows:

[0096] (39);

[0097] In the formula: N represents the number of shared charging piles connected to distribution network node j, and N represents the number of electric vehicle users.

[0098] In step 2, the lower-level shared model is solved using the Gurobi solver.

[0099] In step 3, the evaluation indicators for the impact of the shared charging piles on the power distribution network are as follows:

[0100] System network loss It is the sum of active power losses in each branch of the distribution network, reflecting the economic efficiency of the distribution network after electric vehicles are connected:

[0101] (40);

[0102] Maximum daily peak-valley difference This refers to the degree of deviation of the load from the mean, describing the level of load fluctuation. The smaller the value, the smaller the load fluctuation.

[0103] (41);

[0104] Node voltage offset extreme value β3: The maximum voltage offset of all nodes during all scheduling periods, which can be used to analyze the impact of charging control strategy on node voltage.

[0105] (42);

[0106] Daily voltage offset cumulative value Defined as the sum of the offsets of all nodes during all scheduling periods throughout the day in the distribution network, it can be used to assess the impact of the sharing strategy on the distribution network voltage level.

[0107] (43);

[0108] Transformer load factor β5: The ratio of apparent power obtained from the transformer outlet node voltage U and outlet line current I to the transformer's rated capacity Stran. β5 can measure whether a new load peak is formed after electric vehicle sharing.

[0109] (44);

[0110] This invention addresses the issues from the perspectives of centralized control and multi-entity collaborative control on the distribution network side, forming a systematic strategy for optimizing the utilization of charging facilities and its theory and method for coordinated operation with the distribution network. It proposes a multi-time period two-level decision model, where the upper level decides the charging time and power of controllable electric vehicles, and the lower level decides the charging location of controllable and uncontrollable electric vehicles.

[0111] Meanwhile, the continuous nonlinear constraint of charging time is reconstructed into a linear constraint by introducing the concept of difference and auxiliary array. The coupling constraint relaxation of power and time variables is achieved through the principal subproblem of the generalized Benders decomposition algorithm. The bi-level model is then solved using the Gurobi solver, and the impact of the proposed decision model on improving the voltage quality of the distribution network is analyzed.

[0112] This invention can provide specific guidance for optimizing the utilization of charging piles that are beneficial to the operation of power distribution networks. Attached Figure Description

[0113] Figure 1 This is a schematic diagram of the charging pile sharing process under centralized control of the power distribution network;

[0114] Figure 2 This involves analyzing two scenarios encompassed by the difference array constraint.

[0115] Figure 3 This is the solution process for the two-layer matching model of shared charging piles;

[0116] Figure 4 It is an IEEE 33-node distribution network system;

[0117] Figure 5 This refers to the number of shared charging stations for 500 uncontrollable electric vehicles in different scenarios.

[0118] Figure 6 This refers to the situation of shared charging piles under different modes under the controllable charging of electric vehicles. Detailed Implementation

[0119] See attached document Figure 1-6 To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below. The embodiments described in this application are merely some embodiments of this invention, and not all embodiments. Based on the spirit of this invention, other embodiments obtained by those skilled in the art without creative effort are all within the protection scope of this invention.

[0120] In this embodiment, the distribution network simulation analysis is performed using the IEEE 33-node standard example, with a reference voltage of 12.66 kV and a reference power of 10 MW. All power flow parameters below are per-unit values, and the voltage amplitude boundaries are... , Branch complex power amplitude boundary The system is connected to the upstream power grid via two 2500kVA / 35kV / 0.4kV transformers operating in parallel. A total of 500 electric vehicles are included in the optimized utilization, each with a battery capacity of 35kWh, a maximum charging power of 7kW, a minimum charging power of 1kW, and a charging efficiency of 0.9. The base load for each time period is as follows:

[0121]

[0122] Based on NHTS2017 data, Monte Carlo simulation was used to obtain the data for any electric vehicle. i Scheduled charging time slots [t] arr,i ,t le,i Initial State of Charge (SOC) O,i Desired State of Charge (SOC) E The value is 0.9. Assume that each of the distribution network nodes 3, 10, 13, 17, 21, 26 and 30 (hereinafter referred to as the nodes including the charging piles) is connected to 100 shared charging piles.

[0123] First, we analyze the impact of the sharing strategy on the power distribution network under the uncontrollable charging of electric vehicles:

[0124] Assuming the charging of 500 electric vehicles is uncontrollable, the optimal sharing scheme for charging piles is calculated directly based on the lower-level vehicle-charging-pile spatial matching model. In scenarios 1, 2, and 3, the optimized utilization does not consider centralized control of the power distribution network; electric vehicle users randomly select shared charging piles. Scenario 4 is the experimental group.

[0125] Scenario 1: The number of shared piles with pile nodes is 70, 70, 70, 80, 70, 70, 70.

[0126] Scenario 2: The number of shared piles with pile nodes are 80, 80, 80, 20, 80, 80, and 80 respectively.

[0127] Scenario 3: The number of shared piles in the pile node is 0, 0, 100, 100, 100, 100, 100.

[0128] Scenario 4: Call the optimal sharing scheme calculated by the lower-level matching model. The number of shared piles including pile nodes are 100, 100, 0, 0, 100, 100, and 100 respectively.

[0129] Furthermore, the impact of different sharing scenarios on the power distribution network is compared and analyzed using the evaluation indicators proposed in Section 4:

[0130]

[0131] The system's network loss, node voltage deviation extremes, daily cumulative voltage deviation, and average transformer load rate are all superior to those in other scenarios. The daily maximum peak-to-valley difference is the same across all scenarios because the charging time and power of uncontrollable electric vehicles are fixed, resulting in no difference in grid-connected charging power across scenarios and time periods.

[0132] Then, the impact of the sharing strategy under controllable electric vehicle charging on the power distribution network is analyzed:

[0133] Four sets of modes were set up for comparison:

[0134] Mode A: Uncontrollable charging mode, where the electric vehicle begins constant power charging the moment it arrives at the charging station and continues until fully charged. The charging period and power are uncontrollable.

[0135] Mode B: Charging period shifting mode only. The charging power and charging time of the electric vehicle are constant, and the load can only be transferred by shifting the charging period.

[0136] Mode C: Charging power scheduling mode only, the time range of electric vehicles staying at shared charging stations [ , It continuously charges internally, and the power level can only be changed.

[0137] Mode D: Controllable charging mode, which is the two-layer scheduling mode proposed in this paper. It can simultaneously schedule shared charging time and power under the constraint of continuous charging time of electric vehicles.

[0138] Comparison of the impact of different sharing strategies on the power distribution network:

[0139]

[0140] Considering the controllable charging of electric vehicles, modes B, C, and D all regulate the shared charging time and power, thereby optimizing the load distribution of electric vehicles and making the network loss, maximum peak-valley difference, extreme value of node voltage deviation, cumulative value of daily voltage deviation, and average load rate of transformers of the distribution network system better than the uncontrollable charging mode of electric vehicles.

[0141] Guiding the matching of shared bike charging stations from the power distribution network side has multiple advantages:

[0142] First, all shared charging stations need to obtain power from the power distribution network. Compared to charging station companies, the vehicle-charging station matching led by the power distribution network is easier to integrate charging stations from various brands.

[0143] Second, the metering and settlement channels for charging piles and power distribution networks are smooth, and the construction cost of the shared platform is low.

[0144] Third, it can alleviate the impact of the huge charging demand on the operation of the power distribution network when connected to the grid at the same time.

[0145] Fourth, the theoretical and pricing research foundation for guiding changes in charging time, power, and location on the distribution network side is good, and it can be quickly applied to the charging guidance of shared vehicle charging piles.

[0146] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.

Claims

1. A method for optimizing the utilization of electric vehicle charging piles, primarily driven by the power distribution network side, characterized in that... Includes the following steps: Step 1: Using the shared charging time and power of charging piles as decision variables, and taking into account the charging power constraints of electric vehicle users and the continuous charging time constraints of electric vehicles, establish an upper-level sharing model for shared vehicles and charging piles based on the peak-valley variance optimal objective. Step 2: Using electric vehicle charging locations as decision variables, and considering data on electric vehicle charging demand and distribution network basic power load, establish a lower-level sharing model for shared vehicle charging stations with the optimal goal of reducing network losses. Step 3: Select five indicators—system network loss, daily maximum peak-to-valley difference, extreme value of node voltage deviation, cumulative value of daily voltage deviation, and transformer load rate—to evaluate the impact of the charging pile sharing strategy on power grid operation. In step 1, the objective function of the upper-layer shared model is as follows: The upper-level sharing model aims to reduce the peak-valley variance of the load in different time periods. The decision variables are the sharing time interval and power of the charging piles. The objective function is as follows: (1)； In the formula: This represents the load variance after electric vehicle loads are connected to the distribution network; This represents the total load of the distribution network in time period t; This represents the average load over the entire scheduling cycle; T represents the time of the scheduling cycle. (2)； In the formula: Indicates the base load; Let N represent the base load of node j during time period t; N represents the number of electric vehicle users participating in the sharing. Indicates the charging power of electric vehicles; This represents the charging power of the k-th electric vehicle at time t; Indicates the total load of the distribution network; (3)。 2. The method for optimizing the utilization of electric vehicle charging piles dominated by the distribution network side as described in claim 1, characterized in that: In step 1, the constraint on the continuity of charging time for electric vehicle users is as follows: To meet the requirements of continuous charging status and flexible charging duration, the charging period [ , The charging status indicator within [ ] All values ​​must be 1, during non-charging periods. All are 0; At the same time, unlike uncontrollable electric vehicles, the charging start time of controllable electric vehicles... Both the end time (tend) and k can be within the shared time period. , [Internal flexibility;] However, to avoid frequent charging interruptions, the charging time state must meet the following continuous constraints: (8)； (9)； (10)； (11)。 3. The method for optimizing the utilization of electric vehicle charging piles dominated by the distribution network side as described in claim 2, characterized in that: In step 1, the constraint that the charging time of the electric vehicle user is continuous is a non-convex nonlinear constraint. By introducing the difference concept, the reconstructed time constraint is a linear constraint, and the difference variable is defined. Mark the charging status of the k-th electric vehicle user at two adjacent times t and t-1. , difference, For integer variables, all Construct a difference array and introduce an auxiliary array. , The absolute value terms are linearized to be integer variables between 0 and 1, as follows: (18)； (19)； (20)； (21)。 4. The method for optimizing the utilization of electric vehicle charging piles dominated by the distribution network side as described in claim 1, characterized in that: In step 1, the upper-layer shared model is solved using the generalized Benders decomposition algorithm, which decomposes the main problem and sub-problems to relax the coupling constraints of power and time, as follows: First, solve the subproblem to update the upper bound space of the scheduling target and form a new constraint, namely the cut plane backfilling main problem; Then, the lower bound space of the scheduling target is obtained by solving the main problem; Finally, the subproblems and the main problem are solved iteratively until the upper bound space and the lower bound space are consistent, at which point the iteration ends and the optimal solution is obtained.

5. The method for optimizing the utilization of electric vehicle charging piles dominated by the distribution network side as described in claim 1, characterized in that: In step 2, the objective function of the lower-level shared model is as follows: (23)； In the formula: T is the total number of time periods in the scheduling cycle. The length of the scheduling period; This indicates the network loss in the distribution network; iij,t represents the current flowing through branch lij in time period t, and L represents the set of branches.

6. The method for optimizing the utilization of electric vehicle charging piles dominated by the distribution network side as described in claim 1, characterized in that: In step 2, the optimal power flow problem of the distribution network in the lower-level shared model is transformed into a second-order cone programming problem to facilitate solving by commercial solvers, as detailed below: (36)。 7. The method for optimizing the utilization of electric vehicle charging piles dominated by the distribution network side as described in claim 1, characterized in that: In step 3, the evaluation indicators for the impact of the shared charging piles on the power distribution network are as follows: System network loss It is the sum of active power losses in each branch of the distribution network, reflecting the economic efficiency of the distribution network after electric vehicles are connected: (40)； Maximum daily peak-valley difference This refers to the degree of deviation of the load from the mean, describing the level of load fluctuation. The smaller the value, the smaller the load fluctuation. (41)； Node voltage offset extreme value β3: The maximum voltage offset of all nodes during all scheduling periods, which can be used to analyze the impact of charging control strategy on node voltage. (42)； Daily voltage offset cumulative value Defined as the sum of the offsets of all nodes during all scheduling periods throughout the day in the distribution network, it can be used to assess the impact of the sharing strategy on the distribution network voltage level. (43)； Transformer load factor β5: The ratio of apparent power obtained from the transformer outlet node voltage U and outlet line current I to the transformer's rated capacity Stran; β5 can measure whether a new load peak is formed after electric vehicle sharing. (44)。

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