A historical load-based electric vehicle time-of-use step charging control method and system

Abstract

The present application relates to electric vehicle charging control technical field, propose a kind of based on historical load electric vehicle time-of-use step charging control method and system, comprising: obtaining the historical load of target distribution area, time-of-use electricity price and electric vehicle charging load demand;Based on the historical load, calculate time-of-use reference load;Based on the time-of-use reference load, time-of-use electricity price and electric vehicle charging load demand, build time-of-use step charging control model;With total charging fee minimum and load fluctuation degree minimum as target, the time-of-use charging control model is solved, and the optimal control scheme is obtained, and the electric vehicle charging strategy of target distribution area is controlled by the optimal control scheme.Compared with prior art, the present application considers historical load data, improves the time-of-use charging control degree of detail, effectively relieves the pressure of power grid peak period.

Description

Technical Field

[0001] This invention belongs to the field of electric vehicle charging control technology, specifically relating to a time-sharing tiered charging control method and system for electric vehicles based on historical load. Background Technology

[0002] In recent years, with increasing global attention to environmental protection and sustainable development, coupled with advancements in electric vehicle technology and supportive national policies, the number of electric vehicles has increased significantly. This rapid growth is accompanied by concentrated charging of electric vehicles, which may pose a potential threat to the stability and safety of the power distribution network, exacerbating grid load pressure and even triggering overload risks. Time-of-use pricing strategies guide electric vehicle users to charge during off-peak hours when electricity prices are lower, based on grid load conditions. This ensures users' charging needs are met while alleviating pressure on the grid during peak hours, achieving a balance between power supply and demand and stable grid operation.

[0003] Currently, existing technologies for time-of-use electric vehicle charging neglect the differences in regional load and the micro-characteristics of distribution substations, relying too heavily on a uniform time-of-use pricing strategy, making it difficult to accurately match the actual needs of different regions. Due to significant differences in historical load, electricity consumption structure, and electric vehicle charging behavior among various distribution substations, existing technologies cannot effectively solve the problem of localized overload caused by uneven regional load distribution. Furthermore, insufficient consideration of dynamic load changes, distributed energy access, and the charging demand characteristics of users in different regions further exacerbates the incompatibility of grid regulation, leading to reduced grid operating efficiency. Summary of the Invention

[0004] To overcome the shortcomings of the prior art, which ignores the differences in regional loads and leads to uneven power dispatch, the present invention provides a time-sharing tiered charging control method and system for electric vehicles based on historical loads.

[0005] To achieve the above-mentioned technical effects, the technical solution of the present invention is as follows:

[0006] A time-sharing tiered charging control method for electric vehicles based on historical load includes the following steps:

[0007] Obtain the historical load, time-of-use electricity price, and electric vehicle charging load demand of the target distribution area; calculate the time-of-use reference load based on the historical load.

[0008] A time-sharing tiered charging control model is constructed based on the time-sharing reference load, time-sharing electricity price, and electric vehicle charging load demand.

[0009] The time-sharing charging control model is solved with the objectives of minimizing total charging costs and minimizing load fluctuations to obtain the optimal control scheme. The electric vehicle charging strategy of the target distribution area is then controlled through the optimal control scheme.

[0010] This invention also proposes a time-sharing tiered charging control system for electric vehicles based on historical load, the system comprising:

[0011] Reference load calculation module: used to obtain the historical load, time-of-use electricity price, and electric vehicle charging load demand of the target distribution area; and to calculate the time-of-use reference load based on the historical load;

[0012] Solution Module: It is equipped with a time-sharing tiered charging control model, which is used to solve the time-sharing charging control model with the objectives of minimizing total charging costs and minimizing load fluctuations, so as to obtain the optimal control scheme, and control the electric vehicle charging strategy of the target distribution area through the optimal control scheme.

[0013] The present invention also proposes an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the time-sharing tiered charging control method for electric vehicles based on historical load as described in the present invention.

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0015] This invention constructs a time-sharing tiered charging control model by combining historical load data of the target distribution area, which can effectively adapt to the load characteristics of different regional power grids and fully consider regional differences. Secondly, it balances the load brought by electric vehicle charging with the optimization objective of "minimizing load fluctuations", reducing local load peaks and fluctuation risks. The charging strategy is optimized with the objective of "minimizing total charging costs", which effectively reduces the charging costs for users and guides users to charge during off-peak hours, thereby alleviating peak-hour pressure. Attached Figure Description

[0016] Figure 1 This is a flowchart of a time-sharing tiered charging control method for electric vehicles based on historical load.

[0017] Figure 2 This is a graph showing the time-of-use charging cost curve for electric vehicles in a certain distribution station area.

[0018] Figure 3 A comparison diagram of the load before and after time-staggered charging control for a certain distribution station.

[0019] Figure 4 This is an architecture diagram of a time-sharing tiered charging control system for electric vehicles based on historical load. Detailed Implementation

[0020] The accompanying drawings are for illustrative purposes only and should not be construed as limiting the invention.

[0021] It will be understood by those skilled in the art that some well-known descriptions may be omitted in the accompanying drawings.

[0022] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0023] Example 1

[0024] This embodiment proposes a time-sharing tiered charging control method for electric vehicles based on historical load, such as... Figure 1 The diagram shown is a flowchart of the time-sharing tiered charging control method for electric vehicles based on historical load in this embodiment.

[0025] The time-sharing tiered charging control method for electric vehicles based on historical load proposed in this embodiment includes the following steps:

[0026] Obtain the historical load, time-of-use electricity price, and electric vehicle charging load demand of the target distribution area; calculate the time-of-use reference load based on the historical load.

[0027] A time-sharing tiered charging control model is constructed based on the time-sharing reference load, time-sharing electricity price, and electric vehicle charging load demand.

[0028] The time-sharing charging control model is solved with the objectives of minimizing total charging costs and minimizing load fluctuations to obtain the optimal control scheme. The electric vehicle charging strategy of the target distribution area is then controlled through the optimal control scheme.

[0029] In this embodiment, a time-sharing tiered charging control model is constructed by combining historical load data, time-of-use pricing, and electric vehicle charging load demand of the target distribution area. This achieves a site-specific optimization strategy that effectively adapts to the load characteristics of different regional power grids and fully considers regional differences. Secondly, with "minimizing load fluctuation" as the optimization objective, charging time periods are allocated according to load, balancing the load brought by electric vehicle charging, reducing local load peaks and fluctuation risks, and improving the stability and reliability of power grid operation. Finally, the charging strategy is optimized with "minimizing total charging costs" as the objective, effectively reducing users' charging costs and guiding users to charge during off-peak hours, thereby alleviating peak-hour pressure and achieving synergistic optimization of user economy and power grid operation efficiency.

[0030] In an optional embodiment, the historical load includes load data for each moment within a certain number of periods. The time-of-use reference load for each moment within the target period is calculated based on the historical load, and its expression is as follows:

[0031]

[0032] Among them, P r,j λ is the reference load at time j within the target period time r.t P is the reference load factor for the t-th cycle time; t,j Let be the historical load at time j in the t-th cycle; k is the number of cycles included in the historical load data.

[0033] As an example, the period is 24 hours and k is 7.

[0034] In this embodiment, the historical load includes load data for each moment within several periods. Based on this, the time-of-use reference load for each moment within the target period is calculated, fully considering the load variation patterns over different time periods and reflecting the actual load characteristics of the target distribution area, providing scientific data support for charging control strategies. By using load data from multiple periods, the technical solution can dynamically adapt to the periodicity and fluctuation characteristics of regional loads, avoiding the problem of a "one-size-fits-all" approach with fixed time-of-use pricing. Simultaneously, it effectively disperses charging loads, reducing peak-hour pressure on the power grid from concentrated charging and ensuring the safe and stable operation of the power grid. Furthermore, this design supports load balancing optimization, reduces equipment losses due to high load operation, and provides reference data for subsequent multi-objective optimization.

[0035] In an optional embodiment, the time-sharing tiered charging control model includes total charging cost, load fluctuation level, and constraints; the step of solving the time-sharing charging control model includes: finding that at least one of the total charging cost or load fluctuation level reaches a minimum value under the constraints.

[0036] In this embodiment, the time-sharing tiered charging control model incorporates total charging costs, load fluctuation levels, and constraints into its model structure. By solving under constraints, it minimizes either the total charging cost or the load fluctuation level, thereby achieving an effective balance between user economy and grid stability.

[0037] Alternatively, the total charging cost is calculated based on time-of-use electricity pricing and electric vehicle charging load demand, as shown in the following formula:

[0038]

[0039] Where f1 is the total charging cost; N is the number of electric vehicles; M is the number of times sampled within the period; P EVij e represents the charging power of the i-th electric vehicle at time j; j Let x be the charging price at time j; ij This represents the charging state of the i-th electric vehicle at time j, when x ij =0 indicates that the i-th electric vehicle is not charging at time j, when x ij =1 indicates that the i-th electric vehicle is charging at time j;

[0040] The load fluctuation level is calculated based on the electric vehicle charging load demand and time-of-use reference load, and its calculation expression is as follows:

[0041]

[0042] Where f2 represents the degree of load fluctuation; P r,j The reference load at time j within the target period time r; P j The total load value represents the sum of the reference load and the charging load at time j. This represents the average load.

[0043] As an example, M is set to 96.

[0044] In this embodiment, by minimizing the total charging cost, the user's charging cost is significantly reduced; by minimizing the degree of load fluctuation, the allocation of charging load is optimized, reducing the pressure of peak load on the power grid operation and improving the stability and security of the power grid operation.

[0045] Alternatively, a multi-objective normalized optimization can be performed on the total charging cost and the degree of load fluctuation, as shown in the following expression:

[0046]

[0047] Among them, e min The minimum charging electricity price; e max This represents the maximum charging electricity price.

[0048] In this embodiment, the time-sharing tiered charging control model needs to avoid the influence of dimensional differences in multi-objective functions. Therefore, normalization optimization is required to transform the multi-objective functions into a single objective function. Normalization optimization eliminates the unfairness or imbalance caused by dimensional differences in different objective functions, allowing the model to fairly weigh the importance of each objective during optimization. After normalization, the model can unify multiple objectives, such as minimizing total charging costs and minimizing load fluctuations, under a single optimization framework, thereby improving the efficiency and accuracy of the solution process. Furthermore, this method makes the optimization results more reasonable, achieving a scientific balance between different optimization objectives. This ensures that the final strategy meets both the user's economic needs and the grid's stability requirements, improving the model's reliability and applicability in practical applications.

[0049] In one optional embodiment, the constraints include at least one of the following: charging power demand constraints, charging duration constraints, number of charging vehicles constraints, and power distribution network load rate constraints.

[0050] Specifically, the expression for the charging power demand constraint is as follows:

[0051] SOC exi ≤SOC endi ≤1

[0052] Among them, SOC exi The expected state of battery after the i-th electric vehicle has finished charging; SOC endi This refers to the battery state after the i-th electric vehicle has finished charging.

[0053] Charging time constraint: The charging time required for a single electric vehicle to meet its charging demand, expressed as follows:

[0054]

[0055] Where: SOC sti Battery state before charging the i-th electric vehicle; T endi The moment when charging of the i-th electric vehicle ends; T sti The time at which charging of the i-th electric vehicle begins; P i The charging power of the i-th electric vehicle; C i Let be the battery capacity of the i-th electric vehicle.

[0056] Specifically, for electric vehicle charging users, the initial charging time must align with the normal daily routines of ordinary residents, with the following additional charging time constraints:

[0057]

[0058] Charging vehicle quantity constraint: The current number of charging vehicles must not exceed the total number of electric vehicles.

[0059] Distribution network load factor constraint: For distribution networks with time-of-use tiered charging control, regardless of whether they are under heavy overload before the time-of-use tiered charging control, heavy overload of the distribution network should be avoided as much as possible during the time-of-use tiered charging control day. The expression is as follows:

[0060] P j ≤0.8S N η

[0061] Among them, S N Let η be the capacity of the distribution transformer, and η be the power factor of the distribution transformer.

[0062] In this embodiment, charging demand constraints ensure that each electric vehicle meets the user's expected battery state requirements after charging, improving the practicality and user acceptance of the strategy. Charging time constraints dynamically calculate the required time based on charging power, battery capacity, and charging state, while also considering the normal daily routines of ordinary residents to avoid disrupting users' daily lives and improve user experience. Charging vehicle quantity constraints limit the number of electric vehicles charging simultaneously to the maximum capacity of the charging facilities, preventing equipment damage or efficiency reduction due to overload and improving system safety and stability. Distribution network load factor constraints effectively prevent heavy overload in the distribution network during the implementation of time-sharing tiered charging control. By dynamically optimizing charging load distribution through constraints, the system avoids exceeding the capacity and power factor limits of distribution transformers, ensuring the reliability of distribution network operation. This embodiment enhances the adaptability of the time-sharing tiered charging control model to complex real-world scenarios through multiple constraint settings. It not only meets the charging needs of individual users but also optimizes the overall operating efficiency and safety of the distribution network, making the model more scientific, accurate, and reliable.

[0063] In an optional embodiment, the optimal control scheme includes a time-sharing tiered charging arrangement scheme for electric vehicles and a power control scheme for electric vehicle charging piles; the time-sharing tiered charging arrangement scheme controls the charging status of electric vehicles; and the power control scheme for electric vehicle charging piles controls the charging power of electric vehicles.

[0064] In this embodiment, the optimal control scheme combines the electric vehicle time-sharing tiered charging arrangement scheme with the charging pile power control scheme to achieve refined management and dynamic optimization of the charging process. By rationally allocating charging time through the time-sharing tiered charging arrangement scheme and dynamically controlling the charging status of electric vehicles, the scheme effectively alleviates the peak load pressure on the power grid and reduces user charging costs. By flexibly adjusting the charging power through the charging pile power control scheme, the scheme meets user charging needs while reducing equipment overload and losses, significantly improving charging efficiency, economy, and the safety of power grid operation.

[0065] As an example, a distribution transformer in a certain substation has a capacity of 315 kVA and collects historical load information and time-of-use pricing information. A total of 10 electric vehicles are subject to time-of-use charging control, with an average battery capacity of 100 kWh and an average charging power of 7 kW. The initial charging battery capacity follows a random distribution of the maximum battery capacity within the range [0.05, 0.25], and the final charging battery capacity follows a random distribution of the maximum battery capacity within the range [0.9, 1]. Figure 2 The figure shows the time-of-use charging cost curve for electric vehicles in this distribution station area. A detailed calculation example was performed on a selected day as the time-of-use tiered charging control day, resulting in the electric vehicle time-of-use tiered charging arrangement scheme shown in Table 1.

[0066] Table 1. Time-sharing tiered charging arrangement plan for electric vehicles in a certain power station area.

[0067]

[0068] As shown in the table above, since the base load of this area is a dual-peak load during midday and evening, the time-sharing tiered charging control scheme can achieve the optimal control target by distributing the charging during off-peak periods. The required charging time also varies depending on the initial battery capacity of the electric vehicle.

[0069] The power control scheme for electric vehicle charging piles is shown in Table 2.

[0070] Table 2 Power Control Scheme for Time-Sharing Tiered Charging Piles for Electric Vehicles in a Certain Area

[0071]

[0072] As shown in Table 1, if no method is adopted to control the power of the charging piles for electric vehicles in the area, the area is prone to heavy overload during the periods of 13:00-14:30 in the afternoon and 20:00-23:00 at night. Therefore, it is necessary to implement time-sharing and tiered charging pile power control to avoid heavy overload of the equipment in the area, which leads to the control scheme in Table 2.

[0073] like Figure 3 The image shows a comparison of the load before and after time-lapse tiered charging control for this distribution station. Figure 3 As can be seen, after implementing the time-sharing tiered charging control method for electric vehicles based on historical load proposed in this embodiment, the blue line is significantly lower than the red line for randomly connected charging loads. This demonstrates that the method of this invention can achieve time-sharing charging control of electric vehicles in a region based on historical load, effectively mitigating grid load fluctuations and optimizing power resource allocation.

[0074] Example 2

[0075] This embodiment proposes a time-sharing tiered charging control system for electric vehicles based on historical load, applying the time-sharing tiered charging control method for electric vehicles based on historical load proposed in Embodiment 1. For example... Figure 4 The diagram shown is an architecture diagram of a time-sharing tiered charging control system for electric vehicles based on historical load, according to this embodiment.

[0076] This embodiment proposes a time-sharing tiered charging control system for electric vehicles based on historical load, including:

[0077] Reference load calculation module: used to obtain the historical load, time-of-use electricity price, and electric vehicle charging load demand of the target distribution area; and to calculate the time-of-use reference load based on the historical load;

[0078] Solution Module: It is equipped with a time-sharing tiered charging control model, which is used to solve the time-sharing charging control model with the objectives of minimizing total charging costs and minimizing load fluctuations, so as to obtain the optimal control scheme, and control the electric vehicle charging strategy of the target distribution area through the optimal control scheme.

[0079] It is understood that the system in this embodiment corresponds to the method in Embodiment 1 above, and the options in Embodiment 1 above are also applicable to this embodiment, so they will not be described again here.

[0080] Example 3

[0081] This embodiment proposes a computer device, including a memory and a processor. The memory stores computer-readable instructions, wherein when the computer-readable instructions are executed by the processor, the processor performs the steps of a time-sharing tiered charging control method for electric vehicles based on historical load proposed in Embodiment 1.

[0082] Example 4

[0083] This embodiment proposes a storage medium storing computer-readable instructions, wherein when the computer-readable instructions are executed by a processor, they implement the steps of the time-sharing tiered charging control method for electric vehicles based on historical load proposed in Embodiment 1.

[0084] By way of example, the storage medium includes, but is not limited to, USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks or optical disks, and other media capable of storing program code.

[0085] By way of example, the instructions, programs, code sets, or instruction sets may be implemented using conventional programming languages.

[0086] By way of example, the processor includes, but is not limited to, smartphones, personal computers, servers, network devices, etc., for executing all or part of the steps of the time-sharing tiered charging control method for electric vehicles based on historical load described in Example 1.

[0087] The terminology used in the accompanying drawings is for illustrative purposes only and should not be construed as limiting the scope of this patent.

[0088] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the implementation of the present invention. Those skilled in the art can make other variations or modifications based on the above description. It is neither necessary nor possible to exhaustively describe all embodiments here. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the claims of the present invention.

Claims

1. A time-sharing tiered charging control method for electric vehicles based on historical load, characterized in that, Includes the following steps: Obtain the historical load, time-of-use electricity price, and electric vehicle charging load demand of the target distribution area; calculate the time-of-use reference load based on the historical load. A time-sharing tiered charging control model is constructed based on the time-sharing reference load, time-sharing electricity price, and electric vehicle charging load demand. The time-sharing tiered charging control model is solved with the objectives of minimizing total charging costs and minimizing load fluctuations to obtain the optimal control scheme. The electric vehicle charging strategy of the target distribution area is then controlled through the optimal control scheme. The historical load includes load data for each moment within a certain number of periods; the time-of-use reference load for each moment within the target period is calculated based on the historical load, and its expression is as follows: Among them, P r,j λ is the reference load at time j within the target period time r. t P is the reference load factor for the t-th cycle time; t,j Let be the historical load at time j in the t-th cycle; k is the number of cycles included in the historical load data. The time-sharing tiered charging control model includes total charging cost, load fluctuation degree, and constraints; the steps for solving the time-sharing tiered charging control model include: under the constraints, finding that at least one of the total charging cost or load fluctuation degree reaches a minimum value. The total charging cost is calculated based on time-of-use electricity pricing and electric vehicle charging load demand, and the calculation formula is as follows: Where f1 is the total charging cost; N is the number of electric vehicles; M is the number of times sampled within the period; P EVij e represents the charging power of the i-th electric vehicle at time j; j Let x be the charging price at time j; ij This represents the charging state of the i-th electric vehicle at time j, when x ij =0 indicates that the i-th electric vehicle is not charging at time j, when x ij =1 indicates that the i-th electric vehicle is charging at time j; The load fluctuation level is calculated based on the electric vehicle charging load demand and time-of-use reference load, and its calculation expression is as follows: Where f2 represents the degree of load fluctuation; P r,j The reference load at time j within the target period time r; P j The total load value represents the sum of the reference load and the charging load at time j. This represents the average load.

2. The time-sharing tiered charging control method for electric vehicles based on historical load according to claim 1, characterized in that, The total charging cost and load fluctuation level are optimized using a multi-objective normalization process, and the expression is as follows: Among them, e min The minimum charging electricity price; e max This represents the maximum charging electricity price.

3. The time-sharing tiered charging control method for electric vehicles based on historical load according to claim 1, characterized in that, The constraints include at least one of the following: charging power demand constraints, charging duration constraints, number of charging vehicles constraints, and power distribution network load rate constraints.

4. The time-sharing tiered charging control method for electric vehicles based on historical load according to any one of claims 1 to 3, characterized in that, The optimal control scheme includes a time-sharing tiered charging arrangement scheme for electric vehicles and a power control scheme for electric vehicle charging piles; the time-sharing tiered charging arrangement scheme for electric vehicles is used to control the charging status of electric vehicles; the power control scheme for electric vehicle charging piles is used to control the charging power of electric vehicles.

5. A time-sharing tiered charging control system for electric vehicles based on historical load, employing the time-sharing tiered charging control method for electric vehicles based on historical load as described in any one of claims 1 to 4, characterized in that, The system includes: Reference load calculation module: used to obtain the historical load, time-of-use electricity price, and electric vehicle charging load demand of the target distribution area; and to calculate the time-of-use reference load based on the historical load; Solution Module: It is equipped with a time-sharing tiered charging control model, which is used to solve the time-sharing tiered charging control model with the objectives of minimizing total charging costs and minimizing load fluctuations, so as to obtain the optimal control scheme, and control the electric vehicle charging strategy of the target distribution area through the optimal control scheme.

6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the time-sharing tiered charging control method for electric vehicles based on historical load as described in any one of claims 1 to 4.

7. A storage medium having computer-readable instructions stored thereon, characterized in that, When the computer-readable instructions are executed by the processor, they implement the time-sharing tiered charging control method for electric vehicles based on historical load as described in any one of claims 1 to 4.

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