Multi-stage charging facility planning method considering distributed energy and energy storage

Abstract

The invention provides a multi-stage charging facility planning method considering distributed energy and energy storage, and relates to the technical field of urban charging infrastructure collaborative planning. The method comprises the steps of S1, modeling and operation of an EVCS, including load flow calculation after grid connection of the EVCS, ESS modeling, DG modeling and multi-stage charging facility modeling; s2, carrying out charging facility capacity modeling, and determining the optimal capacity of the charging facility through variable definition and constraint by taking elimination of vehicle charging queuing as a target; s3, power distribution network reinforcement modeling: introducing a reinforcement coefficient to optimize the line capacity of the power distribution network in combination with the energy characteristics of the charging station; s4, constructing an objective function, integrating the energy consumption cost, the investment cost and the reinforcement cost, realizing the minimization of the total cost of the whole planning cycle, and determining the optimal operation mode of the DG and the ESS; s5, scene parameters are set, an optimal scene output result is obtained, and annual costs under different cases are compared; according to the method, the cost is minimized, the optimal operation mode of the DG and the ESS is determined, the long-term operation cost is reduced, and the operation pressure of a power grid is reduced.

Description

Technical Field

[0001] This invention relates to the field of collaborative planning technology for urban charging infrastructure, and in particular to a multi-level charging facility planning method that considers distributed energy and energy storage. Background Technology

[0002] Against the backdrop of global energy structure transformation and the advancement of "dual-carbon" goals, the inherent defects of fossil fuels have become a key bottleneck restricting sustainable social development. On the one hand, the combustion of fossil fuels produces a large amount of harmful pollutants, and the released carbon dioxide is a major anthropogenic factor contributing to global warming. On the other hand, as a non-renewable resource, the limited reserves of fossil fuels cannot support long-term energy demand, making the shift towards cleaner and more diversified energy consumption an inevitable trend. Electric vehicles (EVs), with their zero emissions and high energy efficiency, are widely recognized as a core solution for replacing traditional gasoline vehicles and reducing dependence on fossil fuels. However, public acceptance and adoption of electric vehicles are still limited by the sophistication of charging infrastructure—if charging stations are poorly laid out, power supply is insufficient, or the charging experience is unpleasant, it will directly reduce the convenience of using electric vehicles and hinder their market promotion.

[0003] The current planning and operation of electric vehicle charging stations (EVCSs) suffer from the following shortcomings: 1. Single power supply mode, resulting in significant grid load pressure: Existing EVCSs generally rely solely on the distribution network for power supply, failing to effectively integrate distributed energy and energy storage systems. During peak charging periods, the concentrated charging of a large number of electric vehicles leads to a surge in distribution network load, easily causing problems such as line overload, voltage fluctuations, and even local power outages. This not only affects the safe and stable operation of the power grid but may also reduce user experience due to charging interruptions. Furthermore, the reliance on grid power makes EVCS operating costs significantly susceptible to electricity price fluctuations; high electricity prices during peak hours directly drive up charging service costs. 2. Single charging facility power, resulting in poor supply-demand adaptability: Traditional charging stations often use charging piles with uniform power, failing to consider the differentiated charging needs of electric vehicles. In real-world scenarios, some electric vehicles initially have sufficient charge and only require a small amount of additional power to meet their subsequent travel needs. Forcing the use of high-power charging piles would result in energy waste and equipment idleness. Conversely, some electric vehicles initially have extremely low charge and urgently need rapid charging. If only low-power charging piles can be used, charging time would be significantly prolonged, leading to user queues and reduced charging efficiency. This "one-size-fits-all" approach to charging infrastructure configuration not only increases ineffective investment in charging facilities but also fails to meet diverse user needs, hindering the improvement of charging station service quality. Third, existing research often neglects the coordinated optimization of the distribution network and charging stations in EVCS planning. When EVCS is connected to the distribution network, if the existing line capacity is not specifically reinforced, it will further exacerbate the risks to grid operation; while simple grid reinforcement will lead to a surge in investment costs, making it difficult to achieve a balance between economy and safety. Summary of the Invention

[0004] The main objective of this invention is to provide a multi-level charging facility planning method that considers distributed energy and energy storage, thereby solving the problems of low power supply reliability, poor user experience, and difficulty in cost control in existing EVCS systems.

[0005] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is: a multi-level charging facility planning method considering distributed energy resources and energy storage, comprising the following steps: S1: Modeling and operation of EVCS, including power flow calculation after EVCS grid connection, ESS modeling, DG modeling and multi-level charging facility modeling; S2: Charging facility capacity modeling, with the goal of eliminating vehicle charging queues, determines the optimal capacity of charging facilities through variable definition and constraints; S3: Distribution network reinforcement modeling, combined with the energy characteristics of charging stations, introduces reinforcement coefficients to optimize the distribution network line capacity; S4: Objective function construction, integrating energy consumption cost, investment cost and reinforcement cost, to minimize the total cost throughout the planning cycle, and to determine the optimal operating mode of DG and ESS; S5: Preset scene parameters, obtain the optimal output results for the scene, and compare the annualized cost under different cases.

[0006] In the preferred embodiment, the power flow calculation after EVCS grid connection in step S1 includes: S101: Acquires connection data between the standard bus distribution network and the charging station, treats multi-level charging facilities as loads and DG and PV as injected power, and adjusts power flow according to the ESS charging and discharging period. S102: System stability is ensured through power limit equations and voltage limit equations, the formulas are as follows: Power and voltage limits, the formula is: (1); (2); In the formula: for Timetable The active power flowing through; For the line The maximum active power flowing through it; For distribution network nodes The voltage amplitude; and These are the maximum and minimum voltage amplitudes, respectively; and These are the distribution network node set and the time set, respectively; S103: Establish power flow balance equations for nodes not connected to charging stations and nodes connected to charging stations respectively, incorporating load power, electric vehicle charging demand under different charging power, energy storage charging and discharging power, distributed generator power, and photovoltaic power to maintain power balance. The formulas are as follows: (3); (4); In the formula: for Time Node The load power; , and The first electric vehicles Time Node Select the rated charging power of slow charging piles, medium-speed charging piles, and fast charging piles; and They are respectively Time Node The ESS charging power and discharging power are as follows; for Time Node DG power below; Time Node PV power below; and These are the sets of distribution network nodes that are not connected to charging stations and the sets of distribution network nodes that are connected to charging stations, respectively. This is a set of vehicle indexes.

[0007] In the preferred embodiment, ESS modeling in step S1 includes the following steps: S104: Confirm that each time period is only in a charging or discharging state, define the rated power and storage efficiency, where the rated power variable is the rated power of the node's ESS, and the storage efficiency variable is the ESS efficiency, with the following formulas: (5); (6); (7); (8); (9); In the formula: For nodes The rated power of the ESS For ESS efficiency; S105: Calculate the ESS energy level for each time period. The energy level is updated with battery energy at unit intervals, where the battery energy variable is the ESS battery energy at each time point, and the unit interval variable is the unit interval duration. The formulas are as follows: (10); (11); In the formula, for Time Node ESS battery energy; The interval duration is expressed in units.

[0008] In the preferred embodiment, the DG modeling in S1 includes: S106: Model the distributed generation (DG) as a supplementary power generation system. Its operating power is constrained by the rated power, where the rated power variable is the rated power of the distributed generator. The operating model formula for the DG is: (12); In the formula, This is the rated power of DG.

[0009] In the preferred embodiment, the modeling of the multi-level charging infrastructure includes: The charging power is selected as slow, medium or fast based on the initial energy of the electric vehicle. The initial energy variable is the initial energy, the electric vehicle capacity variable is the capacity of the electric vehicle, and the time-node energy variable is the energy of the electric vehicle at the time node. Electric vehicles are classified according to their initial energy. A first threshold and a second threshold are preset for battery capacity. The two thresholds divide the battery capacity into three equal intervals from large to small. If the initial energy of the electric vehicle is within the third interval, the vehicle is charged through a slow charging facility. If the initial energy is within the second interval where the battery is at full capacity, a medium-speed charging facility is used. If the initial energy is within the first interval, a fast charger is used.

[0010] In the preferred scheme, the first threshold and the second threshold are preset to one-third and two-thirds of the battery capacity, respectively. Electric vehicles are classified according to their initial energy, as expressed by: (13); (14); (15); (16); (17); (18); In the formula: Initial energy; For the capacity of electric vehicles; For the first electric vehicles Time Node The amount of electricity; , and These represent the maximum charging power for fast charging, medium charging, and slow charging, respectively.

[0011] In the preferred embodiment, step S2 involves modeling the charging facility capacity and optimizing capacity constraints through variables, including: S201: Set a binary variable to indicate whether the electric vehicle enters the charging station at a given time point, where 1 indicates entry and 0 indicates otherwise; S202: Calculate the number of electric vehicles in the charging station for each time period by summing the binary variables, where the variable representing the number of electric vehicles is the number of electric vehicles in the charging station at each time point. The formula is: (19); In the formula, for Time Node The number of electric vehicles within the EVCS; S203: Apply a capacity constraint to ensure that the number of electric vehicles does not exceed the charging facility capacity, where the charging facility capacity variable is the charging facility capacity of the charging station, and the charging facility capacity constraint formula is: (20); In the formula, The charging facility capacity for EVCS; S204: Optimize the charging facility capacity as a design variable and set the parking capacity to the maximum number of electric vehicles to achieve instant charging.

[0012] In the preferred embodiment, the distribution network hardening modeling in S3 utilizes hardening coefficients in conjunction with the energy storage system and distributed generator strategy, including: S301: Define the maximum capacity of each line as the base capacity multiplied by the reinforcement factor; S302: Optimize the reinforcement coefficient using an objective function, the formula of which is: (twenty one); In the formula, This is the line reinforcement coefficient; S303: Combine the ESS charging and discharging strategy and DG power injection to adjust network capacity expansion; S304: Obtain line length data and incorporate it into the reinforcement calculation, where the line length variable is the length of the line, in order to optimize the overall network stability.

[0013] In the preferred embodiment, step S4 defines an objective function to calculate various costs and determines the optimal configuration of the charging station through optimization, including: S401: Calculate energy consumption cost based on time-of-use electricity pricing, EVCS energy consumption cost for: (twenty two); In the formula, for Electricity price at any given time; S402: Calculate the annualized investment cost of ESS The formula is: (twenty three); In the formula, The investment cost per unit capacity of ESS batteries; Investment cost per unit rated power of ESS battery; The discount rate; For the number of years of operation; S403: Calculate the annualized investment cost of multi-level charging infrastructure based on the unit capacity investment cost. Annualized investment cost of parking spaces The formula is: (twenty four); (25); In the formula, The unit capacity investment cost of charging facilities; Space required for each parking space; This refers to the unit investment cost of a charging station.

[0014] In the preferred embodiment, the calculation of various costs, and the optimization of the operating modes of ESS and DG by comprehensively considering the costs to determine the optimal configuration, further includes: S404: Calculate the annualized investment cost of power distribution network reinforcement. DG annualized investment cost Annualized investment cost of PV The formulas are as follows: (26); (27); (28); In the formula, For the line Reinforcement investment cost per unit length; For the line Length; DG unit investment cost; The unit fuel cost of DG; The unit investment cost per PV; S405: The final planning cost is the sum of the above costs. The proposed plan uses minimizing these costs as its objective function, as shown in the formula: min (29); In the formula: For the final planning cost.

[0015] This invention provides a multi-level charging facility planning method considering distributed energy resources and energy storage. Through modeling and operating an EVCS (Electric Vehicle Control System), the PV, DG (Distributed Generation), and ESS (Energy Storage System) configured in the EVCS form a multi-energy complementary power supply system, effectively eliminating reliance on the distribution network. Aiming to eliminate vehicle charging queuing, the method models the charging facility capacity, determining the optimal capacity through variable definition and constraints. The charging facility capacity is optimized to the "maximum number of vehicles within the station," ensuring that vehicles entering the EVCS at any given time can immediately start charging, completely solving the "queueing" problem of traditional charging stations. The method also includes distribution network reinforcement modeling, introducing a "line reinforcement coefficient" as an optimization variable, combined with ESS and DG... With the flexible adjustment capability of the multi-level charging strategy, targeted reinforcement is carried out only on lines with significant load increases, minimizing grid reinforcement investment costs and achieving an optimal balance between safety and economy. The objective function is constructed to determine the optimal operating mode of DG and ESS, and the scenario parameters are preset to obtain the optimal output results of the scenario. The annualized costs under different cases are compared. Through the integration and optimization of the whole cycle cost by the objective function, the investment in various facilities is precisely controlled, the short-term financial pressure is reduced, and the economic feasibility of the project is improved. Through multi-energy synergy, the carbon emissions of EVCS throughout its entire life cycle are significantly reduced, providing technical support for the low-carbon transformation of the electric vehicle industry and energy system. Attached Figure Description

[0016] The present invention will be further described below with reference to the accompanying drawings and embodiments: Figure 1 This is a schematic diagram of the connection structure between the EVCS located at node 10 and the IEEE 30 distribution network in this invention; Figure 2 This is a flowchart of the planning method of the present invention; Figure 3 This is the photovoltaic output diagram of the present invention; Figure 4 This is a comparison chart of annualized costs in different cases of this invention. Detailed Implementation

[0017] Example 1 like Figure 1-4 As shown, a multi-level charging facility planning method considering distributed energy resources and energy storage includes the following steps: S1: Modeling and operation of EVCS, including power flow calculation after EVCS grid connection, ESS modeling, DG modeling and multi-level charging facility modeling.

[0018] S2: Charging facility capacity modeling, with the goal of eliminating vehicle charging queues, determines the optimal capacity of charging facilities through variable definition and constraints.

[0019] S3: Distribution network reinforcement modeling, combined with the energy characteristics of charging stations, introduces reinforcement coefficients to optimize the distribution network line capacity; S4: Objective function construction, integrating energy consumption cost, investment cost and reinforcement cost, to minimize the total cost throughout the planning cycle, and to determine the optimal operating mode of DG and ESS.

[0020] S5: Set scenario parameters, obtain the optimal output results for the scenario, and compare the annualized costs under different cases.

[0021] This embodiment first connects to the charging station via an IEEE 30 bus, thus considering the power flow distribution of the charging station. Secondly, the charging station is equipped with an Energy Storage System (ESS), a Diesel Generator (DG), and Photovoltaic (PV), and has multi-level charging facilities. Therefore, the ESS, DG, and PV are modeled, and the rated power of fast, medium, and slow charging piles is determined. Next, considering the impact on the distribution network after the EVCS is connected, the distribution network needs to be reinforced and its line capacity increased. Finally, the objective function is determined, and various costs are optimized.

[0022] This embodiment, through modeling and operation of the EVCS, establishes a multi-energy complementary power supply system composed of PV, DG, and ESS, effectively eliminating reliance on the distribution network. With the goal of eliminating vehicle charging queues, charging facility capacity is modeled, and the optimal capacity is determined through variable definition and constraints. The charging facility capacity is optimized to the "maximum number of vehicles within the station," ensuring that vehicles entering the EVCS at any given time can immediately start charging, completely resolving the "queueing" problem of traditional charging stations. Distribution network reinforcement modeling introduces a "line reinforcement coefficient" as an optimization variable. Combined with the flexible adjustment capabilities of ESS, DG, and multi-level charging strategies, targeted reinforcement is applied only to lines with significant load increases, minimizing grid reinforcement investment costs and achieving an optimal balance between "safety and economy." An objective function is constructed to determine the optimal operating mode of DG and ESS. Through the integrated optimization of the entire lifecycle cost using the objective function, precise control of various facility investments is achieved, reducing short-term financial pressure and improving project economic feasibility. Through multi-energy synergy, carbon emissions throughout the EVCS's lifecycle are significantly reduced, providing technical support for the low-carbon transformation of the electric vehicle industry and energy systems.

[0023] S1.1: Modeling and running of EVCS.

[0024] This embodiment uses the IEEE standard 30 bus distribution network and connects it to a charging station located at node 10. The charging station is equipped with an ESS, DG, and PV. The ESS injects power into the distribution network during the discharge period and consumes power from the grid during the charging period. Equations (1) and (2) represent the power and voltage limits of the line.

[0025] S101: Acquires connection data between the standard bus distribution network and the charging station, treats multi-level charging facilities as loads and DG and PV as injected power, and adjusts power flow according to the ESS charging and discharging period.

[0026] S102: System stability is ensured through power limit equations and voltage limit equations, the formulas are as follows: (1); (2); In the formula: for Timetable The active power flowing through; For the line The maximum active power flowing through it; For distribution network nodes The voltage amplitude; and These are the maximum and minimum voltage amplitudes, respectively; and These are the distribution network node set and the time set, respectively.

[0027] S103: Establish power flow balance equations for nodes not connected to charging stations and nodes connected to charging stations respectively, incorporating load power, electric vehicle charging demand under different charging power, energy storage charging and discharging power, distributed generator power, and photovoltaic power to maintain power balance. The formulas are as follows: (3); (4); In the formula: for Time Node The load power; , and The first electric vehicles Time Node Select the rated charging power of slow charging piles, medium-speed charging piles, and fast charging piles; and They are respectively Time Node The ESS charging power and discharging power are as follows; for Time Node DG power below; Time Node PV power below; and These are the sets of distribution network nodes that are not connected to charging stations and the sets of distribution network nodes that are connected to charging stations, respectively. This is a set of vehicle indexes.

[0028] S1.2 ESS modeling.

[0029] In this embodiment, the ESS is installed on the EVCS to increase operational flexibility and reduce planning costs. The ESS is modeled using equations (5) to (9). In equations (5) and (6), it is confirmed that the battery can only operate in either charging or discharging state in each time period. The rated power of the ESS is defined by equations (7) and (8).

[0030] S104: Confirm that each time period is only in a charging or discharging state, define the rated power and storage efficiency, where the rated power variable is the rated power of the node's ESS, and the storage efficiency variable is the ESS efficiency, with the following formulas: (5); (6); (7); (8); (9); In the formula: For nodes The rated power of the ESS For ESS efficiency.

[0031] S105: Calculate the ESS energy level for each time period. The energy level is updated by the battery energy at unit intervals. The battery energy variable is the ESS battery energy at each time point, and the unit interval variable is the unit interval duration. The energy of the ESS in each time period is calculated according to formula (10). The rated capacity of the ESS is defined by formula (11): (10); (11); In the formula, for Time Node ESS battery energy; The interval duration is expressed in units.

[0032] S1.3: DG modeling.

[0033] Adding distributed generation (DG) to charging stations as a supplementary power generation system can help cope with peak electricity price periods.

[0034] S106: Model the distributed generation (DG) as a supplementary power generation system. Its operating power is constrained by the rated power, where the rated power variable is the rated power of the distributed generator. The operating model formula for the DG is: (12); In the formula, This is the rated power of DG.

[0035] S1.4: Modeling of multi-level charging facilities.

[0036] In the preferred scheme, slow, medium or fast charging power is selected based on the initial energy classification of the electric vehicle, where the initial energy variable is the initial energy, the electric vehicle capacity variable is the capacity of the electric vehicle, and the time node energy variable is the energy of the electric vehicle at the time node.

[0037] Electric vehicles are classified according to their initial energy. A first threshold and a second threshold are preset for battery capacity. The two thresholds divide the battery capacity into three equal intervals from large to small. If the initial energy of the electric vehicle is within the third interval, the vehicle is charged through a slow charging facility. If the initial energy is within the second interval where the battery is at full capacity, a medium-speed charging facility is used. If the initial energy is within the first interval, a fast charger is used.

[0038] In this embodiment, the first threshold and the second threshold are 1 / 3 and 2 / 3 of the battery capacity, respectively.

[0039] Electric vehicles are classified according to their initial energy, as expressed by: (13); (14); (15); (16); (17); (18); In the formula: Initial energy; For the capacity of electric vehicles; For the first electric vehicles Time Node The amount of electricity; , and These represent the maximum charging power for fast charging, medium charging, and slow charging, respectively.

[0040] S2: Charging facility capacity modeling.

[0041] This embodiment models the capacity of the charging facilities within the EVCS through the following steps.

[0042] S201: First, set up a binary variable. , representing the first A number of electric vehicles Time Node Whether to enter EVCS: 1 if enter, 0 otherwise.

[0043] The number of vehicles in the charging station during each time period is calculated using (19), and the capacity constraint of the charging facility is given by formula (20).

[0044] S202: Calculate the number of electric vehicles in the charging station for each time period by summing two variables, where the variable representing the number of electric vehicles in the charging station at each time point is: (19); In the formula, for Time Node The number of electric vehicles in the EVCS.

[0045] S203: Apply capacity constraints to ensure that the number of electric vehicles does not exceed the charging facility capacity, where the charging facility capacity variable is the charging facility capacity of the charging station, and the charging facility capacity constraint formula is: (20); In the formula, The charging facility capacity for EVCS.

[0046] S204: Optimize the charging facility capacity as a design variable and set the parking capacity to the maximum number of electric vehicles to enable instant charging.

[0047] This embodiment uses charging facility capacity as a design variable and optimizes it through planning. Parking capacity equals the maximum number of electric vehicles within the charging station, ensuring that all vehicles entering the station at any given time can begin charging immediately without queuing. While this increases the investment cost of the charging station, it also improves driver comfort and increases public acceptance of electric vehicles.

[0048] S3: Power distribution network reinforcement modeling.

[0049] After configuring EVCS in the distribution network, the network needs to be reinforced and line capacity increased to meet the high power demand of charging stations. This embodiment combines grid reinforcement with ESS, DG, and flexible charging strategies to optimize network reinforcement costs.

[0050] S301: Define the maximum capacity of each line as the base capacity multiplied by the reinforcement factor.

[0051] S302: Optimize the reinforcement coefficient through an objective function. The reinforcement coefficient is an optimization variable in this embodiment, achieved by planning the objective function, the formula of which is: (twenty one); In the formula, This is the line reinforcement coefficient.

[0052] In equation (21), the maximum capacity of each line is defined as the base capacity multiplied by the reinforcement factor.

[0053] S303: Combines ESS charging and discharging strategies with DG power injection to adjust network capacity expansion.

[0054] S304: Obtain line length data and incorporate it into the reinforcement calculation, where the line length variable is the length of the line, in order to optimize the overall network stability.

[0055] S4: Objective function.

[0056] The planning cost in this embodiment is expressed by the following formulas (22) to (28).

[0057] S401: Calculate energy consumption cost based on time-of-use electricity pricing, EVCS energy consumption cost for: (twenty two); In the formula, for Electricity price at any given moment.

[0058] S402: Calculate the annualized investment cost of ESS The formula is: (twenty three); In the formula, The investment cost per unit capacity of ESS batteries; Investment cost per unit rated power of ESS battery; The discount rate; This refers to the number of years the equipment has been in operation.

[0059] S403: Calculate the annualized investment cost of multi-level charging infrastructure based on the unit capacity investment cost. Annualized investment cost of parking spaces The formula is: (twenty four); (25); In the formula, The unit capacity investment cost of charging facilities; Space required for each parking space; This refers to the unit investment cost of a charging station.

[0060] S404: Calculate the annualized investment cost of power distribution network reinforcement. DG annualized investment cost Annualized investment cost of PV The formulas are as follows: (26); (27); (28); In the formula, For the line Reinforcement investment cost per unit length; For the line Length; DG unit investment cost; The unit fuel cost of DG; The unit investment cost is PV.

[0061] S405: The final planning cost is the sum of the above costs. The proposed plan uses minimizing these costs as its objective function, as shown in the formula: min (29); In the formula: For the final planning cost.

[0062] This embodiment designs an EVCS equipped with multiple energy sources, optimizing the design of charging station capacity, charging facilities, battery storage system, and network hardening to minimize costs. The optimal operating modes for DG and ESS are determined through modeling.

[0063] S5: Obtain the optimal output result for the scenario, compare the annualized cost under different cases, and perform result analysis.

[0064] S5.1: Parameter settings.

[0065] This embodiment uses an IEEE 30-node distribution network as a case study, with node 10 equipped with an EVCS. The network's base power and voltage are 10 MVA and 12.66 kV, respectively. The charging station is supported by DG, ESS, multi-level charging infrastructure, and PV. For the photovoltaic system, four typical days representing spring, summer, autumn, and winter are used to characterize the calendar year in the optimal configuration problem, such as... Figure 3 As shown in Table 1, the economic parameters are set as shown in Table 2, and the time-of-use electricity price is set as shown in Table 3.

[0066] Table 1 Economic Parameter Settings

[0067] Table 2 Non-economic parameter settings

[0068] Table 3 Time-of-use Electricity Prices

[0069] S5.2: Analysis Results (1) Optimal configuration result Table 4 shows the current planned configuration results. The output indicates that fast charging has a rated power approximately 46% higher than medium charging, and medium charging has a rated power approximately 124% higher than slow charging. This increases the flexibility of electric vehicle charging while reducing charging time and planning costs. The ideal capacity of the charging station is optimized for 22 charging piles, meaning the charging station requires 22 parking spaces to charge vehicles. The rated power and capacity of the ESS are 223kW and 111.5kWh, respectively. The rated power of the DG is also set to 10kW.

[0070] Table 4 Optimal Configuration

[0071] (2) Percentage of line reinforcement The energy required by the charging station is provided by the power grid, and one of the planning objectives is to strengthen the power grid lines. The reinforcement percentage is used as a design variable and optimized through planning, as shown in Table 5. The results show that the current network requires significant reinforcement on the line connecting node 1 and node 10 (where the charging station is located), while other lines in the network do not require expansion.

[0072] Table 5. Line Reinforcement Status

[0073] (3) Cost-benefit analysis like Figure 4 As shown, the annualized planning cost is RMB 3,296,370.831 per year when all proposed facilities are included. However, in the planning scheme considering only fast charging, the annualized planning cost is RMB 4,872,134.197 per year, indicating that configuring charging facilities with various power levels significantly reduces the planning cost. Furthermore, each infrastructure element has a significant impact on the total cost. In contrast, if ESS, DG, or line reinforcement measures are excluded from the model, the annualized planning cost will increase to RMB 3,307,678.286 per year, RMB 3,307,690.478 per year, and RMB 4,641,724.115 per year, respectively.

[0074] This embodiment designs an EVCS equipped with multiple energy sources, optimizes the design of charging station capacity, charging facilities, ESS, network reinforcement, etc., minimizes costs, and determines the optimal operating mode of diesel generator and battery energy storage system through modeling.

[0075] The above embodiments are merely preferred technical solutions of the present invention and should not be considered as limitations on the present invention. The scope of protection of the present invention should be limited to the technical solutions described in the claims, including equivalent substitutions of the technical features described in the claims. That is, equivalent substitutions and improvements within this scope are also within the scope of protection of the present invention.

Claims

1. A multi-level charging facility planning method considering distributed energy resources and energy storage, characterized in that, Includes the following steps: S1: Modeling and operation of EVCS, including power flow calculation after EVCS grid connection, ESS modeling, DG modeling and multi-level charging facility modeling; S2: Charging facility capacity modeling, with the goal of eliminating vehicle charging queues, determines the optimal capacity of charging facilities through variable definition and constraints; S3: Distribution network reinforcement modeling, combined with the energy characteristics of charging stations, introduces reinforcement coefficients to optimize the distribution network line capacity; S4: Objective function construction, integrating energy consumption cost, investment cost and reinforcement cost, to minimize the total cost throughout the planning cycle, and to determine the optimal operating mode of DG and ESS; S5: Preset scene parameters, obtain the optimal output results for the scene, and compare the annualized cost under different cases.

2. The multi-level charging facility planning method considering distributed energy resources and energy storage according to claim 1, characterized in that, In S1, the power flow calculation after EVCS grid connection includes: S101: Acquires connection data between the standard bus distribution network and the charging station, treats multi-level charging facilities as loads and DG and PV as injected power, and adjusts power flow according to the ESS charging and discharging period. S102: System stability is ensured through power limit equations and voltage limit equations, the formulas are as follows: Power and voltage limits, the formula is: (1)； (2)； In the formula: for Timetable The active power flowing through; For the line The maximum active power flowing through it; For distribution network nodes The voltage amplitude; and These are the maximum and minimum voltage amplitudes, respectively; and These are the distribution network node set and the time set, respectively; S103: Establish power flow balance equations for nodes not connected to charging stations and nodes connected to charging stations respectively, incorporating load power, electric vehicle charging demand under different charging power, energy storage charging and discharging power, distributed generator power, and photovoltaic power to maintain power balance. The formulas are as follows: (3)； (4)； In the formula: for Time Node The load power; , and The first electric vehicles Time Node Select the rated charging power of slow charging piles, medium-speed charging piles, and fast charging piles; and They are respectively Time Node The ESS charging power and discharging power are as follows; for Time Node DG power below; Time Node PV power below; and These are the sets of distribution network nodes that are not connected to charging stations and the sets of distribution network nodes that are connected to charging stations, respectively. This is a set of vehicle indexes.

3. The multi-level charging facility planning method considering distributed energy resources and energy storage according to claim 1, characterized in that, In S1, ESS modeling includes the following steps: S104: Confirm that each time period is only in a charging or discharging state, define the rated power and storage efficiency, where the rated power variable is the rated power of the node's ESS, and the storage efficiency variable is the ESS efficiency, with the following formulas: (5)； (6)； (7)； (8)； (9)； In the formula: For nodes The rated power of the ESS For ESS efficiency; S105: Calculate the ESS energy level for each time period. The energy level is updated with battery energy at unit intervals, where the battery energy variable is the ESS battery energy at each time point, and the unit interval variable is the unit interval duration. The formulas are as follows: (10)； (11)； In the formula, for Time Node ESS battery energy; The interval duration is expressed in units.

4. The multi-level charging facility planning method considering distributed energy resources and energy storage according to claim 1, characterized in that, In S1, DG modeling includes: S106: Model the distributed generation (DG) as a supplementary power generation system. Its operating power is constrained by the rated power, where the rated power variable is the rated power of the distributed generator. The operating model formula for the DG is: (12)； In the formula, This is the rated power of DG.

5. The multi-level charging facility planning method considering distributed energy resources and energy storage according to claim 1, characterized in that, The modeling of the multi-level charging infrastructure includes: The charging power is selected as slow, medium or fast based on the initial energy of the electric vehicle. The initial energy variable is the initial energy, the electric vehicle capacity variable is the capacity of the electric vehicle, and the time-node energy variable is the energy of the electric vehicle at the time node. Electric vehicles are classified according to their initial energy. A first threshold and a second threshold are preset for battery capacity. The two thresholds divide the battery capacity into three equal intervals from large to small. If the initial energy of the electric vehicle is within the third interval, the vehicle is charged through a slow charging facility. If the initial energy is within the second interval where the battery is at full capacity, a medium-speed charging facility is used. If the initial energy is within the first interval, a fast charger is used.

6. The multi-level charging facility planning method considering distributed energy resources and energy storage according to claim 5, characterized in that, The first and second thresholds are preset to one-third and two-thirds of the battery capacity, respectively. Electric vehicles are classified according to their initial energy, as expressed by: (13)； (14)； (15)； (16)； (17)； (18)； In the formula: Initial energy; For the capacity of electric vehicles; For the first electric vehicles Time Node The amount of electricity; , and These represent the maximum charging power for fast charging, medium charging, and slow charging, respectively.

7. The multi-level charging facility planning method considering distributed energy resources and energy storage according to claim 1, characterized in that, The S2 section models the capacity of charging facilities and optimizes capacity constraints through variables, including: S201: Set a binary variable to indicate whether the electric vehicle enters the charging station at a given time point, where 1 indicates entry and 0 indicates otherwise; S202: Calculate the number of electric vehicles in the charging station for each time period by summing the binary variables, where the variable representing the number of electric vehicles is the number of electric vehicles in the charging station at each time point. The formula is: (19)； In the formula, for Time Node The number of electric vehicles within the EVCS; S203: Apply a capacity constraint to ensure that the number of electric vehicles does not exceed the charging facility capacity, where the charging facility capacity variable is the charging facility capacity of the charging station, and the charging facility capacity constraint formula is: (20)； In the formula, The charging facility capacity for EVCS; S204: Optimize the charging facility capacity as a design variable and set the parking capacity to the maximum number of electric vehicles to achieve instant charging.

8. The multi-level charging facility planning method considering distributed energy resources and energy storage according to claim 1, characterized in that, The distribution network hardening modeling in S3 utilizes hardening coefficients combined with the energy storage system and distributed generator strategy, including: S301: Define the maximum capacity of each line as the base capacity multiplied by the reinforcement factor; S302: Optimize the reinforcement coefficient using an objective function, the formula of which is: (21)； In the formula, This is the line reinforcement coefficient; S303: Combine the ESS charging and discharging strategy and DG power injection to adjust network capacity expansion; S304: Obtain line length data and incorporate it into the reinforcement calculation, where the line length variable is the length of the line, in order to optimize the overall network stability.

9. The multi-level charging facility planning method considering distributed energy resources and energy storage according to claim 1, characterized in that, Step S4 defines an objective function to calculate various costs and determines the optimal configuration of the charging station through optimization, including: S401: Calculate energy consumption cost based on time-of-use electricity pricing, EVCS energy consumption cost for: (22)； In the formula, for Electricity price at any given time; S402: Calculate the annualized investment cost of ESS The formula is: (23)； In the formula, The investment cost per unit capacity of ESS batteries; Investment cost per unit rated power of ESS battery; The discount rate; For the number of years of operation; S403: Calculate the annualized investment cost of multi-level charging infrastructure based on the unit capacity investment cost. Annualized investment cost of parking spaces The formula is: (24)； (25)； In the formula, The unit capacity investment cost of charging facilities; Space required for each parking space; This refers to the unit investment cost of a charging station.

10. The multi-level charging facility planning method considering distributed energy resources and energy storage according to claim 9, characterized in that, The calculation of various costs, and the optimization of the ESS and DG operating modes by comprehensively considering these costs to determine the optimal configuration, also includes: S404: Calculate the annualized investment cost of power distribution network reinforcement. DG annualized investment cost Annualized investment cost of PV The formulas are as follows: (26)； (27)； (28)； In the formula, For the line Reinforcement investment cost per unit length; For the line Length; DG unit investment cost; The unit fuel cost of DG; The unit investment cost per PV; S405: The final planning cost is the sum of the above costs. The proposed plan uses minimizing these costs as its objective function, as shown in the formula: min (29); In the formula: For the final planning cost.

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