A charging station active-voltage step droop curve hierarchical optimization method and system

Abstract

The application discloses a charging station active-voltage step droop curve layered optimization method and system. The method comprises the following steps: constructing a single electric vehicle SoC model and a charging station SoC model after aggregation of electric vehicles; establishing a charging station SoC interval conditional risk value optimization model, obtaining the SoC interval of the charging station and the corresponding power adjustable range in each scheduling period through rolling optimization, and reporting to a power distribution network dispatching center; at the power distribution network level, based on the determined power adjustable range and considering the source-load uncertainty of the power distribution network, a charging station active-voltage step droop curve optimization model is established, the optimal step droop curve of each scheduling period is solved, and the step droop curve is issued to the charging station; the charging station decomposes the segmented power of the step droop curve, obtains the charging power of each electric vehicle corresponding to different segments, and dynamically adjusts the charging active power of the electric vehicle according to the real-time voltage measurement value and the power decomposition result of the current scheduling period.

Description

Technical Field

[0001] This invention belongs to the field of power distribution networks, specifically relating to a hierarchical optimization method and system for the active power-voltage step droop curve of a charging station. Background Technology

[0002] The penetration rate of renewable energy in distribution networks is gradually increasing. However, renewable energy output exhibits significant intermittency and volatility. Simultaneously, distribution network load exhibits high uncertainty due to factors such as user electricity consumption behavior and industrial restructuring. Therefore, the dual uncertainty of renewable energy and load exacerbates the complexity of power flow distribution in distribution networks, leading to voltage deviation and fluctuation problems, and threatening the safe operation of the distribution network.

[0003] Meanwhile, electric vehicles have received widespread attention due to their energy-saving and emission-reduction advantages. As sales of new energy vehicles continue to climb, the scale of electric vehicle charging stations is also expanding. It is noteworthy that most electric vehicle owners spend more time parking at charging stations than they actually need to charge at maximum power to reach the required amount of electricity. This phenomenon indicates that electric vehicles possess significant potential as a controllable load resource during parking periods. For example, when renewable energy output is excessive and grid voltage is high, electric vehicles can adaptively increase charging power to consume excess energy and lower grid voltage; conversely, when renewable energy output is insufficient and grid voltage is low, electric vehicles can adaptively reduce charging power to decrease electrical load and raise grid voltage. To achieve this, it is necessary to accurately determine the adjustable power range of charging stations, enabling them to participate in voltage regulation of the distribution network. No relevant solutions have yet been proposed in the current technology. Summary of the Invention

[0004] Objective: This invention proposes a hierarchical optimization method and system for the active power-voltage step droop curve of charging stations, considering the aggregation of electric vehicle system SoC (System-on-Chips) intervals. This provides a hierarchical coordination framework for power distribution network operation to coordinate the optimization of electric vehicle charging station system SoC intervals and the optimization of step droop curves. Simultaneously, it fully establishes an active power-voltage step droop curve model for the charging station, optimizes droop parameters, and performs power decomposition on the step droop curve to obtain the charging power of each electric vehicle in different segments.

[0005] Technical solution: A hierarchical optimization method and system for the active power-voltage step droop curve of a charging station, comprising the following steps:

[0006] A hierarchical optimization method for the active power-voltage step droop curve of a charging station, characterized by the following steps:

[0007] (1) Based on the charging behavior data of electric vehicle users, including the time of entering the charging station, the time of leaving the charging station, the initial state of charge (SoC) when entering the charging station, and the expected SoC when leaving the charging station, a single electric vehicle SoC model is constructed, and then a charging station SoC model after the aggregation of electric vehicles is constructed.

[0008] (2) Considering the uncertainty of the number of electric vehicles arriving at the charging station and the charging behavior, a conditional risk value optimization model for the SoC interval of the charging station is established. The optimization objective is to minimize the charging cost and conditional risk value. The SoC interval of the charging station is obtained by rolling optimization in each scheduling period. Based on the SoC interval, the power adjustable range of the charging station in the first period of the rolling optimization cycle is calculated and the power range is reported to the distribution network dispatch center.

[0009] (3) At the distribution network system level, considering the uncertainty of renewable energy output and load in the distribution network, based on the power adjustable range reported by the charging station in each scheduling period, an optimization model of the active power-voltage step droop curve of the charging station is established, and the optimal step droop curve for each scheduling period is obtained and sent to the charging station.

[0010] (4) Based on the step droop curve of the current scheduling period, the charging station decomposes the power of each segment of the droop curve to obtain the charging power of each electric vehicle corresponding to different segments of power, and dynamically adjusts the charging active power of electric vehicles according to the real-time voltage measurement value and the power decomposition result.

[0011] Furthermore, the single electric vehicle SoC model in step (1) includes:

[0012] (a) The charging power of electric vehicles during parking periods should meet the following power range:

[0013]

[0014] Where t represents the time period index, m is the vehicle index, i is the power distribution network node index, and s represents the electric vehicle scenario newly arriving at the charging station. Let denot s be the charging power of electric vehicle m parked at charging station i at node i in scenario s during time period t. The maximum charging power allowed for electric vehicles. This refers to the time interval for car parking.

[0015] (b) The charging power of electric vehicles during non-parking periods is 0:

[0016]

[0017] (c) Each electric vehicle will only be dispatched during the designated parking period, which is as follows:

[0018]

[0019] in, and These are the arrival and departure times of the vehicles, respectively.

[0020] (d) The SoC constraints for electric vehicle parking periods are as follows:

[0021]

[0022] in, For electric vehicle SoC, For the battery capacity of electric vehicles, SoC max and SoC min These are the maximum and minimum SoCs allowed for electric vehicles, respectively. The electric vehicle charging efficiency is represented by Δt, which is the unit charging scheduling time. Constraint (4) indicates that the electric vehicle operates according to the charging efficiency during the parking period. The process of SoC change during charging; constraint (5) represents the range constraint of the upper bound of SoC of electric vehicle during parking period;

[0023] (e) The SoC constraints for electric vehicle entry and exit times are as follows:

[0024]

[0025] in, For the initial SoC of electric vehicles, Let be the expected SoC when the electric vehicle leaves; constraint (6) means taking the initial SoC of the electric vehicle as the entry time t. in The SoC at that time; constraint (7) requires that the SoC be greater than the SoC the owner originally hoped to achieve when the electric vehicle leaves.

[0026] Furthermore, in step (1), the SoC model of the charging station after electric vehicle aggregation includes:

[0027] (f) The power constraints of the charging station are as follows:

[0028]

[0029] in, The power consumed by the charging station M is the maximum charging power of the entire charging station. t,i,s Let s be the set of electric vehicles m parked at node i in time period t under scenario s; constraint (8) means that the power consumed by the charging station is the sum of the charging power of all electric vehicles; constraint (9) means that the overall power consumed by the charging station should meet the power limit.

[0030] (g) The cost constraints for charging stations are as follows:

[0031]

[0032] in, For power The corresponding charging cost, Pr t The charging electricity price for time period t;

[0033] (h) The SoC model of the charging station after electric vehicle aggregation is as follows:

[0034]

[0035] in, For the charging station SoC; constraint (11) calculate the charging station SoC after the aggregation of electric vehicles based on the SoC of each vehicle in the charging station and the rated capacity of the battery.

[0036] Furthermore, in step (2), the SoC interval conditional value-at-risk optimization model for the charging station is as follows:

[0037]

[0038] in, The conditional value of risk is the upper bound of the SoC range. Let T be the conditional value of risk for the lower bound of the SoC interval, T be the set of time period indices, S be the set of electric vehicle scenarios s that have just arrived at the charging station, and N be the conditional value of risk for the lower bound of the SoC interval. s Let β1 and β2 be the weight coefficients for different objectives, representing the total number of scenarios. This is the upper bound of the SoC range. This is the lower bound of the SoC range. and θ These represent the maximum and minimum values ​​of the SoC range width, respectively. and For auxiliary variables; Formula (12) indicates that the optimization objective is to minimize charging cost and conditional risk value; Constraint (13) indicates the SoC range width limit; Constraint (14) indicates the range limit of SoC range values; Constraints (15)-(17) are the upper bound conditional risk value constraints of SoC; Constraints (18)-(20) are the lower bound conditional risk value constraints of SoC.

[0039] Furthermore, in step (2), the SoC range of the charging station is obtained sequentially through rolling optimization in each scheduling period, and the power adjustable range of the charging station corresponding to the first period of the rolling optimization cycle is calculated based on the SoC range. The calculation method is as follows:

[0040]

[0041] in, This is the minimum power that the charging station can upload to the distribution network, and it will serve as the lower limit of the step droop curve. This represents the maximum power that the charging station uploads to the power distribution network and will serve as the upper limit of the step droop curve. The SoC is determined one hour before the charging station; Formula (21) calculates the minimum power of the charging station; Formula (22) calculates the maximum power of the charging station.

[0042] Furthermore, in step (3), based on the adjustable power range reported by the charging station during each scheduling period, an optimization model for the active power-voltage step droop curve of the charging station is established, including:

[0043] (a) The expression for the sag curve of the electric vehicle charging station steps is shown below:

[0044]

[0045] in, The active power (V) of charging station i at node i during time period t under scenario c, where there is uncertainty in the output and load of renewable energy in the distribution network. t,i,c Here, n is the node voltage value, and n is an odd number used to represent the number of segments in the stepped sag curve. P represents the voltage value at the inflection point of the sag curve of the charging station steps. 1,2,…,n,t,i The power values ​​for each segment of the sag curve of the charging station steps;

[0046] (b) The power constraints for each segment are shown below:

[0047]

[0048] Constraint (24) limits the range of power values ​​for each segment;

[0049] (c) The inflection point voltage constraint is shown below:

[0050]

[0051] in, V and These represent the minimum and maximum allowable voltages, respectively, and ζ represents the length of each segment of the stepped sag curve.

[0052] The minimum threshold value must be greater than; constraint (25) indicates the order of magnitude and constraint range of each inflection point voltage; constraint (26) indicates that there must be a certain distance between two adjacent inflection point voltage values.

[0053] Furthermore, in step (3), the optimal step droop curve for each scheduling period is obtained and sent to the charging station, including:

[0054] The expression for the step droop curve is reconstructed based on the Big M method. First, n binary decision variables are defined, with the following constraints:

[0055]

[0056] in, is the binary decision variable for the stepped droop curve; constraint (27) ensures that only a certain segment of the droop curve is activated for each charging station in each scenario at each time period by restricting the value of the binary variable; constraint (28) represents the power of the charging station;

[0057] Since constraint (28) contains bilinear terms, the power of the charging station is represented by introducing auxiliary variables and multiple inequality constraints as follows:

[0058]

[0059] in, M is an auxiliary variable; M is a specified positive number that is greater than the maximum charging power of the charging station; constraint (29) introduces an auxiliary variable to represent the power of the charging station; constraints (30)-(31) represent the values ​​of each auxiliary variable under different binary variable values;

[0060] The voltages corresponding to each segment of the stepped sag curve are represented by multiple inequality constraints as shown below:

[0061]

[0062] Where v is a specified positive number, less than one-thousandth of the maximum per-unit value of voltage, used to represent the step at the segment inflection point; constraint (32) indicates that the node voltage is located between the two inflection point voltages of the corresponding segment of the step droop curve under different binary variable values;

[0063] The linear power flow constraint model for the distribution network is established as follows:

[0064]

[0065] Among them, P t,hi,c and Q t,hi,c P represents the active power and reactive power flowing through branch hi, respectively. t,ij,c and Q t,ij,c Vij represents the active power and reactive power flowing through branch ij, respectively, and V0 is the reference voltage. and These represent the active power generation of the photovoltaic system and the reactive power of the photovoltaic inverter, respectively. and These represent the active power and reactive power of the load, respectively, r ij Let x be the resistance of branch ij. ijLet ij be the reactance of branch; constraint (33) represents the active power balance of the node; constraint (34) represents the reactive power balance of the node; constraint (35) represents the voltage drop of the node;

[0066] The voltage constraints for distribution network nodes are established as follows:

[0067]

[0068] Constraint (36) represents a limitation on the node voltage range;

[0069] The power constraints for distribution network branches are established as follows:

[0070]

[0071] in, Indicates the maximum apparent power allowed for the branch; constraint (37) indicates the power range limit for the branch;

[0072] The constraints for the photovoltaic inverter are as follows:

[0073]

[0074] in, This refers to the minimum reactive power allowed by the photovoltaic inverter. This refers to the maximum reactive power allowed by the photovoltaic inverter. The apparent power capacity of the photovoltaic inverter is given by constraint (38); constraint (39) represents the reactive power limit of the photovoltaic inverter; constraint (39) represents that the sum of the squares of the photovoltaic active power and the photovoltaic inverter reactive power cannot exceed the square of its apparent power capacity; constraints (37) and (39) are linearized using the polygon approximation method.

[0075] The optimization model for the stepped sag curve is established as follows:

[0076]

[0077] Where, N c Let I be the number of source-load uncertainty scenarios, L be the set of distribution network nodes i, L be the set of branches ij, and C be the set of source-load uncertainty scenarios c. For network loss, For node voltage offset, N i ω1 and ω2 are the weighting coefficients for different objectives, respectively; Formula (40) indicates that the optimization objective is to minimize power loss and node voltage offset; Formula (41) is used to calculate power loss; Formula (42) is used to calculate node voltage offset;

[0078] The model is solved using a stochastic optimization method to obtain the power values ​​and inflection point voltage values ​​of each segment of the stepped droop curve, which are then sent to the charging station.

[0079] Furthermore, in step (4), the charging station decomposes the power of each segment of the stepped droop curve, as shown in the following model:

[0080]

[0081] in A binary indicator variable for regulating the charging power of electric vehicles. To optimize the power of each segment of the obtained stepped droop curve, The charging power of each charging station is evenly distributed to each electric vehicle according to the minimum segmented power of the stepped droop curve. The charging power of each electric vehicle is the change in charging power when the charging station adjusts its power from the minimum segment power upwards according to the stepped droop curve; the optimization objective (43) represents the number of electric vehicles whose charging power changes are minimized; constraint (44) represents the charging power distribution of electric vehicles under the power of the first to the nth segment when the charging station adjusts its power according to the stepped droop curve, where (44a) represents the power decomposition of the minimum segment power of the droop curve, (44b) represents the power decomposition of the power of the second segment power of the droop curve, and so on up to the power decomposition of the nth segment power; constraint (45) represents the variable power constraint of electric vehicles during the stepped droop curve adjustment process, and the variable power range is the maximum charging power of the electric vehicle and difference.

[0082] A hierarchical optimization system for the active power-voltage step droop curve of a charging station includes:

[0083] The charging station-side processing module is configured to construct a single electric vehicle SoC model based on the charging behavior data of electric vehicle users, including the time of entering the charging station, the time of leaving the charging station, the initial state of charge (SoC) when entering the charging station, and the expected SoC when leaving the charging station, and then construct a charging station SoC model after aggregating electric vehicles.

[0084] Considering the uncertainty of the number of electric vehicles arriving at the charging station and their charging behavior, a conditional risk value optimization model for the SoC interval of the charging station is established. The optimization objective is to minimize the charging cost and conditional risk value. The SoC interval of the charging station is obtained through rolling optimization in each scheduling period. Based on the SoC interval, the power adjustable range of the charging station in the first period of the rolling optimization cycle is calculated, and the power range is reported to the distribution network dispatch center.

[0085] Based on the stepped droop curve of the current scheduling period obtained from the distribution network processing module, the power of each segment of the droop curve is decomposed to obtain the charging power of each electric vehicle corresponding to different segment power, and the charging active power of electric vehicles is dynamically adjusted according to the real-time voltage measurement value and the power decomposition result.

[0086] The distribution network processing module is configured to consider the uncertainty of renewable energy output and load in the distribution network system at the distribution network system level. Based on the power adjustable range reported by the charging station in each scheduling period, it establishes an optimization model of the active power-voltage step droop curve of the charging station, solves the optimal step droop curve for each scheduling period, and sends it to the charging station.

[0087] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the hierarchical optimization method for the active power-voltage step droop curve of a charging station as described above.

[0088] Compared with the prior art, the present invention has the following beneficial effects:

[0089] (1) Unlike battery energy storage systems, which are fixed-capacity devices, electric vehicle charging stations have varying capacities depending on the number of electric vehicles parked at different times. Furthermore, charging stations only have charging functionality, meaning they can only serve as loads on the distribution network. Battery energy storage, on the other hand, can both charge and discharge. Therefore, the SoC range optimization for electric vehicle charging stations differs from that for battery energy storage. This invention optimizes the SoC range of charging stations to meet the charging needs of all electric vehicle owners, obtaining the adjustable power range of each charging station and enabling it to participate in voltage regulation of the distribution network. Considering that electric vehicles are not adapted to frequent power changes, a stepped active-voltage droop curve for charging stations is established, allowing for adjustment within a limited power range. This can improve the voltage quality of the distribution network to a certain extent, reduce power loss, and provide a new technical path for achieving safe and economical operation of the distribution network. By coordinating the SoC range conditional risk value optimization of charging stations with active-voltage stepped droop control, the SoC range and stepped droop curve parameters of the charging stations are optimized, and local droop control is implemented to minimize power loss and voltage deviation in the distribution network.

[0090] (2) Traditional droop modeling output power changes frequently with voltage changes, which will affect electric vehicles and is undesirable for electric vehicle users. This invention proposes a stepped droop curve model suitable for electric vehicle charging stations, and a corresponding droop curve power decomposition method, which can efficiently adjust the charging power of electric vehicles while avoiding the impact of frequent power adjustments on electric vehicles. Attached Figure Description

[0091] Figure 1 This is a diagram illustrating the overall framework of the method of this invention;

[0092] Figure 2 This refers to the 33-node system used in this embodiment of the invention;

[0093] Figure 3The SoC range for each charging station in this embodiment of the invention is the optimized range for the entire day.

[0094] Figure 4 The stepped droop curves optimized for each charging station during the 10:00-11:00 time period in this embodiment of the invention are shown.

[0095] Figure 5 In this embodiment of the invention, the charging power allocated to each electric vehicle by each charging station is based on the second segment of the stepped downward curve during the 10:00-11:00 time period. Detailed Implementation

[0096] The technical solution of the present invention will be further described below with reference to the accompanying drawings.

[0097] This embodiment discloses a hierarchical optimization method and system for the active power-voltage step droop curve of a charging station, the specific framework of which is as follows: Figure 1 As shown, and using Figure 2 The 33-node system shown implements the method proposed in this invention. The system is equipped with 8 photovoltaic units, 6 wind turbine units, and 4 electric vehicle charging stations. The capacity parameters of the photovoltaic and wind turbine units are shown in Table 1.

[0098] Table 1. Photovoltaic and wind turbine capacity parameters

[0099]

[0100] Specifically, the following steps are included:

[0101] Step 1: Based on electric vehicle user charging behavior data, including the time of entering the charging station, the time of leaving the charging station, the initial state of charge (SoC) upon entering the charging station, and the expected SoC upon leaving the charging station, construct a single electric vehicle SoC model, and then construct a charging station SoC model aggregated from electric vehicles. Specifically, this includes:

[0102] (a) The charging power constraints for electric vehicles during parking periods are as follows:

[0103]

[0104] Where t represents the time period index, m is the vehicle index, i is the power distribution network node index, and s represents the electric vehicle scenario newly arriving at the charging station. Let denot s be the charging power of electric vehicle m parked at charging station i at node i in scenario s during time period t. The time interval for car parking. Constraint (1) indicates that the charging power should meet the power range during the parking period.

[0105] (b) The charging power constraints for electric vehicles during non-parking periods are as follows:

[0106]

[0107] Constraint (2) indicates that the charging power of the electric vehicle is 0 during non-parking periods.

[0108] (c) Parking time intervals are indicated as follows:

[0109]

[0110] in, and These represent the arrival and departure times of the vehicles, respectively. Formula (3) represents the parking time interval for each electric vehicle. Scheduling is only performed for each vehicle during its parking time.

[0111] (d) The SoC constraints for electric vehicle parking periods are as follows:

[0112]

[0113] in, For electric vehicle SoC, For the battery capacity of electric vehicles, SoC max and SoC min These are the maximum and minimum SoCs allowed for electric vehicles, respectively. Let Δt represent the charging efficiency of the electric vehicle, where Δt is the unit charging scheduling time. Constraint (4) indicates that the electric vehicle operates according to the charging efficiency during the parking period. The process of SoC change during charging. Constraint (5) represents the range constraint of the upper bound of SoC of electric vehicle during parking period.

[0114] (e) The SoC constraints for electric vehicle entry and exit times are as follows:

[0115]

[0116] in, For the initial SoC of electric vehicles, Let be the expected SoC when the electric vehicle leaves. Constraint (6) indicates that the initial SoC of the electric vehicle is taken as the entry time t. in The SoC at that time. Constraint (7) requires that the SoC be greater than the SoC originally expected by the owner when the electric vehicle leaves.

[0117] (f) The power constraints of the charging station are as follows:

[0118]

[0119] in, The power consumed by the charging station M is the maximum charging power of the entire charging station.t,i,s Let be the set of electric vehicles m parked at node i in scenario s during time period t. Constraint (8) indicates that the power consumed by the charging station is the sum of the charging power of all electric vehicles. Constraint (9) indicates that the overall power consumed by the charging station should meet the power limit.

[0120] (g) The cost constraints for charging stations are as follows:

[0121]

[0122] in, For power The corresponding charging cost, Pr t Let be the charging electricity price for time period t. Constraint (10) calculates the charging cost of the charging station.

[0123] (h) The SoC model of the charging station after electric vehicle aggregation is as follows:

[0124]

[0125] in, For the charging station SoC. Constraint (11) Calculate the charging station SoC after aggregating electric vehicles based on the SoC of each vehicle in the charging station and the rated capacity of the battery. Here, the numerator is obtained by multiplying the SoC of a single vehicle by the corresponding battery capacity to obtain the electrical energy of each vehicle, and then the electrical energy of these vehicles is accumulated; the denominator is obtained by accumulating the battery capacity of each vehicle to obtain the total capacity of the charging station.

[0126] Step 2: Considering the uncertainty of the number of newly arriving electric vehicles and their charging behavior, establish a conditional value-at-risk (VAT) optimization model for the charging station's System of Cost (SoC) interval. Sequentially, obtain the SoC interval of the charging station through rolling optimization in each scheduling period. Based on this SoC interval, calculate the adjustable power range of the charging station corresponding to the first period of the rolling optimization cycle. Report this power range sequentially to the distribution network dispatch center, including:

[0127] (a) Constructing a SoC interval conditional value-at-risk optimization model for each charging station based on the number of electric vehicles in each scenario:

[0128]

[0129]

[0130] in, The conditional value of risk is the upper bound of the SoC range. Let T be the conditional value of risk for the lower bound of the SoC interval, T be the set of time period indices t, S be the set of electric vehicle scenarios s that have just arrived at the charging station, and N be the conditional value of risk for the lower bound of the SoC interval. s Let β1 and β2 be the weight coefficients for different objectives, representing the total number of scenarios. This is the upper bound of the SoC range. This is the lower bound of the SoC range. and θ These represent the maximum and minimum values ​​of the SoC range width, respectively. and For auxiliary variables. Formula (12) indicates that the optimization objective is to minimize charging cost and conditional risk value. Constraint (13) indicates the SoC range width limit. Constraint (14) indicates the range limit of the SoC range values. Constraints (16)-(17) are the upper bound conditional risk value constraints of SoC. Constraints (19)-(20) are the lower bound conditional risk value constraints of SoC.

[0131] (b) Obtain the lower bound of the SoC range of the charging station through rolling optimization in each scheduling period. and the Upper Realm Figure 3 This illustration shows the optimized all-day SoC range for four charging stations in an embodiment of the present invention. Based on this SoC range, the adjustable power range of the charging station corresponding to the first scheduling period of the rolling optimization cycle is calculated, and this power range is uploaded to the distribution network dispatch center. The calculation method is as follows:

[0132]

[0133] in, This is the minimum power that the charging station can upload to the distribution network, and it will serve as the lower limit of the step droop curve. This represents the maximum power that the charging station uploads to the power distribution network and will serve as the upper limit of the step droop curve. The SoC is determined one hour prior to the charging station. Formula (21) calculates the minimum power of the charging station. Formula (22) calculates the maximum power of the charging station.

[0134] Step 3: At the distribution network system level, considering the uncertainty of renewable energy output and load in the distribution network, based on the adjustable power range reported by the charging station in each scheduling period, establish an optimization model for the active power-voltage step droop curve of the charging station, solve for the optimal step droop curve for each scheduling period, and send it to the charging station, including:

[0135] (a) The expression for the sag curve of the electric vehicle charging station steps is shown below:

[0136]

[0137] in, V represents the active power of charging station i at time period t under scenario c, where renewable energy output and load in the distribution network are uncertain. t,i,c Here, n is the node voltage value, and n is an odd number used to represent the number of segments in the stepped sag curve. P represents the voltage value at the inflection point of the sag curve of the charging station steps. 1,2,…,n,t,i Here are the power values ​​for each segment of the sag curve of the charging station's steps. Formula (23) represents the mathematical expression for the sag curve of the steps.

[0138] (b) The power constraints for each segment are shown below:

[0139]

[0140] Constraint (24) limits the range of power values ​​for each segment.

[0141] (c) The inflection point voltage constraint is shown below:

[0142]

[0143] Among them, V and Let represent the minimum and maximum allowable voltages, respectively, and let ζ represent the minimum threshold that the length of each segment of the stepped droop curve must be greater than. Constraint (25) represents the order of magnitude of the voltages at each inflection point and the constraint range. Constraint (26) represents that there must be a certain distance between two adjacent inflection point voltage values.

[0144] (d) The expression of the step droop curve is reconstructed based on the Big M method. First, n binary decision variables are defined, with the following constraints:

[0145]

[0146] in, Let be the binary decision variable for the stepped droop curve. Constraint (27) ensures that only a certain segment of the droop curve is activated for each charging station in each scenario and time period by restricting the values ​​of the binary variable. Constraint (28) represents the charging station power.

[0147] (e) Since constraint (28) contains a bilinear term, the power of the charging station is represented by introducing auxiliary variables and multiple inequality constraints as follows:

[0148]

[0149] in, M is a large positive number that must be greater than the maximum charging power of the charging station. Constraint (29) introduces an auxiliary variable to represent the charging station power. Constraints (30)-(31) represent the values ​​of each auxiliary variable under different binary variable values.

[0150] (f) The voltages corresponding to each segment of the stepped sag curve are expressed by multiple inequality constraints as shown below:

[0151]

[0152] Where v is a very small positive number, which can be less than one-thousandth of the maximum per-unit value of the voltage, and is used to represent the step at the segment inflection point. Constraint (32) indicates that the node voltage is located between the two inflection point voltages of the corresponding segment of the step droop curve under different binary variable values.

[0153] (g) The linear power flow constraint model of the distribution network is established as follows:

[0154]

[0155] Among them, P t,hi,c and Q t,hi,c P represents the active power and reactive power flowing through branch hi, respectively. t,ij,c and Q t,ij,c Vij represents the active power and reactive power flowing through branch ij, respectively, and V0 is the reference voltage. and These represent the active power generation of the photovoltaic system and the reactive power of the photovoltaic inverter, respectively. and These represent the active power and reactive power of the load, respectively, r ij Let x be the resistance of branch ij. ij Let be the reactance of branch ij. Constraint (33) represents the active power balance of the node. Constraint (34) represents the reactive power balance of the node. Constraint (35) represents the voltage drop at the node.

[0156] (h) Establish the following voltage constraints for distribution network nodes:

[0157]

[0158] Constraint (36) represents the limitation on the range of node voltages.

[0159] (i) Establish the following power constraints for distribution network branches:

[0160]

[0161] in, This indicates the maximum apparent power allowed for the branch. Constraint (37) indicates the power range limit for the branch.

[0162] (j) The constraints for the photovoltaic inverter are established as follows:

[0163]

[0164] in, This refers to the minimum reactive power allowed by the photovoltaic inverter. This refers to the maximum reactive power allowed by the photovoltaic inverter. Let be the apparent power capacity of the photovoltaic inverter. Constraint (38) represents the reactive power limit of the photovoltaic inverter. Constraint (39) represents that the sum of the squares of the photovoltaic active power and the photovoltaic inverter reactive power cannot exceed the square of its apparent power capacity. Constraints (37) and (39) are linearized using a polygon approximation method.

[0165] (k) The optimization model for the stepped sag curve is established as follows:

[0166]

[0167] Where, N c Let I be the number of source-load uncertainty scenarios, L be the set of distribution network nodes i, L be the set of branches ij, and C be the set of source-load uncertainty scenarios c. For network loss, For node voltage offset, N i Let ω1 and ω2 be the total number of nodes in the distribution network, and let ω1 and ω2 be the weighting coefficients for different objectives. Equation (40) indicates that the optimization objective is to minimize power loss and node voltage offset. Equation (41) is used to calculate power loss. Equation (42) is used to calculate node voltage offset.

[0168] The model is solved using a stochastic optimization method to obtain the power values ​​and inflection point voltage values ​​of each segment of the stepped droop curve, which are then sent to the charging stations. In this embodiment of the invention, the stepped droop curves optimized by four charging stations during the 10:00-11:00 time period are shown below. Figure 4 As shown.

[0169] Step 4: Based on the stepped droop curve of the current scheduling period, the charging station decomposes the power of each segment of the droop curve to obtain the charging power of each electric vehicle corresponding to different segments, such as... Figure 5 As shown, the charging active power of electric vehicles is dynamically adjusted based on real-time voltage measurements and power decomposition results.

[0170] The power decomposition model is as follows:

[0171]

[0172] in A binary indicator variable for regulating the charging power of electric vehicles. To optimize the power of each segment of the obtained stepped droop curve, The charging power of each charging station is evenly distributed to each electric vehicle according to the minimum segmented power of the stepped droop curve. This refers to the change in charging power of each electric vehicle when the charging station adjusts its power sequentially upwards from the minimum segment power according to the stepped droop curve. Optimization objective (43) represents minimizing the number of electric vehicles with varying charging power. Constraint (44) represents the charging power allocation of electric vehicles under the power of segments 1 to n when the charging station adjusts its power according to the stepped droop curve, where (44a) represents the power decomposition of the minimum segment power of the droop curve, (44b) represents the power decomposition of the power of the second segment power of the droop curve, and so on up to the power decomposition of the nth segment power. Constraint (45) represents the variable power constraint of electric vehicles during the stepped droop curve adjustment process, where the variable power range is the maximum charging power of the electric vehicle and... difference.

[0173] To verify the effectiveness of the proposed hierarchical optimization method for the active power-voltage step droop curve of charging stations, 1000 sets of distribution network load and photovoltaic and wind power output scenarios were randomly generated to simulate the real-time uncertainty during the scheduling period. A stepless droop control method was used for comparison. This method optimizes and keeps the charging power constant for each scheduling period. The comparison results for the 10:00-11:00 period are shown below:

[0174] Table 2 Comparison results of different methods during the 10:00-11:00 time period

[0175]

[0176] During the 10:00-11:00 time period, the method proposed in this invention achieves the lowest average power loss and average voltage deviation. The results demonstrate that the method proposed in this invention has significant advantages for the safe and economical operation of distribution networks.

[0177] This invention also provides a hierarchical optimization system for the active power-voltage step droop curve of a charging station, comprising:

[0178] The charging station-side processing module is configured to construct a single electric vehicle SoC model based on the charging behavior data of electric vehicle users, including the time of entering the charging station, the time of leaving the charging station, the initial state of charge (SoC) when entering the charging station, and the expected SoC when leaving the charging station, and then construct a charging station SoC model after aggregating electric vehicles.

[0179] Considering the uncertainty of the number of electric vehicles arriving at the charging station and their charging behavior, a conditional risk value optimization model for the SoC interval of the charging station is established. The optimization objective is to minimize the charging cost and conditional risk value. The SoC interval of the charging station is obtained through rolling optimization in each scheduling period. Based on the SoC interval, the power adjustable range of the charging station in the first period of the rolling optimization cycle is calculated, and the power range is reported to the distribution network dispatch center.

[0180] Based on the stepped droop curve of the current scheduling period obtained from the distribution network processing module, the power of each segment of the droop curve is decomposed to obtain the charging power of each electric vehicle corresponding to different segment power, and the charging active power of electric vehicles is dynamically adjusted according to the real-time voltage measurement value and the power decomposition result.

[0181] The distribution network processing module is configured to consider the uncertainty of renewable energy output and load in the distribution network system at the distribution network system level. Based on the power adjustable range reported by the charging station in each scheduling period, it establishes an optimization model of the active power-voltage step droop curve of the charging station, solves the optimal step droop curve for each scheduling period, and sends it to the charging station.

[0182] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the hierarchical optimization method for the active power-voltage step droop curve of a charging station as described above.

[0183] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, apparatus (systems), computer devices, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0184] This invention is described with reference to a flowchart of a method according to embodiments of the invention. It should be understood that each step in the flowchart and combinations thereof can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing device, generate instructions for implementing the process. Figure 1 A device for a function specified in one or more processes.

[0185] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 The function specified in one or more processes.

[0186] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 Steps of a specified function in one or more processes.

Claims

1. A hierarchical optimization method for the active power-voltage step droop curve of a charging station, characterized in that, Includes the following steps: (1) Based on the charging behavior data of electric vehicle users, including the time of entering the charging station, the time of leaving the charging station, the initial state of charge (SoC) when entering the charging station, and the expected SoC when leaving the charging station, a single electric vehicle SoC model is constructed, and then a charging station SoC model after the aggregation of electric vehicles is constructed. (2) Considering the uncertainty of the number of electric vehicles arriving at the charging station and the charging behavior, a conditional risk value optimization model for the SoC interval of the charging station is established. The optimization objective is to minimize the charging cost and conditional risk value. The SoC interval of the charging station is obtained by rolling optimization in each scheduling period. Based on the SoC interval, the power adjustable range corresponding to the first period of the rolling optimization cycle of the charging station is calculated. The power adjustable range is reported to the distribution network dispatch center. (3) At the distribution network system level, considering the uncertainty of renewable energy output and load in the distribution network, based on the power adjustable range reported by the charging station in each scheduling period, an optimization model of the active power-voltage step droop curve of the charging station is established, and the optimal step droop curve for each scheduling period is obtained and sent to the charging station. (4) Based on the step droop curve of the current scheduling period, the charging station decomposes the power of each segment of the droop curve to obtain the charging power of each electric vehicle corresponding to different segment power, and dynamically adjusts the charging active power of electric vehicles according to the real-time voltage measurement value and the power decomposition result.

2. The method according to claim 1, characterized in that, The single electric vehicle SoC model in step (1) includes: (a) The charging power of electric vehicles during parking periods should meet the following power range: in, Indicates a time period index. For car indexing, For distribution network node indexing, For electric vehicles newly arriving at charging stations, For the scene Next node Charging station during time period Parked electric vehicles The charging power, The maximum charging power allowed for electric vehicles. This refers to the time interval for car parking. (b) The charging power of electric vehicles during non-parking periods is 0: (c) Each electric vehicle will only be dispatched during the designated parking period, which is as follows: in, and These are the arrival and departure times of the vehicles, respectively. (d) The SoC constraints for electric vehicle parking periods are as follows: in, For electric vehicle SoC, For the battery capacity of electric vehicles, and These are the maximum and minimum SoCs allowed for electric vehicles, respectively. This indicates the charging efficiency of electric vehicles. The unit charging scheduling time length; constraint (4) indicates that the electric vehicle follows the charging scheduling time during the parking period. The process of SoC change during charging; constraint (5) represents the range constraint of the upper bound of SoC of electric vehicle during parking period; (e) The SoC constraints for electric vehicles entering and exiting the vehicle are as follows: in, For the initial SoC of electric vehicles, The expected SoC when the electric vehicle leaves; constraint (6) indicates that the initial SoC of the electric vehicle is taken as the entry time. The SoC at that time; constraint (7) requires that the SoC be greater than the SoC the owner originally hoped to achieve when the electric vehicle leaves.

3. The method according to claim 2, characterized in that, In step (1), the SoC model of the charging station after electric vehicle aggregation includes: (f) The power constraints of the charging station are as follows: in, The power consumed by the charging station This represents the maximum charging power of the entire charging station. For the scene Next node Charging station during time period Parked electric vehicles The set of; constraint (8) means that the power consumed by the charging station is the sum of the charging power of all electric vehicles; constraint (9) means that the overall power consumed by the charging station should meet the power limit; (g) The cost constraints for charging stations are as follows: in, For power The corresponding charging cost, For time period The charging electricity price; (h) The SoC model of the charging station after electric vehicle aggregation is as follows: in, For the charging station SoC; constraint (11) calculate the charging station SoC after the aggregation of electric vehicles based on the SoC of each vehicle in the charging station and the rated capacity of the battery.

4. The method according to claim 3, characterized in that, In step (2), the SoC interval conditional value-at-risk optimization model for the charging station is as follows: s.t. in, The conditional value of risk is the upper bound of the SoC range. The conditional value of risk is the lower bound of the SoC range. Time period index The set, For electric vehicles arriving at charging stations The set, The total number of scenes, , This is the upper bound of the SoC range. This is the lower bound of the SoC range. and These represent the maximum and minimum values ​​of the SoC range width, respectively. , , and For auxiliary variables; Formula (12) indicates that the optimization objective is to minimize charging cost and conditional risk value; Constraint (13) indicates the SoC range width limit; Constraint (14) indicates the range limit of SoC range values; Constraints (15)-(17) are the upper bound conditional risk value constraints of SoC; Constraints (18)-(20) are the lower bound conditional risk value constraints of SoC.

5. The method according to claim 4, characterized in that, In step (2), the SoC range of the charging station is obtained sequentially through rolling optimization in each scheduling period, and the power adjustable range of the charging station in the first period of the rolling optimization cycle is calculated based on the SoC range. The calculation method is as follows: in, This is the minimum power that the charging station can upload to the distribution network, and it will serve as the lower limit of the step droop curve. This represents the maximum power that the charging station uploads to the power distribution network and will serve as the upper limit of the step droop curve. The SoC is determined one hour before the charging station; Formula (21) calculates the minimum power of the charging station; Formula (22) calculates the maximum power of the charging station.

6. The method according to claim 5, characterized in that, In step (3), based on the adjustable power range reported by the charging station during each scheduling period, an optimization model for the active power-voltage step droop curve of the charging station is established, including: (a) The expression for the sag curve of the electric vehicle charging station steps is shown below: in, Uncertainty scenarios for renewable energy output and load in the distribution network Next node Charging station during time period The charging active power, The node voltage value. An odd number is used to represent the number of segments in a stepped sag curve. This refers to the voltage value at the inflection point of the sag curve of the charging station's steps. The power values ​​for each segment of the sag curve of the charging station steps; (b) The power constraints for each segment are shown below: Constraint (24) limits the range of power values ​​for each segment; (c) The inflection point voltage constraint is shown below: in, and These represent the minimum and maximum permissible voltages, respectively. The minimum threshold value that the length of each segment of the stepped droop curve must be greater than is indicated; constraint (25) indicates the order of magnitude of the voltage at each inflection point and the constraint range; constraint (26) indicates that there must be a certain distance between the voltage values ​​of two adjacent inflection points.

7. The method according to claim 6, characterized in that, In step (3), the optimal step droop curve for each scheduling period is obtained and sent to the charging station, including: The expression for the step droop curve is reconstructed based on the Big M method. First, n binary decision variables are defined, with the following constraints: in, , , , …, is the binary decision variable for the stepped droop curve; constraint (27) ensures that only a certain segment of the droop curve is activated for each charging station in each scenario at each time period by restricting the value of the binary variable; constraint (28) represents the power of the charging station; Since constraint (28) contains a bilinear term, the power of the charging station is represented by introducing an auxiliary variable and multiple inequality constraints as follows: in, , M is an auxiliary variable; M is a specified positive number with a value greater than the maximum charging power of the charging station; constraint (29) introduces an auxiliary variable to represent the power of the charging station; constraints (30)-(31) represent the values ​​of each auxiliary variable under different binary variable values; The voltages corresponding to each segment of the stepped sag curve are represented by multiple inequality constraints as shown below: in, It is a specified positive number, less than one-thousandth of the maximum per-unit voltage value, used to represent the step at the segment inflection point; constraint (32) indicates that the node voltage is located between the two inflection point voltages of the corresponding segment of the step droop curve under different binary variable values; The linear power flow constraint model for the distribution network is established as follows: in, and Branch roads Active and reactive power are circulating. and Branch roads Active and reactive power are circulating. For reference voltage, These represent the active power generation of the photovoltaic system and the reactive power of the photovoltaic inverter, respectively. and These represent the active power and reactive power of the load, respectively. branch road resistance, branch road Reactance; constraint (33) represents the active power balance of the node; constraint (34) represents the reactive power balance of the node; constraint (35) represents the voltage drop of the node; The voltage constraints for distribution network nodes are established as follows: Constraint (36) represents a limitation on the node voltage range; The power constraints for distribution network branches are established as follows: in, Indicates the maximum apparent power allowed for the branch; constraint (37) indicates the power range limit for the branch; The constraints for the photovoltaic inverter are as follows: in, This refers to the minimum reactive power allowed by the photovoltaic inverter. This refers to the maximum reactive power allowed by the photovoltaic inverter. The apparent power capacity of the photovoltaic inverter is given by constraint (38); constraint (39) represents the reactive power limit of the photovoltaic inverter; constraint (39) represents that the sum of the squares of the photovoltaic active power and the photovoltaic inverter reactive power cannot exceed the square of its apparent power capacity; constraints (37) and (39) are linearized using the polygon approximation method. The optimization model for the stepped sag curve is established as follows: s.t. in, The number of scenarios with uncertain source-load conditions. For distribution network nodes The set, branch road The set, For source-load uncertainty scenarios The set, For network loss, For node voltage offset, This represents the total number of nodes in the distribution network. and These are the weighting coefficients for different objectives; Formula (40) indicates that the optimization objective is to minimize power loss and node voltage offset; Formula (41) is used to calculate power loss; Formula (42) is used to calculate node voltage offset; The model is solved using a stochastic optimization method to obtain the power values ​​and inflection point voltage values ​​of each segment of the stepped droop curve, which are then sent to the charging station.

8. The method according to claim 7, characterized in that, In step (4), the charging station decomposes the power of each segment of the stepped droop curve, as shown in the following model: st in A binary indicator variable for regulating the charging power of electric vehicles. To optimize the power of each segment of the obtained stepped droop curve, The charging power of each charging station is evenly distributed to each electric vehicle according to the minimum segmented power of the stepped droop curve. The charging power of each electric vehicle changes as the charging station adjusts its power from the minimum segment power upwards according to the stepped droop curve. The optimization objective (43) represents the number of electric vehicles whose charging power changes are minimized. The constraint (44) represents the charging power distribution of electric vehicles under the power of the first to the nth segment when the charging station adjusts its power according to the stepped droop curve. Here, (44a) represents the power decomposition of the minimum segment power of the droop curve, (44b) represents the power decomposition of the power of the second segment power of the droop curve, and so on up to the power decomposition of the nth segment power. The constraint (45) represents the variable power constraint of electric vehicles during the stepped droop curve adjustment process. The variable power range is the maximum charging power of the electric vehicle and the maximum charging power of the electric vehicle. difference.

9. A hierarchical optimization system for the active power-voltage step droop curve of a charging station, characterized in that, include: The charging station-side processing module is configured to construct a single electric vehicle SoC model based on the charging behavior data of electric vehicle users, including the time of entering the charging station, the time of leaving the charging station, the initial state of charge (SoC) when entering the charging station, and the expected SoC when leaving the charging station, and then construct a charging station SoC model after aggregating electric vehicles. Considering the uncertainty of the number of electric vehicles arriving at the charging station and their charging behavior, a conditional risk value optimization model for the SoC interval of the charging station is established. The optimization objective is to minimize the charging cost and conditional risk value. The SoC interval of the charging station is obtained through rolling optimization in each scheduling period. Based on the SoC interval, the power adjustable range of the charging station in the first period of the rolling optimization cycle is calculated, and the power adjustable range is reported to the distribution network dispatch center. Based on the stepped droop curve of the current scheduling period obtained from the distribution network processing module, the power of each segment of the droop curve is decomposed to obtain the charging power of each electric vehicle corresponding to different segment power, and the charging active power of electric vehicles is dynamically adjusted according to the real-time voltage measurement value and the power decomposition result. The distribution network processing module is configured to consider the uncertainty of renewable energy output and load in the distribution network system at the distribution network system level. Based on the power adjustable range reported by the charging station in each scheduling period, it establishes an optimization model of the active power-voltage step droop curve of the charging station, solves the optimal step droop curve for each scheduling period, and sends it to the charging station.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the hierarchical optimization method for the active power-voltage step droop curve of the charging station as described in any one of claims 1-8.

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