Electric vehicle ordered charging optimization method based on aggregation regulation potential evaluation

Abstract

The invention discloses an electric vehicle ordered charging optimization method based on aggregation adjustment potential evaluation, and relates to the field of electric power system analysis and calculation, and the method comprises the steps: calculating charging intention coefficients of an electric vehicle user under the condition of adjustable increase and charging load translation; calculating a comprehensive adjustable potential value of the electric vehicle load; according to the electric vehicle load comprehensive adjustable potential value, under the condition that a preset constraint condition and a power grid peak clipping and valley filling principle are met, the charging time and the charging load capacity are determined; based on an aggregation management principle, performing priority ranking considering evaluation factors on the electric vehicle cluster by using a ranking algorithm; and determining an object group based on a priority ranking result, quantifying parameters of the established electric vehicle ordered charging model according to the object group, and obtaining an electric vehicle charging plan reflecting the priority. The regulation potential of the charging load of the electric vehicle is fully excavated, and orderly charging of the electric vehicle and safe and economical operation of a power system are achieved.

Description

Technical Field

[0001] The present invention relates to the field of power system analysis and calculation, and in particular to an electric vehicle orderly charging optimization method based on aggregate regulation potential evaluation. Background Art

[0002] In order to meet the challenges of global warming and the increasing shortage of fossil energy, it is necessary to peak carbon dioxide emissions and begin to gradually decrease, and reduce carbon dioxide emissions to zero or close to zero. Among them, electric vehicles are an important carrier for implementing clean alternatives and electric energy alternatives in the transportation field.

[0003] On the one hand, as an electric load, electric vehicles are environmentally friendly, flexible, and responsive, and are a potential resource for flexible regulation of the power system. On the other hand, the growth of a large number of electric vehicle charging loads has also increased the demand for transformation, expansion, and operation regulation of the distribution and use of power systems.

[0004] Challenges of existing orderly charging technology for electric vehicles include:

[0005] How to fully tap the regulation potential of electric vehicle charging load and give play to its aggregation effect, so as to achieve orderly charging of electric vehicles and safe and economical operation of power system under the premise of ensuring reliable supply of electric vehicle charging load.

[0006] Currently, no effective solution has been proposed for the problems in the related technologies. Summary of the invention

[0007] In view of the problems in the related art, the present invention proposes an electric vehicle orderly charging optimization method based on aggregate regulation potential evaluation to overcome the above-mentioned technical problems existing in the existing related art.

[0008] To this end, the specific technical solution adopted by the present invention is as follows:

[0009] An electric vehicle orderly charging optimization method based on aggregate regulation potential evaluation, the electric vehicle orderly charging optimization method based on aggregate regulation potential evaluation comprises:

[0010] S 1. Configure the electric vehicle charging load demand, and calculate the charging willingness coefficient of electric vehicle users under the adjustable increase and charging load shift conditions in combination with the electric vehicle charging electricity price;

[0011] S2. Calculate the comprehensive adjustable potential value of electric vehicle load based on the charging willingness coefficient under the adjustable increase condition and the charging load shift condition, combined with the charging yield rate and initial charging load of electric vehicle users;

[0012] S3. Determine the charging time and charging load according to the comprehensive adjustable potential value of the electric vehicle load and under the conditions of satisfying the pre-set constraints and the principle of peak load reduction and valley filling of the power grid;

[0013] S4. Based on the aggregation management principle, the electric vehicle cluster is prioritized by considering the evaluation factors using a ranking algorithm;

[0014] S5. Determine the object group based on the priority ranking result, quantify the parameters of the established electric vehicle orderly charging model according to the object group, and obtain the electric vehicle charging plan reflecting the priority.

[0015] Furthermore, the calculation of the charging willingness coefficient of electric vehicle users under the adjustable increase condition includes:

[0016] Obtain the initial electricity price, period electricity price, initial charging demand and period charging demand of the selected period, and calculate the rate of change of period charging demand to period electricity price;

[0017] Divide the initial electricity price by the initial charging demand and multiply it by the rate of change of the charging demand during the period to the electricity price during the period to obtain the charging willingness coefficient of the car user under the adjustable increase condition.

[0018] Furthermore, the calculation of the charging willingness coefficient of electric vehicle users under the condition that the charging load can be shifted includes:

[0019] Obtaining the charging load demand of electric vehicle users and the initial charging demand of electric vehicle users in the selected time period, as well as the time period electricity price and the initial electricity price of another time period, and calculating the change rate of the charging load demand of electric vehicle users in the selected time period to the time period electricity price of another time period;

[0020] Divide the initial electricity price of another period by the initial electric vehicle user charging demand of the selected period, and multiply it by the rate of change of the electric vehicle user charging load demand in a certain period to the period electricity price of another period, to obtain the charging willingness coefficient of the car user when the charging load can be shifted.

[0021] Furthermore, the comprehensive adjustable potential value of electric vehicle load includes the adjustable increase potential value of electric vehicle user charging load and the time shift potential value of charging load.

[0022] Furthermore, the calculation of the adjustable increase potential value of the charging load of electric vehicle users includes:

[0023] Obtain the initial charging load of electric vehicle users in the selected period, the initial charging price of electric vehicle users, the final charging price of electric vehicle users, and the charging willingness coefficient of electric vehicle users under the adjustable increase condition;

[0024] The charging yield rate of the electric vehicle user in the selected period is calculated by the initial charging price of the electric vehicle user and the final charging price of the electric vehicle user, and then multiplied by the charging willingness coefficient of the electric vehicle user in the selected period under the adjustable increase condition and the initial charging load of the electric vehicle user in turn, to obtain the adjustable increase potential value of the charging load of the electric vehicle user in the selected period.

[0025] Furthermore, the calculation of the potential value of the charging load time shift of electric vehicle users includes:

[0026] Obtain the initial charging load of electric vehicle users in the selected period, as well as the charging willingness coefficient, initial charging price of electric vehicles and final charging price of electric vehicle users in other periods of the day when the charging load of electric vehicle users can be shifted;

[0027] Traverse other time periods throughout the day, calculate the charging yield rate of electric vehicle users in the corresponding time period, and multiply it by the charging willingness coefficient of electric vehicle users in the corresponding time period when the charging load can be shifted, and obtain the preliminary results of the corresponding time period;

[0028] All preliminary results for other time periods throughout the day are summed up and multiplied by the initial charging load of electric vehicle users in the selected time period to obtain the time shift potential value of the charging load of electric vehicle users in the selected time period.

[0029] Furthermore, the pre-set constraints include: user expected power, electric vehicle charging power constraints and transformer capacity safety constraints.

[0030] Furthermore, based on the aggregation management principle, the priority ranking of the evaluation factors of the electric vehicle cluster is carried out using a sorting algorithm, including:

[0031] Aggregate a number of electric vehicles to obtain an electric vehicle cluster; use the superior-inferior solution distance method to prioritize each electric vehicle in the electric vehicle cluster based on the evaluation factors, and arrange the electric vehicles from large to small according to the comprehensive quantitative value of potential;

[0032] Among them, the evaluation factors include the distance between the electric vehicle and the charging station, the battery capacity of the electric vehicle and the charging power of the electric vehicle.

[0033] Furthermore, the superior and inferior solution distance method is used to prioritize each electric vehicle in the electric vehicle cluster based on the evaluation factors, and the electric vehicles are arranged from large to small according to the comprehensive quantitative value of potential, including:

[0034] An evaluation matrix including several evaluation attributes of electric vehicle clusters is constructed according to the evaluation factors, and the evaluation matrix is ​​normalized and weighted to obtain a weighted normative matrix;

[0035] Set the positive ideal solution and negative ideal solution of each evaluation attribute, and calculate the comprehensive quantitative value of the electric vehicle's potential based on the distance from the evaluation attribute of the electric vehicle to the positive ideal solution and the negative ideal solution;

[0036] Arrange the electric vehicles from large to small according to their corresponding comprehensive potential quantitative values.

[0037] Furthermore, the objective function of the orderly charging model of electric vehicles is:

[0038]

[0039] Where, the objective function f is the charging cost of electric vehicle users, k is the charging willingness coefficient of electric vehicle users, t is the unit time duration, T is the optimization period, N is the number of electric vehicles, and c is the charging cost of electric vehicle users. ps represents the penalty cost for insufficient charging capacity per unit of electricity, l represents the distance from the i-th electric vehicle to the charging pile, c i0 represents the cost per kilometer consumed by the i-th electric vehicle to the charging station, E v It represents the charging power demand that cannot be met due to the load capacity limitation during the optimization period, that is, the charging capacity shortage, E i represents the electric energy required by the i-th electric vehicle (kWh); S i It indicates whether the i-th electric vehicle is in the adjustable time period at the current moment. When it is 1, it means that the electric vehicle is in the scheduling time at time period t. When it is 0, it means that the electric vehicle is not in the scheduling time. The constraints of the orderly charging model of electric vehicles include charging power constraints, electric vehicle user charging demand, user power demand balance constraints and priority constraints.

[0040] The beneficial effects of the present invention are:

[0041] (1) The present invention starts from the temporal and spatial translation characteristics of electric vehicle charging load, quantitatively evaluates the adjustable potential of electric vehicles, and provides a decision-making basis for the flexible interaction between large-scale electric vehicle charging load and power grid, thereby helping power grid operators to formulate more effective load management and scheduling strategies, ensure the safety and stability of the power grid, maximize the use of clean energy, and reduce peak and valley load differences.

[0042] (2) The superior-inferior solution distance method is used to prioritize the charging needs of electric vehicle users and incorporate them into the electric vehicle charging optimization model. On the premise of meeting the charging needs of users, the user cost is minimized and flexible interaction with the power grid is achieved. Through effective load management, the impact of charging load on the power grid is reduced, and the stability and reliability of the power grid are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0044] Figure 1 is a flow chart of an electric vehicle orderly charging optimization method based on aggregate regulation potential assessment according to an embodiment of the present invention;

[0045] Figure 2 is a flowchart according to a specific application of an embodiment of the present invention;

[0046] Figure 3 It is a schematic diagram of the adjustable potential of electric vehicles;

[0047] Figure 4 This is a typical daily load diagram for a city;

[0048] Figure 5 It is a schematic diagram of the comprehensive load power after scheduling. DETAILED DESCRIPTION

[0049] To further illustrate each embodiment, the present invention provides drawings, which are part of the disclosure of the present invention and are mainly used to illustrate the embodiments and can be used in conjunction with the relevant descriptions in the specification to explain the operating principles of the embodiments. With reference to these contents, ordinary technicians in the field should be able to understand other possible implementations and advantages of the present invention. The components in the figures are not drawn to scale, and similar component symbols are generally used to represent similar components.

[0050] According to an embodiment of the present invention, a method for optimizing orderly charging of electric vehicles based on aggregate regulation potential assessment is provided.

[0051] The present invention is further described with reference to the accompanying drawings and specific embodiments. Figure 1-3 As shown, the method for optimizing orderly charging of electric vehicles based on aggregated regulation potential evaluation according to an embodiment of the present invention includes:

[0052] S1. Configure the electric vehicle charging load demand, and calculate the charging willingness coefficient of electric vehicle users under the adjustable increase and charging load shift conditions in combination with the electric vehicle charging electricity price;

[0053] S2. Calculate the comprehensive adjustable potential value of electric vehicle load based on the charging willingness coefficient under the adjustable increase condition and the charging load shift condition, combined with the charging yield rate and initial charging load of electric vehicle users;

[0054] S3. Determine the charging time and charging load according to the comprehensive adjustable potential value of the electric vehicle load and under the conditions of satisfying the pre-set constraints and the principle of peak load reduction and valley filling of the power grid;

[0055] S4. Based on the aggregation management principle, the electric vehicle cluster is prioritized by considering the evaluation factors using a ranking algorithm;

[0056] S5. Determine the object group based on the priority ranking result, quantify the parameters of the established electric vehicle orderly charging model according to the object group, and obtain the electric vehicle charging plan reflecting the priority.

[0057] In order to facilitate understanding of the above technical solutions of the present invention, the working principle or operation mode of the present invention in the actual process is described in detail below.

[0058] 1. Establish a single electric vehicle charging and discharging model to analyze the state of charge (State Of Charge SOC) of the electric vehicle and its adjustable state

[0059] SOC definition: The ratio of the remaining capacity after a period of use or long-term non-use to the capacity of its fully charged state:

[0060]

[0061] In the formula, C r A is the remaining capacity of the battery after use; C is the capacity of the battery in a fully charged state.

[0062] Assuming that the charging and discharging power of electric vehicles is constant, the SOC change of the vehicle battery is calculated according to the basic model of ordinary battery charging and discharging:

[0063]

[0064] Where, t 0 is the initial time of charging and discharging of electric vehicle i; t is the current time of charging and discharging of electric vehicle i; SOC i (t 0 ) is the electric vehicle i at the initial time t 0 State of charge; SOC i (t) is the charge state of electric vehicle i at the current time t; P i is the charging and discharging power of electric vehicle i; η i is the charging and discharging efficiency of electric vehicle i; C i is the battery capacity of electric vehicle i.

[0065] Through SOC, the current charge and discharge adjustment space can be obtained, as shown in the following formula:

[0066] SOC i，min≤SOC i (t)≤SOC i，max (3)

[0067] In the formula, SOC i，min SOC is the lower limit of the state of charge of electric vehicle i; i，max is the upper limit of the state of charge of electric vehicle i.

[0068] 2. Evaluate the adjustable potential of electric vehicle charging load from the perspectives of time shift and load increase

[0069] In smart charging mode, electric vehicles have good charging and discharging characteristics and a certain amount of charging and discharging adjustment space. Therefore, they can achieve optimized interaction with the power grid by changing the time and space distribution of their charging.

[0070] When the charging load power of electric vehicles is the same, the user's charging time and charging load can be changed through time-of-use electricity prices, incentive compensation and other mechanisms. On the premise of meeting the user's expected power constraints, charging pile power constraints, and distribution transformer capacity constraints, the electric vehicle charging cost can be flexibly adjusted according to the peak shaving and valley filling needs of the power grid, thereby realizing the regulation of charging load and charging time.

[0071] like Figure 3 As shown, Figure 3 This is a schematic diagram of the adjustable potential of electric vehicles, in which:

[0072] D is the most ideal load curve state of the overall charging load of electric vehicles; ΔD t The difference between the regulated load value and the unregulated load value of the electric vehicle at time t can be defined as the adjustable potential of the electric vehicle at time t. After regulation, the load curve can be flattened, while meeting the charging needs of the electric vehicle.

[0073] Among the various factors that affect the charging behavior of electric vehicle users, price plays the biggest role. If the price is higher than the electricity price recognized by the electric vehicle charging user, the user will choose to reduce electricity consumption. Conversely, when the price is low, the electric vehicle charging user can increase its electricity consumption. Therefore, the electric vehicle charging price usually reflects the willingness of electric vehicle charging users to adjust their electricity consumption, and demand response can be achieved in a very short time.

[0074] In order to show the increase in the demand for charging by electric vehicle users when the final price drops due to the incentive price, the price elasticity of demand can be used to express it, that is, the price elasticity of demand represents the coefficient of the willingness of electric vehicle users to charge:

[0075]

[0076] Where: k represents the electric vehicle user charging willingness coefficient; D represents the electric vehicle charging load demand; c represents the electric vehicle charging electricity price; D 0 and c 0 Represents the initial electricity load and initial electricity price.

[0077] Generally, there are two situations for adjusting the charging load of electric vehicles due to different electricity prices in different time periods:

[0078] (1) Evaluation of the potential for increasing the overall load of electric vehicle charging load

[0079] The total amount of charging load can be adjusted: the charging load of electric vehicles cannot be transferred from one time period to another, or the current charging period is a non-dispatchable time. At this time, the demand for electric vehicle charging load only changes with the electricity price in the current period.

[0080] The user charging willingness coefficient under the condition that the electric vehicle charging load can be adjusted is defined as:

[0081]

[0082] In the formula, k r The charging willingness coefficient of electric vehicle users in the adjustable time period; D r and c r Denote the charging demand and electricity price in period r respectively; D r0 and c r0 They represent the initial charging demand in period r and the initial electricity price in period r respectively.

[0083] Assume that the real-time electricity price stipulates that each hour is a node, and c r0 The market is proposed to clear the price before the electric vehicle charging load demand response. Assume that the electricity price in the rth hour is c r0 Adjust to c r , in the rth hour, the electric vehicle user gives a certain response, and the electric vehicle charging load is determined by D r0 Adjust to D r , assuming that the income of electric vehicle charging users in the rth hour is:

[0084] M(D r )=N(D r )-c r D r (6)

[0085] ΔD r =D r -D r0 (7)

[0086] Where: M(D r ) is the total charging profit of electric vehicle charging users in the rth hour; N(Dr ) is the total charging revenue of electric vehicle charging users in the rth hour; c r D r The charging cost for electric vehicle users in the rth hour. ΔD r It is the difference between the charging load of electric vehicle charging users in the rth hour and the initial charging load.

[0087] In order to maximize the benefits of electric vehicle charging users, it is necessary to is 0:

[0088]

[0089] The total charging revenue for electric vehicle charging users is N(D r ) is Taylor expanded, retaining the quadratic terms and ignoring the higher-order terms:

[0090]

[0091] Where: N(D r0 ) is the charging load D r0 The revenue generated. ΔD r It is the difference between the electric vehicle user charging load at the rth hour and the initial charging load.

[0092] Assuming that the initial demand of each node is optimal when implementing real-time electricity prices, we have:

[0093]

[0094] From equations (5) and (12), we can conclude that:

[0095]

[0096] Substitute (11) and (13) into (10):

[0097]

[0098] Substituting (14) into (9) we obtain:

[0099]

[0100] Under the implementation of real-time electricity prices, if the electric vehicle is to obtain the charging load increment of the electric vehicle user in the i-th hour, that is, the charging load adjustable increase potential value α 1 , the electric vehicle response willingness coefficient during the adjustable increase period of electric vehicles must be considered at the same time. From (15) and (7), we can get the adjustable increase potential value α of the electric vehicle user charging load in the rth hour: 1 for:

[0101]

[0102] Where D r0 k is the initial charging load of electric vehicle users in the rth hour; r is the charging willingness coefficient of electric vehicle users in the adjustable time period; c r0 is the initial charging price of electric vehicle users in the rth hour; c r is the final charging price for electric vehicle users in the rth hour.

[0103] (2) Evaluation of the potential for shifting charging load time for electric vehicles

[0104] Charging load time can be shifted: the charging load demand of electric vehicles can be shifted from peak hours to non-peak hours or valley hours. Changes in electricity prices during a certain period will cause the load demand to shift to other periods, but the total charging load demand of electric vehicles will not change.

[0105] The user charging willingness coefficient when the electric vehicle charging load can be shifted is defined as:

[0106]

[0107] In the formula, k s represents the charging willingness coefficient of electric vehicle users in the shiftable time period; D r and c s They represent the charging load demand of electric vehicle users in period r and the electricity price in period s respectively; D r0 and c s0 They represent the initial charging demand of electric vehicle users in period r and the initial electricity price in period s. If the electricity price in period s decreases, the charging demand of electric vehicle users in period r will decrease and part of it will be transferred to period s.

[0108] According to the derivation process of equations (17) and (16), assuming that the electricity price at hour s is c s0 Adjust to c s , in the rth hour, the electric vehicle charging user gives a certain response, and the charging load is determined by D r0 Adjust to D r , assuming Continuously, r, s = 1, 2, ... 24 (r ≠ s), then the increase in the charging load demand of electric vehicle users in the rth hour, that is, the potential value of the charging load time shift of electric vehicle users α 2 for:

[0109]

[0110] Where: D r0 k is the initial charging load of electric vehicle users in the rth hour; s is the charging willingness coefficient of electric vehicle users in the shiftable time period; c s0is the initial charging price of electric vehicle users in the sth hour; c s The final charging price for electric vehicle users in the sth hour.

[0111] (3) Adjustable potential assessment constraints

[0112] During the regulation process, the constraints that need to be considered are as follows:

[0113] 1) User expected power consumption

[0114] In order to ensure that the electric vehicle reaches the desired charge level when leaving the vehicle, r , then through rr 0 After the scheduling of the time period, the minimum amount of electricity that the electric vehicle load power can provide is D min，r Greater than the expected charge D when leaving at time r r .

[0115]

[0116] D min，r ≥D r -D r0 (20)

[0117] In the formula, C i is the battery capacity; η i is the charging efficiency of the i-th electric vehicle; D r0 The initial charge for charging electric vehicles; P i，t is the charging power of the i-th electric car at time t.

[0118] 2) Electric vehicle charging power constraints

[0119] 0≤P i，t ≤P max (twenty one)

[0120] Where P i，t is the charging power of the i-th electric vehicle at time t; P max The maximum charging power for electric vehicles.

[0121] 3) Transformer capacity safety constraints

[0122] P t (t)≤P cap (twenty two)

[0123] Where: P t (t) is the power of the transformer at time t; P cap is the transformer capacity.

[0124] In the case of implementing real-time electricity prices, in order to obtain the charging load that enables electric vehicle charging users to obtain the maximum charging benefits, it is necessary to consider both the adjustable increase potential value and the time shift potential value of the electric vehicle user's charging load. Combining equations (16) and (18), the comprehensive adjustable potential value α of the electric vehicle load is obtained:

[0125] α=α 1 +α 2 (twenty three)

[0126] 3. Use the TOPSIS algorithm to sort the large-scale aggregated electric vehicles and obtain priorities.

[0127] Since the load regulation potential of individual electric vehicles is small and the randomness is strong, a certain number of electric vehicles need to be aggregated and managed in a unified manner. The TOPSIS (Topology of Superior and Inferior Solutions) sorting algorithm is proposed to consider the distance between the electric vehicle and the charging station, the battery capacity of the electric vehicle, and the charging power of the electric vehicle to prioritize each electric vehicle, so as to select suitable electric vehicles for scheduling at different time periods.

[0128] Based on the TOPSIS (superiority and inferiority solution distance method) sorting method, the dispatchable priorities of the N electric vehicles currently waiting to join the orderly charging are sorted, so as to quantitatively describe the dispatchable space of electric vehicle charging load:

[0129] Q={Q i |i=1,2,…,N} (24)

[0130] Where: Q is the electric vehicle cluster.

[0131] The decision maker determines the weight vector W, normalizes the original data matrix, calculates the distance between each evaluation object and the optimal solution and the worst solution, and obtains the relative closeness between each evaluation object and the optimal solution. The specific process is as follows:

[0132] (1) Construct an evaluation matrix A containing N electric vehicles and M evaluation attributes = (a ij ) M×N , where the jth attribute of the i-th vehicle is a ij .

[0133]

[0134] The evaluation factors for electric vehicles participating in scheduling mainly consider three factors: the distance between the electric vehicle and the charging station, the battery capacity of the electric vehicle, and the charging power of the electric vehicle.

[0135] (2) Normalize the evaluation matrix A to obtain B = (b ij ) M×N , construct the weighted normative matrix Z = (zij ) M×N , the decision maker determines the weight vector W, and the weighted norm matrix Z = (z ij ) M×N Determined by the following formula:

[0136] W=[ω 1 ,ω 2 ,…ω M ] (26)

[0137]

[0138] (3) Determine the positive ideal solution and negative ideal solution for each attribute. The positive ideal solution and negative ideal solution are defined as follows:

[0139] 1) The positive ideal solution for the jth attribute is The negative ideal solution is The positive attribute is That is, the positive ideal solution of this property Related to the maximum value of this attribute; negative attribute is Z 0 , which is the negative ideal solution of this property Related to the minimum value of the attribute. A positive ideal solution is the most ideal value that the attribute can reach, and a negative ideal solution is the least ideal value that the attribute can reach.

[0140]

[0141] Calculate the distance from each electric vehicle participating in the orderly charging schedule to the positive ideal solution Distance to negative ideal solution

[0142]

[0143] Calculate the dispatchable priority R of each electric vehicle i :

[0144]

[0145] The calculated comprehensive ranking value R i is the potential comprehensive quantitative value, and each electric vehicle is divided according to the corresponding potential comprehensive quantitative value R i Arrange from largest to smallest, reflecting priorities.

[0146] The three selected evaluation indicators are ranked according to their distance from their positive ideal solutions or negative ideal solutions. The positive ideal solutions and negative ideal solutions of each indicator are different, so upper and lower limits are required.

[0147] R min ≤R i <R max (34)

[0148] In the formula, R min is the lower limit of priority; R max The upper limit of priority.

[0149] 4. Propose an optimization method for orderly charging of electric vehicles based on the evaluation of aggregate regulation potential

[0150] Before charging, electric car owners set information through the human-computer interaction interface and set ideal values ​​for various charging factors according to their personal needs, including the distance between the electric car and the charging station, the battery capacity of the electric car, and the charging power of the electric car. The aggregator will transmit this information to the central control center, which will summarize and process the information. According to the owner's travel chain and the real-time charging pile status information of each charging station, each factor is sorted according to the TOPSIS algorithm to obtain a comprehensive ranking value, as shown in Table 1 and Table 2. Through the comprehensive ranking value, the appropriate object group is determined, and some parameters of the established electric vehicle orderly charging model are quantified to obtain a charging plan after the response priority. After the central control center comes up with a plan, it will send the information to the aggregator. The owner can get the charging information through the human-computer interaction interface. The owner can choose the most suitable charging plan for offline response based on this information and his own situation.

[0151] According to formulas (34) and (35), the positive ideal solutions and negative ideal solutions of the three elements in the evaluation object are selected. The results are shown in Table 1:

[0152] Table 1 Positive and negative ideal solutions for various indicators

[0153]

[0154] The Euclidean distances of the three elements are calculated by equations (36)-(39): And the comprehensive ranking value R i , the results are shown in Table 2:

[0155] Table 2 Euclidean distance and comprehensive ranking value

[0156]

[0157] An orderly charging optimization model for electric vehicles is established. This model achieves the temporal and spatial shift of electric vehicle charging load through the guidance of price signals and the intervention of control measures, prevents overload of power distribution equipment during peak load periods, improves the utilization rate of power distribution equipment, and alleviates the demand for power grid expansion.

[0158] (1) Objective function

[0159]

[0160] Where, the objective function f is the charging cost of electric vehicle users, k is the charging willingness coefficient of electric vehicle users; t is the unit time duration; T is the optimization period; N is the number of electric vehicles; cps is the penalty cost of insufficient charging capacity per unit power; l is the distance from the i-th electric vehicle to the charging pile; c i0 E is the cost per kilometer consumed by the i-th electric vehicle to the charging station; v To optimize the charging power demand that cannot be met due to the load capacity limitation during the cycle, that is, the charging power shortage. i is the electric energy required by the i-th electric vehicle (kWh); S i It indicates whether the i-th electric vehicle is in the adjustable time period at the current moment. When it is 1, it means that the electric vehicle is in the scheduling time during time period t; when it is 0, it means that the electric vehicle is not in the scheduling time.

[0161] (2) Constraints

[0162] 1) Charging power constraints

[0163]

[0164] Where: P i，t is the charging load power of the i-th electric vehicle at time t; is the lower limit of the charging load power of the i-th electric vehicle at time t; is the maximum upper limit of the charging load of the i-th electric vehicle at time t.

[0165] 2) Charging demand of electric vehicle users

[0166] Electric vehicles must be able to meet the user's travel needs when leaving, and are limited by battery capacity, namely:

[0167]

[0168] Where: E i，t0 is the initial power of the i-th electric car at time t; E i，t is the amount of electricity required for the i-th electric vehicle to travel at time t; Δt i is the charging time of the i-th electric vehicle; ΔE i The electric energy added to the i-th electric car; η i is the charging efficiency of the i-th electric vehicle; C i is the battery capacity of the i-th electric car.

[0169] 3) User power demand balance constraints

[0170] 0≤E 1 ≤0.12·S i ·E i0(39)

[0171] 0≤E 2 ≤0.12·S i ·E i0 (40)

[0172] Where: E 1 It is expressed as the load that can be transferred at the current moment; E 2 It is expressed as the load that can be transferred at the current moment; E i0 is the initial charging demand of the ith electric vehicle in period t; S i Is the i-th electric car in the adjustable period at the current moment?

[0173] The charging time of the electric vehicles participating in the scheduling changes, but the overall charging load of the electric vehicles does not change. Therefore, the original charging power of all users should be equal to the charging power of all charging users after optimization:

[0174]

[0175] Where: T is the number of time periods in the optimization cycle within a day; N is the number of electric vehicle charging piles; E i0 is the initial charging demand of the ith electric vehicle in period t; E i Optimize the charging capacity of the i-th electric vehicle in period t.

[0176] 5) Priority constraints

[0177] According to Table 2, the priority ranking can be obtained:

[0178] Z 3 >Z 1 >Z 2 (42)

[0179] Where: Z 1 , Z 2 , Z 3 Represents 3 groups of evaluation objects respectively, and selects the optimal Z 3 .

[0180] Based on a typical daily load curve of a city, the example simulates the operation of 1 million electric vehicles. The user demand load curve for electric vehicles at different driving times is as follows: Figure 4 As shown:

[0181] Under the selection and specific model according to the comprehensive ranking of Tables 1 and 2, Figure 4The figure shows the optimization of the charging load of electric vehicles for the conventional load. It can be seen that during the peak load periods of 7:00-10:00 in the daytime and 17:00-21:00 in the evening, electric vehicles reduce the peak load by up to 25kW, which plays a role in peak reduction. During the low load periods of 13:00-15:00 in the daytime and 23:00-6:00 in the evening, the low load is increased by up to 30kW, which plays a role in valley filling. Figure 5 It can be seen intuitively that the load curve "band" after optimization is flatter, achieving the effect of peak shaving and valley filling, which proves that the proposed orderly charging optimization method can effectively reduce the peak-to-valley difference of the load curve in the study area.

[0182] Aiming at the characteristics of small load regulation potential and strong randomness of single electric vehicles, this invention proposes an optimization method for orderly charging of electric vehicles based on aggregated regulation potential evaluation. Firstly, the dispatchable mode of a single electric vehicle is analyzed, and its adjustable potential is calculated from the two aspects of the time shift and load increase of the electric vehicle charging load; secondly, the Technique for Order Preference by Similarity to Ideal Solution TOPSIS is used to aggregate and sort the vehicles that meet the scheduling requirements according to different factors, so as to select suitable electric vehicles to participate in the grid scheduling in the optimization process of different time periods. Finally, an optimization method for orderly charging of aggregated electric vehicles is proposed, and an optimization model for orderly charging of electric vehicles is established to realize the increase and time shift of the charging load of electric vehicles, alleviate the expansion demand of the power grid, and achieve the effect of peak shaving and valley filling.

[0183] In summary, the present invention starts from the temporal and spatial translation characteristics of electric vehicle charging load, quantitatively evaluates the adjustable potential of electric vehicles, and provides a decision-making basis for the flexible interaction between large-scale electric vehicle charging load and power grid, thereby helping power grid operators to formulate more effective load management and scheduling strategies, ensure the safety and stability of the power grid, maximize the use of clean energy, and reduce peak and valley load differences. The superior and inferior solution distance method is used to prioritize the charging needs of electric vehicle users and incorporate them into the electric vehicle charging optimization model. On the premise of meeting the user's charging needs, the user cost is minimized and the flexible interaction with the power grid is achieved. Through effective load management, the impact of charging load on the power grid is reduced, and the stability and reliability of the power grid are improved.

[0184] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for optimizing orderly charging of electric vehicles based on aggregate regulation potential assessment, characterized in that: The electric vehicle orderly charging optimization method based on the evaluation of aggregate regulation potential includes: S1. Configure the electric vehicle charging load demand, and calculate the charging willingness coefficient of electric vehicle users under the adjustable increase and charging load shift conditions in combination with the electric vehicle charging electricity price; S2. Calculate the comprehensive adjustable potential value of electric vehicle load based on the charging willingness coefficient under the adjustable increase condition and the charging load shift condition, combined with the charging yield rate and initial charging load of electric vehicle users; S3. Determine the charging time and charging load according to the comprehensive adjustable potential value of the electric vehicle load and under the conditions of satisfying the pre-set constraints and the principle of peak load reduction and valley filling of the power grid; S4. Based on the aggregation management principle, the electric vehicle cluster is prioritized by considering the evaluation factors using a ranking algorithm; S5. Determine the object group based on the priority ranking result, quantify the parameters of the established electric vehicle orderly charging model according to the object group, and obtain the electric vehicle charging plan reflecting the priority.

2. The method for optimizing orderly charging of electric vehicles based on aggregate regulation potential assessment according to claim 1, characterized in that: The calculation of the charging willingness coefficient of electric vehicle users under adjustable conditions includes: Obtain the initial electricity price, period electricity price, initial charging demand and period charging demand of the selected period, and calculate the rate of change of period charging demand to period electricity price; Divide the initial electricity price by the initial charging demand and multiply it by the rate of change of the charging demand during the period to the electricity price during the period to obtain the charging willingness coefficient of the car user under the adjustable increase condition.

3. The method for optimizing orderly charging of electric vehicles based on aggregate regulation potential assessment according to claim 1 is characterized in that: Calculation of the charging willingness coefficient of electric vehicle users under the condition that the charging load can be shifted includes: Obtaining the charging load demand of electric vehicle users and the initial charging demand of electric vehicle users in the selected time period, as well as the time period electricity price and the initial electricity price of another time period, and calculating the change rate of the charging load demand of electric vehicle users in the selected time period to the time period electricity price of another time period; Divide the initial electricity price of another period by the initial electric vehicle user charging demand of the selected period, and multiply it by the rate of change of the electric vehicle user charging load demand in a certain period to the period electricity price of another period, to obtain the charging willingness coefficient of the car user when the charging load can be shifted.

4. The method for optimizing orderly charging of electric vehicles based on aggregate regulation potential assessment according to claim 1 is characterized in that: The comprehensive adjustable potential value of the electric vehicle load includes the adjustable increase potential value of the electric vehicle user charging load and the time shift potential value of the charging load.

5. The method for optimizing orderly charging of electric vehicles based on aggregate regulation potential assessment according to claim 4 is characterized in that: The calculation of the adjustable increase potential value of electric vehicle user charging load includes: Obtain the initial charging load of electric vehicle users in the selected period, the initial charging price of electric vehicle users, the final charging price of electric vehicle users, and the charging willingness coefficient of electric vehicle users under the adjustable increase condition; The charging yield rate of the electric vehicle user in the selected period is calculated by the initial charging price of the electric vehicle user and the final charging price of the electric vehicle user, and then multiplied by the charging willingness coefficient of the electric vehicle user in the selected period under the adjustable increase condition and the initial charging load of the electric vehicle user in turn, to obtain the adjustable increase potential value of the charging load of the electric vehicle user in the selected period.

6. The method for optimizing orderly charging of electric vehicles based on aggregate regulation potential assessment according to claim 4 is characterized in that: Calculation of the potential value of electric vehicle user charging load time shifting includes: Obtain the initial charging load of electric vehicle users in the selected period, as well as the charging willingness coefficient, initial charging price of electric vehicles and final charging price of electric vehicle users in other periods of the day when the charging load of electric vehicle users can be shifted; Traverse other time periods throughout the day, calculate the charging yield rate of electric vehicle users in the corresponding time period, and multiply it by the charging willingness coefficient of electric vehicle users in the corresponding time period when the charging load can be shifted, and obtain the preliminary results of the corresponding time period; All preliminary results for other time periods throughout the day are summed up and multiplied by the initial charging load of electric vehicle users in the selected time period to obtain the time shift potential value of the charging load of electric vehicle users in the selected time period.

7. The method for optimizing orderly charging of electric vehicles based on aggregate regulation potential assessment according to claim 1, characterized in that: The pre-set constraints include: User expected power consumption, electric vehicle charging power constraints and transformer capacity safety constraints.

8. The method for optimizing orderly charging of electric vehicles based on aggregate regulation potential assessment according to claim 1 is characterized in that: The method of prioritizing the evaluation factors of the electric vehicle cluster based on the aggregation management principle and using the sorting algorithm includes: Aggregate a number of electric vehicles to obtain an electric vehicle cluster; use the superior-inferior solution distance method to prioritize each electric vehicle in the electric vehicle cluster based on the evaluation factors, and arrange the electric vehicles from large to small according to the comprehensive quantitative value of potential; Among them, the evaluation factors include the distance between the electric vehicle and the charging station, the battery capacity of the electric vehicle and the charging power of the electric vehicle.

9. The method for optimizing orderly charging of electric vehicles based on aggregate regulation potential assessment according to claim 8, characterized in that: The method of using the superior and inferior solution distance method to prioritize each electric vehicle in the electric vehicle cluster based on the evaluation factors and arranging the electric vehicles from large to small according to the comprehensive quantitative value of potential includes: An evaluation matrix including several evaluation attributes of electric vehicle clusters is constructed according to the evaluation factors, and the evaluation matrix is ​​normalized and weighted to obtain a weighted normative matrix; Set the positive ideal solution and negative ideal solution of each evaluation attribute, and calculate the comprehensive quantitative value of the electric vehicle's potential based on the distance from the evaluation attribute of the electric vehicle to the positive ideal solution and the negative ideal solution; Arrange the electric vehicles from large to small according to their corresponding comprehensive potential quantitative values.

10. The method for optimizing orderly charging of electric vehicles based on aggregate regulation potential assessment according to claim 1, characterized in that: The objective function of the electric vehicle orderly charging model is: Where, the objective function f is the charging cost of electric vehicle users, k is the charging willingness coefficient of electric vehicle users, t is the unit time duration, T is the optimization period, N is the number of electric vehicles, and c is the charging cost of electric vehicle users. ps represents the penalty cost for insufficient charging capacity per unit of electricity, l represents the distance from the i-th electric vehicle to the charging pile, c i0 represents the cost per kilometer consumed by the i-th electric vehicle to the charging station, E v It represents the charging power demand that cannot be met due to the load capacity limitation during the optimization period, E i represents the electric energy required by the jth electric vehicle; S i Indicates whether the i-th electric car is in the adjustable period at the current moment; The constraints of the electric vehicle orderly charging model include charging power constraints, electric vehicle user charging demand, user power demand balance constraints and priority constraints.

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