Space-time scheduling optimization method and system for electric vehicles considering user false information

Abstract

The application discloses a kind of considering user false information's electric vehicle space-time scheduling optimization method and system, belong to electric vehicle optimization scheduling field.Scheduling center needs to minimize overall charging cost under the charging demand constraint of satisfying all electric vehicles, user once lies report demand, possibly will reduce own charging cost, but the charging cost of other electric vehicle users will rise, leading to the rise of total cost, to solve this problem, based on the space-time two-dimensional electric vehicle optimization scheduling framework, a new charging cost billing mechanism is designed, considering the influence of the charging strategy reported by user on optimization scheduling result, so that individual charging cost minimization and overall charging cost minimization tend to consistency.Not only reduce the charging cost of each user after participating in scheduling, but also avoid the phenomenon that user chooses to report false information to gain profit when participating in scheduling, better encourage user to participate in scheduling, improve optimization effect.

Description

Technical Field

[0001] This invention belongs to the field of electric vehicle optimization scheduling, and more specifically, relates to a method and system for optimizing the time-sharing scheduling of electric vehicles that takes into account false user information. Background Technology

[0002] With the increasing depletion of traditional fossil fuels and the vigorous development of renewable energy, the energy structure is gradually changing. Electric vehicles, as a new energy mode of transportation, have experienced rapid development in recent years. However, the large-scale integration of electric vehicles into the power grid will inevitably bring significant impacts, leading to increased peak-valley differences and decreased power quality. Research shows that the orderly charging of electric vehicles can achieve peak shaving and valley filling effects on the power grid, improving its operational efficiency. Furthermore, the dispatchable capacity of electric vehicles can be utilized to participate in the ancillary services market, aiding in grid frequency regulation. Therefore, electric vehicles possess enormous potential for development. Thus, researching optimal scheduling methods for electric vehicles in both spatiotemporal dimensions to ensure orderly charging by electric vehicle users is of great significance for ensuring the safe operation of the power grid.

[0003] However, the key to solving the problem lies in how to improve the enthusiasm of electric vehicle users to participate in optimized scheduling and achieve a reasonable distribution of benefits. Traditional benefit distribution methods, based on game theory, cannot guarantee that users will report their true information, which leads to a decrease in optimization effectiveness. In existing research considering false user information, some literature proposes a secure energy trading system based on consortium blockchains, which uses historical data to detect false or malicious charging behavior. Other literature models electric vehicle travel routes, charging times, and user psychology to calculate a comprehensive evaluation index of electric vehicles for charging stations, thus accurately reflecting the charging preferences of electric vehicles. However, most of these studies rely on machine learning methods to detect the authenticity of user-reported information, which cannot guarantee the reliability of the results. Therefore, a new billing mechanism is needed to incentivize electric vehicle users to participate in scheduling and report their true charging needs. Summary of the Invention

[0004] To address the aforementioned deficiencies or improvement needs of existing technologies, this invention provides a method and system for optimizing electric vehicle charging schedules that takes into account false user information. The purpose of this method is to incentivize users to report their actual charging needs and improve the overall optimization effect.

[0005] To achieve the above objectives, according to one aspect of the present invention, a method for optimizing the time and temperature of an electric vehicle considering false user information is provided, comprising:

[0006] S1. Using the minimum charging cost of electric vehicles as the objective function, determine the constraints of each decision variable in the objective function, and construct a spatiotemporal optimization scheduling model for electric vehicles;

[0007] S2. Based on the charging needs reported by electric vehicle users, including arrival time, departure time and charging amount, the dispatch center solves the spatiotemporal optimization scheduling model of electric vehicles according to the maximum charging rate to obtain the set of shortest charging times for each electric vehicle.

[0008] S3. The dispatch center sequentially selects each element in the set of shortest charging times, updates the arrival time and departure time variables in the constraints, solves the optimization scheduling model again, and selects the arrival and departure time variables that maximize the objective function as the most stringent charging time selection for the electric vehicle user.

[0009] S4. By comparing the charging information actually reported by electric vehicle users with the most stringent charging time selection determined by the dispatch center for that user, the marginal contribution of electric vehicle users to the optimized dispatch effect is quantified.

[0010] S5. Add the electric vehicle charging cost and marginal contribution quantification results obtained from solving the original optimization scheduling model to obtain the final charging cost for electric vehicle users.

[0011] Furthermore, the marginal contribution of electric vehicle users to the optimized scheduling effect is quantified using the following expression:

[0012]

[0013] U ini This represents the total charging cost before optimized scheduling. The representative reported the charging strategy as follows: The overall charging cost after participating in the scheduling. This represents the charging cost for the remaining n-1 users (excluding user i) before they participate in the scheduling process. This means that when the reported charging strategy is {c i ,c -i When the other n-1 users participate in the scheduling, the charging cost is calculated.

[0014] Furthermore, the spatiotemporal optimal scheduling model for electric vehicles is as follows:

[0015]

[0016] The cost of charging an electric vehicle i Let i be the travel cost of electric vehicle i.

[0017] Furthermore, the specific solution process for the spatiotemporal optimization scheduling model of electric vehicles is as follows:

[0018] The original optimization problem is decomposed into two coupled decision variables—charging rate and charging station selection—using the coordinate descent method, resulting in two subproblems. The decision variables for each subproblem are then updated alternately using sequential quadratic programming until the two decision variables converge, yielding the optimized charging scheme.

[0019] The present invention also provides a time-conditioning optimization system for electric vehicles that takes into account false user information, comprising:

[0020] The scheduling model construction module is used to determine the constraints of each decision variable in the objective function by taking the minimum charging cost of electric vehicles as the objective function, and to construct a spatiotemporal optimization scheduling model for electric vehicles.

[0021] The scheduling model solving module is used to solve the spatiotemporal optimization scheduling model of electric vehicles based on the charging needs reported by electric vehicle users on the previous day, including arrival time, departure time and charging amount. The scheduling center solves the model based on the maximum charging rate to obtain the set of shortest charging times for each electric vehicle.

[0022] In the charging time update module, the scheduling center sequentially selects each element in the set of shortest charging times, updates the arrival time and departure time variables in the constraints, solves the optimized scheduling model again, and selects the arrival and departure time variables that maximize the objective function as the most stringent charging time selection for the electric vehicle user.

[0023] The charging demand contribution quantification module is used to quantify the marginal contribution of electric vehicle users to the optimized scheduling effect by comparing the charging information actually reported by electric vehicle users with the most stringent charging time selection determined by the scheduling center for that user.

[0024] The charging cost incentive compensation module adds the electric vehicle charging cost and marginal contribution quantification results obtained from the original optimized scheduling model to serve as the final charging cost for electric vehicle users.

[0025] Overall, the above-described technical solutions conceived by this invention can achieve the following beneficial effects compared with the prior art.

[0026] This invention, based on a spatiotemporal dual-dimensional electric vehicle optimal scheduling framework, designs a novel charging cost billing mechanism. It considers the impact of user-reported charging strategies on the optimal scheduling results, ensuring that minimizing individual charging costs aligns with minimizing overall charging costs. This not only reduces the charging cost for each user participating in scheduling but also prevents users from profiting by reporting false information, thus better incentivizing user participation and improving optimization effectiveness. Attached Figure Description

[0027] Figure 1This is a schematic diagram showing the distribution of electric vehicles and charging stations according to an embodiment of the present invention;

[0028] Figure 2 This is a schematic diagram of the main steps of the electric vehicle charging and discharging optimization scheduling method according to an embodiment of the present invention;

[0029] Figure 3 This is a schematic diagram of the main steps of the charging cost billing mechanism in an embodiment of the present invention. Detailed Implementation

[0030] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0031] This invention designs a charging cost billing mechanism based on a spatiotemporal dual-dimensional scheduling framework for electric vehicles (EVs) to avoid users reporting false information. Its purpose is to reduce the charging costs of all EVs participating in the scheduling and decrease the peak load on charging stations. The participants in the entire optimized scheduling process include EVs, a central dispatch center, and all charging stations in the region under the control of the dispatch center. EV users first report their charging information to the dispatch center the day before, including arrival time, departure time, and charging demand. The dispatch center then optimizes this information, obtaining the optimized charging strategy for the next day and the best charging station selection. The optimization results are sent to subordinate charging stations and EV users respectively. The next day, EV users go to the designated charging station according to the optimization results, and the charging station charges the EVs according to the optimized charging strategy. Subsequently, based on the obtained charging strategy, a new and reasonable billing mechanism is designed to recalculate the charging cost for each EV user, ensuring that each user's charging cost decreases compared to before participating in the optimized scheduling, and that reporting their true information maximizes the cost reduction, i.e., the highest benefit.

[0032] To facilitate understanding of this invention, the applicable background of the method of this invention will be briefly introduced first, such as... Figure 1 The diagram illustrates a region with multiple electric vehicles N = {1, 2, ..., n}, where n represents the number of electric vehicles, and multiple charging stations S = {1, 2, ..., s}, where s represents the number of charging stations. The entire optimization interval is τ = {1, 2, ..., T}. All charging stations in this region are managed by a central dispatch center, which is responsible for providing optimized charging strategies. and w i =(w i,1 ,w i,2 ,…,w i,sPreviously, electric vehicle users were required to report their charging needs to the dispatch center, including arrival time and departure time. and charging amount R i The dispatch center needs to minimize the overall charging cost and provide the optimal charging strategy for the next day while satisfying the charging needs of all electric vehicles. Therefore, the charging needs reported by users affect the constraints of the problem. If a user falsely reports their needs, they may reduce their own charging cost, but the charging cost of other electric vehicle users will increase, resulting in an overall increase in cost. This is something the dispatch center needs to avoid.

[0033] like Figure 2 The diagram shows the main steps of the electric vehicle charging and discharging optimization scheduling method according to an embodiment of the present invention. Preferably, the pre-constructed electric vehicle charging optimization scheduling model includes the charging cost of the electric vehicle:

[0034]

[0035] In the above formula, The charging cost for electric vehicle i, where s represents the number of charging stations in the area, considering T time periods. Indicates the charging rate, w ij Indicates charging station selection, p j (t) represents the electricity price at charging station j at time t. The model uses a time-of-use pricing mechanism.

[0036] p j (t)=αL j (t)+β (2)

[0037] In the above formula, α and β > 0, representing electricity price coefficients. j (t) represents the electrical load of charging station j at time t, where

[0038]

[0039] In the above formula, The amount of charge given to electric vehicle i at time t, i.e., the charging strategy, w ij For 0 / 1 variables, when w ij =1, indicating that electric vehicle i travels to charging station j. Because spatial scheduling is considered, the travel cost of the electric vehicle still needs to be taken into account.

[0040]

[0041] In the above formula, For the distance cost of electric vehicle i, d ijLet γ represent the distance between electric vehicle i and charging station j, and let γ,δ≥0 be the distance cost coefficient. In summary, the objective function is to minimize the overall charging cost of the electric vehicle:

[0042]

[0043] In the above formula, U tol This represents the overall charging cost, including the charging costs and travel costs for n electric vehicles. Furthermore, the constraints include the following: maximum dispatch distance constraint (6), battery capacity constraint (7), charging demand constraint (8), charging balance constraint (9), charging power constraint (10), and charging station peak constraint (11):

[0044]

[0045] In the above formula, E i This represents the rated battery capacity of electric vehicle i. This represents the battery capacity status of the electric vehicle when it arrives at the charging station. η represents the minimum battery capacity of electric vehicle i, and η is the coefficient for energy consumption per kilometer. This is the capacity safety limit to ensure the operation of the electric vehicle, meaning that the electric vehicle must be able to reach the corresponding charging station before the battery reaches the minimum capacity.

[0046]

[0047] In the above formula This represents the battery capacity of electric vehicle i at time t. This represents the maximum battery capacity of electric vehicle i. This ensures that the electric vehicle battery remains between its minimum and maximum capacity range throughout the entire scheduling period.

[0048]

[0049] In the above formula R represents the arrival and departure times of electric vehicle i, respectively. i This represents the charging demand of electric vehicle i. This ensures that the charging demand is met within the time frame of the electric vehicle's arrival and departure from the station. Secondly, charging balance constraints must also be met at each moment.

[0050]

[0051] Charging power balance limit is

[0052]

[0053] In the above formula This represents the minimum charging rate of electric vehicle i at any given moment. This represents the highest charging rate of electric vehicle i at any given moment.

[0054]

[0055] In the above formula, L max This represents the maximum load that a charging station can provide at any given time. This ensures that at any given moment, the maximum charging load of the charging station cannot exceed the maximum load that can be provided.

[0056] Based on the aforementioned objective function and constraints, a spatiotemporal optimization scheduling model for electric vehicles is constructed. The problem is decomposed using the coordinate descent method and solved quickly to obtain the charging strategy for the following day, achieving orderly charging of electric vehicles and reducing charging costs and peak loads at charging stations. However, this charging cost-based billing has inherent flaws. To minimize overall charging costs, some users may experience increased costs, which is unfair and may discourage them from participating in the scheduling process, thus compromising the mechanism's operation. Furthermore, users may intentionally report false charging demands to obtain lower costs, harming the overall optimal outcome. Since the scheduling center cannot verify the authenticity of user-reported information, this invention designs a new billing mechanism to incentivize users to report truthful information.

[0057] like Figure 3 This is a schematic diagram illustrating the main steps of the billing mechanism in this embodiment of the invention. The difference in charging costs before and after a user participates in scheduling is defined as the revenue gained from participating in scheduling, denoted as v. i (c i ,c -i )

[0058]

[0059] In the above formula U represents the charging cost of electric vehicle user i before it participates in the dispatch process. i (c i ,c -i The denoted ) represents the charging cost after participating in the dispatch system. It's important to note that once electric vehicle i participates in the dispatch system, its charging cost is affected by the charging demands c reported by other electric vehicle users also participating in the dispatch system. -i The impact. Undoubtedly, electric vehicle users always hope to complete their charging tasks faster and at lower charging costs. Therefore, users might report false arrival and departure times to deceive the dispatch center, causing it to prioritize their charging needs and increase their charging urgency. However, this would harm other users. Therefore, false information reported by users should satisfy property (1-2):

[0060] (1) Record the user's actual arrival and departure times as follows: and The false arrival and departure times are and The false arrival and departure times reported by the user are within the range of the actual possible arrival and departure times, i.e.

[0061]

[0062] (2) Under the constraint of the maximum charging rate, the amount of charging time reported by the user can meet the user's charging demand, i.e.

[0063]

[0064] The number of false information messages that meet the above properties is not unique. Let the set of false information messages reported by users be denoted as . q represents the total number of false information items.

[0065] First, the dispatch center receives information c reported by the user. i Based on properties (3-5), the user's shortest charging time strategy is obtained, denoted as...

[0066] (3) Shortest arrival time for users Departure and departure times The reported arrival time Departure and departure times Within the range, that is

[0067]

[0068] (4) Let the user's shortest charging time be denoted as . Under the constraint of the maximum charging rate, the user's charging needs can be met in the shortest charging time, that is...

[0069]

[0070] (5) If the user's charging time is less than the minimum charging time, then under the constraint of the maximum charging rate, the user's charging needs cannot be met, i.e.

[0071]

[0072] Shortest charging time strategy that satisfies the above properties Not unique, the set of user's shortest charging time strategies is denoted as k represents the total number of shortest charging time strategies.

[0073] After determining the shortest charging strategy for user i, the dispatch center sequentially sets the shortest charging strategies. Substitute each element in the equation into the constraints corresponding to the objective function, and let... That is, by changing the constraints of the original problem and re-optimizing the objective function, a set of solutions can be obtained.

[0074]

[0075] Then, find the set The element with the largest median value, i.e.

[0076]

[0077] And define the charging strategy for electric vehicle user i at this time as follows: This can be interpreted as the most stringent charging strategy, as it results in the highest overall cost, in order to obtain... Subsequently, based on the VCG mechanism, the evaluation index M is designed considering both the user's own schedulable value and the impact on the benefits of other users. i Specifically, a user's own schedulable value refers to the value calculated by the scheduling center based on user i's charging strategy c. i For its stringent charging strategy If the overall benefit is still significant at this point, it indicates that the user's own dispatchable value is low, because even if the user relaxes their charging strategy, the overall benefit improvement will be minimal. The impact on the benefits of other users refers to the impact on the benefits of user i when their charging strategy is c. i If other users experience greater benefits, then this strategy is more valuable in improving overall efficiency. Based on this, the following evaluation index M is designed. i

[0078]

[0079] In the above formula, v i and v j The meaning is shown in equation (12). The first term on the right represents the charging strategy when user i uses the most stringent charging strategy. At that time, the sum of the charging cost reductions for all participating electric vehicle users is the sum of the benefits. The second term on the right is the sum of the benefits when user i reports charging strategy c. i At that time, the sum of the reductions in charging costs for other users is the sum of the benefits gained by other users. This is analogous to the previous discussion of user benefits v. i (c i ,c -i Substituting the definition of (12) into the above formula, we can obtain M. i A further expression, namely

[0080]

[0081] In the above formula, Uini This represents the total charging cost before optimized scheduling, i.e.

[0082]

[0083] The representative reported the charging strategy as follows: The total charging cost after participating in the scheduling, i.e.

[0084]

[0085] Similarly, This represents the charging cost of the remaining n-1 users (excluding user i) before they participate in the scheduling process.

[0086]

[0087] This means that when the reported charging strategy is {c i ,c -i When}, the charging cost after the other n-1 users participate in the scheduling, that is

[0088]

[0089] Get M i Afterwards, the new charging cost for electric vehicle users is... It consists of two parts, the first part U i It is derived from the product of the charging strategy at each time step and the electricity price at each time step. The second part, M... i It is calculated by the dispatch center, that is

[0090]

[0091] Below, we will demonstrate that the design of this mechanism satisfies individual rationality and incentive compatibility.

[0092] Theorem 1: The new charging cost billing mechanism satisfies individual rationality, that is, the charging cost after a user participates in the scheduling will be less than or equal to the charging cost when not participating in the scheduling.

[0093] Proof: Under the new charging cost billing mechanism, the benefit for user i is... This means that the benefit is equal to the charging cost when users do not participate in the scheduling. and new charging costs The difference, that is

[0094]

[0095] Substituting expressions (21) and (27) into... The expression yields

[0096]

[0097] In the above formula The charging strategy for user i is the most stringent charging strategy. At that time, the charging cost for all users. tol (c i ,c -i The charging strategy for user i is c. i At that time, the charging cost for all users. Because the most stringent charging strategy is determined for user i. This narrows the feasible region of the original optimization problem, therefore the optimization result will inevitably become worse, meaning the overall charging cost will definitely increase. therefore

[0098] Theorem 2: The new charging cost billing mechanism satisfies incentive compatibility, that is, the benefit obtained by users who report true information is always greater than or equal to the benefit obtained by users who report false information.

[0099] Proof: First, define the symbols. User i reports the actual charging strategy. Use of proceeds Indicates, using This indicates the most stringent charging strategy determined by the dispatch center at this time; reporting false charging strategies. Use of proceeds Indicates, using This represents the most stringent charging strategy determined by the dispatch center at this point. To prove the theorem holds, we will prove... As long as it is greater than or equal to 0, the benefit obtained from Theorem 1 is sufficient. The expression can be obtained by substituting it in.

[0100]

[0101] Analyzing the above formula, let's denote... The set of shortest charging strategies for user i is determined as follows: Depend on The set of shortest charging strategies for user i is determined as follows: As can be seen from (13), the actual charging strategy It's definitely better than fake charging strategies. Wider, therefore, set The elements in the set must be in the set. Found, that is According to property (19), we can obtain And because and In comparison, the feasible region is narrowed when solving the original optimization problem, resulting in... In summary

[0102] This invention takes into account the temporal and spatial characteristics of electric vehicles and designs a new charging cost billing mechanism based on the spatiotemporal optimization scheduling model of electric vehicles. It satisfies both individual rationality and incentive compatibility, and can ensure that electric vehicle users actively participate in the optimization scheduling and report their actual charging needs.

[0103] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for optimizing the time-of-use temperature of electric vehicles considering false user information, characterized in that, include: S1. Using the minimum charging cost of electric vehicles as the objective function, determine the constraints of each decision variable in the objective function, and construct a spatiotemporal optimization scheduling model for electric vehicles; S2. Based on the charging needs reported by electric vehicle users, including arrival time, departure time and charging amount, the dispatch center solves the spatiotemporal optimization scheduling model of electric vehicles according to the maximum charging rate to obtain the set of shortest charging times for each electric vehicle. S3. The dispatch center sequentially selects each element in the set of shortest charging times, updates the arrival time and departure time variables in the constraints, solves the optimization scheduling model again, and selects the arrival and departure time variables that maximize the objective function as the most stringent charging time selection for the electric vehicle user. S4. By comparing the charging information actually reported by electric vehicle users with the most stringent charging time selection determined by the dispatch center for that user, the marginal contribution of electric vehicle users to the optimized dispatch effect is quantified. S5. The electric vehicle charging cost and marginal contribution quantification results obtained after solving the optimized scheduling model in S2 are added together to obtain the final charging cost for electric vehicle users; The marginal contribution of electric vehicle users to the optimized scheduling effect is quantified using the following expression: This represents the total charging cost before optimized scheduling. The representative reported the charging strategy as follows: The overall charging cost after participating in the scheduling. Represents removing users ,other The charging cost before each user participates in the scheduling The representative reported the charging strategy as follows: At that time, others The charging cost after each user participates in the scheduling.

2. The method for optimizing the time and temperature of an electric vehicle considering false user information as described in claim 1, characterized in that, The spatiotemporal optimal scheduling model for electric vehicles is as follows: For electric vehicles The charging cost, For electric vehicles The cost of travel.

3. The method for optimizing the time and temperature of an electric vehicle considering false user information according to claim 2, characterized in that, The specific solution process for the spatiotemporal optimization scheduling model of electric vehicles is as follows: The original optimization problem is decomposed into two coupled decision variables—charging rate and charging station selection—using the coordinate descent method, resulting in two subproblems. The decision variables for each subproblem are then updated alternately using sequential quadratic programming until the two decision variables converge, yielding the optimized charging scheme.

4. A time-conditioning optimization system for electric vehicles that considers false user information, characterized in that, include: The scheduling model construction module is used to determine the constraints of each decision variable in the objective function by taking the minimum charging cost of electric vehicles as the objective function, and to construct a spatiotemporal optimization scheduling model for electric vehicles. The scheduling model solving module is used to solve the spatiotemporal optimization scheduling model of electric vehicles based on the charging needs reported by electric vehicle users on the previous day, including arrival time, departure time and charging amount. The scheduling center solves the model based on the maximum charging rate to obtain the set of shortest charging times for each electric vehicle. In the charging time update module, the scheduling center sequentially selects each element in the set of shortest charging times, updates the arrival time and departure time variables in the constraints, solves the optimized scheduling model again, and selects the arrival and departure time variables that maximize the objective function as the most stringent charging time selection for the electric vehicle user. The charging demand contribution quantification module is used to quantify the marginal contribution of electric vehicle users to the optimized scheduling effect by comparing the charging information actually reported by electric vehicle users with the most stringent charging time selection determined by the scheduling center for that user. The charging cost incentive compensation module adds the electric vehicle charging cost and the marginal contribution quantification result after solving the optimized scheduling model in the scheduling model solving module, and uses it as the final charging cost for electric vehicle users. The marginal contribution of electric vehicle users to the optimized scheduling effect is quantified using the following expression: This represents the total charging cost before optimized scheduling. The representative reported the charging strategy as follows: The overall charging cost after participating in the scheduling. Represents removing users ,other The charging cost before each user participates in the scheduling The representative reported the charging strategy as follows: At that time, others The charging cost after each user participates in the scheduling.

5. A time-conditioning optimization device for electric vehicles that considers false user information, characterized in that, include: processor; The processor is coupled to a memory for storing computer programs or instructions, and the processor is used to execute the computer programs or instructions in the memory, such that the electric vehicle time-conditioning optimization method considering false user information as described in any one of claims 1-3 is executed.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein, when the computer program is executed by a processor, it controls the device where the storage medium is located to perform a time-conditioning optimization method for electric vehicles that takes into account false user information as described in any one of claims 1-3.

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