Electric vehicle aggregator electricity market operation optimization system considering user willingness

Abstract

The invention discloses an electric vehicle aggregator electric power market operation optimization system considering user intentions, and the system comprises a user classification and dynamic excitation module which comprises a real-time data collection interface; an anxiety cost calculation unit; the probability classification unit is used for generating a user classification probability factor and a dynamic response strategy through a profit-cost balance algorithm; the excitation strategy interface is used for outputting differentiated charging excitation price and discharging subsidy price; the cluster energy and power boundary generation module comprises a charge and discharge control interface; the boundary aggregation unit is used for dynamically generating a real-time energy boundary and a power boundary of the EV cluster according to a user classification result; the multi-stage market collaborative optimization module comprises a time sequence data interface which is connected with multi-stage market electricity price and frequency modulation requirements; the optimization modeling unit is used for establishing a multi-stage income objective function and solving an optimal charging and discharging scheduling strategy through an optimization algorithm; and the control instruction output interface is used for sending charging, discharging and frequency modulation instructions to the EV cluster.

Description

Technical Field

[0001] The present invention relates to technical fields such as smart grid and electric vehicle aggregator operation optimization, and in particular to an electric vehicle aggregator power market operation optimization system taking user wishes into account. Background Art

[0002] As the global energy mix transitions toward a low-carbon economy, the green development of the transportation and power sectors, as significant sources of carbon emissions, is crucial to addressing climate change. The large-scale deployment of electric vehicles (EVs) provides flexible, distributed energy storage resources for the power system. In theory, this can enhance the grid's ability to absorb renewable energy through aggregators participating in power market transactions and frequency regulation services. However, existing technologies still face the following limitations in practical applications:

[0003] Insufficient user behavior modeling:

[0004] Existing studies are mostly based on idealized assumptions (such as users fully responding to scheduling instructions) and do not fully consider the psychological and economic costs of users' actual participation in regulation (such as concerns about battery loss and loss of time flexibility). This leads to significant deviations between user response rate predictions and actual conditions, restricting the feasibility of scheduling strategies.

[0005] Single market coordination mechanism:

[0006] Current methods often focus on a single market scenario (such as optimizing only energy market revenue), lack a comprehensive analysis of the multi-stage interactive impact of the day-ahead and real-time markets, and find it difficult to dynamically coordinate charging and discharging plans with frequency regulation capacity allocation, thus limiting the maximization of the aggregator's comprehensive revenue.

[0007] Grid security constraints simplification:

[0008] In EV cluster control, existing technologies often use static power constraint models, which do not fully consider the dynamic mutual exclusivity of charging and discharging behaviors and the energy boundary change rate limit. This may cause the risk of instantaneous power exceeding the limit and threaten the stable operation of the power grid.

[0009] Optimization algorithms have limited adaptability:

[0010] Traditional optimization algorithms are prone to falling into local optimality when dealing with complex models with multiple objectives and multiple constraints, and fail to effectively combine user classification characteristics to dynamically adjust the solution strategy, resulting in scheduling plans deviating from actual needs. Summary of the Invention

[0011] To address the shortcomings and deficiencies of existing technologies, the present invention provides a system and method for optimizing the electricity market operations of electric vehicle aggregators that takes user preferences into account. This system achieves a win-win situation for both users and aggregators, as well as safe grid operation, through the following innovative designs:

[0012] User classification and dynamic incentive mechanism:

[0013] Based on the battery loss anxiety cost (quantifying the user's sensitivity to charging and discharging losses) and the flexible car use time anxiety cost (modeling time sensitivity through an exponential decay function), combined with the user's benefit-cost psychological trade-off, the user classification probability factor (A1 / A2 / A3) and response strategy are dynamically generated, and differentiated charging incentive prices (real-time electricity price difference) and discharge subsidy prices (day-ahead price + loss compensation) are formulated to accurately match user participation.

[0014] Cluster Energy and Power Boundary Modeling:

[0015] Through the charge and discharge state exclusive variable (ξ i (t) is a 0-1 variable, its value is 1, indicating that the i-th EV is in the charging state at time t, and its value is 0, indicating that the i-th EV is in the discharging state at time t) constrains the charging and discharging behavior of a single EV, and dynamically aggregates the SOC demand to generate the real-time energy boundary (E + (t), E - (t) are the upper and lower energy boundaries of the EV cluster at time t) and the power boundary (P + (t), P - (t) are the upper and lower power boundaries of the EV cluster at time t, respectively, and the instantaneous power is limited by the energy change rate to ensure the safety of the power grid.

[0016] Multi-stage market collaborative optimization:

[0017] A multi-stage objective function (maxF = -F1-F2+F3+F4+F5) is established, which covers the day-ahead electricity purchase cost, real-time frequency regulation cost, capacity benefit (performance score correction), mileage benefit (frequency regulation signal normalization processing) and charging subsidy benefit. An improved particle swarm algorithm (dynamic inertia weight + penalty function constraint) is used to solve the optimal scheduling strategy to achieve the synergistic maximization of the benefits in the energy-frequency regulation market.

[0018] Technical Effect: While ensuring the charging needs of EV users, the model significantly increases the revenue of aggregators and reduces the battery loss and time cost of users participating in V2G. The model takes into account both solution efficiency and engineering applicability, providing flexible support for high-proportion new energy power grids.

[0019] The present invention specifically adopts the following technical solutions:

[0020] An electric vehicle aggregator power market operation optimization system taking user preferences into account includes:

[0021] User classification and dynamic incentive module, including:

[0022] Real-time data collection interface for obtaining charging needs, battery status and market data of electric vehicle users;

[0023] Anxiety cost calculation unit, which generates user anxiety cost parameters based on the battery loss model and the flexible vehicle use time model;

[0024] Probabilistic classification unit, which generates user classification probability factors and dynamic response strategies through a benefit-cost trade-off algorithm;

[0025] Incentive strategy interface, outputs differentiated charging incentive prices and discharge subsidy prices;

[0026] Cluster energy and power boundary generation module, including:

[0027] Charge and discharge control interface, receiving the charge and discharge constraints and SOC requirements of a single EV;

[0028] The boundary aggregation unit dynamically generates the real-time energy boundary and power boundary of the EV cluster based on the user classification results, and limits the power change rate to ensure grid security;

[0029] Multi-stage market collaborative optimization module:

[0030] Time series data interface, access to multi-stage market electricity prices and frequency regulation requirements;

[0031] Optimize the modeling unit, establish a multi-stage benefit objective function and solve the optimal charging and discharging scheduling strategy through the optimization algorithm. The objective function covers frequency regulation capacity benefit, frequency regulation mileage benefit, electricity purchase cost and subsidy benefit;

[0032] Control command output interface, sending charging, discharging and frequency modulation commands to the EV cluster.

[0033] In the above system design, the main functions of each module are:

[0034] User classification module generates dynamic user response strategies based on battery loss anxiety cost and flexible vehicle use time anxiety cost;

[0035] The boundary generation module dynamically generates the cluster power boundary through charge and discharge mutual exclusion constraints and energy change rate limits;

[0036] The execution module is optimized, combining multi-stage market differences with the improved particle swarm algorithm to solve the optimal scheduling strategy.

[0037] Furthermore, in the anxiety cost calculation unit:

[0038] The additional charge and discharge loss of a single EV is calculated by integrating the charge and discharge power with the loss coefficient;

[0039] The battery loss model calculates the battery loss anxiety cost based on the additional charge and discharge loss of a single EV and the user psychological rejection coefficient θ>1, which is used to amplify the user's sensitivity to battery loss. The calculation of the battery loss anxiety cost introduces a binomial distribution response variable to dynamically determine whether the user responds to the incentive strategy.

[0040] The flexible car use time model quantifies the user's anxiety cost of the time sensitivity of flexible car use through an exponential decay function. The parameters of the exponential decay function include a time-sensitive coefficient for controlling the growth rate of the anxiety cost over time, a maximum online time for adjusting the decay rate of the anxiety cost over time, and a dynamic calibration based on the user's historical behavior data.

[0041] Furthermore, the benefit-cost trade-off algorithm maps user anxiety cost to a classification probability factor through a membership function, wherein the shape of the membership function is dynamically calibrated according to historical user behavior data.

[0042] Furthermore, the operating logic of the charge and discharge control interface is:

[0043] The charge and discharge power constraints are controlled by mutually exclusive charge and discharge state variables to ensure that the charge and discharge behaviors are mutually exclusive;

[0044] The SOC demand constraint ensures that the minimum power demand is met when the user is off-grid.

[0045] Furthermore, the power boundary generation logic of the boundary aggregation unit includes:

[0046] Dynamically limit instantaneous power through the energy change rate formula to prevent grid overload;

[0047] The energy boundary is generated by aggregating SOC demand constraints to ensure that the user meets the minimum power demand when off-grid.

[0048] The power change rate limit of the boundary aggregation unit is dynamically calculated through the time derivative of the energy boundary to prevent the instantaneous power from exceeding the grid safety threshold.

[0049] Furthermore, the optimization algorithm in the optimization modeling unit is an improved particle swarm optimization algorithm, including:

[0050] Dynamically adjust the inertia weight to balance global search and local convergence, and the weight decreases linearly with the number of iterations;

[0051] Energy and power boundary constraints are handled through penalty functions; the penalty function coefficient is proportional to the degree of violation of the energy and power boundary constraints;

[0052] Frequency regulation capacity benefits are calculated by adjusting the performance score to determine the matching degree between the declared capacity and the actual frequency regulation effect;

[0053] The FM mileage benefit is calculated by normalizing the FM signal, averaging the FM indication signal by time interval, and then separating the upward / downward FM mileage.

[0054] Furthermore, the generation logic of the differentiated charging incentive price is:

[0055] Charging incentive prices are dynamically adjusted based on the difference between the real-time energy market electricity price and the aggregator’s electricity price;

[0056] The discharge subsidy price is generated by adding the day-ahead discharge price and battery loss compensation.

[0057] Furthermore, it also includes:

[0058] User interaction terminal, providing feedback to users on regulation benefits and charging and discharging plans;

[0059] The grid safety verification interface synchronizes power boundaries to the grid dispatching system and triggers protection actions.

[0060] And, a method for optimizing the electricity market operation of electric vehicle aggregators taking into account user willingness, comprising the following steps:

[0061] Based on the battery loss anxiety cost and flexible vehicle use time anxiety cost of electric vehicle users, the user categories are dynamically divided through the mathematical model of benefit-cost psychological trade-off, and the user classification probability factors and dynamic response strategies are generated;

[0062] Based on the user classification results, differentiated charging incentive prices and discharge subsidy prices are formulated:

[0063] Charging incentive prices are dynamically adjusted based on the difference between the real-time energy market electricity price and the aggregator’s electricity price;

[0064] The discharge subsidy price is generated by adding the day-ahead discharge price and the battery loss compensation;

[0065] The real-time energy and power boundaries of the EV cluster are generated by aggregating the charging and discharging power constraints and SOC demand constraints of a single EV:

[0066] Combining user-based charging and discharging strategies with the day-ahead and real-time market price differences, a multi-stage revenue optimization model was established. The objective function encompasses electricity purchase costs, frequency regulation fees, capacity benefits, mileage benefits, and subsidy benefits.

[0067] An improved particle swarm algorithm is used to solve the optimal scheduling strategy. The global search and local convergence are balanced by dynamically adjusting the inertia weight. The energy and power boundary constraints are processed through the penalty function to maximize the synergistic benefits of the energy-frequency modulation market.

[0068] Furthermore, the method for dynamically classifying users includes:

[0069] The battery loss anxiety cost is calculated based on the extra charge and discharge loss of a single EV, the user's psychological rejection coefficient, and a response variable that follows a binomial distribution.

[0070] The time anxiety cost of flexible car use quantifies the user's sensitivity to flexible car use time through an exponential decay function. The parameters include time sensitivity coefficient, decay rate and maximum online time.

[0071] The real-time energy boundary and power boundary of the EV cluster generated by aggregation are specifically:

[0072] The charge and discharge power constraints are controlled by mutually exclusive charge and discharge state variables to ensure that the charge and discharge behaviors are mutually exclusive;

[0073] The power boundary is dynamically adjusted according to the time derivative of the energy boundary to limit the instantaneous power from exceeding the grid safety threshold.

[0074] And, an electronic device includes a memory, a processor, and a computer program stored in the memory and runnable on the processor, characterized in that the processor implements the steps of the above method when executing the program.

[0075] A non-transitory computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of the above method when executed by a processor.

[0076] Compared with the prior art, the present invention and its preferred embodiments have at least the following beneficial effects:

[0077] Win-win situation for users and aggregators:

[0078] By quantifying the user's anxiety costs related to battery loss and flexible vehicle usage time, and combining them with dynamic incentive mechanisms (such as charging incentives based on real-time electricity price differences and discharge subsidies based on day-ahead prices and losses), we can accurately match user participation willingness. While ensuring charging needs, we can significantly improve user response rates, reduce the hidden costs of user participation in V2G, and maximize the comprehensive benefits of aggregators in the energy-frequency modulation market.

[0079] Coordinated optimization of power grid security and market capacity:

[0080] Based on the mutually exclusive constraints of charge and discharge states and the dynamic aggregation model of energy boundaries, the real-time power and energy operating boundaries of the EV cluster are generated. By limiting the power change rate, instantaneous overload of the power grid is prevented, ensuring the safety and stability of the power grid. At the same time, the user classification-driven capacity allocation strategy effectively improves the matching degree between the capacity declared in the frequency regulation market and the actual regulation demand, and enhances the reliability of the frequency regulation response.

[0081] Multi-stage market benefits and engineering applicability:

[0082] A multi-stage objective function covering day-ahead energy purchase, real-time frequency regulation costs, and capacity / mileage benefits is established, and an improved optimization algorithm is combined for efficient solution. The model takes into account both solution efficiency and engineering practicality, providing a feasible flexible resource scheduling solution for a high proportion of new energy power grids, with both theoretical innovation and potential for large-scale application. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments:

[0084] Figure 1 This is a flowchart of the construction of the electric vehicle aggregator power market operation optimization system taking user wishes into account according to an embodiment of the present invention. DETAILED DESCRIPTION

[0085] Hereinafter, specific embodiments of the present application will be described in detail with reference to the accompanying drawings. Based on these detailed descriptions, those skilled in the art will be able to clearly understand the present application and implement the present application. Without violating the principles of the present application, the features of different embodiments may be combined to obtain new implementations, or certain features of certain embodiments may be substituted to obtain other preferred implementations.

[0086] To make the features and advantages of the present invention more clearly understood, the following embodiments are specifically described in detail with reference to the accompanying drawings.

[0087] like Figure 1 As shown, the embodiment of the present invention provides a construction and design process of an operation optimization system for electric vehicle aggregators to participate in the electricity market under a charging and discharging incentive mechanism that takes into account user willingness, including the following steps:

[0088] Step 1: Collect and screen information on whether electric vehicles (EVs) can participate in scheduling. EV aggregators perform the following process to optimize the operations of EVs participating in scheduling.

[0089] Step 2: Classify EV users based on their willingness to participate in regulation.

[0090] Step 3: Determine the charging incentive price and discharging subsidy price by adjusting the charging incentive mechanism and discharging incentive mechanism of EV aggregators;

[0091] Step 4: Obtain the upper and lower bound models of energy and power of the EV cluster through a single EV;

[0092] Step 5: Taking into account the timing of EV charging and discharging regulation and control and the frequency regulation needs of the multi-stage market, the aggregator's revenue is divided into charging costs, power costs, capacity benefits, mileage benefits, charging benefits, and subsidy costs. A multi-stage revenue optimization model for the aggregator is established, and an operating strategy is formulated with the goal of maximizing operating benefits.

[0093] As a preferred solution of this embodiment, in step 2, EV users are classified according to their willingness to participate in regulation. The user's willingness to participate is an important factor affecting the available regulation energy and revenue of EV aggregators. The battery charge and discharge loss and the time sacrificed in participating in regulation are also important factors affecting the user's willingness to participate in regulation. The user classification criteria are as follows:

[0094] Step 2-1: Additional battery loss per EV regulated by EV aggregators for:

[0095]

[0096] in, and The power loss during EV charging and discharging, and are the additional charge and discharge power, t c With t d are the additional charging and discharging time, t c0 With t d0 are the initial charge and discharge time within the additional charge and discharge time, Δt c and Δt d are the charging and discharging time periods within the additional charging and discharging time, respectively.

[0097] Step 2-2, the battery loss anxiety cost w for a single user b for:

[0098]

[0099] v~B(1,δ)

[0100] Among them, θ is the user rejection psychological coefficient, θ>1, and v is a 0-1 variable that obeys the binomial distribution B(1,δ).

[0101] Step 2-3, the anxiety cost of flexible car use time w for a single user f for:

[0102]

[0103] t∈[t1,t2],k1∈[0,1],k2∈(-∞,0)∪(0,+∞)

[0104] Where t is a certain moment during the EV's grid-connected period, T is the maximum grid-connected time of the EV cluster to which the EV belongs, η is the conversion factor, t1 and t2 are the grid-connected time and grid-off time of the EV, respectively, and k1 and k2 are decision factors.

[0105] Step 2-4: The user's psychological trade-off between participating in the aggregator's regulation and providing backup and sacrificing time costs. a for:

[0106]

[0107] Among them, f u,r is the expected reserve income of a single user participating in regulation, τ is the conversion factor, The maximum flexible car use anxiety cost of a single user participating in the regulation is calculated by the membership function. a The relationship between the two is characterized, and users are divided into clusters A1, A2, and A3, representing "strong participation intention", "indifferent participation intention" and "weak participation intention" respectively.

[0108] Steps 2-5: Method for determining users’ willingness to participate in regulation:

[0109]

[0110] 0 <Z<Y<X<1

[0111] Among them, w i is a 0-1 variable that reflects the willingness of a single user, and X, Y, and Z are probability factors that reflect the strength of the participation willingness of users in each cluster.

[0112] As a result, the modeling of user classification and dynamic incentive modules was formed.

[0113] As a preferred solution of this embodiment, in step 4, a day is divided into different study periods, and the proportion of the three types of EV users participating in the regulation is estimated. A computer is used to generate the type, starting SOC, and charging time of a single EV based on the obtained proportions. The power operation constraints and energy operation constraints of the single EV are specifically as follows:

[0114]

[0115] Among them, ξ i (t) is a 0-1 variable, where its value is 1 and it indicates that the i-th EV is in the charging state at time t, and its value is 0 and it indicates that the i-th EV is in the discharging state at time t, which is used to constrain the EV from charging and discharging at the same time. c,i (t) and p d,i (t) are the charging and discharging power of the ith EV at time t; v iis a 0-1 variable, and its value is 1, indicating that the i-th EV responds to the regulation, t i,0 is the charging start time of the i-th EV, t i,1 is the adjustable end time of the charging incentive mechanism for the i-th EV, t i,2 is the adjustable end time of the i-th EV’s response to the discharge reward mechanism.

[0116] The energy operation constraints are as follows:

[0117]

[0118] Among them, s i (t) is the SOC of the i-th EV at time t, s i,need is the SOC required by the i-th EV.

[0119] As a preferred solution of this embodiment, in step 4, establishing the upper and lower bounds of energy and power of the EV cluster specifically includes:

[0120]

[0121] Among them, E + (t), E - (t) are the upper and lower energy boundaries of the EV cluster at time t, N i EV aggregators can control the number of EVs, P + (t), P - (t) are the upper and lower bounds of the EV cluster power at time t.

[0122] Thus, the modeling of the cluster energy and power boundary generation module is completed.

[0123] As a preferred solution of this embodiment, the aggregator's revenue in step 5 is divided into five parts: electricity purchase costs in the day-ahead energy market, frequency regulation costs obtained through real-time frequency regulation, capacity revenue in the frequency regulation market, mileage revenue in the frequency regulation market, and charging revenue and subsidies provided by the aggregator obtained by EV response scheduling. The optimization objective function is thus obtained, where the aggregator's revenue specifically includes:

[0124] In step 5-1, the aggregator's electricity purchase cost F1 in the day-ahead energy market is:

[0125]

[0126] Among them, r Chr (k) is the electricity price in the energy market k days before the end of the period, P Chr (k) is the charging plan power for time period k formulated by the aggregator, K is the number of time periods in the entire study period, and Δk is the time period interval.

[0127] In step 5-2, the aggregator participates in real-time frequency modulation and obtains the frequency modulation fee F2 as follows:

[0128]

[0129] Among them, r RT (k) is the electricity price in the real-time energy market during period k, P UP (k), P DN (k) are the upward and downward FM powers of the aggregator in time period k, respectively.

[0130] In step 5-3, the capacity benefit F3 of the aggregator participating in the frequency regulation market is:

[0131]

[0132] Among them, r RC (k) is the frequency regulation capacity price in period k, P RC (k) is the frequency modulation capacity of the aggregator in time period k, and λ is the performance score.

[0133] In step 5-4, the mileage revenue F4 of the EV aggregator participating in the frequency modulation market is:

[0134]

[0135] in,

[0136]

[0137] Among them, r M (k) is the frequency regulation mileage electricity price in period k, m UP (k), m DN (k) are the upward and downward frequency modulation output mileage of the EV cluster in time period k; N m is the number of time intervals of the FM signal in period k, A(j,t) is the jth FM indication signal A(j,t)∈[-1,1] released by the FM market at time t, and time t belongs to period k.

[0138] In step 5-5, the EV responds to the dispatch and obtains the charging income and subsidy F5 given by the aggregator:

[0139]

[0140] Where P(k) is the net power of the EV that responds to the stimulus during period k, P0(k) is the net power of the EV that does not respond to the stimulus during period k, and P EVA (k) is the net power of the aggregator’s response for the time period kEV, r CS (k) is the charging electricity price published by the EV aggregator for the time period k, Δr CS (k) is the charging incentive price that EV aggregators use to incentivize users during period k.

[0141] Steps 5-6, based on the need to maximize the aggregator's profit, with the goal of maximizing the total profit of the aggregator participating in the energy-frequency modulation market, establish the objective function F:

[0142] maxF=-F1-F2+F3+F4+F5.

[0143] As a preferred solution of this embodiment, the constraints of the optimization problem in step 5 include:

[0144] (1) Energy boundary constraint:

[0145]

[0146] Among them, E - (k), E + (k) are the upper and lower energy boundaries of the kEV cluster in the time period, respectively.

[0147] (2) Power boundary constraints:

[0148] P-(k)≤P(k)≤P + (k)

[0149] Among them, P - (k), P + (k) are the upper and lower bounds of the power of the kEV cluster in the time period, respectively.

[0150] (3) Upward and downward frequency modulation power non-negative constraints:

[0151]

[0152] Thus, a multi-stage market collaborative optimization module is constructed.

[0153] As a preferred solution of this embodiment, considering the advantages of the PSO algorithm, an improved PSO algorithm is used in step 5 to solve the model.

[0154] In this embodiment, based on the specific system model scenario, the improvements and adjustments to the PSO algorithm include:

[0155] Dynamically adjust the inertia weight to balance global search and local convergence, and the weight decreases linearly with the number of iterations;

[0156] Energy and power boundary constraints are handled through penalty functions; the penalty function coefficient is proportional to the degree of violation of the energy and power boundary constraints;

[0157] Frequency regulation capacity benefits are calculated by adjusting the performance score to determine the matching degree between the declared capacity and the actual frequency regulation effect;

[0158] The FM mileage benefit is calculated by normalizing the FM signal, averaging the FM indication signal by time interval, and then separating the upward / downward FM mileage.

[0159] In one embodiment, an operation optimization system for electric vehicle aggregators participating in the electricity market under a charging and discharging incentive mechanism that takes user intention into account is provided, comprising:

[0160] User classification and dynamic incentive module, including:

[0161] Real-time data collection interface for obtaining charging needs, battery status and market data of electric vehicle users;

[0162] Anxiety cost calculation unit, which generates user anxiety cost parameters based on the battery loss model and the flexible vehicle use time model;

[0163] Probabilistic classification unit, which generates user classification probability factors and dynamic response strategies through a benefit-cost trade-off algorithm;

[0164] Incentive strategy interface, outputs differentiated charging incentive prices and discharge subsidy prices;

[0165] Cluster energy and power boundary generation module, including:

[0166] Charge and discharge control interface, receiving the charge and discharge constraints and SOC requirements of a single EV;

[0167] The boundary aggregation unit dynamically generates the real-time energy boundary and power boundary of the EV cluster based on the user classification results, and limits the power change rate to ensure grid security;

[0168] Multi-stage market collaborative optimization module:

[0169] Time series data interface, access to multi-stage market electricity prices and frequency regulation requirements;

[0170] Optimize the modeling unit, establish a multi-stage benefit objective function and solve the optimal charging and discharging scheduling strategy through the optimization algorithm. The objective function covers frequency regulation capacity benefit, frequency regulation mileage benefit, electricity purchase cost and subsidy benefit;

[0171] Control command output interface, sending charging, discharging and frequency modulation commands to the EV cluster.

[0172] In the above system design, the main functions of each module are:

[0173] User classification module generates dynamic user response strategies based on battery loss anxiety cost and flexible vehicle use time anxiety cost;

[0174] The boundary generation module dynamically generates the cluster power boundary through charge and discharge mutual exclusion constraints and energy change rate limits;

[0175] The execution module is optimized, combining multi-stage market differences with the improved particle swarm algorithm to solve the optimal scheduling strategy.

[0176] Regarding the specific limitations of the operational optimization system for electric vehicle aggregators participating in the electricity market under a charging and discharging incentive mechanism that takes into account user intentions, please refer to the limitations of the operational optimization method for electric vehicle aggregators participating in the electricity market under a charging and discharging incentive mechanism that takes into account user intentions, and will not be repeated here. The various modules in the operational optimization system for electric vehicle aggregators participating in the electricity market under the aforementioned charging and discharging incentive mechanism that takes into account user intentions can be implemented in whole or in part through software, hardware, or a combination thereof. Each of the aforementioned modules can be embedded in or independent of a processor in a computer device in hardware form, or can be stored in a memory in the computer device in software form, so that the processor can call and execute the corresponding operations of each of the aforementioned modules.

[0177] The optimized scheduling strategy obtained by the scheme of the present invention can effectively maximize the total operating revenue of aggregators participating in the frequency modulation market. The optimization strategy obtained by this scheme can effectively improve the revenue of EV aggregators, reduce the user costs of V2G, and achieve a win-win situation for both parties. The model is simple and easy to solve, and has certain theoretical and engineering value.

[0178] Based on the same inventive concept, the present invention also provides a computer device, which includes: one or more processors and a memory for storing one or more computer programs; the program includes program instructions, and the processor is used to execute the program instructions stored in the memory. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is used to implement one or more instructions, specifically for loading and executing one or more instructions in a computer storage medium to implement the above method.

[0179] It should be further explained that, based on the same inventive concept, the present invention also provides a computer storage medium having a computer program stored thereon, which executes the above method when executed by a processor. The storage medium can be any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electrical, magnetic, infrared, or semiconductor system, device or component, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device or component.

[0180] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that a specific feature, structure, material, or characteristic described in conjunction with that embodiment or example is included in at least one embodiment or example of the present disclosure. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0181] The above shows and describes the basic principles, main features and advantages of the present disclosure. Those skilled in the art should understand that the present disclosure is not limited to the above embodiments. The above embodiments and descriptions are merely illustrative of the principles of the present disclosure. Various changes and improvements may be made to the present disclosure without departing from the spirit and scope of the present disclosure, and such changes and improvements shall fall within the scope of the present disclosure.

[0182] The present invention is not limited to the above-mentioned optimal implementation mode. Anyone can derive various other forms of electric vehicle aggregator power market operation optimization systems that take into account user wishes under the inspiration of the present invention. All equal changes and modifications made according to the scope of the patent application of the present invention should be covered by the scope of the present invention.

Claims

1. An electric vehicle aggregator power market operation optimization system taking into account user intentions, characterized by: include: User classification and dynamic incentive module, including: Real-time data collection interface for obtaining charging needs, battery status and market data of electric vehicle users; Anxiety cost calculation unit, which generates user anxiety cost parameters based on the battery loss model and the flexible vehicle use time model; Probabilistic classification unit, which generates user classification probability factors and dynamic response strategies through a benefit-cost trade-off algorithm; Incentive strategy interface, outputs differentiated charging incentive prices and discharge subsidy prices; Cluster energy and power boundary generation module, including: Charge and discharge control interface, receiving the charge and discharge constraints and SOC requirements of a single EV; The boundary aggregation unit dynamically generates the real-time energy boundary and power boundary of the EV cluster based on the user classification results, and limits the power change rate to ensure grid security; Multi-stage market collaborative optimization module: Time series data interface, access to multi-stage market electricity prices and frequency regulation requirements; Optimize the modeling unit, establish a multi-stage benefit objective function and solve the optimal charging and discharging scheduling strategy through the optimization algorithm. The objective function covers frequency regulation capacity benefit, frequency regulation mileage benefit, electricity purchase cost and subsidy benefit; Control command output interface, sending charging, discharging and frequency modulation commands to the EV cluster.

2. The electric vehicle aggregator power market operation optimization system taking user intention into account according to claim 1 is characterized by: In the anxiety cost calculation unit: The additional charge and discharge loss of a single EV is calculated by integrating the charge and discharge power with the loss coefficient; The battery loss model calculates the battery loss anxiety cost based on the additional charge and discharge loss of a single EV and the user psychological rejection coefficient θ>1, which is used to amplify the user's sensitivity to battery loss. The calculation of the battery loss anxiety cost introduces a binomial distribution response variable to dynamically determine whether the user responds to the incentive strategy. The flexible car use time model quantifies the user's anxiety cost of the time sensitivity of flexible car use through an exponential decay function. The parameters of the exponential decay function include a time-sensitive coefficient for controlling the growth rate of the anxiety cost over time, a maximum online time for adjusting the decay rate of the anxiety cost over time, and a dynamic calibration based on the user's historical behavior data.

3. The electric vehicle aggregator power market operation optimization system taking user intention into account according to claim 1 is characterized by: The benefit-cost trade-off algorithm maps user anxiety cost to a classification probability factor through a membership function, where the shape of the membership function is dynamically calibrated according to historical user behavior data.

4. The electric vehicle aggregator power market operation optimization system taking user intention into account according to claim 1 is characterized by: The operating logic of the charge and discharge control interface is: The charge and discharge power constraints are controlled by mutually exclusive charge and discharge state variables to ensure that the charge and discharge behaviors are mutually exclusive; The SOC demand constraint ensures that the minimum power demand is met when the user is off-grid.

5. The electric vehicle aggregator power market operation optimization system taking user intention into account according to claim 1 is characterized by: The power boundary generation logic of the boundary aggregation unit includes: Dynamically limit instantaneous power through the energy change rate formula to prevent grid overload; Energy boundaries are generated by aggregating SOC demand constraints to ensure that users meet the minimum power demand when they are off-grid. The power change rate limit of the boundary aggregation unit is dynamically calculated through the time derivative of the energy boundary to prevent the instantaneous power from exceeding the grid safety threshold.

6. The electric vehicle aggregator power market operation optimization system taking user intention into account according to claim 1 is characterized by: The optimization algorithm in the optimization modeling unit is an improved particle swarm optimization algorithm, including: Dynamically adjust the inertia weight to balance global search and local convergence, and the weight decreases linearly with the number of iterations; Energy and power boundary constraints are handled through penalty functions; the penalty function coefficient is proportional to the degree of violation of the energy and power boundary constraints; Frequency regulation capacity benefits are calculated by adjusting the performance score to determine the matching degree between the declared capacity and the actual frequency regulation effect; The FM mileage benefit is calculated by normalizing the FM signal, averaging the FM indication signal by time interval, and then separating the upward / downward FM mileage.

7. The electric vehicle aggregator power market operation optimization system taking user intention into account according to claim 1 is characterized by: The generation logic of the differentiated charging incentive price is as follows: Charging incentive prices are dynamically adjusted based on the difference between the real-time energy market electricity price and the aggregator’s electricity price; The discharge subsidy price is generated by adding the day-ahead discharge price and battery loss compensation.

8. The electric vehicle aggregator power market operation optimization system taking user intention into account according to claim 1 is characterized by: Also includes: User interaction terminal, providing feedback to users on regulation benefits and charging and discharging plans; The grid safety verification interface synchronizes power boundaries to the grid dispatching system and triggers protection actions.

9. A method for optimizing the electricity market operation of electric vehicle aggregators taking into account user preferences, characterized in that: The following steps are involved: Based on the battery loss anxiety cost and flexible vehicle use time anxiety cost of electric vehicle users, the user categories are dynamically divided through the mathematical model of benefit-cost psychological trade-off, and the user classification probability factors and dynamic response strategies are generated; Based on the user classification results, differentiated charging incentive prices and discharge subsidy prices are formulated: Charging incentive prices are dynamically adjusted based on the difference between the real-time energy market electricity price and the aggregator’s electricity price; The discharge subsidy price is generated by adding the day-ahead discharge price and the battery loss compensation; The real-time energy and power boundaries of the EV cluster are generated by aggregating the charging and discharging power constraints and SOC demand constraints of a single EV: Combining user-based charging and discharging strategies with the day-ahead and real-time market price differences, a multi-stage revenue optimization model was established. The objective function encompasses electricity purchase costs, frequency regulation fees, capacity benefits, mileage benefits, and subsidy benefits. An improved particle swarm algorithm is used to solve the optimal scheduling strategy. The global search and local convergence are balanced by dynamically adjusting the inertia weight. The energy and power boundary constraints are processed through the penalty function to maximize the synergistic benefits of the energy-frequency modulation market.

10. The method for optimizing electric vehicle aggregator power market operations taking into account user preferences according to claim 9, characterized in that: The method for dynamically classifying users includes: The battery loss anxiety cost is calculated based on the extra charge and discharge loss of a single EV, the user's psychological rejection coefficient, and a response variable that follows a binomial distribution. The time anxiety cost of flexible car use quantifies the user's sensitivity to flexible car use time through an exponential decay function. The parameters include time sensitivity coefficient, decay rate, and maximum online time. The real-time energy boundary and power boundary of the EV cluster generated by aggregation are specifically: The charge and discharge power constraints are controlled by mutually exclusive charge and discharge state variables to ensure that the charge and discharge behaviors are mutually exclusive; The power boundary is dynamically adjusted according to the time derivative of the energy boundary to limit the instantaneous power from exceeding the grid safety threshold.

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