Method and device for evaluating participation of aggregator in power market bidding, and storage medium

Abstract

The invention relates to the technical field of electric power market bidding, and discloses an evaluation method and device for participation of an aggregator in electric power market bidding and a storage medium, and the method comprises the steps: obtaining market data of an electric power market and automobile related data of an electric automobile connected to the electric power market, the automobile related data comprises operation data of the electric automobile and user behavior data of an electric automobile user; constructing an energy constraint model of an electric vehicle group according to the operation data of the electric vehicles; constructing a user response probability model according to the user behavior data of the electric vehicle user; constructing an interval optimization model based on market fluctuation according to the energy constraint model, the user response probability model and the market data of the electricity market; and performing bidding evaluation on the interval optimization model solution to obtain a bidding strategy of the electric vehicle aggregator participating in the power market bidding.

Description

Technical Field

[0001] This application relates to the field of electricity market bidding technology, specifically to an evaluation method, apparatus, and storage medium for aggregators participating in electricity market bidding. Background Technology

[0002] With the development of new energy sources and smart grids, electric vehicles (EVs), due to their flexible charging and discharging characteristics, are gradually becoming an important adjustable energy storage resource in the power system. Electric Vehicle Aggregators (EVAs) can participate in both the energy market and the frequency regulation market simultaneously by uniformly managing the charging and discharging behavior of multiple EVs, achieving peak shaving and valley filling as well as economic benefits. However, in a complex and volatile market environment, the joint bidding optimization of aggregators still faces significant uncertainties and model limitations.

[0003] Most electric vehicle aggregators' bidding methods in the electricity market are based on deterministic optimization or static robust optimization frameworks. These typically assume that the fluctuation ranges of electricity prices, frequency signals, and user responses are fixed, and achieve bidding robustness through worst-case constraints. While this approach is stable when market volatility is low, the fixed confidence interval fails to reflect the real-time characteristics of electricity market changes when electricity prices or frequency regulation signals fluctuate drastically. Furthermore, its static parameter settings lack adaptability, making it difficult to cope with dynamic changes in the electricity market.

[0004] In user behavior modeling, traditional methods often use fixed psychological curves or linear functions to describe stimulus responses, failing to consider user behavioral differences and the influence of environmental factors. In reality, user responses are random and volatile, influenced by travel demand, time preferences, and external factors. Ignoring these characteristics can lead to biases in aggregators' estimates of adjustable capacity, affecting frequency regulation reliability and stability, and consequently impacting the rational dispatch of the power grid.

[0005] As can be seen from the above, the relevant technologies have shortcomings in describing market uncertainty and modeling user response, which limits the accuracy, flexibility and adaptability of electric vehicle aggregators' decision-making in multi-market environments. Bidding strategies may not achieve effective utilization of grid resources and may affect the safety of grid operation. Summary of the Invention

[0006] This application aims to address at least the shortcomings of the evaluation model for aggregators participating in electricity market bidding, which are deficient in terms of market uncertainty description, user response modeling, and risk control. These shortcomings limit the reliability, flexibility, and adaptability of electric vehicle aggregators' decision-making in multi-market environments, and may lead to technical problems such as bidding strategies failing to achieve effective utilization of grid resources and affecting grid operation safety.

[0007] To address the aforementioned technical problems, embodiments of this application provide an evaluation method for aggregators participating in electricity market bidding, comprising:

[0008] Acquire market data from the electricity market and vehicle-related data of electric vehicles connected to the electricity market, wherein the vehicle-related data includes operating data of electric vehicles and user behavior data of electric vehicle users;

[0009] An energy constraint model for the electric vehicle population is constructed based on the operating data of the electric vehicles.

[0010] A user response probability model is constructed based on the user behavior data of the electric vehicle users.

[0011] An interval optimization model based on market fluctuations is constructed based on the energy constraint model, the user response probability model, and the market data of the electricity market.

[0012] The solution of the interval optimization model is evaluated to obtain the bidding strategy for electric vehicle aggregators to participate in the electricity market bidding.

[0013] In some embodiments, constructing an energy constraint model for a group of electric vehicles based on the operating data of the electric vehicles includes:

[0014] Determine the energy balance equation for the energy evolution of a single electric vehicle;

[0015] Determine the battery safety constraints that a single electric vehicle must meet during operation;

[0016] The energy constraint model for a single electric vehicle is determined based on the energy balance equation and the battery safety constraints.

[0017] The energy constraint models of each individual electric vehicle are summarized to obtain the overall energy constraint model of the electric vehicle aggregator, wherein the overall energy constraint model includes the total power and total energy of the electric vehicle aggregator.

[0018] In some embodiments, the energy balance equation is:

[0019] ;

[0020] in, Let be the battery level of the i-th electric vehicle at time t. , These are charging power and discharging power, respectively. The scheduling time step;

[0021] The battery safety constraints include:

[0022] ;

[0023] ;

[0024] ;

[0025] in, and The minimum and maximum allowable battery capacity for electric vehicles; Maximum charging / discharging power limit for electric vehicles;

[0026] The expression for the overall energy constraint model is:

[0027] ;

[0028] ;

[0029] ;

[0030] in, Contribute to the overall efforts of the aggregator; N represents the total energy storage of the aggregator; N represents the number of aggregating vehicles.

[0031] In some embodiments, a user response probability model is constructed based on the user behavior data of the electric vehicle users, including:

[0032] Based on the nonlinear response characteristics of the electric vehicle user to the excitation signal, a piecewise probabilistic response model is determined; wherein, the piecewise probabilistic response model is:

[0033] ;

[0034] in, To incentivize compensation levels; The response sensitivity of user i; and These are the dead zone and the saturation excitation threshold, respectively. This represents the maximum response probability.

[0035] In some embodiments, the method further includes:

[0036] Based on user behavior fluctuations and external influences, the response probability in the segmented probability response model is represented by an interval, wherein the response probability is expressed as:

[0037] ;

[0038] in, This represents the expected response probability. This is a fluctuation parameter that reflects the magnitude of uncertainty in user behavior.

[0039] In some embodiments, an interval optimization model based on market fluctuations is constructed according to the energy constraint model, the user response probability model, and the market data of the electricity market, including:

[0040] The energy boundary and dispatchable power are determined based on the energy constraint model of the electric vehicle population.

[0041] Determine the uncertainty range of market signals;

[0042] Based on the schedulable power, energy boundary, uncertainty interval, and user response probability, an interval optimization model is constructed with expected return and variance risk as the objectives.

[0043] In some embodiments, the uncertainty range includes the uncertainty range of energy market prices, the uncertainty range of frequency modulation market prices, and the uncertainty range of frequency modulation signals.

[0044] The uncertainty range of the energy market price is:

[0045] ;

[0046] The uncertainty range of the FM market price is:

[0047] ;

[0048] in, For energy market electricity prices, For up and down frequency modulation prices, , These represent the prediction error ranges;

[0049] The uncertainty range of the frequency modulation signal is:

[0050] ;

[0051] in, This is a frequency deviation signal. The mean, Standard deviation The confidence factor.

[0052] In some embodiments, the objective function of the interval optimization model is:

[0053] ;

[0054] in, For expected returns, For the variance of revenue, As a risk adjustment factor;

[0055] In the objective function, the total revenue of the electric vehicle aggregator in the electricity market is:

[0056] ;

[0057] in, Total revenue for aggregators; Baseline power; and For up and down frequency modulation capacity; and Separate the positive and negative components of the frequency modulation signal; Cost per unit of battery degradation; To incentivize prices;

[0058] The constraints of the interval optimization model are:

[0059] ;

[0060] in, and These represent the upper and lower limits of the power available to the aggregator, respectively. and These are the upper and lower limits of the aggregation energy constraint, respectively.

[0061] In some embodiments, the bidding strategy includes: baseline power planning Up-modulation capacity allocation Down-modulation capacity allocation Incentive compensation level and expected optimal return .

[0062] This application embodiment also provides an evaluation device for aggregators participating in electricity market bidding, including:

[0063] The acquisition module is configured to acquire market data of the electricity market and vehicle-related data of electric vehicles connected to the electricity market, wherein the vehicle-related data includes the operation data of electric vehicles and the user behavior data of electric vehicle users;

[0064] The first model building module is configured to build an energy constraint model for the electric vehicle group based on the operating data of the electric vehicles.

[0065] The second model building module is configured to build a user response probability model based on the user behavior data of the electric vehicle users.

[0066] The third model building module is configured to build an interval optimization model based on market fluctuations based on the energy constraint model, the user response probability model, and the market data of the electricity market.

[0067] The model solving module is configured to evaluate the bids for the interval optimization model to obtain the bidding strategies for electric vehicle aggregators to participate in electricity market bidding.

[0068] In some embodiments, the first model building module is further configured to:

[0069] Determine the energy balance equation for the energy evolution of a single electric vehicle;

[0070] Determine the battery safety constraints that a single electric vehicle must meet during operation;

[0071] The energy constraint model for a single electric vehicle is determined based on the energy balance equation and the battery safety constraints.

[0072] The energy constraint models of each individual electric vehicle are summarized to obtain the overall energy constraint model of the electric vehicle aggregator, wherein the overall energy constraint model includes the total power and total energy of the electric vehicle aggregator.

[0073] In some embodiments, the energy balance equation is:

[0074] ;

[0075] in, Let be the battery level of the i-th electric vehicle at time t. , These are charging power and discharging power, respectively. The scheduling time step;

[0076] The battery safety constraints include:

[0077] ;

[0078] ;

[0079] ;

[0080] in, and The minimum and maximum allowable battery capacity for electric vehicles; Maximum charging / discharging power limit for electric vehicles;

[0081] The expression for the overall energy constraint model is:

[0082] ;

[0083] ;

[0084] ;

[0085] in, Contribute to the overall efforts of the aggregator; N represents the total energy storage of the aggregator; N represents the number of aggregating vehicles.

[0086] In some embodiments, the second model building module is further configured to:

[0087] Based on the nonlinear response characteristics of the electric vehicle user to the excitation signal, a piecewise probabilistic response model is determined; wherein, the piecewise probabilistic response model is:

[0088] ;

[0089] in, To incentivize compensation levels; The response sensitivity of user i; and These are the dead zone and the saturation excitation threshold, respectively. This represents the maximum response probability.

[0090] In some embodiments, the second model building module is further configured to:

[0091] Based on user behavior fluctuations and external influences, the response probability in the segmented probability response model is represented by an interval, wherein the response probability is expressed as:

[0092] ;

[0093] in, This represents the expected response probability. This is a fluctuation parameter that reflects the magnitude of uncertainty in user behavior.

[0094] In some embodiments, the third model building module is further configured to:

[0095] The energy boundary and dispatchable power are determined based on the energy constraint model of the electric vehicle population.

[0096] Determine the uncertainty range of market signals;

[0097] Based on the schedulable power, energy boundary, uncertainty interval, and user response probability, an interval optimization model is constructed with expected return and variance risk as the objectives.

[0098] In some embodiments, the uncertainty range includes the uncertainty range of energy market prices, the uncertainty range of frequency modulation market prices, and the uncertainty range of frequency modulation signals.

[0099] The uncertainty range of the energy market price is:

[0100] ;

[0101] The uncertainty range of the FM market price is:

[0102] ;

[0103] in, For energy market electricity prices, For up and down frequency modulation prices, , These represent the prediction error ranges;

[0104] The uncertainty range of the frequency modulation signal is:

[0105] ;

[0106] in, This is a frequency deviation signal. The mean, Standard deviation The confidence factor.

[0107] In some embodiments, the objective function of the interval optimization model is:

[0108] ;

[0109] in, For expected returns, For the variance of revenue, As a risk adjustment factor;

[0110] In the objective function, the total revenue of the electric vehicle aggregator in the electricity market is:

[0111] ;

[0112] in, Total revenue for aggregators; Baseline power; and For up and down frequency modulation capacity; and Separate the positive and negative components of the frequency modulation signal; Cost per unit of battery degradation; To incentivize prices;

[0113] The constraints of the interval optimization model are:

[0114] ;

[0115] in, and These represent the upper and lower limits of the power available to the aggregator, respectively. and These are the upper and lower limits of the aggregation energy constraint, respectively.

[0116] In some embodiments, the bidding strategy includes: baseline power planning Up-modulation capacity allocation Down-modulation capacity allocation Incentive compensation level and expected optimal return .

[0117] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described evaluation method for aggregators participating in electricity market bidding.

[0118] This application also provides an electronic device, including at least a memory and a processor, wherein the memory stores a computer program, and the processor implements the above-described evaluation method for aggregators participating in electricity market bidding when executing the computer program in the memory.

[0119] The evaluation method, apparatus, and storage medium for aggregators participating in electricity market bidding provided in this application embodiment acquire market data of the electricity market and vehicle-related data of electric vehicles connected to the electricity market. The vehicle-related data includes operating data of electric vehicles and user behavior data of electric vehicle users. An energy constraint model for the electric vehicle group is constructed based on the operating data of the electric vehicles. A user response probability model is constructed based on the user behavior data of the electric vehicle users. An interval optimization model based on market fluctuations is constructed based on the energy constraint model, the user response probability model, and the market data of the electricity market. The interval optimization model is solved to evaluate bidding, resulting in a bidding strategy for electric vehicle aggregators participating in electricity market bidding. This paper introduces a dynamic uncertainty description mechanism on the basis of the traditional robust optimization framework. By constructing a four-layer linkage system of "electric vehicle aggregator schedulable resource model (energy constraint model) - user response probability model - market uncertainty interval - adaptive interval optimization model", it comprehensively considers multiple uncertainties and realizes the coordinated optimization of electric vehicle aggregators' participation in the energy market and frequency regulation market, improving the accuracy, flexibility and adaptability of bidding strategies. According to the bidding strategy, it can reasonably carry out power allocation, frequency regulation and other services, realize the effective utilization of grid resources, and ensure the safe operation of the grid. In addition, the constructed interval optimization model can reflect market fluctuations in real time, avoiding the problem of traditional robust optimization being too conservative, and further improving the flexibility and adaptability of bidding strategies. Attached Figure Description

[0120] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0121] Figure 1 This is a first flowchart of the evaluation method for aggregators participating in electricity market bidding, as exemplified in this application.

[0122] Figure 2 Another flowchart illustrating the evaluation method for aggregators participating in electricity market bidding according to an embodiment of this application;

[0123] Figure 3 This is another flowchart illustrating the evaluation method for aggregators participating in electricity market bidding, as described in this application.

[0124] Figure 4 This is a schematic diagram of the structure of the evaluation method for aggregators participating in electricity market bidding according to an embodiment of this application. Detailed Implementation

[0125] Various embodiments and features of this application are described herein with reference to the accompanying drawings.

[0126] It should be understood that various modifications can be made to the embodiments described herein. Therefore, the above description should not be considered as limiting, but merely as an example of embodiments. Other modifications within the scope and spirit of this application will be apparent to those skilled in the art.

[0127] The accompanying drawings, which are included in and form part of this specification, illustrate embodiments of the present application and, together with the general description of the present application given above and the detailed description of the embodiments given below, serve to explain the principles of the present application.

[0128] These and other features of this application will become apparent from the following description of preferred forms of embodiments given as non-limiting examples, with reference to the accompanying drawings.

[0129] It should also be understood that although this application has been described with reference to some specific examples, those skilled in the art can certainly implement many other equivalent forms of this application, which have the features described in the claims and are therefore all within the scope of protection defined herein.

[0130] The above and other aspects, features and advantages of this application will become more apparent when taken in conjunction with the accompanying drawings and in view of the following detailed description.

[0131] Specific embodiments of this application are described thereafter with reference to the accompanying drawings; however, it should be understood that the claimed embodiments are merely examples of this application, which can be implemented in various ways. Well-known and / or repeated functions and structures are not described in detail to avoid unnecessary or redundant details that could obscure the application. Therefore, the specific structural and functional details claimed herein are not intended to be limiting, but merely serve as the basis and representative basis for the claims to teach those skilled in the art to use this application in a variety of substantially any suitable detailed structures.

[0132] This specification may use the phrases “in one embodiment,” “in another embodiment,” “in yet another embodiment,” or “in other embodiments,” all of which may refer to one or more of the same or different embodiments according to this application.

[0133] Example 1

[0134] Figure 1 A flowchart illustrating the evaluation method for aggregators participating in electricity market bidding, according to an embodiment of this application, is shown. Figure 1 As shown in the embodiments of this application, an evaluation method for aggregators participating in electricity market bidding is provided, including:

[0135] S101: Obtain market data from the electricity market and vehicle-related data of electric vehicles connected to the electricity market.

[0136] The electricity market includes the energy market and the frequency regulation market. The energy market is the market for trading active power, while the frequency regulation market focuses on power system frequency regulation services.

[0137] The vehicle-related data includes operational data of electric vehicles and user behavior data of electric vehicle users. Operational data for electric vehicles includes initial battery level, capacity, rated power, expected connection and disconnection times, and charging / discharging status of each vehicle. User behavior data includes historical incentive levels, response probabilities, and travel time patterns. Electricity market data includes market price forecast data and frequency regulation signal data; for example, it can be used to forecast electricity prices for the day-ahead energy market. FM market upward and downward price forecasts Frequency deviation signal sequences, etc.

[0138] In this step, information from multiple sources can be collected and processed uniformly to serve as the input dataset for subsequent model building.

[0139] S102: Construct an energy constraint model for the electric vehicle group based on the operating data of the electric vehicles.

[0140] In this step, an energy constraint model for the electric vehicle group can be constructed based on the operational data of the electric vehicle terminals, thereby determining the dispatchable capacity of the electric vehicle aggregator (hereinafter referred to as the aggregator) for the vehicle group. This energy constraint model serves as the dispatchable resource model for the electric vehicle aggregator.

[0141] S103: Construct a user response probability model based on the user behavior data of the electric vehicle users.

[0142] Electric vehicle users' participation behavior is highly sensitive to incentive prices. Therefore, in this step, user behavior data is considered, and a user response probability model is constructed based on the user behavior data of electric vehicle users. This realizes probability-based user response modeling, enhances the accuracy of user behavior description, and facilitates the assessment of the impact of user behavior on the resource scheduling of electric vehicle aggregators.

[0143] S104: Construct an interval optimization model based on market fluctuations according to the energy constraint model, the user response probability model, and the market data of the electricity market.

[0144] In this step, considering the uncertainty of market signals, after constructing the energy constraint model and the user response probability model, the dispatchable power range for each time period can be extracted from the energy constraint model. Taking into account the uncertainty of user behavior and market data, the user response probability and market uncertainty ranges are determined, and a range optimization model based on user behavior fluctuations and market fluctuations is constructed, achieving a dynamic characterization of user behavior and market uncertainty. This range optimization model can target expected returns and variance risk, effectively controlling bidding risks while improving market competitiveness and avoiding impacts on grid safety operation.

[0145] S105: Perform a bid evaluation on the solution of the interval optimization model to obtain the bidding strategy for electric vehicle aggregators to participate in the electricity market bidding.

[0146] After constructing the interval optimization model, solving the interval optimization model will yield the optimal joint bidding strategy for electric vehicle aggregators.

[0147] The evaluation method for aggregators participating in electricity market bidding provided in this application embodiment acquires market data of the electricity market and vehicle-related data of electric vehicles connected to the electricity market. The vehicle-related data includes operating data of electric vehicles and user behavior data of electric vehicle users. An energy constraint model for the electric vehicle group is constructed based on the operating data of the electric vehicles. A user response probability model is constructed based on the user behavior data of the electric vehicle users. An interval optimization model based on market fluctuations is constructed based on the energy constraint model, the user response probability model, and the market data of the electricity market. The interval optimization model is solved to evaluate bidding, resulting in a bidding strategy for electric vehicle aggregators participating in electricity market bidding. This method is applicable to traditional... Based on the robust optimization framework, a dynamic uncertainty description mechanism is introduced. By constructing a four-layer linkage system of "electric vehicle aggregator schedulable resource model (energy constraint model) - user response probability model - market uncertainty interval - adaptive interval optimization model", multiple uncertainties are comprehensively considered, realizing the coordinated optimization of electric vehicle aggregators' participation in the energy market and frequency regulation market, improving the accuracy, flexibility and adaptability of bidding strategies. According to the bidding strategy, power allocation, frequency regulation and other services can be reasonably carried out to achieve effective utilization of grid resources and ensure the safe operation of the grid. In addition, the constructed interval optimization model can reflect market fluctuations in real time, avoiding the problem of traditional robust optimization being too conservative, further improving the flexibility and adaptability of bidding strategies.

[0148] The entire process of evaluating the above-mentioned aggregator's participation in electricity market bidding can be regarded as a series of abstractions from the "physical layer" to the "decision layer": the physical layer describes the battery energy conservation relationship of the electric vehicle group, the behavioral layer characterizes the user participation characteristics, the market layer reflects the fluctuations of market electricity prices and frequency regulation signals, and the optimization layer achieves the dynamic optimization of the bidding strategy under these constraints.

[0149] In some embodiments, such as Figure 2 As shown, in step S102, constructing an energy constraint model for the electric vehicle group based on the operating data of the electric vehicles includes:

[0150] S1021: Determine the energy balance equation for the energy evolution of a single electric vehicle.

[0151] The number of vehicles managed by the electric vehicle aggregator is denoted as N, and the scheduling cycle is discretized into T time periods.

[0152] The energy evolution of each vehicle follows the following energy balance equation:

[0153] (1)

[0154] in, Let be the battery level of the i-th electric vehicle at time t. , These are charging power and discharging power, respectively. This is the scheduling time step.

[0155] The above energy balance equation reflects the cumulative characteristics of energy evolution over time: the charging and discharging process of an electric vehicle directly determines its state in the next period.

[0156] S1022: Determine the battery safety constraints that a single electric vehicle must meet during operation.

[0157] Considering the physical constraints of the battery and the limitations of its charging and discharging power, electric vehicles must meet the following battery safety constraints during operation:

[0158] (2)

[0159] (3)

[0160] (4)

[0161] in, and The minimum and maximum allowable battery capacity for electric vehicles; Maximum charging / discharging power limit for electric vehicles.

[0162] S1023: Determine the energy constraint model for a single electric vehicle based on the energy balance equation and the battery safety constraints.

[0163] S1024: Summarize the energy constraint models of each individual electric vehicle to obtain the overall energy constraint model of the electric vehicle aggregator, wherein the overall energy constraint model includes the total power and total energy of the electric vehicle aggregator.

[0164] Summarizing the energy constraint models of individual electric vehicles, the energy expression for the electric vehicle aggregator is obtained as follows:

[0165] (5)

[0166] (6)

[0167] in, Contribute to the overall efforts of the aggregator; N represents the total energy storage of the aggregator; N represents the number of aggregating vehicles.

[0168] Furthermore, the above energy satisfies the population energy conservation principle:

[0169] (7)

[0170] In steps S1021 to S1024 above, the dispatchable energy (dispatchable power) of the electric vehicle aggregator can be determined based on the charging and discharging capabilities of the electric vehicle.

[0171] In some embodiments, step S103, constructing a user response probability model based on the user behavior data of the electric vehicle user, includes:

[0172] S1031: Determine the piecewise probabilistic response model based on the nonlinear response characteristics of the electric vehicle user to the excitation signal.

[0173] Electric vehicle users' participation behavior is highly sensitive to incentive prices. To characterize different psychological thresholds and response differences among users, and considering the nonlinear response characteristics of electric vehicle users to incentive signals, the response probability is determined as a piecewise function in this step:

[0174] (8)

[0175] in, To incentivize compensation levels; The response sensitivity of user i; and These are the dead zone and the saturation excitation threshold, respectively. This represents the maximum response probability.

[0176] The piecewise function described above reflects the user behavior mechanism of "insufficient incentives resulting in no response - moderate incentives leading to linear growth - excessive incentives leading to saturation." Employing a piecewise probabilistic response function to characterize the dynamic response of electric vehicle users to incentive prices takes into account user behavior fluctuations and individual differences, making aggregators' forecasts of adjustable capacity more reliable and improving the reliability and stability of grid dispatch. Furthermore, the behavioral model based on incentive response curves and probability intervals effectively addresses the problem of overly idealized user response assumptions in traditional methods, providing a more accurate characterization of user response behavior.

[0177] In some embodiments, the method further includes:

[0178] S1032: Based on user behavior fluctuations and external influences, the response probability in the segmented probability response model is represented by an interval.

[0179] The response probability is represented as the following interval:

[0180] (9)

[0181] in, This represents the expected response probability. This is a fluctuation parameter that reflects the magnitude of uncertainty in user behavior.

[0182] Different users have different travel plans and psychological thresholds, and the external environment also has different effects on user behavior. Therefore, this application further considers the interval uncertainty of response probability and adopts interval representation. It can be a fluctuation parameter fitted based on historical participation data. It can dynamically reflect the impact of factors such as time, season, and weather on user behavior, thereby more realistically constraining adjustable resources in optimization decisions.

[0183] In some embodiments, such as Figure 3 As shown, in step S104, a range optimization model based on market fluctuations is constructed according to the energy constraint model, the user response probability model, and the market data of the electricity market, including:

[0184] S1041: Determine the schedulable power and energy boundary of the electric vehicle aggregator based on the energy constraint model and the user response probability model.

[0185] Specifically, in this step, the energy boundary (aggregation energy constraint) and dispatchable power of the electric vehicle aggregator can be determined by the energy constraint model, and the dispatchable power range of the electric vehicle aggregator in each time period can be accurately predicted.

[0186] S1042: Determine the uncertainty range of market signals.

[0187] Market signal fluctuations include energy market price fluctuations, frequency modulation market price fluctuations, and frequency modulation signal fluctuations. Therefore, market signal fluctuations can be characterized by constructing an uncertainty interval for market signals, which is the confidence interval for market signals.

[0188] Energy market prices and frequency regulation market prices are modeled as the following uncertainty range:

[0189] (11) (12)

[0190] in, For energy market electricity prices, For up and down frequency modulation prices, , These represent the prediction error ranges.

[0191] The uncertainty interval for a frequency modulated signal is:

[0192] (13)

[0193] in, This is a frequency deviation signal. The mean, Standard deviation The confidence factor.

[0194] The uncertainty range of the aforementioned market signals is a dynamically changing range, which can be dynamically updated through the sliding window and exponential weighted average (EWMA) method. This allows the range optimization model to reflect market fluctuations in real time, avoiding the problem of overly conservative robust optimization in traditional models, and improving the flexibility and adaptability of bidding strategies.

[0195] S1043: Based on the schedulable power, energy boundary, uncertainty interval, and user response probability, construct an interval optimization model with expected return and variance risk as the objectives.

[0196] In this step, an adaptive interval optimization model is constructed under multiple uncertainties, with the objectives of expected return and variance risk. These multiple uncertainties include changes in electric vehicle operating parameters, changes in user response, and market volatility.

[0197] The objective function of the interval optimization model is:

[0198] (14)

[0199] in, For expected returns, For the variance of revenue, It serves as a risk adjustment factor.

[0200] In the objective function, the total revenue of the electric vehicle aggregator in the electricity market is:

[0201] (15)

[0202] in, For the total revenue of the aggregator, For baseline power, and For up and down frequency modulation capacity, and These are the positive and negative components of the frequency modulation signal, respectively. For the unit degradation cost of the battery, To incentivize prices.

[0203] The constraints of the interval optimization model are:

[0204] (16)

[0205] in, and These represent the upper and lower limits of the power available to the aggregator, respectively. and These are the upper and lower limits of the aggregation energy constraint, respectively.

[0206] In this embodiment, a dual-objective optimization mechanism of return and risk is constructed. By introducing expected value and variance terms into the objective function, the model can maximize returns while suppressing return volatility, preventing profit declines under extreme market conditions. This achieves robustness of the bidding strategy. Furthermore, by introducing a risk adjustment factor into the objective function, balancing expected returns and risk variance, the model can automatically balance returns and robustness according to market conditions, enhancing market competitiveness and risk control capabilities.

[0207] In some embodiments, when evaluating bids by solving the interval optimization model in step S105, the solver can be called to solve the interval optimization model and output the optimal bidding strategy.

[0208] The output bidding strategy includes: baseline power plan Up-modulation capacity allocation Down-modulation capacity allocation Incentive compensation level and expected optimal return The solver is preferably a mixed-integer quadratic programming (MIQP) solver.

[0209] Among them, the baseline power plan Electricity purchase and sale plans are formulated based on energy price fluctuations, charging during periods of low electricity prices and discharging during periods of high electricity prices to reduce costs or generate arbitrage profits; frequency regulation capacity (including the allocation of up-regulation capacity) and down-modulation capacity allocation Based on the frequency regulation price differences, priority is given to obtaining high-yield regulation services, while buffers are reserved to cope with signal fluctuations and ensure grid security; incentive compensation level By balancing user willingness to respond and costs through linear zone settings, and ensuring sufficient aggregated EV resources to support bidding capabilities, the aforementioned baseline power plan... Up-modulation capacity allocation Down-modulation capacity allocation Incentive compensation level It can adapt to multiple uncertainties within a robust framework, achieving a balance between maximizing bidding returns and ensuring strategy robustness. It can also rationally allocate electricity and configure corresponding frequency regulation services based on bidding strategies to guarantee grid security.

[0210] Furthermore, the interval optimization model can automatically update parameters based on real-time data during operation, for example, updating the risk adjustment factor. and confidence multiple This enables a closed-loop scheduling of prediction-optimization-feedback, improving the model's real-time performance and robustness, thereby significantly enhancing the robustness and market adaptability of the bidding strategy.

[0211] In summary, this application's embodiments determine the dispatchable power of the power grid by establishing an energy constraint model and a user response probability model for the electric vehicle population. Then, under multiple uncertainties, an adaptive interval optimization model with expected returns and variance risk as objectives is constructed to solve for the optimal joint bidding strategy of electric vehicle aggregators. Simultaneously, a piecewise probability response model is introduced into the user response probability model to characterize user behavior. The market parameter interval is dynamically adjusted through a sliding window and exponential weighted average. Furthermore, a risk adjustment factor is added to the interval optimization model, which enables a coordinated balance between expected bidding returns and volatility risk.

[0212] Example 2

[0213] Figure 4 A schematic diagram of the structure of an evaluation device for aggregators participating in electricity market bidding, according to an embodiment of this application, is shown. Figure 4 As shown in the embodiment of this application, an evaluation device for aggregators participating in electricity market bidding is also provided, comprising:

[0214] The acquisition module 10 is configured to acquire market data of the electricity market and vehicle-related data of electric vehicles connected to the electricity market, wherein the vehicle-related data includes the operation data of electric vehicles and the user behavior data of electric vehicle users;

[0215] The first model building module 20 is configured to build an energy constraint model for the electric vehicle group based on the operating data of the electric vehicles.

[0216] The second model building module 30 is configured to build a user response probability model based on the user behavior data of the electric vehicle users.

[0217] The third model building module 40 is configured to build an interval optimization model based on market fluctuations based on the energy constraint model, the user response probability model, and the market data of the electricity market.

[0218] The model solving module 50 is configured to evaluate the bids of the interval optimization model to obtain the bidding strategy for electric vehicle aggregators to participate in the electricity market bidding.

[0219] In some embodiments, the first model building module 20 is further configured to:

[0220] Determine the energy balance equation for the energy evolution of a single electric vehicle;

[0221] Determine the battery safety constraints that a single electric vehicle must meet during operation;

[0222] The energy constraint model for a single electric vehicle is determined based on the energy balance equation and the battery safety constraints.

[0223] The energy constraint models of each individual electric vehicle are summarized to obtain the overall energy constraint model of the electric vehicle aggregator, wherein the overall energy constraint model includes the total power and total energy of the electric vehicle aggregator.

[0224] In some embodiments, the energy balance equation is:

[0225] ;

[0226] in, Let be the battery level of the i-th electric vehicle at time t. , These are charging power and discharging power, respectively. The scheduling time step;

[0227] The battery safety constraints include:

[0228] ;

[0229] ;

[0230] ;

[0231] in, and The minimum and maximum allowable battery capacity for electric vehicles; Maximum charging / discharging power limit for electric vehicles;

[0232] The expression for the overall energy constraint model is:

[0233] ;

[0234] ;

[0235] ;

[0236] in, Contribute to the overall efforts of the aggregator; N represents the total energy storage of the aggregator; N represents the number of aggregating vehicles.

[0237] In some embodiments, the second model building module 30 is further configured to:

[0238] Based on the nonlinear response characteristics of the electric vehicle user to the excitation signal, a piecewise probabilistic response model is determined; wherein, the piecewise probabilistic response model is:

[0239] ;

[0240] in, To incentivize compensation levels; The response sensitivity of user i; and These are the dead zone and the saturation excitation threshold, respectively. This represents the maximum response probability.

[0241] In some embodiments, the second model building module 30 is further configured to:

[0242] Based on user behavior fluctuations and external influences, the response probability in the segmented probability response model is represented by an interval, wherein the response probability is expressed as:

[0243] ;

[0244] in, This represents the expected response probability. This is a fluctuation parameter that reflects the magnitude of uncertainty in user behavior.

[0245] In some embodiments, the third model building module 40 is further configured to:

[0246] The energy boundary and dispatchable power are determined based on the energy constraint model of the electric vehicle population.

[0247] Determine the uncertainty range of market signals;

[0248] Based on the schedulable power, energy boundary, uncertainty interval, and user response probability, an interval optimization model is constructed with expected return and variance risk as the objectives.

[0249] In some embodiments, the uncertainty range includes the uncertainty range of energy market prices, the uncertainty range of frequency modulation market prices, and the uncertainty range of frequency modulation signals.

[0250] The uncertainty range of the energy market price is:

[0251] ;

[0252] The uncertainty range of the FM market price is:

[0253] ;

[0254] in, For energy market electricity prices, For up and down frequency modulation prices, , These represent the prediction error ranges;

[0255] The uncertainty range of the frequency modulation signal is:

[0256] ;

[0257] in, This is a frequency deviation signal. The mean, Standard deviation The confidence factor.

[0258] In some embodiments, the objective function of the interval optimization model is:

[0259] ;

[0260] in, For expected returns, For the variance of revenue, As a risk adjustment factor;

[0261] In the objective function, the total revenue of the electric vehicle aggregator in the electricity market is:

[0262] ;

[0263] in, Total revenue for aggregators; Baseline power; and For up and down frequency modulation capacity; and Separate the positive and negative components of the frequency modulation signal; Cost per unit of battery degradation; To incentivize prices;

[0264] The constraints of the interval optimization model are:

[0265] ;

[0266] in, and These represent the upper and lower limits of the power available to the aggregator, respectively. and These are the upper and lower limits of the aggregation energy constraint, respectively.

[0267] In some embodiments, the bidding strategy includes: baseline power planning Up-modulation capacity allocation Down-modulation capacity allocation Incentive compensation level and expected optimal return .

[0268] The evaluation device for aggregators participating in electricity market bidding provided in this application corresponds to the evaluation method for aggregators participating in electricity market bidding in the above embodiments. Any option in the evaluation method embodiment for aggregators participating in electricity market bidding is also applicable to the embodiment of the evaluation device for aggregators participating in electricity market bidding, and will not be repeated here.

[0269] Example 3

[0270] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described evaluation method for aggregators participating in electricity market bidding.

[0271] The computer-readable storage medium in this application embodiment 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, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. In this application embodiment, the 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, apparatus, or device; for example, it can be the aforementioned memory.

[0272] The computer programs of embodiments of this application can be organized into one or more computer-executable components or modules. Various aspects of this application can be implemented with any number and combination of such components or modules. For example, aspects of this application are not limited to the specific computer-executable instructions or specific components or modules shown in the drawings and described herein. Other embodiments may include different computer-executable instructions or components having more or fewer functions than those shown and described herein.

[0273] Example 4

[0274] This application also provides an electronic device, including at least a memory and a processor, wherein the memory stores a computer program, and the processor implements the above-described evaluation method for aggregators participating in electricity market bidding when executing the computer program in the memory.

[0275] In some embodiments, the processor executing a computer program may be a processing device that includes one or more general-purpose processing devices, such as a microprocessor, a central processing unit (CPU), a graphics processing unit (GPU), etc. More specifically, the processor may be a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a processor that runs other instruction sets, or a processor that runs a combination of instruction sets. The processor may also be one or more special-purpose processing devices, such as application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), system-on-a-chip (SoCs), etc.

[0276] The memory may be a read-only memory (ROM), random access memory (RAM), phase-change random access memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), electrically erasable programmable read-only memory (EEPROM), other types of random access memory (RAM), flash drives or other forms of flash memory, cache, registers, static memory, optical disc read-only memory (CD-ROM), digital versatile optical disc (DVD) or other optical storage, magnetic tape cassette or other magnetic storage devices, or any other possible non-transitory medium used to store information or instructions that can be accessed by computer equipment.

[0277] The electronic devices in this application embodiment may include, but are not limited to, fixed terminal devices such as servers, desktop computers, and digital TVs, as well as mobile terminal devices such as in-vehicle devices (e.g., head-up displays), handheld devices (e.g., mobile phones, tablets, etc.), and wearable devices (e.g., smartwatches, smart bracelets, etc.).

[0278] Although the subject matter has been described using language specific to structural features and / or methodological logic, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. Rather, the specific features and actions described above are merely illustrative examples of implementing the claims.

Claims

1. A method for evaluating aggregators participating in electricity market bidding, characterized in that, include: Acquire market data from the electricity market and vehicle-related data of electric vehicles connected to the electricity market, wherein the vehicle-related data includes operating data of electric vehicles and user behavior data of electric vehicle users; An energy constraint model for the electric vehicle population is constructed based on the operating data of the electric vehicles. A user response probability model is constructed based on the user behavior data of the electric vehicle users. An interval optimization model based on market fluctuations is constructed based on the energy constraint model, the user response probability model, and the market data of the electricity market. The solution of the interval optimization model is evaluated to obtain the bidding strategy for electric vehicle aggregators to participate in the electricity market bidding.

2. The method according to claim 1, characterized in that, An energy constraint model for the electric vehicle population is constructed based on the operating data of the electric vehicles, including: Determine the energy balance equation for the energy evolution of a single electric vehicle; Determine the battery safety constraints that a single electric vehicle must meet during operation; The energy constraint model for a single electric vehicle is determined based on the energy balance equation and the battery safety constraints. The energy constraint models of each individual electric vehicle are summarized to obtain the overall energy constraint model of the electric vehicle aggregator, wherein the overall energy constraint model includes the total power and total energy of the electric vehicle aggregator.

3. The method according to claim 2, characterized in that, The energy balance equation is: ； in, Let be the battery level of the i-th electric vehicle at time t. , These are charging power and discharging power, respectively. The scheduling time step; The battery safety constraints include: ； ； ； in, and The minimum and maximum allowable battery capacity for electric vehicles; Maximum charging / discharging power limit for electric vehicles; The expression for the overall energy constraint model is: ； ； ； in, Contribute to the overall efforts of the aggregator; N represents the total energy storage of the aggregator; N represents the number of aggregating vehicles.

4. The method according to claim 1, characterized in that, A user response probability model is constructed based on the user behavior data of the electric vehicle users, including: Based on the nonlinear response characteristics of the electric vehicle user to the excitation signal, a piecewise probabilistic response model is determined; wherein, the piecewise probabilistic response model is: ； in, To incentivize compensation levels; The response sensitivity of user i; and These are the dead zone and the saturation excitation threshold, respectively. This represents the maximum response probability.

5. The method according to claim 4, characterized in that, The method further includes: Based on user behavior fluctuations and external influences, the response probability in the piecewise probabilistic response model is represented by an interval, wherein the response probability is expressed as: ； in, This represents the expected response probability. This is a fluctuation parameter that reflects the magnitude of uncertainty in user behavior.

6. The method according to claim 1, characterized in that, Based on the energy constraint model, the user response probability model, and the electricity market data, a range optimization model based on market fluctuations is constructed, including: The energy boundary and dispatchable power are determined based on the energy constraint model of the electric vehicle population. Determine the uncertainty range of market signals; Based on the schedulable power, energy boundary, uncertainty interval, and user response probability, an interval optimization model is constructed with expected return and variance risk as the objectives.

7. The method according to claim 6, characterized in that, The uncertainty range includes the uncertainty range of energy market prices, the uncertainty range of frequency modulation market prices, and the uncertainty range of frequency modulation signals. The uncertainty range of the energy market price is: ； The uncertainty range of the FM market price is: ； in, For energy market electricity prices, For up and down frequency adjustment prices, , These represent the prediction error ranges; The uncertainty range of the frequency modulation signal is: ； in, This is a frequency deviation signal. The mean, Standard deviation, The confidence factor.

8. The method according to claim 6, characterized in that, The objective function of the interval optimization model is: ； in, For expected returns, For the variance of revenue, As a risk adjustment factor; In the objective function, the total revenue of the electric vehicle aggregator in the electricity market is: ； in, Total revenue for aggregators; Baseline power; and For up and down frequency modulation capacity; and Separate the positive and negative components of the frequency modulation signal; Cost per unit of battery degradation; To incentivize prices; The constraints of the interval optimization model are: ； in, and These represent the upper and lower limits of the power available to the aggregator, respectively. and These are the upper and lower limits of the aggregation energy constraint, respectively.

9. The method according to claim 1, characterized in that, The bidding strategy includes: baseline power planning. Up-modulation capacity allocation Down-modulation capacity allocation Incentive compensation level and expected optimal return .

10. An evaluation device for aggregators participating in electricity market bidding, characterized in that, include: The acquisition module is configured to acquire market data of the electricity market and vehicle-related data of electric vehicles connected to the electricity market, wherein the vehicle-related data includes the operation data of electric vehicles and the user behavior data of electric vehicle users; The first model building module is configured to build an energy constraint model for the electric vehicle group based on the operating data of the electric vehicles. The second model building module is configured to build a user response probability model based on the user behavior data of the electric vehicle users. The third model building module is configured to build an interval optimization model based on market fluctuations based on the energy constraint model, the user response probability model, and the market data of the electricity market. The model solving module is configured to evaluate the bids for the interval optimization model to obtain the bidding strategies for electric vehicle aggregators to participate in electricity market bidding.

11. The evaluation device for aggregators participating in electricity market bidding according to claim 10, characterized in that, The first model building module is also configured as follows: Determine the energy balance equation for the energy evolution of a single electric vehicle; Determine the battery safety constraints that a single electric vehicle must meet during operation; The energy constraint model for a single electric vehicle is determined based on the energy balance equation and the battery safety constraints. The energy constraint models of each individual electric vehicle are summarized to obtain the overall energy constraint model of the electric vehicle aggregator, wherein the overall energy constraint model includes the total power and total energy of the electric vehicle aggregator.

12. The evaluation device for aggregators participating in electricity market bidding according to claim 11, characterized in that, The energy balance equation is: ； in, Let be the battery level of the i-th electric vehicle at time t. , These are charging power and discharging power, respectively. The scheduling time step; The battery safety constraints include: ； ； ； in, and The minimum and maximum allowable battery capacity for electric vehicles; Maximum charging / discharging power limit for electric vehicles; The expression for the overall energy constraint model is: ； ； ； in, Contribute to the overall efforts of the aggregator; N represents the total energy storage of the aggregator; N represents the number of aggregating vehicles.

13. The evaluation device for aggregators participating in electricity market bidding according to claim 10, characterized in that, The second model building module is also configured as follows: Based on the nonlinear response characteristics of the electric vehicle user to the excitation signal, a piecewise probabilistic response model is determined; wherein, the piecewise probabilistic response model is: ； in, To incentivize compensation levels; The response sensitivity of user i; and These are the dead zone and the saturation excitation threshold, respectively. This represents the maximum response probability.

14. The evaluation device for aggregators participating in electricity market bidding according to claim 13, characterized in that, The second model building module is also configured as follows: Based on user behavior fluctuations and external influences, the response probability in the piecewise probabilistic response model is represented by an interval, wherein the response probability is expressed as: ； in, This represents the expected response probability. This is a fluctuation parameter that reflects the magnitude of uncertainty in user behavior.

15. The evaluation device for aggregators participating in electricity market bidding according to claim 10, characterized in that, The third model construction module is also configured as follows: The energy boundary and dispatchable power are determined based on the energy constraint model of the electric vehicle population. Determine the uncertainty range of market signals; Based on the schedulable power, energy boundary, uncertainty interval, and user response probability, an interval optimization model is constructed with expected return and variance risk as the objectives.

16. The evaluation device for aggregators participating in electricity market bidding according to claim 15, characterized in that, The uncertainty range includes the uncertainty range of energy market prices, the uncertainty range of frequency modulation market prices, and the uncertainty range of frequency modulation signals. The uncertainty range of the energy market price is: ； The uncertainty range of the FM market price is: ； in, For energy market electricity prices, For up and down frequency adjustment prices, , These represent the prediction error ranges; The uncertainty range of the frequency modulation signal is: ； in, This is a frequency deviation signal. The mean, Standard deviation, The confidence factor.

17. The evaluation device for aggregators participating in electricity market bidding according to claim 15, characterized in that, The objective function of the interval optimization model is: ； in, For expected returns, For the variance of revenue, As a risk adjustment factor; In the objective function, the total revenue of the electric vehicle aggregator in the electricity market is: ； in, Total revenue for aggregators; Baseline power; and For up and down frequency modulation capacity; and Separate the positive and negative components of the frequency modulation signal; Cost per unit of battery degradation; To incentivize prices; The constraints of the interval optimization model are: ； in, and These represent the upper and lower limits of the power available to the aggregator, respectively. and These are the upper and lower limits of the aggregation energy constraint, respectively.

18. The evaluation device for aggregators participating in electricity market bidding according to claim 10, characterized in that, The bidding strategy includes: baseline power planning. Up-modulation capacity allocation Down-modulation capacity allocation Incentive compensation level and expected optimal return .

19. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method of any one of claims 1 to 9.

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