Power grid frequency modulation optimization method, medium and program product

Abstract

The invention discloses a power grid frequency modulation optimization method, a medium and a program product in the technical field of electric vehicle and power grid interaction, and the method comprises the steps: building a single electric vehicle model in multiple network access states, and determining an operation boundary condition when a single electric vehicle interacts with power grid energy; establishing a relationship between the incentive electricity price and the user responsivity based on the Weber-Fechner law; evaluating the time sequence response of the electric vehicle under the excitation electricity price in the whole network access time period, and establishing an electric vehicle cluster model according to the operation boundary condition and the user responsivity when the single electric vehicle interacts with the power grid energy; and a collaborative frequency modulation operation optimization objective function of the electric vehicle aggregator and the frequency modulation unit is established, and collaborative operation of the electric vehicle aggregator and the frequency modulation unit is realized. The method can effectively improve the responsivity of an electric vehicle user to power grid frequency modulation, the high responsivity of the user not only expands the range of frequency modulation resources, but also enhances the flexibility in the frequency modulation process, and optimizes the power grid frequency modulation effect.

Description

Technical Field

[0001] The present invention relates to the technical field of interaction between electric vehicles and power grids, and in particular to a power grid frequency modulation optimization method, medium and program product. Background Art

[0002] With the large-scale access of new energy to the power grid, the volatility of the power grid frequency has increased significantly, bringing challenges to the power grid frequency regulation. The resulting system frequency instability problem has become increasingly prominent. Traditional frequency regulation units have long response lags and low ramp rates, making it difficult to track rapid changes in power. The configuration of a considerable amount of battery energy storage for frequency regulation units is an effective way to improve the performance of frequency regulation units. It can effectively utilize the rapid response capability of battery energy storage to improve the frequency regulation effect. However, the current large-scale battery energy storage investment and operating costs are high. In recent years, the number of electric vehicles in the world has continued to grow, and the market penetration rate has continued to increase. After aggregation, electric vehicles have the characteristics of large regulation capacity and fast response speed. The joint frequency regulation mode of electric vehicles and frequency regulation units complementing each other after aggregation has become a new idea. At present, some studies on the participation of electric vehicle aggregators in the frequency regulation market have failed to accurately characterize the response willingness of electric vehicle users, have not deeply explored the relationship between user response willingness and price incentives, and it is difficult to accurately establish a model that reflects user responsiveness. Existing research on the coordinated frequency regulation of electric vehicle aggregators and frequency regulation units has failed to take into account the fact that the frequency regulation capacity of electric vehicle aggregators is affected by the behavior of electric vehicles compared with energy storage, and has lacked consideration of the impact of electric vehicle responsiveness on their frequency regulation benefits. In the coordinated frequency regulation between electric vehicle aggregators and frequency regulation units, it is difficult to clarify the frequency regulation contribution of units and aggregators, and has failed to consider the impact of user responsiveness on the joint frequency regulation benefits. Summary of the invention

[0003] The purpose of the present invention is to overcome the deficiencies in the prior art and to provide a power grid frequency regulation optimization method, medium and program product, which can effectively improve the user's responsiveness to power grid frequency regulation, thereby expanding the scope of frequency regulation resources, enhancing the flexibility and adaptability of the frequency regulation process, and achieving the effect of optimizing power grid frequency regulation.

[0004] To achieve the above object, the present invention is implemented by adopting the following technical solutions:

[0005] In a first aspect, the present invention provides a method for optimizing power grid frequency regulation, comprising:

[0006] Establish a single electric vehicle model in multiple grid-connected states, use the single electric vehicle model to simulate the energy exchange process between the electric vehicle and the grid in different time periods, and determine the operating boundary conditions of a single electric vehicle when interacting with the grid energy;

[0007] Evaluate the differences in users' psychological tolerance for participating in two-way vehicle-grid interaction, and establish the relationship between incentive electricity price and user responsiveness based on the Weber-Fechner law;

[0008] Evaluate the timing response of electric vehicles under incentive electricity prices during the entire grid access period, establish an electric vehicle cluster model based on the operating boundary conditions and user responsiveness of a single electric vehicle when interacting with grid energy, and use the electric vehicle cluster model to determine the operating boundary conditions of the electric vehicle cluster when interacting with grid energy;

[0009] According to the operating boundary conditions of the electric vehicle cluster when interacting with the grid energy, the constraints of the electric vehicle aggregator's frequency regulation are set, and an optimization objective function for the coordinated frequency regulation operation of the electric vehicle aggregator and the frequency regulation unit is established. Combined with the relationship between the incentive electricity price and the user's responsiveness, the highest comprehensive benefit of the electric vehicle aggregator and the frequency regulation unit is taken as the goal, and the coordinated operation of the electric vehicle aggregator and the frequency regulation unit is achieved to optimize the grid frequency regulation effect.

[0010] Furthermore, the operating boundary conditions of the single electric vehicle when interacting with the grid energy include the upper and lower limits of the charging and discharging power of the single electric vehicle and the upper and lower limits of the SOC battery state of charge;

[0011] Single electric vehicle in the period The upper and lower limits of the charging and discharging power for energy exchange with the power grid are shown in the following formula:

[0012] ;

[0013] Where: are the upper and lower limits of the power for the i-th electric vehicle to exchange energy with the power grid at time t; is the maximum charging power; is the maximum discharge power;

[0014] The single electric vehicle model in multiple grid-connected states takes into account the charging state, discharging state and idle state of the single electric vehicle:

[0015] (1) Charging status: The electric vehicle absorbs electrical energy at the real-time charging power;

[0016] (2) Idle state: the electric vehicle has no power transmission;

[0017] (3) Discharge state: The electric vehicle discharges electrical energy at real-time discharge power;

[0018] The calculation formula of the SOC of a single electric vehicle in three states is expressed as follows:

[0019] ;

[0020] Where: is the SOC state of the i-th electric vehicle at time t; and are the charging and discharging power of the i-th electric vehicle at time t; and are the charging and discharging efficiencies of the i-th electric vehicle at time t; For the i-th electric car Charging time in time; is the battery capacity of the electric vehicle; is the state of the response mode of the i-th electric vehicle; CM is Charging Mode, indicating that the electric vehicle is in a charging state; IM is Idle Mode, indicating that the electric vehicle is in an idle state; DM is Discharging Mode, indicating that the electric vehicle is in a discharging state;

[0021] According to the calculation formula of the SOC of the single electric vehicle in three states, it can be obtained that the single electric vehicle in the time period The upper and lower limits of the SOC battery state of charge for energy exchange with the power grid are shown in the following formula:

[0022] ;

[0023] Where: are the upper and lower limits of the SOC battery state of charge of the i-th electric vehicle at time t; for the study time interval; Minimum SOC to prevent over-discharge; The required SOC for ending charging of electric vehicles.

[0024] Furthermore, based on the Weber-Fechner law, the relationship between the incentive electricity price and the user responsiveness is established, and the relationship is expressed as follows:

[0025] ;

[0026] Where: is the response rate of electric vehicles when the incentive electricity price is r; is the electric vehicle response coefficient; c is the electric vehicle response constant; in the same time period, a single electric vehicle can only be charged or discharged when connected to the grid. When a single electric vehicle charges in response to the incentive electricity price r, r= Substitute into the formula to calculate the response rate ; When a single electric vehicle discharges in response to the incentive electricity price r, r= Substitute into the formula to calculate the response rate ;

[0027] For electric vehicles that discharge in response to the incentive electricity price r, additional compensation is required for the battery loss cost caused by the discharge, based on the battery loss cost and the charging incentive price Setting the discharge subsidy price given by EV aggregators As shown below:

[0028] ;

[0029] Where: Subsidize the price for discharge; To provide incentive price for charging; Compensate for the discharge battery loss of electric vehicles during the entire discharge period;

[0030] According to the relationship between the incentive electricity price and the user's responsiveness, the response threshold of different users to the change of the incentive electricity price is determined. The response threshold includes the upper limit of the user's charging response rate under different incentive electricity prices. , Charging response rate lower limit , upper limit of discharge response rate and the lower limit of discharge response rate ;

[0031]

[0032]

[0033]

[0034]

[0035] Where: The starting response price for the upper limit of the charging response rate; The upper limit cutoff response price of the charging response rate; The starting response price for the lower limit of the charging response rate; The lower limit response price of the charging response rate; is the starting response price for the upper limit of the discharge response rate; is the upper cutoff response price for the discharge response rate; is the lower limit starting response price of the discharge response rate; It is the lower limit cutoff response price of the discharge response rate.

[0036] Furthermore, the discharge subsidy price is set and the charging incentive electricity price When the proportion of users responding to the discharge subsidy mechanism and the proportion of responding to charging incentive mechanisms They are evenly distributed within their upper and lower limits. The response ratios of the charging incentive mechanism and the discharging subsidy mechanism are shown in the following formula:

[0037] ;

[0038] Where: Represents a uniform distribution.

[0039] Furthermore, the timing response of electric vehicles under incentive electricity prices is evaluated during the entire grid access period, including:

[0040] Considering the EV response charging incentive mechanism, the regulation period of EV aggregators during EV charging is as follows:

[0041] ;

[0042] Where: express It represents the minimum time required for the i-th electric vehicle to complete the charging demand; represents the charging period of the i-th electric vehicle; is the adjustable time period for the i-th electric vehicle to respond to the charging incentive mechanism; represents the i-th electric vehicle responding to the charging incentive mechanism, otherwise ;

[0043] Considering the EV response discharge subsidy mechanism, the regulation period during which the EV aggregator can discharge the EV is as follows:

[0044] ;

[0045] Where: is the adjustable period for the i-th electric vehicle to respond to the discharge subsidy mechanism; The i-th electric vehicle responds to the discharge subsidy mechanism, otherwise ; for and The maximum value of The i-th electric vehicle responds to the charging incentive mechanism or the discharging subsidy mechanism, otherwise ;

[0046] The single electric vehicle can only be charged or discharged in the same time period when it is connected to the grid, and the charging and discharging power at each moment is subject to the upper and lower limits of the charging and discharging power. In the non-grid time period, the charging and discharging power of the single electric vehicle is 0. The power operation constraint of the single electric vehicle is as follows:

[0047] ;

[0048] Where: is a 0-1 variable used to control the i-th vehicle to be unable to charge and discharge at the same time. The i-th vehicle is charged at time t, otherwise it is discharged;

[0049] The SOC of the single electric vehicle at each moment during the time period of access to the grid is subject to the upper and lower limits of the SOC battery state of charge and the user's charging demand. During the non-grid access time period, the SOC of the single electric vehicle that can participate in regulating the grid frequency is 0. The energy operation constraint of the single electric vehicle is as follows:

[0050] ;

[0051] An electric vehicle cluster model is established according to the operating boundary conditions and user responsiveness of a single electric vehicle when interacting with grid energy, and the operating boundary conditions of the electric vehicle cluster when interacting with grid energy are determined using the electric vehicle cluster model, including:

[0052] Generate the charging starting SOC and charging time of a single electric vehicle through Monte Carlo sampling to simulate the randomness of electric vehicle charging and discharging;

[0053] Based on the relationship between the incentive electricity price and the user responsiveness established according to the Weber-Fechner law, the response rate of the electric vehicle user to the charging incentive is calculated. and the response rate to the discharge subsidy , and determine whether the electric vehicle responds to charging incentives or discharging subsidies based on the response rate, thereby determining the 0-1 variable , and The value of

[0054] According to the charging starting SOC, charging time and 0-1 variable of the single electric vehicle , , , calculate the operating boundary of the single electric vehicle when interacting with the grid energy, and determine the upper and lower limits of its charging and discharging power and the upper and lower limits of the SOC battery state of charge within a given period of time;

[0055] Repeat the above process, summarize the energy and power boundaries of individual electric vehicles, and obtain the operating boundary conditions of the electric vehicle cluster when interacting with the grid energy. The operating boundary conditions of the electric vehicle cluster when interacting with the grid energy include the boundaries of the energy and power operation of the electric vehicle cluster, as shown in the following formula:

[0056] ;

[0057] Where: are the upper and lower bounds of the energy of the electric vehicle cluster at time t; are the upper and lower bounds of the power of the electric vehicle cluster at time t; The number of electric vehicles that can be controlled by electric vehicle aggregators.

[0058] Furthermore, the constraint conditions for the frequency modulation of the electric vehicle aggregator are set according to the operation boundary of the electric vehicle cluster when interacting with the grid energy, including:

[0059] The charging plan energy determined by the electric vehicle cluster at each time period and the net energy after responding to charging incentives or discharging subsidies It is subject to the constraints of the energy operation boundary of this period, as shown in the following formula:

[0060] ;

[0061] Where: represents the charging power planned by the EV aggregator during time period k; is the time period length; represents the net power of the electric vehicle in response to the charging incentive or discharging subsidy during the k period; are the upper and lower bounds of the energy of the electric vehicle cluster in time period k;

[0062] The electric vehicle aggregator responds to the changes in the frequency modulation signal by controlling the charging and discharging power of each electric vehicle in the electric vehicle cluster. The net power of the electric vehicle cluster responds to the charging incentive or discharging subsidy. Each time period is subject to the constraints of the power operation boundary of the time period, as shown in the following formula:

[0063] ;

[0064] Where: are the upper and lower bounds of the power of the electric vehicle cluster in time period k;

[0065] Upward frequency modulation power of electric vehicle clusters at different time periods , Downward frequency modulation power All are non-negative:

[0066] ;

[0067] The economic factors considered in the objective function include the electricity purchase cost of the electric vehicle aggregator, the operating cost of the electric vehicle aggregator, the operating cost of the frequency regulation unit, the frequency regulation capacity income and the mileage income. The objective function is shown in the following formula:

[0068] ;

[0069] Where: It is the combined revenue of EV aggregators and frequency regulators; The cost of electricity purchases by electric vehicle aggregators in the day-ahead energy market; Cost of operating in the real-time market for EV aggregators; Frequency regulation capacity revenue for EV aggregators and frequency regulation units; Frequency regulation mileage revenue for electric vehicle aggregators and frequency regulation units; Charging incentives and discharge subsidies issued by electric vehicle aggregators to users, as well as charging revenues obtained from users; It is the operating cost of the frequency regulation unit;

[0070] in:

[0071] ;

[0072] Where: K is the number of time periods in the entire research cycle; is the electricity price in the k-period in the day-ahead energy market;

[0073] ;

[0074] ;

[0075] Where: is the electricity price in the real-time energy market in time period k; The real-time charging power of the electric vehicle aggregator at k time period within the day; The difference between the day-ahead electricity purchase and the day-time electricity consumption is calculated as the real-time electricity price. Settlement, To provide for imbalance penalties for double-settled electricity market arbitrage; is the day-ahead bidding electricity; is the actual electricity consumption during the day; are two constants; ;

[0076] ;

[0077] Where: is the frequency regulation capacity electricity price in period k; and are the frequency regulation capacities of the EV aggregator and frequency regulation unit in period k respectively;

[0078] ;

[0079] ;

[0080] Where: is the frequency regulation mileage electricity price in time period k; and are the frequency regulation mileage of the electric vehicle aggregator and the frequency regulation unit in time period k, respectively; , are the upward and downward frequency modulation powers of EV aggregators in time period k, respectively;

[0081] ;

[0082] Where: is the net power responded by the EV aggregator in period k; is the net power of electric vehicles that do not respond to charging incentives or discharge subsidies; is the charging incentive price issued by the EV aggregator in time period k; is the EV charging price published by the EV aggregator in time period k; The discharge subsidy fee given by the electric vehicle aggregator to the electric vehicle users who participated in the discharge in response to the discharge subsidy, Charging incentive fees for electric vehicle aggregators to provide to electric vehicle users who participate in charging in response to the charging incentives, Charging charges given to EV aggregators for EV clusters;

[0083] ;

[0084] In the formula: NG is the number of frequency-regulated units; NT is the number of time periods; is the linear coal consumption cost of the frequency regulation unit; is the no-load cost of the frequency regulation unit; The cost of starting the frequency regulating unit; The cost of shutting down frequency regulation units; is the output power of frequency modulation unit g in time period t; is a 0-1 variable, Indicates the operating status of frequency-regulated unit g in time period t, For online operation, To be out of operation; Indicates the start-up action of the frequency-modulating unit g at time t, To start the action, For other status; Indicates the start-up action of the frequency-regulated unit g at time t, To start the action, For other status.

[0085] Furthermore, the power grid frequency regulation optimization method also includes: setting frequency regulation constraints of the frequency regulation units, the output power of the frequency regulation units being limited by their maximum output and minimum power output, while maintaining necessary standby power margin.

[0086] Furthermore, the frequency regulation constraint conditions of the frequency regulation unit are set, including:

[0087] Any frequency-regulating unit g is in the on state in any time period t. The actual output power of the frequency-regulating unit in operation is subject to its maximum output power and its minimum output power constraints, as shown in the following formula:

[0088] ;

[0089] Where: Indicates the operating status of frequency-regulated unit g in time period t, For online operation, To be out of operation; is the output power of frequency modulation unit g in time period t; is the maximum output power of frequency modulation unit g; is the minimum output power of frequency modulation unit g;

[0090] The frequency regulating unit g is subject to its minimum opening time TU g and minimum shutdown time TD g The restrictions are as follows:

[0091] ;

[0092] Where: Indicates the start-up action of the frequency-regulated unit g at time t, To start the action, For other status; Indicates the start-up action of the frequency-regulated unit g at time t, To start the action, For other status;

[0093] In any time period t, the frequency-regulating unit in the online operation state can be shut down but not started; in any time period t, the frequency-regulating unit in the out-of-operation state can be started but not shut down, as shown in the following formula:

[0094] ;

[0095] The sum of the power generated by all frequency-regulating units in operation in any time period t needs to meet the load demand in the time period t and maintain the reserve power margin, as shown in the following formula:

[0096] ;

[0097] Where: is the number of frequency regulation units; is the frequency regulation load in time period t.

[0098] In a second aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, characterized in that when the program is executed by a processor, the steps of the above-mentioned power grid frequency optimization method are implemented.

[0099] In a third aspect, the present invention provides a computer program product, comprising a computer program / instruction, characterized in that the computer program / instruction implements the steps of the above-mentioned power grid frequency optimization method when executed by a processor.

[0100] Compared with the prior art, the present invention has the following beneficial effects:

[0101] The present invention constructs a single electric vehicle model under multiple grid-connected states, simulates the energy exchange process between the electric vehicle and the grid in different time periods, determines the operating boundary conditions of a single electric vehicle in energy interaction, and thus optimizes the overall energy management and regulation.

[0102] By evaluating the psychological tolerance of users in two-way vehicle-grid interaction and combining the Weber-Fechner law, the present invention accurately describes the perception of electric vehicle users on changes in incentive electricity prices and establishes the relationship between incentive electricity prices and user responsiveness. This method can effectively capture the sensitivity and behavioral changes of different users to incentive electricity prices, and by reasonably adjusting the incentive electricity prices, more electric vehicle users can be encouraged to participate in the grid frequency regulation process.

[0103] The user's response to the incentive electricity price is nonlinear. Based on the Weber-Fechner law, the present invention accurately depicts the relationship between the user's willingness to respond and price changes, ensuring that electric vehicle users make charging and discharging decisions under appropriate incentive conditions. By establishing a model that reflects the user's responsiveness, the user's response potential is deeply excavated, allowing electric vehicle aggregators to more effectively regulate user behavior and improve the overall frequency modulation effect.

[0104] By evaluating the timing response of electric vehicles under charging and discharging incentives during the full grid access period, the present invention further establishes an electric vehicle cluster model, realizes the synergy of electric vehicle clusters in frequency regulation, and makes scheduling more flexible and dynamic. The model enables electric vehicle aggregators to more accurately predict the frequency regulation response capability of electric vehicle clusters and optimize frequency regulation scheduling on this basis.

[0105] In addition, the present invention constructs an optimization objective function for the coordinated frequency regulation operation of electric vehicle aggregators and frequency regulation units, takes the maximization of comprehensive benefits as the goal, and effectively combines user responsiveness and the operating boundary of electric vehicle clusters to achieve efficient coordinated operation of electric vehicle aggregators and frequency regulation units. This optimization method improves the accuracy and stability of grid frequency regulation, while also increasing the economic benefits of electric vehicle aggregators and frequency regulation units.

[0106] The present invention can fully utilize the flexible energy storage capacity of electric vehicles during the frequency modulation process, significantly improve the frequency modulation response speed and efficiency of the power grid, reduce the risk of instability caused by frequency fluctuations, and has broad application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0107] Figure 1 It is the relationship diagram between charging incentive electricity price and upper and lower limits of user responsiveness;

[0108] Figure 2 It is the relationship diagram between the discharge subsidy electricity price and the upper and lower limits of user responsiveness;

[0109] Figure 3 It is a graph showing the relationship between incentive electricity price and comprehensive frequency regulation revenue;

[0110] Figure 4 This is the frequency modulation output optimization result diagram of scenario 1;

[0111] Figure 5 This is the result diagram of frequency modulation output optimization in scenario 2;

[0112] Figure 6 This is the result diagram of frequency modulation output optimization in scenario 3;

[0113] Figure 7 It is a flow chart of the power grid frequency optimization method provided in Example 1 of the present invention. DETAILED DESCRIPTION

[0114] The technical solution of the present invention is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present application and the specific features in the embodiments are detailed descriptions of the technical solution of the present application, rather than limitations on the technical solution of the present application. In the absence of conflict, the embodiments of the present application and the technical features in the embodiments can be combined with each other.

[0115] Embodiment 1:

[0116] This embodiment provides a method for optimizing power grid frequency regulation, which is characterized by comprising:

[0117] Establish a single electric vehicle model in multiple grid-connected states, use the single electric vehicle model to simulate the energy exchange process between the electric vehicle and the grid in different time periods, and determine the operating boundary conditions of a single electric vehicle when interacting with the grid energy;

[0118] Evaluate the differences in users' psychological tolerance for participating in two-way vehicle-grid interaction, and establish the relationship between incentive electricity price and user responsiveness based on the Weber-Fechner law;

[0119] Evaluate the timing response of electric vehicles under incentive electricity prices during the entire grid access period, establish an electric vehicle cluster model based on the operating boundary conditions and user responsiveness of a single electric vehicle when interacting with grid energy, and use the electric vehicle cluster model to determine the operating boundary conditions of the electric vehicle cluster when interacting with grid energy;

[0120] According to the operating boundary conditions of the electric vehicle cluster when interacting with the grid energy, the constraints of the electric vehicle aggregator's frequency regulation are set, and an optimization objective function for the coordinated frequency regulation operation of the electric vehicle aggregator and the frequency regulation unit is established. Combined with the relationship between the incentive electricity price and the user's responsiveness, the highest comprehensive benefit of the electric vehicle aggregator and the frequency regulation unit is taken as the goal, and the coordinated operation of the electric vehicle aggregator and the frequency regulation unit is achieved to optimize the grid frequency regulation effect.

[0121] EV aggregators are responsible for coordinating a large number of EVs to participate in the frequency regulation of the power grid. Assume that there are thousands of EVs, and their battery storage capacity is very dispersed and small. A single EV may not be able to provide sufficient frequency regulation services, but EV aggregators can pool these battery vehicles to form a "virtual power plant" to provide useful energy storage resources for the grid. EV aggregators help manage the charging and discharging behavior of each EV and respond quickly to grid needs.

[0122] Frequency regulating units refer to power generation equipment or energy storage systems that are specifically used to regulate the frequency of the power grid. When the frequency of the power grid fluctuates, for example due to unstable power generation from renewable energy sources, the frequency regulating units will maintain the stability of the frequency by increasing or decreasing the output power. As electric vehicles become new energy storage tools, they can serve as mobile frequency regulating units. Under the coordination of electric vehicle aggregators, the batteries of electric vehicles play the role of these "frequency regulating units" and directly participate in frequency regulation through charging and discharging behaviors.

[0123] Firstly, a single electric vehicle model with multiple grid access states is established to determine the operating boundary conditions of a single electric vehicle when interacting with the grid energy, namely the upper and lower limits of the charging and discharging power of the single electric vehicle and the upper and lower limits of the SOC battery state of charge.

[0124] When the battery is charging, the upper SOC boundary is the maximum charge state that the battery can safely reach. Exceeding this upper limit may cause the battery to be overcharged. When the battery is discharging, the lower SOC boundary is the safest minimum charge state of the battery. Falling below this lower limit may cause the battery to be over-discharged. Overcharging or over-discharging of the battery will affect its performance or safety.

[0125] When an electric vehicle is connected to the grid for charging, the upper limit of SOC can limit the charging process to ensure that the battery is not overcharged. For example, the battery management system (BMS) may set an upper limit, such as 80% SOC, to protect the battery.

[0126] When an EV is providing power to the grid, a lower limit on the SOC ensures that the battery is not discharged too low. For example, a lower limit of 30% SOC may be set to ensure that the battery is not over-discharged during the discharge process.

[0127] In actual operation, the SOC boundary will be dynamically adjusted according to the specific grid demand and the actual situation of the battery. When the grid load is peak, the electric vehicle will reduce the battery SOC to an allowable safety lower limit to support grid stability.

[0128] In this embodiment, a single electric vehicle is The upper and lower limits of the charging and discharging power for energy exchange with the power grid are shown in the following formula:

[0129] ;

[0130] Where: are the upper and lower limits of the power for the i-th electric vehicle to exchange energy with the power grid at time t; is the maximum charging power; is the maximum discharge power;

[0131] The single electric vehicle model with multiple access states takes into account the charging state, discharging state and idle state of the single electric vehicle:

[0132] (1) Charging Mode (CM): The electric vehicle absorbs electrical energy at the real-time charging power;

[0133] (2) Idle Mode (IM): The electric vehicle has no power transmission;

[0134] (3) Discharging Mode (DM): The electric vehicle discharges electrical energy at the real-time discharge power;

[0135] The calculation formula of the SOC of a single electric vehicle in three states is expressed as follows:

[0136]

[0137] Where: is the SOC state of the i-th electric vehicle at time t; and are the charging and discharging power of the i-th electric vehicle at time t; and are the charging and discharging efficiencies of the i-th electric vehicle at time t; For the i-th electric car Charging time in time; is the battery capacity of the electric vehicle; is the state of the response mode of the i-th electric vehicle;

[0138] According to the calculation formula of the SOC of a single electric vehicle in three states, it can be concluded that the SOC of a single electric vehicle in the time period The upper and lower limits of the SOC battery state of charge for energy exchange with the power grid are shown in the following formula:

[0139] ;

[0140] Where: are the upper and lower limits of the SOC battery state of charge of the i-th electric vehicle at time t; for the study time interval; Minimum SOC to prevent over-discharge; The required SOC for ending charging of electric vehicles.

[0141] Electric vehicle batteries can adjust their charge and discharge status in a short time because of their fast charging and discharging speed. This responsiveness can help the power grid balance supply and demand in the short term, especially when renewable energy generation fluctuates violently, the rapid response of electric vehicles is crucial for frequency regulation.

[0142] In order to get electric vehicle users to participate in grid frequency regulation, some incentives are needed. In this embodiment, charging incentives and discharge subsidies are used as economic rewards given by electric vehicle aggregators to electric vehicle users. When electric vehicles discharge to support the grid, electric vehicle aggregators issue discharge subsidies to electric vehicle users; when electric vehicles charge to support the grid, electric vehicle aggregators issue charging incentives to electric vehicle users. This incentive mechanism ensures that electric vehicle users are willing to connect their vehicles to the grid frequency regulation system.

[0143] Applying the Weber-Fechner law to the response of electric vehicle users to price incentives can be explained from the perception of electric vehicle users on changes in incentive electricity prices, that is, the relationship between the behavioral decisions of electric vehicle users and changes in incentive electricity prices. Based on the Weber-Fechner law, the relationship between incentive electricity prices and user responsiveness is established, and the relationship is expressed as:

[0144] ;

[0145] Where: is the response rate of electric vehicles when the incentive electricity price is r; is the electric vehicle response coefficient; c is the electric vehicle response constant; in the same time period, a single electric vehicle can only be charged or discharged when connected to the grid. When a single electric vehicle charges in response to the incentive electricity price r, r= Substitute into the formula to calculate the response rate ; When a single electric vehicle discharges in response to the incentive electricity price r, r= Substitute into the formula to calculate the response rate ;

[0146] For electric vehicles that discharge in response to the incentive electricity price r, additional compensation is required for the battery loss cost caused by the discharge, based on the battery loss cost and the charging incentive price Setting the discharge subsidy price given by EV aggregators As shown in the formula:

[0147]

[0148] Where: Subsidize the price for discharge; To provide incentive price for charging; Compensate for the discharge battery loss of electric vehicles during the entire discharge period;

[0149] According to the relationship between the incentive electricity price and the user's responsiveness, the response threshold of different users to the change of the incentive electricity price is determined. The response threshold includes the upper limit of the user's charging response rate under different incentive electricity prices. , Charging response rate lower limit , upper limit of discharge response rate and the lower limit of discharge response rate ;

[0150]

[0151]

[0152]

[0153]

[0154] Where: The starting response price for the upper limit of the charging response rate; The upper limit cutoff response price of the charging response rate; The starting response price for the lower limit of the charging response rate; The lower limit response price of the charging response rate; is the starting response price for the upper limit of the discharge response rate; is the upper cutoff response price for the discharge response rate; is the lower limit starting response price of the discharge response rate; It is the lower limit cutoff response price of the discharge response rate.

[0155] The user's response to electricity price incentives is nonlinear. Based on the Weber-Fechner law, the relationship between the user's response willingness and price changes is accurately described to ensure that electric vehicle users make charging and discharging decisions under appropriate incentive conditions. By reasonably adjusting the incentive electricity price, more electric vehicle users are encouraged to participate in the grid frequency regulation process. Figure 1 , Figure 2 As shown in the figure, the relationship between the charging incentive price and the upper and lower limits of user response and the relationship between the discharge subsidy price and the upper and lower limits of user response are shown. It can be seen from the figure that based on the Weber-Fechner law, the user's response to the charging incentive and discharge subsidy prices presents a nonlinear relationship. This law shows that there is not a simple linear relationship between the price change perceived by the user and the actual price change. The user may have a weak perception of a smaller price change, but when the price change is large, the perception increases significantly.

[0156] The red curve in the figure, which reflects the upper limit of the charge and discharge response rate, indicates the upper limit of user responsiveness under higher incentive electricity prices. As the incentive electricity price increases, the user's willingness to respond approaches saturation, and even if the incentive electricity price is further increased, the improvement in user responsiveness becomes smaller and smaller.

[0157] The blue curve in the figure reflects the lower limit of the charge and discharge response rate. The lower limit curve represents the lower limit of user responsiveness at a lower incentive electricity price. It can be seen that when the incentive electricity price is low, users will hardly make a significant response, but once the incentive electricity price exceeds a certain threshold, user responsiveness begins to increase rapidly.

[0158] The relationship between incentive electricity price and user responsiveness is established based on the Weber-Fechner law, which is particularly important in electric vehicle frequency regulation. By setting incentive electricity prices reasonably, electric vehicle aggregators can mobilize users' enthusiasm for participating in grid frequency regulation, avoid setting too high incentive electricity prices to waste resources, and ensure that users will not fail to respond due to too low incentive electricity prices.

[0159] In setting the discharge subsidy price and the charging incentive electricity price When the proportion of users responding to the discharge subsidy mechanism and the proportion of responding to charging incentive mechanisms They are evenly distributed within their upper and lower limits. The response ratio of the charging incentive mechanism and the discharging subsidy mechanism is shown in the formula:

[0160] ;

[0161] Where: Represents a uniform distribution.

[0162] In practice, it is difficult to accurately obtain the response characteristics of each user, so the assumption of uniform distribution is a reasonable choice made under limited information. It ensures coverage of a wide range of user behaviors without relying on overly detailed user behavior feature data. In general, assuming that the responsiveness of users to charging incentives and discharging subsidies is uniformly distributed within their upper and lower limits can simplify the analysis and ensure the fairness and effectiveness of the frequency modulation mechanism, while effectively reflecting the behavioral differences of different users.

[0163] When electric vehicles are connected to the power grid, the time period during which electric vehicle users charge or discharge in response to charging incentives or discharging subsidies is the time period during which electric vehicle aggregators can regulate these electric vehicle users to participate in grid frequency regulation.

[0164] Considering the EV response charging incentive mechanism, the regulation period that EV aggregators can adjust during EV charging is as follows:

[0165]

[0166] Where: express It represents the minimum time required for the i-th electric vehicle to complete the charging demand; represents the charging period of the i-th electric vehicle; is the adjustable time period for the i-th electric vehicle to respond to the charging incentive mechanism; represents the i-th electric vehicle responding to the charging incentive mechanism, otherwise ;

[0167] Considering the EV response discharge subsidy mechanism, the regulation period during which the EV aggregator can discharge the EV is as follows:

[0168]

[0169] Where: is the adjustable period for the i-th electric vehicle to respond to the discharge subsidy mechanism; The i-th electric vehicle responds to the discharge subsidy mechanism, otherwise ; for and The maximum value of The i-th electric vehicle responds to the charging incentive mechanism or the discharging subsidy mechanism, otherwise ;

[0170] The single electric vehicle can only be charged or discharged in the same time period when it is connected to the grid, and the charging and discharging power at each moment is subject to the upper and lower limits of the charging and discharging power. In the non-grid-connected time period, the charging and discharging power of the single electric vehicle is 0. The power operation constraint of the single electric vehicle is as shown in the formula:

[0171] ;

[0172] Where: is a 0-1 variable used to control the i-th vehicle to be unable to charge and discharge at the same time. The i-th vehicle is charged at time t, otherwise it is discharged;

[0173] The SOC of the single electric vehicle at each moment during the time period of access to the grid is subject to the upper and lower limits of the SOC battery state of charge and the user's charging demand. During the non-grid access time period, the SOC of the single electric vehicle that can participate in regulating the grid frequency is 0. The energy operation constraint of the single electric vehicle is as shown in the formula:

[0174] ;

[0175] An electric vehicle cluster model is established according to the operating boundary conditions and user responsiveness of a single electric vehicle when interacting with grid energy, and the operating boundary conditions of the electric vehicle cluster when interacting with grid energy are determined using the electric vehicle cluster model, including:

[0176] The charging starting SOC and charging duration of a single electric vehicle are generated through Monte Carlo sampling to simulate the randomness of electric vehicle charging and discharging;

[0177] The initial state of SOC is based on the habits of electric vehicle users, assuming that the battery power is between 20% and 80% when entering the charging station. For electric vehicles that are used frequently, their SOC tends to be in a lower range, such as 20% to 40%; while electric vehicles that are used less frequently may enter the charging station when the SOC is higher, such as 60% to 80%. This method can reasonably simulate the diversity of electric vehicle users and the randomness of their usage behavior.

[0178] Based on the relationship between the incentive electricity price and the user responsiveness established according to the Weber-Fechner law, the response rate of the electric vehicle user to the charging incentive is calculated. and in response to the discharge subsidy , and determine whether the electric vehicle responds to charging incentives or discharging subsidies based on the response rate, thereby determining the 0-1 variable , and The value of

[0179] By combining the relationship between incentive electricity prices and user responsiveness, the sensitivity and willingness of electric vehicle users to price incentives are accurately portrayed. By setting appropriate charging incentives and discharging subsidies, the user's responsiveness to the grid frequency regulation needs can be effectively improved, and more electric vehicle users can be encouraged to participate in the two-way interaction of the grid. This not only taps the potential of electric vehicle clusters as mobile energy storage resources, but also enhances the flexibility and response speed of frequency regulation resources.

[0180] According to the charging starting SOC, charging time and 0-1 variable of the single electric vehicle , , , calculate the operating boundary of the single electric vehicle when interacting with the grid energy, and determine the upper and lower limits of its charging and discharging power and the upper and lower limits of the SOC battery state of charge within a given period of time;

[0181] Repeat the above process, summarize the energy and power boundaries of individual electric vehicles, and obtain the operating boundary conditions of the electric vehicle cluster when interacting with the grid energy. The operating boundary conditions of the electric vehicle cluster when interacting with the grid energy include the boundaries of the energy and power operation of the electric vehicle cluster, as shown in the following formula:

[0182] ;

[0183] Where: are the upper and lower bounds of the energy of the electric vehicle cluster at time t; are the upper and lower bounds of the power of the electric vehicle cluster at time t; The number of electric vehicles that can be controlled by electric vehicle aggregators.

[0184] In order to achieve coordinated frequency regulation between electric vehicle aggregators and frequency regulation units, it is necessary to set constraints to ensure that the charging and discharging power and energy operation boundaries of the electric vehicle cluster in each period do not exceed the predetermined limits, and to ensure that the response signal of the electric vehicle cluster meets the frequency regulation requirements. The constraints for the frequency regulation of electric vehicle aggregators are set according to the operation boundaries of the electric vehicle cluster when interacting with the grid energy, including:

[0185] The charging plan energy determined by the electric vehicle cluster at each time period and the net energy after responding to charging incentives or discharging subsidies It is subject to the constraints of the energy operation boundary of this period, as shown in the formula:

[0186] ;

[0187] Where: are the upper and lower energy boundaries of the electric vehicle cluster in time period k.

[0188] The electric vehicle cluster responds to changes in the frequency modulation signal by controlling the charging and discharging power of each electric vehicle. The net power of the electric vehicle cluster in response to charging incentives or discharging subsidies In each period, it is subject to the constraints of the power operation boundary of the period, as shown in the formula:

[0189] ;

[0190] Where: are the upper and lower bounds of the power of the electric vehicle cluster in time period k.

[0191] Upward frequency modulation power of electric vehicle clusters at different time periods , Downward frequency modulation power All are non-negative:

[0192] ;

[0193] , They are the upward and downward frequency modulation powers of the electric vehicle aggregator in time period k respectively; upward frequency modulation power means that when the grid frequency is lower than the target value, such as lower than 50 Hz, the electric vehicle aggregator increases the power supply in the grid by discharging, helping to improve the service capacity of the grid frequency. When the grid power is insufficient, the electric vehicle aggregator controls the discharge of electric vehicles to supplement the power and help restore the frequency. At this time, the battery of the electric vehicle will discharge to the grid and transfer the electric energy in the battery to the grid. For example, if there is a power shortage in the grid, the electric vehicle aggregator can mobilize multiple vehicles to discharge in time period k to provide upward frequency modulation power, such as providing 10 MW of power to the grid, which can increase the grid frequency.

[0194] Downward frequency modulation power refers to the service capacity of electric vehicle aggregators to absorb excess power through charging when the grid frequency is higher than the target value, such as above 50 Hz, to help reduce the grid frequency. When there is excess power on the grid, electric vehicle aggregators control the charging of electric vehicles to consume excess power and help reduce the frequency. At this time, the batteries of electric vehicles will charge from the grid and absorb excess power. For example, when there is excess power on the grid, aggregators can mobilize electric vehicles for charging and absorb excess power in time period k, such as providing 8 MW of downward frequency modulation power to help reduce the grid frequency.

[0195] Taking the highest comprehensive profit of electric vehicle aggregators and frequency modulation units as the goal, an optimization objective function of coordinated frequency modulation operation between electric vehicle aggregators and frequency modulation units is established. The economic factors considered in the objective function include the electricity purchase cost of electric vehicle aggregators, the operating cost of frequency modulation units, frequency modulation capacity income and mileage income. The objective function is shown in the formula:

[0196] ;

[0197] Where: It is the combined revenue of EV aggregators and frequency regulators; The cost of electricity purchases by electric vehicle aggregators in the day-ahead energy market; Cost of operating in the real-time market for EV aggregators; Frequency regulation capacity revenue for EV aggregators and frequency regulation units; Frequency regulation mileage revenue for electric vehicle aggregators and frequency regulation units; Charging incentives and discharge subsidies issued by electric vehicle aggregators to users, as well as charging revenues obtained from users; It is the operating cost of the frequency regulation unit;

[0198] Frequency regulation capacity refers to the maximum frequency regulation power that an EV aggregator can provide to the grid, that is, the total power of the batteries of the EV clusters controlled by the EV aggregator that can be used for frequency regulation services. It represents the potential of EVs to participate in grid frequency regulation, and measures how much power EVs can contribute to the grid to regulate frequency when needed. For example, how much power EVs can provide by discharging when the grid is short of power; how much power they can absorb by charging when the grid has excess power. Assuming that the total frequency regulation capacity of EVs controlled by an EV aggregator is 10 MW, it can provide a maximum of 10 MW of frequency regulation services when frequency regulation needs arise.

[0199] Frequency modulation mileage refers to the amount of frequency modulation services provided by the battery to the grid when the electric vehicle participates in the frequency modulation service. Frequency modulation mileage measures how much electric energy the electric vehicle actually charges or discharges during the frequency modulation process. Frequency modulation mileage is the accumulation of frequency modulation capacity and time, that is, the duration of the frequency modulation service provided by the electric vehicle and the frequency modulation amplitude. Assuming that the electric vehicle discharges 5 kWh in a certain period of time, the frequency modulation mileage is 5 kWh, which means that it contributes 5 kWh of electricity to the grid for frequency regulation during that period of time.

[0200] in:

[0201] ;

[0202] Where: K is the number of time periods in the entire research cycle; is the electricity price in the k-period in the day-ahead energy market; represents the charging power planned by the EV aggregator during time period k; is the time period length;

[0203] ;

[0204] ;

[0205] Where: is the electricity price in the real-time energy market in time period k; The real-time charging power of the electric vehicle aggregator at k time period within the day; The difference between the day-ahead electricity purchase and the day-time electricity consumption is calculated as the real-time electricity price. Settlement, To provide for imbalance penalties for double-settled electricity market arbitrage; is the day-ahead bidding electricity; is the actual electricity consumption during the day; are two constants; ;

[0206] ;

[0207] Where: is the frequency regulation capacity electricity price in period k; and are the frequency regulation capacities of the EV aggregator and frequency regulation unit in period k respectively;

[0208] ;

[0209] ;

[0210] Where: is the frequency regulation mileage electricity price in time period k; and are the frequency modulation mileage of the electric vehicle aggregator and the frequency modulation unit in the k period, respectively, that is, the absolute value of the difference between the output value of the frequency modulation unit in response to the frequency modulation signal at the end of the k period and the output value at the beginning of the k period;

[0211] The frequency regulation capability of an EV aggregator depends on the number of EVs it manages, the remaining charge of each vehicle's battery (SOC), and the willingness of EV owners to participate. In time period k, the EV aggregator needs to balance the allocation of upward and downward frequency regulation power to maximize economic benefits and frequency regulation benefits while ensuring grid frequency stability.

[0212] ;

[0213] Where: The net power of electric vehicles in response to charging incentives or discharging subsidies; is the net power responded by the EV aggregator in period k; is the net power of electric vehicles that do not respond to charging incentives or discharge subsidies; is the charging incentive price issued by the EV aggregator in time period k; is the EV charging price published by the EV aggregator in time period k; The discharge subsidy fee given by the electric vehicle aggregator to the electric vehicle users who participated in the discharge in response to the discharge subsidy, Charging incentive fees for electric vehicle aggregators to provide to electric vehicle users who participate in charging in response to the charging incentives, Charging charges given to EV aggregators for EV clusters;

[0214] ;

[0215] In the formula: NG is the number of frequency-regulated units; NT is the number of time periods; is the linear coal consumption cost of the frequency regulation unit; is the no-load cost of the frequency regulation unit; The cost of starting the frequency regulating unit; The cost of shutting down frequency regulation units; is the output power of frequency modulation unit g in time period t; is a 0-1 variable, Indicates the operating status of frequency-regulated unit g in time period t, For online operation, To be out of operation; Indicates the start-up action of the frequency-regulated unit g at time t, To start the action, For other status; Indicates the start-up action of the frequency-regulated unit g at time t, To start the action, Other states include: standby state, i.e. the unit is not actually involved in frequency regulation, but remains ready to start at any time so that it can be quickly put into operation when the grid needs it; shutdown state, i.e. the unit completely stops running, neither outputs power nor participates in frequency regulation; out of operation state, i.e. the unit is temporarily disconnected from the grid and cannot provide frequency regulation services; maintenance state, the unit enters maintenance state when it is regularly inspected or repaired due to a fault.

[0216] In order to achieve coordinated frequency regulation between EV aggregators and frequency regulation units, constraints need to be set to ensure that the output power of the frequency regulation units is subject to maximum and minimum power limits, while maintaining the necessary reserve power margin to ensure stable operation of the power grid.

[0217] Set the frequency regulation constraints of the frequency regulation units, including:

[0218] Any frequency-regulating unit g is in the on state in any time period t. The output power of the frequency-regulating unit in actual operation is constrained by its maximum output power and its minimum output power, as shown in the formula:

[0219] ;

[0220] Where: Indicates the operating status of frequency-regulated unit g in time period t, For online operation, To be out of operation; is the output power of frequency modulation unit g in time period t; is the maximum output power of frequency modulation unit g; is the minimum output power of frequency modulation unit g.

[0221] The frequency regulating unit g is subject to its minimum opening time TU g (Unit: h) and minimum shutdown time TD g (Unit: h) is limited as shown in the formula:

[0222]

[0223] Where: Indicates the start-up action of the frequency-regulated unit g at time t, To start the action, For other status; Indicates the start-up action of the frequency-regulated unit g at time t, To start the action, For other status.

[0224] Any frequency regulating unit g is in online operation in any time period t, that is, The frequency-regulating units can be shut down but not turned on; any frequency-regulating unit g is out of operation in any time period t, that is, The frequency regulating unit can be turned on but not shut down, as shown in the formula:

[0225]

[0226] The sum of the power generated by all frequency-regulating units in operation in any time period t needs to meet the load demand in the time period t and maintain the reserve power margin, as shown in the formula:

[0227] ;

[0228] Where: is the number of frequency regulation units; is the frequency regulation load in time period t.

[0229] The grid frequency regulation optimization method proposed in the present invention effectively improves the user's responsiveness to grid frequency regulation by taking into account the relationship between incentive electricity prices and user responsiveness, thereby attracting more electric vehicle users to participate in grid frequency regulation. This high responsiveness and wide participation not only expands the scope of frequency regulation resources, but also enhances the flexibility and adaptability in the frequency regulation process, thereby further improving the accuracy and stability of grid frequency regulation. The coordinated work of electric vehicle aggregators and traditional frequency regulation units makes the allocation of frequency regulation resources more reasonable and realizes the joint frequency regulation of multiple subjects. By establishing the objective function of coordinated frequency regulation between electric vehicle aggregators and frequency regulation units, with the goal of maximizing comprehensive benefits, the optimization of grid frequency regulation effects is achieved, thereby ensuring the stability of the grid while improving the economic benefits of the system.

[0230] Embodiment 2:

[0231] In order to further illustrate the grid frequency optimization method of the present invention, the following is an explanation in conjunction with a specific application scenario. The method of the present invention is used to simulate typical domestic electricity market data. It is assumed that 1,000 electric vehicles are connected to the grid in one day, wherein the battery energy storage capacity of each electric vehicle is set to 100 kWh, the rated charging power of the electric vehicle is set to 30 kW, and the charging and discharging efficiencies are both 90%. The Monte Carlo method is used to simulate 1,000 electric vehicles, with one day as the research interval, and one day is divided into 24 hours for research. The Python language is used to call the Docplex model to solve the revenue results of electric vehicle aggregators and frequency regulation units.

[0232] With reference to the performance indicators of the frequency modulation unit that is equipped with energy storage batteries and jointly modulates the frequency with them, the performance indicators of the electric vehicle aggregator and the frequency modulation unit in joint frequency modulation are set. Since the access of electric vehicles to the power grid can be regarded as a kind of battery energy storage, in this embodiment, the performance indicators of the joint frequency modulation of energy storage batteries and frequency modulation units are used as a reference for calculation. The performance indicators when not all electric vehicles participate in the frequency modulation are between the frequency modulation performance indicators of the frequency modulation unit when the frequency modulation unit performs frequency modulation alone and the joint frequency modulation of the electric vehicle aggregator and the frequency modulation unit when all electric vehicles participate in the frequency modulation. The various performance indicator parameters of the joint frequency modulation of the electric vehicle aggregator and the frequency modulation unit when the frequency modulation unit performs frequency modulation alone and all electric vehicles participate in the frequency modulation are shown in Table 1:

[0233]

[0234] In Table 1, k1 represents the regulation rate, k2 represents the response time, k3 represents the regulation accuracy, and kp represents the comprehensive frequency regulation performance index. The detailed definition and calculation method of each performance index have been standardized in the "Frequency Regulation Performance Index Calculation Method" section of the "Guangdong Frequency Regulation Auxiliary Service Market Transaction Implementation Rules", and this embodiment will not be repeated here. As shown in Table 1, after the electric vehicle aggregator and the frequency regulation unit jointly regulate the frequency, the frequency regulation performance index is greatly improved.

[0235] In this embodiment, the frequency-modulating unit is assumed to be jointly powered by two units, and the parameter settings of each frequency-modulating unit are shown in Table 2:

[0236]

[0237] The relationship between the incentive price and the comprehensive frequency modulation benefits is as follows: Figure 3 As shown, different charging incentive prices or discharging subsidy prices are set, and the comprehensive frequency regulation benefits under different charging and discharging price incentives or subsidies are solved respectively. The comprehensive frequency regulation benefits under various charging and discharging price incentives are compared, and the optimal charging incentive price is determined with the goal of maximizing the comprehensive frequency regulation benefit. The discharging subsidy price is set as the sum of the charging incentive price and the battery loss compensation.

[0238] The Monte Carlo method is used to simulate 1,000 electric vehicles, with one day as the research interval, and one day is divided into 24 hours for research. In order to fully consider the impact of operating strategies on the benefits of electric vehicle aggregators and units in the frequency regulation market, the Python language is used to call the Docplex model to solve and analyze the benefits of electric vehicle aggregators and units in the following three scenarios:

[0239] Scenario 1: Electric vehicle aggregators do not participate in power market frequency regulation, do not set charging and discharging incentives or subsidy prices, electric vehicles do not respond to charging incentive mechanisms and discharging subsidy mechanisms, and only frequency regulation units participate in power market frequency regulation;

[0240] Scenario 2: Electric vehicle aggregators only respond to downward frequency modulation signals and only set charging incentive prices. Electric vehicles only respond to charging incentive mechanisms. Electric vehicles that respond to charging incentive mechanisms and frequency modulation units jointly participate in power market frequency modulation.

[0241] Scenario 3: Electric vehicle aggregators respond to all frequency modulation signals, set charging incentives and discharge subsidy prices, electric vehicles respond to charging incentive mechanisms and discharge subsidy mechanisms, and electric vehicle aggregators and frequency modulation units collaborate to participate in power market frequency modulation.

[0242] Scenario 1 uses the frequency modulation unit to independently output in response to the frequency modulation signal. The frequency modulation output is as follows: Figure 4 As shown in the figure, it can be seen that in scenario 1, only two units jointly output frequency regulation. Due to the maximum and minimum output limits of the units and the start-up and shutdown costs, there is a situation where the unit output is greater than the frequency regulation signal demand from 15:00 to 20:00. Due to the limited power response capacity of the frequency regulation unit, the performance index of the frequency regulation unit is low, and the frequency regulation income is low. The income of the unit in scenario 1 is 53,436.2 yuan.

[0243] Scenario 2: Electric vehicles responding to the charging incentive mechanism and frequency modulation units work together to respond to the frequency modulation signal. The frequency modulation output is as follows: Figure 5As shown in the figure, it can be seen that in scenario 2, the electric vehicles participating in charging regulation and the two frequency regulation units jointly output frequency regulation. In the period of responding to the frequency regulation signal, the electric vehicle aggregator participated in the frequency regulation output, which improved the response capability and frequency regulation income of the frequency regulation unit. The income of the frequency regulation unit in scenario 2 is 65,641.2 yuan, and the income of the electric vehicle aggregator is 13,735.3 yuan.

[0244] Scenario 3: Electric vehicle aggregators and frequency modulation units work together to respond to frequency modulation signals. The frequency modulation output is as follows: Figure 6 As shown in the figure, it can be seen that in scenario 3, the electric vehicle aggregator and the two frequency-regulating units jointly output frequency regulation, which significantly improves the defects of unit regulation delay, regulation deviation and regulation reversal, and improves the response capability and frequency regulation income of the frequency-regulating unit. The income of the frequency-regulating unit in scenario 3 is 83,663.5 yuan, and the income of the electric vehicle aggregator is 24,801.0 yuan.

[0245] The optimization results of the benefits of electric vehicle aggregators and frequency regulation units in each scenario are shown in Table 3. When only the frequency regulation units are considered to participate in frequency regulation, the unit performance indicators are low and need to be started and stopped frequently, resulting in the worst economic benefits; when only the electric vehicle clusters participating in the charging incentive mechanism and the frequency regulation units are considered to participate in the market frequency regulation in a coordinated manner, the frequency regulation benefits are significantly improved, but at this time the electric vehicle aggregators can only respond to the downward frequency regulation signal and cannot completely improve the unit frequency regulation performance; when the electric vehicle clusters and the frequency regulation units participate in the market frequency regulation in a coordinated manner, the rapid adjustment of the electric vehicle aggregators can effectively improve the unit frequency regulation performance, so that the comprehensive benefits of the electric vehicle aggregators and the frequency regulation units are maximized.

[0246] The profit results of electric vehicle aggregators and frequency modulation units in the above three scenarios show that the power grid frequency modulation optimization method provided by the present invention can effectively improve the user's responsiveness to power grid frequency modulation and attract more electric vehicle users to participate. Through the collaborative work of electric vehicle aggregators and frequency modulation units, multi-subject joint frequency modulation is realized, the allocation of frequency modulation resources is optimized, and the overall economic benefits of the system are improved while ensuring the stability of the power grid.

[0247] Embodiment three:

[0248] An embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the steps of the power grid frequency optimization method of the above-mentioned embodiment 1 are first implemented.

[0249] The computer-readable storage medium includes: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0250] Embodiment 4:

[0251] An embodiment of the present invention further provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of the power grid frequency optimization method of embodiment 1.

[0252] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0253] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0254] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0255] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0256] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for optimizing power grid frequency regulation, characterized in that: include: Establish a single electric vehicle model in multiple grid-connected states, use the single electric vehicle model to simulate the energy exchange process between the electric vehicle and the grid in different time periods, and determine the operating boundary conditions of a single electric vehicle when interacting with the grid energy; Evaluate the differences in users' psychological tolerance for participating in two-way vehicle-grid interaction, and establish the relationship between incentive electricity price and user responsiveness based on the Weber-Fechner law; Evaluate the timing response of electric vehicles under incentive electricity prices during the entire grid access period, establish an electric vehicle cluster model based on the operating boundary conditions and user responsiveness of a single electric vehicle when interacting with grid energy, and use the electric vehicle cluster model to determine the operating boundary conditions of the electric vehicle cluster when interacting with grid energy; According to the operating boundary conditions of the electric vehicle cluster when interacting with the grid energy, the constraints of the electric vehicle aggregator's frequency regulation are set, and an optimization objective function for the coordinated frequency regulation operation of the electric vehicle aggregator and the frequency regulation unit is established. Combined with the relationship between the incentive electricity price and the user's responsiveness, the highest comprehensive benefit of the electric vehicle aggregator and the frequency regulation unit is taken as the goal, and the coordinated operation of the electric vehicle aggregator and the frequency regulation unit is achieved to optimize the grid frequency regulation effect.

2. The method for optimizing power grid frequency regulation according to claim 1, characterized in that: The operating boundary conditions of a single electric vehicle when interacting with the grid energy include the upper and lower limits of the charging and discharging power of the single electric vehicle and the upper and lower limits of the SOC battery state of charge; Single electric vehicle in the period The upper and lower limits of the charging and discharging power for energy exchange with the power grid are shown in the following formula: ； Where: are the upper and lower limits of the power for the i-th electric vehicle to exchange energy with the power grid at time t; is the maximum charging power; is the maximum discharge power; The single electric vehicle model in multiple grid-connected states takes into account the charging state, discharging state and idle state of the single electric vehicle: (1) Charging status: The electric vehicle absorbs electrical energy at the real-time charging power; (2) Idle state: the electric vehicle has no power transmission; (3) Discharge state: The electric vehicle discharges electrical energy at real-time discharge power; The calculation formula of the SOC of a single electric vehicle in three states is expressed as follows: ； Where: is the SOC state of the i-th electric vehicle at time t; and are the charging and discharging power of the i-th electric vehicle at time t; and are the charging and discharging efficiencies of the i-th electric vehicle at time t; For the i-th electric car Charging time in time; is the battery capacity of the electric vehicle; is the state of the response mode of the i-th electric vehicle; CM is Charging Mode, indicating that the electric vehicle is in a charging state; IM is Idle Mode, indicating that the electric vehicle is in an idle state; DM is Discharging Mode, indicating that the electric vehicle is in a discharging state; According to the calculation formula of the SOC of the single electric vehicle in three states, it can be obtained that the single electric vehicle in the time period The upper and lower limits of the SOC battery state of charge for energy exchange with the power grid are shown in the following formula: ； Where: are the upper and lower limits of the SOC battery state of charge of the i-th electric vehicle at time t; for the study time interval; Minimum SOC to prevent over-discharge; The required SOC for ending charging of electric vehicles.

3. The method for optimizing power grid frequency regulation according to claim 2, characterized in that: The relationship between incentive electricity price and user responsiveness is established based on the Weber-Fechner law, and the relationship is expressed as follows: ； Where: is the response rate of electric vehicles when the incentive electricity price is r; is the electric vehicle response coefficient; c is the electric vehicle response constant; in the same time period, a single electric vehicle can only be charged or discharged when connected to the grid. When a single electric vehicle charges in response to the incentive electricity price r, r= Substitute into the formula to calculate the response rate ; When a single electric vehicle discharges in response to the incentive electricity price r, r= Substitute into the formula to calculate the response rate ; For electric vehicles that discharge in response to the incentive electricity price r, additional compensation is required for the battery loss cost caused by the discharge, based on the battery loss cost and the charging incentive price Setting the discharge subsidy price given by EV aggregators As shown below: ； Where: Subsidize the price for discharge; To provide incentive price for charging; Compensate for the discharge battery loss of electric vehicles during the entire discharge period; According to the relationship between the incentive electricity price and the user's responsiveness, the response threshold of different users to the change of the incentive electricity price is determined. The response threshold includes the upper limit of the user's charging response rate under different incentive electricity prices. , Charging response rate lower limit , upper limit of discharge response rate and the lower limit of discharge response rate ; ； ； ； ； Where: The starting response price for the upper limit of the charging response rate; The upper limit cutoff response price of the charging response rate; The starting response price for the lower limit of the charging response rate; The lower limit response price of the charging response rate; is the starting response price for the upper limit of the discharge response rate; is the upper cutoff response price for the discharge response rate; is the lower limit starting response price of the discharge response rate; It is the lower limit cutoff response price of the discharge response rate.

4. The method for optimizing power grid frequency regulation according to claim 3, characterized in that: Setting the discharge subsidy price and the charging incentive electricity price When the proportion of users responding to the discharge subsidy mechanism and the proportion of responses to charging incentives They are evenly distributed within their upper and lower limits. The response ratios of the charging incentive mechanism and the discharging subsidy mechanism are shown in the following formula: ； Where: Represents a uniform distribution.

5. The method for optimizing power grid frequency regulation according to claim 3, characterized in that: Evaluate the timing response of electric vehicles under incentive electricity prices during the entire grid access period, including: Considering the EV response charging incentive mechanism, the regulation period of EV aggregators during EV charging is as follows: ； Where: express It represents the minimum time required for the i-th electric vehicle to complete the charging demand; represents the charging period of the i-th electric vehicle; is the adjustable time period for the i-th electric vehicle to respond to the charging incentive mechanism; represents the i-th electric vehicle responding to the charging incentive mechanism, otherwise ; Considering the EV response discharge subsidy mechanism, the regulation period during which the EV aggregator can discharge the EV is as follows: ； Where: is the adjustable period for the i-th electric vehicle to respond to the discharge subsidy mechanism; The i-th electric vehicle responds to the discharge subsidy mechanism, otherwise ; for and The maximum value of The i-th electric vehicle responds to the charging incentive mechanism or the discharging subsidy mechanism, otherwise ; The single electric vehicle can only be charged or discharged in the same time period when it is connected to the power grid, and the charging and discharging power at each moment is subject to the upper and lower limits of the charging and discharging power. In the non-grid-connected time period, the charging and discharging power of the single electric vehicle is 0. The power operation constraint of the single electric vehicle is as follows: ； Where: is a 0-1 variable used to control the i-th vehicle to be unable to charge and discharge at the same time. The i-th vehicle is charged at time t, otherwise it is discharged; The SOC of the single electric vehicle at each moment during the time period of access to the grid is subject to the upper and lower limits of the SOC battery state of charge and the user's charging demand. During the non-grid access time period, the SOC of the single electric vehicle that can participate in regulating the grid frequency is 0. The energy operation constraint of the single electric vehicle is as follows: ； An electric vehicle cluster model is established according to the operating boundary conditions and user responsiveness of a single electric vehicle when interacting with grid energy, and the operating boundary conditions of the electric vehicle cluster when interacting with grid energy are determined using the electric vehicle cluster model, including: Generate the charging starting SOC and charging time of a single electric vehicle through Monte Carlo sampling to simulate the randomness of electric vehicle charging and discharging; Based on the relationship between the incentive electricity price and the user responsiveness established according to the Weber-Fechner law, the response rate of the electric vehicle user to the charging incentive is calculated. and the response rate to the discharge subsidy , and determine whether the electric vehicle responds to charging incentives or discharging subsidies based on the response rate, thereby determining the 0-1 variable , and The value of According to the charging starting SOC, charging time and 0-1 variable of the single electric vehicle , , , calculate the operating boundary of the single electric vehicle when interacting with the grid energy, and determine the upper and lower limits of its charging and discharging power and the upper and lower limits of the SOC battery state of charge within a given period of time; Repeat the above process, summarize the energy and power boundaries of individual electric vehicles, and obtain the operating boundary conditions of the electric vehicle cluster when interacting with the grid energy. The operating boundary conditions of the electric vehicle cluster when interacting with the grid energy include the boundaries of the energy and power operation of the electric vehicle cluster, as shown in the following formula: ； Where: are the upper and lower bounds of the energy of the electric vehicle cluster at time t; are the upper and lower bounds of the power of the electric vehicle cluster at time t; The number of electric vehicles that can be controlled by electric vehicle aggregators.

6. The method for optimizing power grid frequency regulation according to claim 5, characterized in that: The constraints for the frequency regulation of the electric vehicle aggregator are set according to the operating boundaries of the electric vehicle cluster when interacting with the grid energy, including: The charging plan energy determined by the electric vehicle cluster at each time period and the net energy after responding to charging incentives or discharging subsidies It is subject to the constraints of the energy operation boundary of this period, as shown in the following formula: ； Where: represents the charging power planned by the EV aggregator during time period k; is the time period length; represents the net power of the electric vehicle in response to the charging incentive or discharging subsidy during the k period; are the upper and lower bounds of the energy of the electric vehicle cluster in time period k; The electric vehicle aggregator responds to the changes in the frequency modulation signal by controlling the charging and discharging power of each electric vehicle in the electric vehicle cluster. The net power of the electric vehicle cluster responds to the charging incentive or discharge subsidy. Each time period is subject to the constraints of the power operation boundary of the time period, as shown in the following formula: ； Where: are the upper and lower bounds of the power of the electric vehicle cluster in time period k; Upward frequency modulation power of electric vehicle clusters at different time periods , Downward frequency modulation power All are non-negative: ； The economic factors considered in the objective function include the electricity purchase cost of the electric vehicle aggregator, the operating cost of the electric vehicle aggregator, the operating cost of the frequency regulation unit, the frequency regulation capacity income and the mileage income. The objective function is shown in the following formula: ； Where: It is the combined revenue of EV aggregators and frequency regulators; The cost of electricity purchases by electric vehicle aggregators in the day-ahead energy market; Cost of operating in the real-time market for EV aggregators; Frequency regulation capacity revenue for EV aggregators and frequency regulation units; Frequency regulation mileage revenue for electric vehicle aggregators and frequency regulation units; Charging incentives and discharge subsidies issued by electric vehicle aggregators to users, as well as charging revenues obtained from users; It is the operating cost of the frequency regulation unit; in: ； Where: K is the number of time periods in the entire research cycle; is the electricity price in the k-period in the day-ahead energy market; ； ； Where: is the electricity price in the real-time energy market in time period k; The real-time charging power of the electric vehicle aggregator at k time period within the day; The difference between the day-ahead electricity purchase and the day-time electricity consumption is calculated as the real-time electricity price. Settlement, To provide for imbalance penalties for double-settled electricity market arbitrage; is the day-ahead bidding electricity; is the actual electricity consumption during the day; are two constants; ; ； Where: is the frequency regulation capacity electricity price in period k; and are the frequency regulation capacities of the EV aggregator and frequency regulation unit in period k respectively; ； ； Where: is the frequency regulation mileage electricity price in time period k; and are the frequency regulation mileage of the electric vehicle aggregator and the frequency regulation unit in time period k, respectively; , are the upward and downward frequency modulation powers of EV aggregators in time period k, respectively; ； Where: is the net power responded by the EV aggregator in period k; is the net power of electric vehicles that do not respond to charging incentives or discharge subsidies; is the charging incentive price issued by the EV aggregator in time period k; is the EV charging price published by the EV aggregator in time period k; The discharge subsidy fee given by the electric vehicle aggregator to the electric vehicle users who participated in the discharge in response to the discharge subsidy, Charging incentive fees for electric vehicle aggregators to provide to electric vehicle users who participate in charging in response to the charging incentives, Charging charges given to EV aggregators for EV clusters; ； In the formula: NG is the number of frequency-regulated units; NT is the number of time periods; is the linear coal consumption cost of the frequency regulation unit; is the no-load cost of the frequency regulation unit; The cost of starting the frequency regulation unit; Cost of shutting down frequency regulating units; is the output power of frequency modulation unit g in time period t; is a 0-1 variable, Indicates the operating status of frequency-regulated unit g in time period t, For online operation, To be out of operation; Indicates the start-up action of the frequency-regulated unit g at time t, To start the action, For other status; Indicates the start-up action of the frequency-modulating unit g at time t, To start the action, For other status.

7. The method for optimizing power grid frequency regulation according to claim 1, characterized in that: Also includes: The frequency regulation constraints of the frequency regulation units are set. The output power of the frequency regulation units is limited by their maximum output and minimum power output, while maintaining the necessary reserve power margin.

8. The method for optimizing power grid frequency regulation according to claim 7, setting the frequency regulation constraint conditions of the frequency regulation unit comprises: Any frequency-regulating unit g is in the on state in any time period t. The actual output power of the frequency-regulating unit in operation is subject to its maximum output power and its minimum output power constraints, as shown in the following formula: ； Where: Indicates the operating status of frequency-regulated unit g in time period t, For online operation, To be out of operation; is the output power of frequency modulation unit g in time period t; is the maximum output power of frequency modulation unit g; is the minimum output power of frequency modulation unit g; The frequency regulating unit g is subject to its minimum opening time TU g and minimum shutdown time TD g The restrictions are as follows: ； Where: Indicates the start-up action of the frequency-modulating unit g at time t, To start the action, For other status; Indicates the start-up action of the frequency-modulating unit g at time t, To start the action, For other status; In any time period t, the frequency-regulating unit in the online operation state can be shut down but not started; in any time period t, the frequency-regulating unit in the out-of-operation state can be started but not shut down, as shown in the following formula: ； The sum of the power generated by all frequency-regulating units in operation in any time period t needs to meet the load demand in the time period t and maintain the reserve power margin, as shown in the following formula: ； Where: is the number of frequency regulation units; is the frequency regulation load in time period t.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the power grid frequency optimization method described in any one of claims 1-8 are implemented.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the steps of the power grid frequency optimization method described in any one of claims 1-8 are implemented.

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