Scheduling system for multi-class electric vehicle cluster to participate in power grid power balance control

Abstract

The invention discloses a scheduling system for multi-class electric vehicle clusters to participate in power grid power balance control, and relates to the field of high-proportion new energy power system scheduling. The system comprises an upper-layer scheduling unit and a lower-layer scheduling unit, the upper-layer scheduling unit performs optimal coordination on the standby reservation capacity of the EV cluster and the conventional unit which cooperatively participate in the full-time scale FCAS to obtain an optimal standby reservation plan of the EV cluster, the conventional unit, the wind power output and the adjusting element, and transmits an EV cluster total scheduling instruction to the lower-layer scheduling unit; and a lower-layer scheduling unit optimizes the allocation of various types of EV resources by taking the benefit maximization of the EV aggregator as a target under the condition of meeting the EV cluster total output and standby reservation constraint conditions determined by the upper layer. According to the method, the frequency safety and stability of the power system can be enhanced and the wind power consumption level can be effectively improved while the EVA income is guaranteed, so that the optimal coordination among the safety, the high quality and the economic operation target of the high-proportion new energy power system can be better realized.

Description

A dispatching system for multiple types of electric vehicle clusters to participate in power grid power balance control Technical Field

[0001] This invention relates to the field of dispatching high-proportion renewable energy power systems, and in particular to a two-layer optimized dispatching system for power system active power balance involving multiple types of electric vehicle clusters. Background Technology

[0002] Frequency Control Ancillary Services (FCAS), as a means of active power balance control in power systems, effectively ensure the safe and stable operation of the power grid frequency. However, with the increasing penetration rate of new energy sources, their intermittent, fluctuating, and random power outputs place ever-increasing demands on system regulation performance. Current grid regulation performance cannot meet the operational needs of future high-proportion new energy power systems. Simultaneously, thermal power units, the mainstay of flexible regulation resources, are gradually being replaced and decommissioned, leading to increasingly insufficient system regulation capacity. These issues pose a severe challenge to the construction of future power systems, necessitating the exploration of new flexible regulation resources. Through synergy with conventional and other regulation resources, the system's flexible regulation capacity and performance can be jointly enhanced, thereby better ensuring the achievement of the goals of safe, high-quality, and economical operation of future power systems.

[0003] The singular travel characteristics of a single type of EV limit its daily dispatchability, while considering complementary and coordinated operations among multiple EV types can circumvent this problem. Based on their ability and performance in providing regulation services to the main grid, EVs can be divided into three categories: 1) Large, centrally managed EVs, such as buses, which are centrally managed by a company and have large battery capacities, making their travel patterns relatively easy to predict; 2) Small, centrally managed EVs, such as government vehicles, which are centrally managed by a company but have relatively small battery capacities, yet their travel characteristics are still relatively easy to predict; 3) Non-centrally managed EVs, such as private cars, which are used by individual users and have small battery capacities, resulting in greater uncertainty in their travel patterns. Therefore, it is necessary to study optimal dispatch schemes that consider the participation of multiple types of electric vehicle clusters in the active power balance control of the power system.

[0004] In existing technologies, all scheduled EVs are usually treated as a single-feature cluster, with few studies considering the collaboration of multiple types of EVs. There is no further research on the operational strategies for coordinating and complementing the scheduling resources of various EVs when different types of EVs participate in actual operation. There is a lack of research on the collaborative operation mechanism of EV clusters with conventional units under the full-time-scale FCAS. There is also a lack of research on the ability to achieve hierarchical coordinated scheduling between the main grid and EVA, which can easily lead to the problem of disharmony between provincial and local scheduling.

[0005] Therefore, enabling multiple types of electric vehicle clusters and conventional generating units to participate in day-ahead scheduling of full-time-scale frequency control ancillary services (FCAS) is an urgent problem to be solved. Summary of the Invention

[0006] In view of the above-mentioned deficiencies of the prior art, the technical problems to be solved by the present invention are: how to achieve coordination between heterogeneous resources with different response characteristics when EVs cooperate with conventional units; how to achieve coordination and complementarity of scheduling resources of different types of EVs within an EV cluster; and how to achieve coordinated operation between scheduling entities at different levels.

[0007] To achieve the above objectives, the present invention provides a scheduling system for multiple types of electric vehicle clusters to participate in power grid power balance control. The power system takes into account the dynamic random energy domain of the EV clusters and includes an upper-level scheduling unit and a lower-level scheduling unit.

[0008] The upper-level scheduling unit makes optimal coordination of the reserve capacity of EV clusters and conventional units participating in the full-time-scale FCAS, obtains the optimal reserve plan for the output of EV clusters, conventional units, wind power and regulating components, and transmits the overall scheduling command of EV clusters to the lower-level scheduling unit.

[0009] The upper-level scheduling unit aims to minimize the daily operating cost of the power grid and optimizes the joint scheduling plan of conventional units and EV clusters. Its objective function is:

[0010] ;

[0011] in, For power grid operating costs; for Operating costs of thermal power units during a given period; The grid compensates EVA for the cost of participating in FCAS; Cost of wind curtailment;

[0012] The lower-level scheduling unit, under the constraints of the total output of the EV cluster and the reserve requirement determined by the upper layer, optimizes the allocation of various EV resources with the goal of maximizing the revenue of EV aggregators. Its objective function is:

[0013] ;

[0014] in, For the benefit of EV aggregators; In numerical and upper-level power grid models equal; The compensation cost paid by aggregators for the revenue of various electric vehicles when they pass the discharge response FCAS; , , , These are the compensation electricity prices paid by EVA to EV users when different types of EVs participate in various FCAS programs.

[0015] Furthermore, the collaborative participation of the EV cluster and conventional units in full-time-scale FCAS includes:

[0016] The second-level step disturbance regulation module allows the EV cluster to participate in second-level step disturbance regulation, thereby obtaining the reserve capacity required for the EV cluster to participate in FFR. And the spare capacity required for EV clusters to participate in PFR ;

[0017] The minute-level non-step disturbance regulation module, with EV clusters participating in minute-level non-step disturbance regulation, yields the coordinated SFR reserve of conventional units and EV clusters. , , and ;

[0018] The hourly-level long-term net load change module involves EV clusters participating in hourly-level long-term net load changes, resulting in shared PR reserve capacity for both conventional units and EV clusters. , , and .

[0019] Furthermore, the EV cluster's participation in second-level step disturbance adjustment includes:

[0020] When the initial frequency change rate Safety constraints not met At that time, the required reserve capacity for EV cluster energy storage to participate in FFR is obtained. for: ;

[0021] When the frequency is at its lowest point Safety constraints not met At that time, the required reserve capacity for EV cluster energy storage to participate in PFR is obtained. for: .

[0022] Furthermore, the coordinated SFR reserve capacity of the conventional units and EV clusters , , and The following relationship must be satisfied:

[0023] ;

[0024] ;

[0025] in, and For each thermal power unit, the SFR (Standardized Reserve Ratio) above and below reserve capacity during this period. and These represent the upper and lower standby capacities of the SFR (Side Frame Rate) for the current time period of the EV cluster energy storage. and This represents the required upper and lower SFR reserve amounts for the system during the current time period.

[0026] Furthermore, conventional units and EV clusters jointly reserve PR spare capacity. , , and satisfy:

[0027] ;

[0028] ;

[0029] in, and The respective PR (Pressure Reserve) capacity for each thermal power unit during this time period. and These represent the PR (Pressure Reserve) capacity and backup capacity for the EV cluster energy storage during the current time period. and The required PR reserve for each time period.

[0030] Furthermore, the aforementioned Operating costs of thermal power units during a given period for:

[0031] ;

[0032] Where G represents the number of thermal power units in the system. , The units Start-up and shutdown costs; For unit g in The cost of generating electricity during a given period; Costs for the unit to participate in SFR; This refers to the cost when the unit enters DPR (Digital Precipitation).

[0033] Furthermore, the cost for the unit to participate in SFR is:

[0034] ;

[0035] The cost of the unit entering DPR for:

[0036] ;

[0037] in, and The units exist SFR (Side Frame Rate) upper and lower reserve capacity for the time period; , The units Reduce output to the critical value of DPR in the first and second gears; , The units Reduce output to the first and second tier DPR unit price; For the unit exist Reserve capacity under PR during the time period.

[0038] Furthermore, the grid compensation covers the cost of EVA participating in FCAS. for:

[0039] ;

[0040] in, ; ; ; ; , , , These are the compensation unit prices ($ / MWh) provided by the grid for the corresponding regulation services provided by EVA; and For EVA in Reserved FFR and PFR capacity for specific time periods; and Reserved SFR upper / lower spare capacity for EVA; and Reserved PR upper / lower spare capacity for EVA.

[0041] Furthermore, the constraints of the upper-level scheduling unit include:

[0042] Power balance constraint: During each stage of daily system operation, the system's active power output and load demand should be kept in balance.

[0043] System spindle reserve constraint: The total spindle reserve constraint during system operation should meet the minimum spindle reserve requirement of the system.

[0044] Thermal power unit constraints include upper and lower limits of unit output, upper and lower limits of unit maximum power ramping constraints, start-up and shutdown time constraints, and upper / lower reserve constraints.

[0045] EVA constraints: Detailed constraints on the power and energy of the EVA;

[0046] Wind turbine constraints: When wind turbines are running, the actual wind power output absorbed by the system shall not exceed the wind power generation output.

[0047] Furthermore, the compensation cost paid by the aggregator for the revenue of the various types of electric vehicles when discharging in response to FCAS. for:

[0048] ;

[0049] The compensation electricity price paid by EVA to EV users when different types of EVs participate in various FCAS programs , , , They are respectively:

[0050] ;

[0051] ;

[0052] ;

[0053] ;

[0054] in, For type The unit price of discharge compensation required for EVs; For type EV in Discharge power over a given period of time; , , , For type The EVA compensation unit price ($ / MWh) required for EVs to participate in various regulatory services. , , , , Represent Types EV in The time period provides spare capacity for EVA to participate in FCAS.

[0055] Compared with the prior art, the present invention has the following technical advantages:

[0056] This invention enables EV clusters and conventional units to participate in full-time-scale FCAS in a coordinated manner, leveraging the flexible and rapid frequency adjustment characteristics of distributed energy storage resources, helping conventional units to undertake backup reserve plans, enabling the system to withstand step disturbances of up to 15% of the maximum base capacity, and enhancing the security of system frequency.

[0057] The scheduling system of this invention takes into account the different optimization objectives of decision-makers at different levels of the power grid and EVA. Compared with the traditional system without the participation of EV clusters, this model reduces the total daily operating cost of the power grid by 9.9% while ensuring the operating benefits of EVA, and provides a feasible solution for realizing the economical and effective participation of EVA in the main grid FCAS.

[0058] The following will further explain the concept, specific structure, and technical effects of the present invention in conjunction with the accompanying drawings, so as to fully understand the purpose, features, and effects of the present invention. Attached Figure Description

[0059] Figure 1 is a structural principle diagram of a scheduling system according to a specific embodiment of the present invention;

[0060] Figure 2 is a schematic diagram of a power system structure according to a specific embodiment of the present invention;

[0061] Figure 3 is a schematic diagram of the energy feasible domain of a single EV according to a specific embodiment of the present invention;

[0062] Figure 4 is a schematic diagram of the process of an EV cluster participating in FFR according to a specific embodiment of the present invention;

[0063] Figure 5 is a schematic diagram of the process of an EV cluster participating in PFR according to a specific embodiment of the present invention;

[0064] Figure 6 is a schematic diagram of the working process of the adjustment system according to a specific embodiment of the present invention;

[0065] Figure 7 is a 10-machine bus configuration diagram according to a specific embodiment of the present invention;

[0066] Figure 8 shows a daily load data curve according to a specific embodiment of the present invention;

[0067] Figure 9 shows the wind power predicted output curve of a specific embodiment of the present invention;

[0068] Figure 10 illustrates the day-ahead scheduling strategy of the 10-machine system of the IEEE-39 node according to a specific embodiment of the present invention. Figure 10a shows the planned output data of the EV cluster, Figure 10b shows the reserve curve of the EV cluster participating in FCAS, Figure 10c shows the reserve curve of the unit's SFR (Save for Free) above, Figure 10d shows the reserve curve of the unit's SFR below, Figure 10e shows the reserve curve of the unit's PR (Reserve for PR above), and Figure 10f shows the reserve curve of the unit's PR below.

[0069] Figure 11 shows the lowest frequency points in various scenarios in a specific embodiment of the present invention;

[0070] Figure 12 shows the initial frequency change rate under various scenarios in a specific embodiment of the present invention;

[0071] Figure 13 shows the EVA reserve curves under three schemes in a specific embodiment of the present invention. Figure 13a shows the EVA reserve curve for scheme 1, Figure 13b shows the EVA reserve curve for scheme 2, and Figure 13c shows the EVA reserve curve for scheme 3. Detailed Implementation

[0072] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, unless otherwise specified, the following embodiments and features described therein can be combined with each other.

[0073] Some exemplary embodiments of the invention have been described for illustrative purposes. It should be understood that the invention may be implemented in other ways not specifically shown in the accompanying drawings.

[0074] As shown in Figure 1, a scheduling system for multiple types of electric vehicle clusters to participate in grid power balance control is provided in a specific embodiment, as shown in Figure 2. The scheduling system includes a quantization unit, an upper-level scheduling unit, and a lower-level scheduling unit. The upper-level scheduling unit takes the power grid as the research object, and the lower-level scheduling unit takes EVA as the research object. The aim is to formulate a resource coordination and scheduling strategy that can minimize the total daily operating cost of the main grid while taking into account the daily operating revenue of EVA.

[0075] The quantization unit models the EV cluster as a generalized virtual energy storage model and quantifies its dispatchable capacity during the daily scheduling phase in order to achieve effective scheduling of the EV cluster.

[0076] In existing technologies, the schedulable capacity of EVs is usually determined by probabilistic modeling or prediction based on historical data. However, existing technologies typically treat all scheduled EVs as a single-feature cluster, with few studies considering the collaboration of multiple types of EVs. There is no further research on the operational strategies for coordinating and complementing the scheduling resources of various EVs when different types of EVs participate in actual operation, thus failing to achieve the quantification of EV schedulable capacity.

[0077] Due to the large number and high uncertainty of individual EVs, the power grid struggles to achieve precise scheduling for each EV. Therefore, it is necessary to aggregate a large number of EVs into large-scale energy storage units (EVA) to participate in grid dispatch. In this embodiment, the power and energy polyhedral constraint set of the generalized energy storage model after aggregating a large number of individual EVs is constructed using the Minkowski addition method. The energy feasible domain of an individual EV is shown in Figure 3. Since the grid connection / disconnection times of individual EVs differ, the grid connection time domain of individual EVs needs to be extended to the entire daily dispatch period, constructing a Minkowski and aggregation model that considers the step characteristics of EV charging and discharging states.

[0078] (1)

[0079] in, This indicates the EV's on-network status throughout the entire time period. A value of 1 indicates that the EV is connected to the grid during that time period; otherwise, it is disconnected from the grid. This demonstrates the step change in EV cluster power caused by EVs entering / leaving the grid. The expression is:

[0080] (2)

[0081] (3)

[0082] (4)

[0083] in, This refers to the number of EVs connected to the network during that period. The state of charge (SoC) of EV users upon network access. Expected SoC for EV users when they go offline and These represent the EV's entry / exit time periods.

[0084] The generalized energy storage model and constraints for the EV cluster are shown below:

[0085] (5a)

[0086] (5b)

[0087] (5c)

[0088] (5d)

[0089] (5e)

[0090] in, A Boolean variable representing the charging and discharging state of an EV (1 for discharging, 0 for charging); and For EV clusters Maximum discharge / charge power limit for a given time period; and For EV clusters Maximum / minimum battery capacity allowed during the time period; and Minimum / maximum SoC to ensure safe operation of EV batteries; , and EV clusters The charging and discharging output and the amount of electricity during the period; and The charging and discharging efficiency of EVs.

[0091] It should be noted that this study assumes all participating EVs are equipped with V2G technology and have signed agreements with the grid, enabling them to participate in grid active power regulation. Based on the energy storage model established for the EV cluster mentioned earlier, the up-and down-adjustment reserve capacity that the EV cluster can provide during the daily dispatch phase through FCAS is as follows:

[0092] (6)

[0093] (7)

[0094] In the formula, and For EV clusters in The available time period allows for adjustments to reserve capacity, either upwards or downwards. This is the maximum output limit of the EV cluster at this time.

[0095] Unlike traditional energy storage stations, EV charging and travel behavior is random, which introduces uncertainty into the dispatchable potential of EV clusters. Based on the principle of classifying EVs according to travel uncertainty and battery capacity, in a specific embodiment, historical data on travel and charging of three types of vehicles—private cars, buses, and official vehicles—were collected and defined as follows:

[0096] (8)

[0097] in, For the first in the dataset Historical data of similar vehicles The number of this type of vehicle in the dataset. For this type of EV Historical data on the trips and charging of EVs The specific information contained herein is as follows:

[0098] (9)

[0099] in, and For the EV's entry / exit time, and Electricity input / output to / from the grid; and This represents the upper limit of the charging / discharging power for the EV; in this embodiment, it is assumed that the upper limit of charging / discharging power is equal for each vehicle. and To ensure the maximum / minimum charge capacity for safe operation of EV power batteries, a characteristic data sequence of the EV cluster was obtained by summing a large number of historical EV data sequences using Minkowski summation.

[0100] (10)

[0101] in, For the first in the dataset EV-like clusters in Time-schedulable capacity data sequence.

[0102] The dimensionality of the aggregated historical data sequence is independent of the number of EVs, effectively reducing the data tensor. Using the aggregated historical data sequences of various vehicle types as the training dataset, a Long Short-Term Memory (LSTM) network is used to obtain the predicted schedulable capacity of each type of EV during the daily scheduling phase. The LSTM network is a commonly used network in existing technologies. Since this embodiment focuses on optimizing scheduling strategies rather than on the prediction model, and the specific structure of the LSTM network is already well-established in existing technologies, detailed modeling of the EV cluster prediction model will not be elaborated here.

[0103] In one specific embodiment, the upper-level scheduling unit makes optimal coordination of the reserve capacity of EV clusters and conventional units participating in the full-time-scale FCAS, obtains the optimal reserve plan for the output of EV clusters, conventional units, wind power and regulating components, and transmits the overall scheduling command of EV clusters to the lower-level scheduling unit.

[0104] The upper-level scheduling unit includes a second-level step disturbance adjustment module, a minute-level non-step disturbance adjustment module, an hour-level long-term net load change module, and an objective function module. The second-level step disturbance adjustment module, the minute-level non-step disturbance adjustment module, and the hour-level long-term net load change module are used to enable EV clusters and conventional units to participate in full-time-scale FCAS modulation in a coordinated manner.

[0105] The second-level step disturbance adjustment module allows the EV cluster to participate in second-level step disturbance adjustment, thereby obtaining the reserve capacity required for the EV cluster to participate in FFR. And the spare capacity required for EV clusters to participate in PFR ;

[0106] During daily operation, the system may experience sudden and significant power shortages. These step disturbances pose a significant threat to the safe operation of the system, potentially leading to underfrequency load shedding (UFLS) and widespread blackouts, directly threatening grid security and economic and social stability. Therefore, reserve capacity must be reserved for such disturbances when formulating day-ahead dispatch plans. The system provides rapid frequency response to suppress frequency decline trends through IFR and PFR, a process typically lasting 0-60 seconds. IFR primarily relies on the kinetic energy of the synchronous generator rotor, spontaneously providing short-term power to mitigate frequency changes when a disturbance occurs. PFR, on the other hand, provides power when the system frequency deviation exceeds the unit's set dead zone. Then, through the autonomous action of the unit's speed governor, power is released or absorbed to restore instantaneous power balance and limit the further expansion of frequency deviation.

[0107] As an energy storage element, EV (Energy Utilization) itself lacks physical rotational inertia. Therefore, it needs to participate in IFR (Inertia-Free Regulation) through FFR (Fixed-Free Regulation) and PFR (Power-Free Regulation) through virtual inertia control. Furthermore, since thermal power units participate in IFR and PFR on a mandatory basis, while EVA (Energy Utilization) as a load-side resource participates in regulation on a paid basis, grid operators should schedule conventional units to participate in IFR and PFR as much as possible. Only when the reserve capacity of conventional units is insufficient should EV (Energy Utilization) be scheduled for auxiliary reserve.

[0108] The system in The RoCoF expression for a time subject to a step disturbance is:

[0109] (11)

[0110] in, The change in frequency This is the power disturbance. The system's rated frequency before the disturbance occurred. The system's rated capacity is the sum of the capacities of all regulating elements in the system that can participate in IFR and PFR during the current time period. Let be the overall inertia time constant of the system, and its expression is:

[0111] (12)

[0112] Where G represents the number of regular units in the system that can participate in IFR and PSR during this period. It is a thermal power unit The inertial time constant, To indicate the unit A Boolean variable representing the start / stop status (1 for start, 0 for stop). For the unit Rated installed capacity, The virtual inertial time constant of the EV cluster. This represents the maximum discharge power of the EV cluster during the current time period.

[0113] Combined with the lowest permissible frequency for safe system operation From equation (12), the RoCoF constraint of the system is obtained as follows:

[0114] (13)

[0115] As shown in Figure 4, when the IFR reserve provided by the conventional units is sufficient... Conversely, when the initial frequency change rate Safety constraints not met At that time, the virtual inertia provided by EV through FFR is expressed as follows:

[0116] (14)

[0117] in, The virtual inertial response coefficient of EV cluster energy storage, and the reserve capacity of EV cluster energy storage participating in FFR. The relationship is:

[0118] (15)

[0119] Therefore, the spare capacity constraint for EV clusters participating in FFR is:

[0120] (16)

[0121] The goal of PFR is to suppress the frequency descent trend after a disturbance, therefore the lowest frequency point of the system after a disturbance. The main indicator for evaluating the PFR capability of a system is expressed as follows:

[0122] (17)

[0123] In the formula, This refers to the gain coefficient of a conventional thermal power unit. The virtual droop control coefficient is used for EV cluster energy storage. Since the frequency response of EV energy storage changes very quickly, its gain coefficient can be approximated as the virtual droop control coefficient.

[0124] For conventional thermal power units, their ability to participate in PFR regulation through droop control is as follows:

[0125] (18)

[0126] In the formula, This is the droop control coefficient for each conventional thermal power unit. This is the maximum linear operating frequency of the unit. This refers to the operating dead zone of the unit's PFR.

[0127] As shown in Figure 5, if the PFR capability of a conventional unit is sufficient, the EV can satisfy the system frequency minimum point constraint without participating in PFR, and the unit's UFLS action will not be triggered. Conversely, when the frequency is at its lowest point Safety constraints not met When an EV cluster needs to participate in PFR, the required reserve capacity for its EVs is... for:

[0128] (19)

[0129] The minute-level non-step disturbance regulation module involves the EV cluster in minute-level non-step disturbance regulation to obtain the coordinated SFR reserve of conventional units and EV clusters. , , and .

[0130] SFR (Simultaneous Frequency Regulation) is the second level of power system frequency control. Its goal is to further eliminate system frequency deviations and restore the frequency to the rated frequency, building upon PFR (Progressive Frequency Regulation). This process typically lasts 30 seconds to 15 minutes. In day-ahead SFR reserve decisions, grid operators need to consider net load fluctuations caused by wind power and load forecasting errors during actual daily operation. Thermal power units and EV (Electric Vehicle) cluster energy storage need to reserve sufficient SFR upper and lower reserve capacity when formulating day-ahead operating plans to cope with minute-level fluctuations in wind power and load.

[0131] The collaborative SFR reserve reservation strategy for conventional units and EV clusters is as follows:

[0132] (20)

[0133] (twenty one)

[0134] in, and For each thermal power unit, the SFR (Standardized Reserve Ratio) above and below reserve capacity during this period. and These represent the upper and lower standby capacities of the SFR (Side Frame Rate) for the current time period of the EV cluster energy storage. and This represents the upstream and downstream SFR reserves required by the system for the current time period. In this embodiment, the upstream and downstream SFR reserves required by the system are taken as 5% of the load forecast and 10% of the wind power forecast.

[0135] The hourly-level long-term net load change module involves the EV cluster in the hourly-level long-term net load change, resulting in a shared reserve PR capacity for both conventional units and the EV cluster. , , and .

[0136] During the daytime operation phase of the system, load changes are inevitable, requiring turbines to track load and adjust output over extended periods to ensure a balance between active power supply and demand. However, due to the uncertainty and anti-peak-shaving characteristics of wind power output, it often cannot be fully absorbed. Whether it's wind curtailment or conventional turbines entering a peak-reduction (DPR) state, it places an additional burden on the system. Furthermore, prolonged deviations in predicted wind turbine output can pose safety risks to the system's active power balance. To prevent negative impacts from the discrepancy between actual and predicted wind power output, conventional turbines and EV clusters should jointly reserve PR (Power Reserve) capacity during the day-ahead dispatch phase.

[0137] (twenty two)

[0138] (twenty three)

[0139] in, and The respective PR (Pressure Reserve) capacity for each thermal power unit during this time period. and These represent the PR (Pressure Reserve) capacity and backup capacity for the EV cluster energy storage during the current time period. and The required PR reserve for each time period.

[0140] To effectively measure the reserve capacity (PR) required by the system to address potential operational risks arising from wind power output deviations during each scheduling period, this embodiment uses a probabilistic statistical method to define the worst-case risk boundary at a given confidence level. This provides a quantifiable risk ceiling for reserve capacity shortages caused by wind power uncertainties, supporting scheduling decisions. Its mathematical expression is:

[0141] (twenty four)

[0142] (25)

[0143] In the formula, This represents the deviation of the wind power forecast value. and They are respectively Actual wind power output and wind power forecast for the specified time period; is the confidence level; VaR is the Value at Risk (VaR) equation.

[0144] The first Deviation of wind power forecast value during the time period If we consider it as a normal distribution with a mean of 0, its probability density function is:

[0145] (26)

[0146] in, for The standard deviation of wind power prediction based on existing centralized day-ahead data can result in an error of 10-21%. This embodiment is based on the normal distribution. The principle is to take:

[0147] (27)

[0148] This yields the required PR reserve for each time period based on VaR at a specific confidence level. For grid operators, the optimal scheduling of EV clusters in PR involves balancing the peak-shaving reserve cost of EV clusters, the cost of conventional units entering DPR state, and the cost of wind curtailment of wind turbines to formulate a coordinated operation strategy for heterogeneous resources. Its core lies in the construction of a day-ahead scheduling optimization model.

[0149] The objective function module aims to minimize the daily operating cost of the power grid and optimize the joint scheduling plan of conventional units and EV clusters. The operating cost of the power grid includes various costs of thermal power units during operation, wind curtailment costs of wind turbines, and compensation costs when using EVs to participate in FCAS.

[0150] (28)

[0151] In the formula, for Operating costs of thermal power units during a given period; The grid compensates EVA for the cost of participating in FCAS; Cost of wind curtailment.

[0152] Operating costs of thermal power units during a given period Including the start-up and shutdown costs of the unit and Power generation cost of the unit Costs of unit participation in SFR And the cost of the unit entering DPR The unit's participation in IFR and PFR is a mandatory adjustment and therefore not included in the cost, as follows:

[0153] (29)

[0154] in,

[0155] (30)

[0156] (31)

[0157] (32)

[0158] (33)

[0159] (34)

[0160] Where G represents the number of thermal power units in the system; , The units Start-up and shutdown costs; A Boolean variable representing the start-up and shutdown status of the unit (1 for start-up, 0 for shutdown); For the unit exist Efforts during a specific time period For the unit exist The cost of generating electricity during a given period; For the unit SFR standby unit price ($ / MWh); and The units exist SFR (Side Frame Rate) upper and lower reserve capacity for the time period; , The units Reduce output to the critical value of DPR in the first and second gears; , The units Reduce output to the first and second tier DPR unit price ($ / MWh); For the unit exist The standby capacity under PR for a given time period. Based on empirical data, in this embodiment, the standby capacity is taken as... , .

[0161] The cost of EVA participating in FCAS is compensated by the power grid. The EVA, including the compensation from the grid to the EVA, participates in the reserve costs of FFR, PFR, SFR, and PR respectively. , , and .

[0162] (35)

[0163] (36)

[0164] (37)

[0165] (38)

[0166] (39)

[0167] in, , , , These are the compensation unit prices ($ / MWh) provided by the grid for the corresponding regulation services provided by EVA; and For EVA in Reserved FFR and PFR capacity for specific time periods; and Reserved SFR upper / lower spare capacity for EVA; and Reserved PR upper / lower spare capacity for EVA;

[0168] The cost of wind curtailment for:

[0169] (40)

[0170] in, Cost per unit of wind curtailment ($ / MWh); For the day before Forecast values ​​for wind power during the specified time period; for The wind turbines are scheduled to operate during certain periods.

[0171] In a specific embodiment, the relevant constraints of the system and various generating units during power grid operation include:

[0172] 1) Power balance constraint: During each stage of daily system operation, the system's active power output and load demand should be kept in balance.

[0173] (41)

[0174] In the formula, for Typical daily load for the time period.

[0175] 2) System spinning reserve constraint: During system operation, the total spinning reserve constraint should meet the minimum spinning reserve requirement of the system.

[0176] (42)

[0177] in, For the unit The upper limit of output; For EV clusters in Increase reserve capacity during certain periods; The minimum rotational reserve factor required by the system is set to 8% in this embodiment.

[0178] 3) Constraints of thermal power units: The constraints of thermal power units during operation are as follows:

[0179] (43)

[0180] (44)

[0181] (45)

[0182] (46)

[0183] (47)

[0184] Equation (43) represents the upper and lower limits of the unit's output. It is a generator set The minimum output technical value; Equation (44) is the maximum up and down power ramping constraint of the unit. and The units The maximum power ramp-up and ramp-down rates for each time period; Equation (45) represents the start-up and shutdown time constraints of the unit. and For the unit The minimum start-up and shutdown times; Equations (46) and (47) are the standby constraints for the unit;

[0185] 4) EVA Constraints: Detailed constraints on the power and energy of EVA are shown in the generalized energy storage model and constraints of the EV cluster. In this embodiment, the overall constraints for EVA as an energy storage element participating in FCAS are given as follows:

[0186] (48)

[0187] (49)

[0188] Equations (48) and (49) represent the upper / lower reserve capacity constraints of EVA for participation in regulation, respectively.

[0189] 5) Wind turbine constraints: During operation, the actual wind power absorbed by the system shall not exceed the wind power generated, i.e.:

[0190] (50)

[0191] In one specific embodiment, the lower-level scheduling unit, under the constraints of the total output and reserve of the EV cluster determined by the upper layer, optimizes the allocation of various EV resources with the goal of maximizing the revenue of EV aggregators, and aims to maximize the daily operating revenue of EVA. This includes compensation for obtaining regulation services from the power grid, and costs. This includes the costs paid by EVA to each type of EV when it participates in regulation through discharge, and the costs when each type of EV provides reserve capacity. Its objective function is:

[0192] (51)

[0193] in, For the benefit of EV aggregators; In numerical and upper-level power grid models equal;

[0194] The required discharge compensation unit price for an EV of type j, and the compensation cost paid by the aggregator's revenue during the discharge response FCAS, are:

[0195] (52)

[0196] , , , The compensation electricity price paid by EVA to EV users when different types of EVs participate in various FCAS programs is as follows:

[0197] (53)

[0198] (54)

[0199] (55)

[0200] (56)

[0201] in, , , , For type The EVA compensation unit price ($ / MWh) required for EVs to participate in various regulatory services. , , , , , Types EV in The time period provides spare capacity for EVA to participate in FCAS; For type The required discharge compensation unit price ($ / MWh) for EVs For type EV in The discharge power during the time period; similarly, , , , These are the compensation electricity prices paid by EVA to EV users when different types of EVs participate in various FCAS (Functional Components and Access Controls), and are part of the operating costs of EVA.

[0202] When EVA schedules multiple types of EV resources to participate in FCAS, it needs to meet the power and energy constraints of each type of EV as well as the related reserve capacity constraints. For each type of EV, the above constraints are shown in Equations (6) and (38), which will not be elaborated further.

[0203] For the lower-level EVA, it is necessary to respond to the total output plan and reserve plan of the EV cluster issued by the power grid through the coordinated scheduling of each type of EV. The constraint set is shown in the table below:

[0204] (57)

[0205] (58)

[0206] (59)

[0207] (60)

[0208] (61)

[0209] Equation (57) represents the total charging and discharging plan constraint of the EV cluster formulated by the upper-level power grid that the lower-level EVs need to satisfy; Equations (58) to (61) represent the reserve capacity balance constraint that each type of EV needs to satisfy for each period of EVA scheduling to participate in various types of FCAS.

[0210] In one specific embodiment, a simulation is performed based on a case system of a 10-machine bus with an IEEE-39 node to analyze and demonstrate the effectiveness and superiority of the present invention. The 10-machine system is shown in Figure 7, where G3, G4, G5, and G6 are frequency modulation units. In this embodiment, the entire day's scheduling time is divided into 96 time periods, each lasting 15 minutes, and is set... =0.5Hz / s, =49.5Hz, the most severe power deficit disturbance that may occur during the daily operation phase of the system is taken as 15% of the system baseline capacity. Unit operating dead zone =0.033Hz, =0.2Hz, =40$ / MWh, =80$ / MWh, EV charge / discharge efficiency and Both are 90%, the upper and lower limits of the safety threshold for EV battery capacity. and EV clusters 20% and 80% of the total electricity consumption during the same period. and The unit price of wind curtailment is $60 / MWh, the unit price of regulating components participating in SFR is $30 / MWh, the unit price of EVA participating in PR is $20 / MWh, and the unit price of system wind curtailment is... It is $70 / MWh, with a confidence level of 70%. The relevant parameters of each conventional unit and various types of EVs in this article are detailed in Tables 1 and 2 below, and the daily load curves and wind power forecast output are shown in Figures 8 and 9, respectively.

[0211] Table 1 Unit Parameters

[0212]

[0213] Table 2 Battery parameters and compensation electricity prices for three types of electric vehicles

[0214]

[0215] The model simulation in this embodiment was run on an Intel(R) Core(TM) i7-10870H CPU @ 2.20GHz processor. Using MATLAB 2023a and the CPLEX solver, the optimal solution was obtained in 270 seconds. The daily operating cost of the upper-level power grid was found to be $612,875, and the daily operating revenue of the lower-level EVA was $78,143. The specific scheduling strategy is shown in Figure 10.

[0216] Figure 10(a) shows the planned output of the EV cluster during the daily dispatch phase. As can be seen from the figure, EVA can help the grid achieve load balance by discharging during the peak load periods of 36-42 and 78-87, and achieve wind power absorption by charging during the off-peak load periods, demonstrating flexible load regulation characteristics.

[0217] Figure 10(b) shows the backup reservation strategies for EV clusters participating in various FCAS operations. As can be seen from the figure, to ensure system... It will not fall to [a certain value] after being subjected to a step disturbance. The following UFLS triggering requires the EV cluster to reserve a certain amount of PFR (Power Free Rate) during the 0-23 and 87-96 periods. This is because a large number of EVs are connected to the system during these periods, which is equivalent to a decrease in the proportion of conventional units in the system. The PFR capacity of thermal power units alone is insufficient to meet the constraints. As shown in Figure 10(b), in order to ensure RoCoF constraints, the EV cluster needs to reserve a certain amount of FFR during the daily operation phase to ensure the system's IFR (Inertia Free Rate) capability. This is also due to the insufficient system inertia caused by the decrease in the proportion of thermal power units in the system. In this model scheme, through the FFR and PFR reserve of the EV cluster, the dynamic characteristics of the system frequency can still meet the predetermined indicators after being subjected to a 15% step disturbance, indicating that this scheme can improve the safety of system frequency operation.

[0218] As shown in Figures 10(b)-10(d), the EV cluster participates in SFR (Stable Flow Rate) reserve, helping the system cope with minute-level disturbances caused by load and wind power net load fluctuations. The total SFR reserve cost of the system is $107,057, with the EV cluster contributing 45.8% of the SFR reserve. Because the EVs assist in SFR reserve, the SFR workload of the generating units is reduced, especially during the 0-18 hour period when the units do not need to reserve SFR, freeing up their output to maintain a higher load factor. During the 0-22 hour and 74-90 hour periods, the units do not need to reserve SFR, making their output more flexible and enabling greater wind power absorption capacity.

[0219] Figures 10(b) and 10(e)-10(f) illustrate the reserve reservation strategies for EV clusters and conventional units when EVA participates in Performance Retention (PR). It can be seen that since PR participation by thermal power units is a mandatory adjustment, while EVA is a paid service, reserving PR reserves for the thermal power units in this example does not incur additional PR costs. Therefore, the optimization results show that the PR reserves in the system are all borne by the thermal power units. However, for peak-shaving reserves, since units may enter a DPR state due to reserving peak-shaving reserves, incurring additional penalty costs, EVA is needed to assist in reservation. As shown in 4(b) and 4(f), EVA assists in assuming most of the system's required PR reserves, effectively preventing the system from entering a more severe DPR state and causing additional economic and component lifespan losses.

[0220] To further illustrate the effectiveness of the strategy proposed in this embodiment, it is compared and analyzed with the following three scenarios.

[0221] Scenario 1: The system has no EV cluster energy storage involved, only conventional thermal power units and wind power units;

[0222] Scenario 2: EV cluster energy storage is introduced into the system, but the EV cluster only follows the load through orderly charging and does not participate in the system's FCAS;

[0223] Scenario 3: EVA is introduced into the system to participate in FCAS. EVA only participates in FFR, PFR and PR, but not SFR.

[0224] These three scenarios were set up to explore the impact of EVA's participation in various types of FCAS to different degrees on the system and the economics of EVA operation, by comparing them with the scheduling scheme proposed in this paper. Table 3 shows the daily operating cost of the power grid, EVA operating revenue, and wind curtailment under each scenario. It can be seen that for the power grid, compared to scenario 1, scenario 2 introduces orderly charging of EVs to assist in absorbing excess wind power, reducing wind curtailment and lowering the daily operating cost of the power grid. In scenario 3, EVA participates in downshaving reserve, effectively reducing the penalty cost of units entering DPR state. The scheme proposed in this paper, on the other hand, also participates in SFR reserve, expanding the output space of units, further improving the system's wind power absorption capacity, and reducing the power grid operating cost. For EVA, since scenario 1 lacks EV energy storage and scenario 2 only follows the load through orderly charging, without EVA participating in controlling the EV cluster to participate in FCAS and thus profiting, there is no EVA revenue in these two scenarios. Scenario 3 lacks SFR compared to the scheduling scheme in this paper, so the EVA revenue is also much lower than the scheme proposed in this paper where EVA participates in FCAS across the entire time scale. This demonstrates that, compared to only participating in a few types of services, the EVA proposed in this paper, by participating in FCAS across the entire timescale, can maximize the economic efficiency of both the grid and the daily operation of the EVA while improving the system's wind power absorption capacity.

[0225] Table 3. Economic Comparison Under Different Scenarios

[0226]

[0227] When the system is subjected to a step disturbance in various scenarios As shown in Figures 11 and 12, in Scenario 1, when there is no EV energy storage in the system, RoCoF The frequency is 49.61 Hz, which is the minimum frequency required for safe system operation. However, at this time the system's RoCoF is 0.53 Hz / s, which is greater than the system's... This could pose a threat to the safe operation of the system. In Scenario 2, the system introduces EV energy storage, which only follows the load through orderly charging and does not participate in FCAS. In this case, the proportion of thermal power units in the system decreases, and the physical inertia of thermal power units alone is insufficient to support the safe operation of the system. In this scenario, RoCoF is higher than the set value. The highest frequency can reach 0.74 Hz / s. This is especially true during the 0-24 hour and 88-96 hour periods. Lower than the preset At its lowest frequency, the frequency is only 49.46Hz, which triggers the system's UFLS, seriously threatening the normal operation of the system. Since both FFR and PFR are involved through EVA, Scenario 3... The RoCoF curve is the same as that of the proposed scheme. This scheme provides the system with virtual inertia and uses virtual droop control to maintain the system's performance above the set value, while also ensuring that its RoCoF remains above the system's set safety threshold. As can be seen from the comparison graph, the proposed model scheme effectively improves the safety of system operation.

[0228] Since different types of EVs have different grid status and battery characteristics when participating in the regulation service, this section explores the scheduling effect of EVA on the system under the above scheme when scheduling the participation of different types of EVs. Due to the special travel characteristics of centrally managed EVs, there may be situations during daily operation where there are few vehicles on the grid, making large-scale scheduling difficult, or even no vehicles on the grid. Private cars, with their high randomness in entering and leaving the grid, have a large on-grid time range after aggregation. Therefore, private cars are the main vehicle type in daily scheduling for both the grid and EVA. The example in this section is based on private car scheduling. Based on the FCAS model for EVA participation across the entire time scale proposed in this paper, we study the impact of the scheduling ratio of different types of EVs on the system scheduling effect. The scheduling scheme is as follows:

[0229] Option 1: Only private cars participate in the dispatching of EVA during daily operation;

[0230] Option 2: During daily operation, EVA will allocate 50% of its resources to private cars and 50% to public buses.

[0231] Option 3: During daily operation, EVA will allocate 50% of its vehicles to private cars and 50% to official vehicles.

[0232] The backup reserve curves and optimization results of EVA participating in FCAS under the three schemes are shown in Table 4 and Figure 13 below, respectively:

[0233] Table 4 Comparison of optimization results under the three schemes

[0234]

[0235] The optimization results show that buses, as representatives of large-scale centralized-managed EVs, have a significant advantage as dispatchable resources participating in FCAS due to their large individual EV battery capacity. Table 4 shows that systems with a high proportion of bus dispatch can effectively reduce grid operating costs and improve the system's wind power absorption capacity. However, for EVA, the higher compensation price for buses participating in FCAS reduces its benefits. Government vehicles, as representatives of small-scale centralized-managed EVs, are only able to connect to charging piles and have dispatchability after relevant units have finished get off work in the evening due to their company management characteristics. This results in the wind curtailment volume of the system under Scheme 3 being higher than other schemes. Figure 13 shows that due to the travel characteristics of private cars and buses, the number of vehicles on the grid is relatively small during the evening peak load period, especially between 73-81. Private cars and buses can provide limited SFR reserve capacity to the grid during this time, while government vehicles, due to their work characteristics, are the main vehicle type providing SFR reserve during this period. Compared to Scheme 3, Scheme 2 increases the SFR reserve capacity of thermal power units by 37.6%. From the perspective of future grid development, increasing the dispatch ratio of government vehicles during peak evening load periods can effectively reduce the SFR reserve tasks of generating units and improve the flexibility of unit output. Private vehicles, due to their highly random grid entry / exit times, large numbers, and wide dispatchable time range, are the main source of dispatchable capacity for EVA in responding to grid regulation during daily operation. For both the grid and EVA, it is possible to flexibly consider using multiple types of EVs to coordinate participation in FCAS to improve economic efficiency or the system's reserve capacity. Therefore, this embodiment can enhance the frequency security and stability of the power system and effectively improve wind power absorption while ensuring EVA benefits, thereby better achieving the optimal coordination between the safety, quality, and economic operation goals of a high-proportion renewable energy power system.

[0236] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.

Claims

1. A dispatching system for multiple types of electric vehicle clusters participating in power grid power balance control, characterized in that, It includes an upper-level dispatching unit and a lower-level dispatching unit. The upper-level dispatching unit optimally coordinates the reserve capacity of EV clusters and conventional units participating in the full-time-scale FCAS (Functional Control System), obtaining the optimal reserve plan for the output of EV clusters, conventional units, wind power, and regulating components, and transmits the overall dispatching command of the EV clusters to the lower-level dispatching unit. The upper-level dispatching unit aims to minimize the daily operating cost of the power grid and optimizes the joint dispatching plan of conventional units and EV clusters. Its objective function is: ;in, For power grid operating costs; for Operating costs of thermal power units during a given period; The grid compensates EVA for the cost of participating in FCAS; The cost of wind curtailment; the lower-level scheduling unit, under the constraints of the total output of the EV cluster and the reserve requirement determined by the upper layer, optimizes the allocation of various types of EV resources with the goal of maximizing the revenue of EV aggregators. Its objective function is: ;in, For the benefit of EV aggregators; In numerical and upper-level power grid models equal; The compensation cost paid by aggregators for the revenue of various electric vehicles when they pass the discharge response FCAS; 、 、 、 These are the compensation electricity prices paid by EVA to EV users when different types of EVs participate in various FCAS programs.

2. The dispatching system for multiple types of electric vehicle clusters participating in power grid power balance control according to claim 1, characterized in that, The EV cluster's collaborative participation with conventional units in full-time-scale FCAS includes: a second-level step disturbance regulation module, where the EV cluster participates in second-level step disturbance regulation to obtain the reserve capacity required for the EV cluster to participate in FFR. And the spare capacity required for EV clusters to participate in PFR The minute-level non-step disturbance regulation module allows EV clusters to participate in minute-level non-step disturbance regulation, obtaining the coordinated SFR reserve of conventional units and EV clusters. 、 、 and The hourly-level long-term net load change module involves the EV cluster participating in hourly-level long-term net load changes, resulting in the joint reservation of PR reserve capacity by conventional units and the EV cluster. 、 、 and 。 3. The dispatching system for multiple types of electric vehicle clusters participating in power grid power balance control according to claim 2, characterized in that, The EV cluster participates in second-level step disturbance regulation, including when the initial frequency change rate... Safety constraints not met At that time, the required reserve capacity for EV cluster energy storage to participate in FFR is obtained. for: When the frequency is at its lowest point Safety constraints not met At that time, the required reserve capacity for EV cluster energy storage to participate in PFR is obtained. for: 。 4. The dispatching system for multiple types of electric vehicle clusters participating in power grid power balance control according to claim 2, characterized in that, The coordinated SFR reserve of conventional units and EV clusters 、 、 and The following relationship must be satisfied: ； ;in, and For each thermal power unit, the SFR (Standardized Reserve Ratio) above and below reserve capacity during this period. and These represent the upper and lower standby capacities of the SFR (Side Frame Rate) for the current time period of the EV cluster energy storage. and This represents the required upper and lower SFR reserve amounts for the system during the current time period.

5. The dispatching system for multiple types of electric vehicle clusters participating in power grid power balance control according to claim 2, characterized in that, Conventional units and EV clusters jointly reserve PR spare capacity 、 、 and satisfy: ； ;in, and The respective PR (Pressure Reserve) capacity for each thermal power unit during this time period. and These represent the PR (Pressure Reserve) capacity and backup capacity for the EV cluster energy storage during the current time period. and The required PR reserve for each time period.

6. The dispatching system for multiple types of electric vehicle clusters participating in power grid power balance control according to claim 2, characterized in that, The Operating costs of thermal power units during a given period for: Where G represents the number of thermal power units in the system. 、 The units Start-up and shutdown costs; For unit g in The cost of generating electricity during a given period; Costs for the unit to participate in SFR; This refers to the cost when the unit enters DPR (Digital Precipitation).

7. The dispatching system for multiple types of electric vehicle clusters participating in power grid power balance control according to claim 6, characterized in that, The cost for the unit to participate in SFR is: The cost of the unit entering DPR for: ;in, and The units exist SFR (Side Frame Rate) upper and lower reserve capacity for the time period; 、 The units Reduce output to the critical value of DPR in the first and second gears; 、 The units Reduce output to the first and second tier DPR unit price; For the unit exist Reserve capacity under PR during the time period.

8. The dispatching system for multiple types of electric vehicle clusters participating in power grid power balance control according to claim 2, characterized in that, The power grid compensates for the cost of EVA participating in FCAS. for: ;in, ； ； ； ； 、 、 、 These are the compensation unit prices ($ / MWh) provided by the grid for the corresponding regulation services provided by EVA; and For EVA in Reserved FFR and PFR capacity for specific time periods; and Reserved SFR upper / lower spare capacity for EVA; and Reserved PR upper / lower spare capacity for EVA.

9. The dispatching system for multiple types of electric vehicle clusters participating in power grid power balance control according to claim 1, characterized in that, The constraints of the upper-level dispatching unit include: power balance constraints: the system's active power output and load demand should be balanced at each stage of daily system operation; system spinning reserve constraints: the total spinning reserve constraint should meet the minimum spinning reserve required by the system during system operation; thermal power unit constraints: including upper and lower limit constraints on unit output, maximum upper and lower power ramping constraints, unit start-up and shutdown time constraints, and unit up / down reserve constraints; EVA constraints: detailed constraints on the power and energy of EVA; wind turbine constraints: when wind turbines are running, the actual wind power output absorbed by the system should not exceed the wind power generation output.

10. A scheduling system for multiple types of electric vehicle clusters participating in power grid balance control according to claim 2, characterized in that, The compensation cost paid by the aggregator for the revenue of the various types of electric vehicles when they pass the discharge response FCAS. for: The compensation electricity price paid by EVA to EV users when different types of EVs participate in various FCAS programs. 、 、 、 They are respectively: ； ； ； ;in, For type The unit price of discharge compensation required for EVs; For type EV in Discharge power over a given period of time; 、 、 、 For type The EVA compensation unit price ($ / MWh) required for EVs to participate in various regulatory services. 、 、 、 、 Represent Types EV in The time period provides spare capacity for EVA to participate in FCAS.

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