Cooperative charging optimization strategy for electric vehicle aggregator EVA participating in day-ahead energy market and frequency modulation auxiliary service market

Abstract

The invention discloses a collaborative charging optimization strategy for an electric vehicle aggregator EVA participating in a day-ahead energy market and a frequency modulation auxiliary service market, belongs to the technical field of energy internet regulation and control, considers the influence of multi-stage income evaluation and an actual frequency modulation effect on income, meets the diversified charging requirements of users, and improves the energy utilization rate of the users. A collaborative charging optimization strategy that EVA participates in a day-ahead energy market and a frequency modulation auxiliary service market is provided, and a control system that EVA participates in peak regulation and frequency modulation of a power grid is constructed to analyze a frequency response effect. In the influence of the actual frequency modulation effect on earnings, in order to improve the frequency modulation quality in the power market, the performance of frequency modulation resources is assessed according to the actual frequency modulation effect, the resources with high scores obtain high earnings, and the frequency modulation earnings are paid according to the assessment result. According to the method, the influence of the frequency modulation effect on earnings is considered, and the value and benefit brought by EVA charging control to a power grid are reflected by researching the charging control of aggregators for aggregating the electric vehicles and realizing marketization transaction.

Description

Technical Field

[0001] The present invention belongs to the field of energy internet regulation technology, and in particular relates to a collaborative charging optimization strategy for electric vehicle aggregators (EVAs) to participate in the day-ahead energy market and the frequency regulation auxiliary service market. Background Art

[0002] With the rapid development of electric vehicles, China's electric vehicle ownership is increasing year by year. A large number of electric vehicles are connected to the grid for charging. Electric vehicle aggregators (EVAs) act as agents, integrating charging and frequency regulation for electric vehicle clusters to participate in electricity market transactions. This allows them to profit from the day-ahead energy market and ancillary services such as frequency regulation.

[0003] In current research and practice across various countries, the time-of-use electricity prices provided by the day-ahead energy market can serve as an incentive to guide EVs to charge at off-peak times based on electricity prices during different time periods. This reduces charging costs through day-ahead optimized scheduling while also achieving peak load shifting and valley filling. Transactions in the frequency regulation ancillary services market involve multiple stages: day-ahead determination of time-of-day reserve capacity, real-time response to system frequency regulation signals, and finally revenue settlement based on actual power output and frequency regulation effectiveness. The revenue settlement requirements of power market mechanisms for frequency regulation are relatively complex. Existing revenue calculation methods for ancillary services markets often include multiple dimensions, such as capacity revenue, energy revenue, and mileage revenue, and incorporate frequency regulation effectiveness assessments. Some studies have designed fractional control methods for EV aggregators (EVAs) participating in frequency regulation based on frequency regulation performance scores and frequency regulation mileage calculations. These methods implement scheduling based on the varying charging behaviors of EVs, achieving more accurate responses to frequency regulation signals and higher frequency regulation revenue. However, these methods lack comprehensive application of the trading mechanisms involving the day-ahead energy and ancillary services markets, fail to fully consider the temporal nature of transactions in different power markets, and struggle to address the issue of EV charging control when day-ahead frequency regulation capacity determination and real-time power regulation revenue settlement are separate.

[0004] Therefore, the existing technology urgently needs a collaborative charging optimization strategy for electric vehicle aggregators (EVAs) to participate in the day-ahead energy market and frequency regulation auxiliary service market to solve this problem. Summary of the Invention

[0005] The purpose of the present invention is to provide a collaborative charging optimization strategy for electric vehicle aggregators (EVA) to participate in the day-ahead energy market and the frequency regulation auxiliary service market, so as to solve the problems existing in the above-mentioned prior art.

[0006] To achieve the above object, the present invention provides the following solutions:

[0007] The present invention provides a collaborative charging optimization strategy for electric vehicle aggregators (EVAs) to participate in the day-ahead energy market and the frequency regulation auxiliary service market, which is characterized by comprising the following steps:

[0008] S1. Settle the day-ahead energy market. In this market, electric vehicle aggregators (EVAs) formulate charging plans for their electric vehicles for the next day, guided by the day-ahead energy price. The goal of day-ahead energy market settlement is to minimize the charging costs for the EV aggregator (EVA).

[0009] S2. Conduct real-time market settlement; during the real-time operation phase, electric vehicles participate in power system frequency regulation;

[0010] S3. Settlements in the frequency modulation market; the electric vehicle aggregator EVA's revenue from the frequency modulation market includes capacity revenue and mileage revenue;

[0011] S4. Establish an optimization model; electric vehicle aggregators (EVAs) aggregate electric vehicles and centrally regulate them, leveraging economies of scale to generate revenue.

[0012] Preferably, in step S1, the charging cost is calculated by the following formula:

[0013]

[0014] Where: F Chr Charging costs for electric vehicle aggregator EVA; Chr (t) is the day-ahead price in period t; p Chr (t) is the charging power during period t; T is the total scheduling period.

[0015] Preferably, step S2 is specifically:

[0016] S21. The independent system operator (ISO) obtains frequency and voltage through a data acquisition and monitoring system (DAMS). The system also calculates the regional control error (RCE) and uses it as the input to a filter. After the RCE error passes through the filter, a frequency modulation signal is generated and sent to the load service provider.

[0017] S22. The load service provider receives the frequency regulation signal from the independent system operator, receives the frequency regulation capacity response signal reported by the frequency regulation resources it manages, and sends the frequency regulation power signal to the electric vehicle aggregator (EVA) and other frequency regulation units it manages;

[0018] S23. The electric vehicle aggregator (EVA) receives the frequency modulation power signal from the load service provider and sends a charging power distribution control signal by controlling the driving signal transmitted to the power electronic converter by the charging pile control unit under its management;

[0019] S24. The charging pile receives the charging power distribution control signal from the electric vehicle aggregator EVA and controls the charging power of the electric vehicles connected to the charging pile, ultimately achieving the purpose of charging and frequency modulation.

[0020] Preferably, in step S2, the electric vehicle aggregator EVA in the real-time market will bring about a change in charging power due to participation in system frequency modulation. The energy benefit obtained from the change is represented by the cost brought by the electric vehicle cluster frequency modulation power:

[0021]

[0022] Where: F RP Cost of frequency regulation for electric vehicle clusters; RT (t) is the real-time market price in period t; p RD (t) is the downward frequency modulation power; p RU (t) is the upward frequency modulation power.

[0023] Preferably, in step S3, the calculation formula is:

[0024]

[0025] p RM (t) = p RC (t)M(t) (6);

[0026] Where: F RC The capacity revenue of electric vehicle aggregator EVA; F RM EVA mileage income for electric vehicle aggregators; C (t) is the capacity price in period t; r M (t) is the mileage price in period t; p RC (t) is the capacity provided by the electric vehicle aggregator EVA during period t; p RM (t) is the frequency modulation mileage provided by the electric vehicle aggregator EVA during period t; M(t) is the frequency modulation mileage; n' is the number of frequency modulation signals in the scheduling period; A Reg,t (i) is the frequency modulation indicator signal given by the market during period t; EVA The performance score of the electric vehicle aggregator EVA indicates how well the frequency modulation resources track the frequency modulation signal and affects the settlement of frequency modulation revenue. Resources with higher scores receive higher revenue.

[0027] Preferably, step S4 is specifically:

[0028] S41. Assume that the electric vehicle aggregator EVA contains N clusters, and use the superscript n to represent the number of the n-th electric vehicle cluster. Establish the objective function with the goal of minimizing cost as follows:

[0029]

[0030] Where: Cost of charging the nth electric vehicle cluster; The cost of frequency regulation for the nth electric vehicle cluster; is the EVA capacity revenue of the EV aggregator for the nth EV cluster; is the mileage revenue of the EV aggregator EVA of the nth EV cluster;

[0031] S42. When controlling electric vehicles to participate in frequency modulation, the electric vehicle aggregator EVA also meets the charging needs of the electric vehicles. The charging power of the electric vehicle aggregator EVA is obtained by the following formula:

[0032]

[0033] Where: is the charging power of the kth vehicle in the electric vehicle cluster n, is the charging power of the nth electric vehicle cluster, N is the total number of electric vehicle clusters, and the charging power of multiple electric vehicle clusters is superimposed to obtain the charging power p of the electric vehicle aggregator EVA EVA (t); K n is the number of electric vehicles owned by electric vehicle cluster n;

[0034] S43. The connection time, the connection time, and the initial battery state of charge (SOC) of the electric vehicle are simulated by a truncated Gaussian distribution. The truncated Gaussian distribution simulation is shown as follows:

[0035]

[0036] Where: is the access time of the kth vehicle in the electric vehicle cluster n, is the departure time of the kth vehicle in the electric vehicle cluster n, is the initial SOC of the kth vehicle in the electric vehicle cluster n; T s n is the lower limit of the charging time of electric vehicles in electric vehicle cluster n; T e n is the upper limit of the charging time of electric vehicles in electric vehicle cluster n; The upper limit of the SOC safety range for electric vehicle cluster n is to prevent deep discharge of electric vehicles and reflect user driving behavior; The lower limit of the SOC safety range for electric vehicle cluster n is to prevent deep discharge of electric vehicles and reflect user driving behavior; is the mean of the Gaussian distribution of the access time, departure time and initial SOC of the electric vehicle cluster n; is the variance of the Gaussian distribution of the access time, departure time and initial SOC of the electric vehicle cluster n;

[0037] S45. When electric vehicles are charging and participating in grid frequency regulation, their charging demand is calculated using the following formula:

[0038]

[0039] Where: The minimum charging requirement for the electric vehicle cluster n; The maximum charging demand for the electric vehicle cluster n; represents the minimum SOC value set to prevent the k-th electric vehicle in the electric vehicle cluster n from overcharging; B is the maximum SOC value set to prevent the k-th electric vehicle in the electric vehicle cluster n from overcharging; nk is the capacity of the kth electric vehicle in electric vehicle cluster n; u is the charging efficiency; Δt is the time interval.

[0040] Preferably, the net charging power of the electric vehicle aggregator EVA is obtained as follows:

[0041]

[0042] Where: is the net charging power of electric vehicle cluster n; Plan power for charging; is the upward frequency modulation power of electric vehicle cluster n; is the downward frequency modulation power of electric vehicle cluster n.

[0043] Preferably, the electric vehicle aggregator EVA Provides FM power for reference response FM signal Specifically: equal Downward FM power minus Modulate power upwards.

[0044] Compared with the prior art, the present invention has achieved the following beneficial technical effects:

[0045] The present invention takes into account the multi-stage revenue evaluation and the impact of actual frequency regulation effect on revenue. While meeting the diverse charging needs of users, it proposes a collaborative charging optimization strategy for electric vehicle aggregators (EVA) to participate in the day-ahead energy market and the frequency regulation auxiliary service market. Based on the frequency regulation capabilities provided by electric vehicles (EVs) in different time periods, a control system for EV aggregators (EVA) to participate in grid peak and frequency regulation is constructed, and the frequency response effect is analyzed.

[0046] In the multi-stage benefit evaluation, the power market clears according to the expected frequency regulation mileage in the day-ahead stage, but the actual frequency regulation needs to be determined according to the status of the real-time stage. Different stages will affect each other, so the present invention designs a real-time automatic power generation control AGC model for electric vehicle aggregators EVA. The aggregators use the designed model to quickly regulate the entire electric vehicle cluster, achieve good control effects, and improve performance scores. Through the proposed multi-stage benefit evaluation considering the day-ahead market optimization and the response of the real-time automatic power generation control AGC model. In the impact of the actual frequency regulation effect on the benefits, in order to improve the quality of frequency regulation, the power market will assess the performance of the frequency regulation resources based on the actual frequency regulation effect. The performance score represents the tracking effect of the frequency regulation resources on the frequency regulation signal and affects the settlement of frequency regulation benefits. Resources with high scores obtain high benefits, and frequency regulation benefits are paid according to the assessment results. In the US PJM market, pursuant to FERC order 755, which implements a pay-for-performance system, PJM assesses the performance of frequency-regulating resources based on their actual frequency-regulating effectiveness. The performance score indicates how well the frequency-regulating resource tracks the frequency-regulating signal and influences the settlement of frequency-regulating revenue. Resources with higher scores receive higher revenue. Therefore, this paper considers the impact of frequency-regulating effectiveness on revenue by studying the charging control of electric vehicles aggregated by aggregators and enabling market-based transactions, demonstrating the value and benefits that electric vehicle aggregators (EVA) charging control bring to the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0048] Figure 1 A block diagram of a model for electric vehicle clusters to participate in secondary frequency regulation of a power grid in an embodiment of a collaborative charging optimization strategy for electric vehicle aggregators (EVAs) to participate in the day-ahead energy market and the frequency regulation auxiliary service market;

[0049] Figure 2 This is a state transition flow chart of electric vehicles in a collaborative charging optimization strategy for electric vehicle aggregators (EVAs) participating in the day-ahead energy market and frequency regulation auxiliary service market according to the present invention;

[0050] Figure 3 This is an equivalent model diagram of an electric vehicle aggregator EVA in a collaborative charging optimization strategy for participating in the day-ahead energy market and the frequency regulation auxiliary service market according to the present invention;

[0051] Figure 4 This is a diagram of the total energy storage model of an electric vehicle aggregator EVA in a collaborative charging optimization strategy for an electric vehicle aggregator EVA to participate in the day-ahead energy market and the frequency regulation auxiliary service market according to the present invention;

[0052] Figure 5 RegD frequency modulation indication signal diagram in an embodiment of the present invention;

[0053] Figure 6 The electricity price map of the power market in the embodiment of the present invention;

[0054] Figure 7 1 is a charging power diagram of different calculation examples in an embodiment of the present invention;

[0055] Figure 8 This is a graph showing changes in charging demand for electric vehicle aggregators (EVAs) under different calculation examples in an embodiment of the present invention;

[0056] Figure 9 This is a diagram showing the charging power optimization results under Example 3 in an embodiment of the present invention;

[0057] Figure 10 This is a diagram of the overall frequency modulation effect in an embodiment of the present invention;

[0058] Figure 11 1 is a diagram showing some frequency modulation effects in an embodiment of the present invention. DETAILED DESCRIPTION

[0059] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0060] The purpose of the present invention is to provide a collaborative charging optimization strategy for electric vehicle aggregators (EVA) to participate in the day-ahead energy market and the frequency regulation auxiliary service market, so as to solve the problems existing in the prior art.

[0061] To address the challenges posed by large-scale EV development to grid operations, EV aggregators (EVAs) need to develop cluster control strategies for charging facilities to mitigate the negative impacts of EV charging on the grid and support grid operations. By implementing appropriate cluster control strategies for EV charging facilities, optimal scheduling of EV charging facilities can be achieved within the current electricity market environment, enabling them to participate in ancillary services such as peak shaving and frequency regulation. Time-of-use electricity prices in the day-ahead energy market can serve as price incentives to regulate the charging process, while frequency regulation revenue from the ancillary services market provides an auxiliary regulatory mechanism for the grid to acquire backup resources and control grid frequency fluctuations. Cluster control strategies for EV charging facilities can optimize the allocation of charging loads to participate in the day-ahead energy and frequency regulation service markets, enabling rapid response to frequency regulation signals and generating market revenue for EV aggregators (EVAs).

[0062] The present invention provides a collaborative charging optimization strategy for electric vehicle aggregators (EVAs) to participate in the day-ahead energy market and the frequency regulation auxiliary service market, which mainly includes the following steps:

[0063] S1. Develop a charging plan based on day-ahead prices to optimize charging costs.

[0064] S2. Real-time market frequency response and dynamic adjustment of charging power;

[0065] S3. Frequency regulation market settlement, including capacity and mileage revenue, taking into account performance scores;

[0066] S4. Establish an optimization model that takes into account charging demand and frequency regulation behavior;

[0067] S5. Introduce constraints to ensure that user charging needs are met without exceeding charging power limits;

[0068] S6. Use the Big M method to construct constraints and manage the frequency modulation state;

[0069] S7. Optimize the EVA model using historical data to improve frequency modulation response performance;

[0070] S8. Simulate to ensure that the frequency modulation power meets the capacity limit.

[0071] This implementation uses the market data from the US PJM market to conduct charging regulation for electric vehicles and electric vehicle clusters participating in the frequency regulation ancillary services market. The PJM (Pennsylvania-New Jersey-Maryland) market provides two frequency regulation indication signals, RegA and RegD, targeting frequency regulation resources with different response times. RegA targets frequency regulation resources with slower response times, such as traditional steam turbines; RegD targets frequency regulation resources with faster response times, such as electric vehicle aggregators (EVAs) and batteries.

[0072] 1.1 Day Ago Energy Market Settlement

[0073] The day-ahead energy market is a market that takes place the day before real-time dispatch. In this market, electric vehicle aggregators (EVAs) plan their electric vehicle charging schedules for the next day, guided by the day-ahead energy price. During this phase, the EV aggregator aims to minimize charging costs, which are calculated using the following formula:

[0074] Charging cost is calculated as follows:

[0075]

[0076] Where: F Chr Charging costs for electric vehicle aggregator EVA; Chr (t) is the day-ahead price in period t; p Chr (t) is the charging power in period t; T is the total scheduling period; under the guidance of energy prices, electric vehicles give full play to their load characteristics and achieve the effect of peak shaving and valley filling.

[0077] 1.2 Real-time market settlement

[0078] The real-time market is an electricity trading market organized to balance energy based on the system's power generation and load conditions, tens of minutes to hours before actual production. In the real-time operation stage, the entire process of electric vehicles participating in power system frequency regulation can be described as follows:

[0079] 1) The independent system operator obtains real-time power system measurement information (frequency, voltage, etc.) through the supervisory control and data acquisition (SCADA) system. The SCADA system calculates the area control error (ACE) and uses it as the input to the filter. After passing the ACE through the filter, the frequency modulation signal is obtained.

[0080] 2) The load service provider receives the frequency modulation signal from the independent system operator, responds to the frequency modulation signal based on the frequency modulation capacity reported by the frequency modulation resources it manages, and distributes the frequency modulation power to the electric vehicle aggregator (EVA) and other frequency modulation units it manages. The frequency modulation resources mentioned above can control adjustable electrical equipment, such as industrial production lines, commercial building air conditioning systems, energy storage equipment, and data center servers.

[0081] 3) The electric vehicle aggregator EVA receives the frequency modulation power signal issued by the load service provider, and sends the frequency modulation power by controlling the driving signal transmitted to the power electronic converter by the charging pile control unit it manages.

[0082] 4) The charging pile receives the charging power distribution control signal from the electric vehicle aggregator (EVA) and controls the charging power of the electric vehicles connected to the charging pile, ultimately achieving the purpose of charging and frequency regulation. In the real-time market, the electric vehicle aggregator (EVA) will cause changes in charging power due to its participation in system frequency regulation. The energy gain obtained from the change is:

[0083]

[0084] Where: F RP Cost of frequency regulation for electric vehicle clusters; RT (t) is the real-time market price in period t; p RD (t) is the downward frequency modulation power; p RU (t) is the upward frequency modulation power;

[0085] 1.3 Settlement of FM Market

[0086] To maintain the safe and stable operation of the power system and guarantee power quality, power generators and power users can provide services beyond normal power production, including frequency regulation, reserve capacity, reactive power regulation, and black start. This paper examines the frequency regulation services provided by electric vehicle aggregators (EVAs). Based on day-ahead charging plans, EV aggregators (EVAs) bid for excess capacity to participate in the frequency regulation market as frequency regulation capacity. Frequency regulation capacity in the PJM market is symmetrical, meaning that the capacity for upward and downward frequency regulation is equal.

[0087] ACE is generated during real-time power system operation. When ACE is less than 0, the independent system operator issues a positive frequency modulation signal, instructing electric vehicles to reduce their planned charging power to fill the power gap. When ACE is greater than 0, the independent system operator issues a negative frequency modulation signal, instructing electric vehicles to increase their planned charging power to consume power. In the PJM market, frequency modulation signals are issued every 2 seconds to dispatch the winning resources and achieve frequency regulation.

[0088] Mileage charges are incurred when frequency regulation reserves are used. Frequency regulation mileage is defined as the sum of the distance traveled by frequency regulation resources during frequency regulation. This is equal to the sum of the absolute values ​​of the differences between two adjacent frequency regulation output points of the frequency regulation resources during a given time period (M1, M2, ..., M5). The revenue generated by electric vehicle aggregators (EVAs) in the frequency regulation market includes capacity revenue and mileage revenue, calculated as follows:

[0089]

[0090]

[0091] p RM (t) = p RC(t)M(t) (6);

[0092] Where: F RC The capacity revenue of electric vehicle aggregator EVA; F RM EVA mileage income for electric vehicle aggregators; C (t) is the capacity price in period t; r M (t) is the mileage price in period t; p RC (t) is the capacity provided by the electric vehicle aggregator EVA during period t; p RM (t) is the frequency modulation mileage provided by the electric vehicle aggregator EVA during period t; M(t) is the frequency modulation mileage; n' is the number of frequency modulation signals in the scheduling period; A Reg,t (i) is the frequency modulation indicator signal given by the market during period t; EVA The performance score of the electric vehicle aggregator EVA indicates how well the frequency modulation resources track the frequency modulation signal and affects the settlement of frequency modulation revenue. Resources with higher scores receive higher revenue.

[0093] 2 Optimization Model

[0094] 2.1 Objective Function

[0095] Electric vehicle aggregators (EVAs) aggregate and centrally regulate electric vehicles, leveraging economies of scale to generate revenue. However, given that EV aggregators' decisions in different markets vary, they reserve frequency regulation capacity in the day-ahead market, preventing this capacity from participating in the energy market. Furthermore, the frequency regulation effectiveness of electric vehicles during real-time operation impacts frequency regulation revenue. Taking these factors into account, the present invention utilizes collaborative optimization to determine the charging and frequency regulation behavior of EV aggregators, maximizing their revenue.

[0096] Assuming that the electric vehicle aggregator EVA contains N electric vehicle clusters, and the superscript n represents the number of the n-th electric vehicle cluster, the objective function with the minimum cost as the goal is:

[0097]

[0098] Where: Cost of charging the nth electric vehicle cluster; The cost of frequency regulation for the nth electric vehicle cluster; is the EVA capacity revenue of the EV aggregator for the nth EV cluster; is the mileage revenue of the electric vehicle aggregator EVA of the nth electric vehicle cluster;.

[0099] 2.2 Electric Vehicle Aggregator EVA Model

[0100] As a frequency modulation resource, electric vehicle aggregators (EVA) differ from traditional electrochemical energy storage in that they must account for the uncertainties of electric vehicle behavior, including their connection and departure times and initial state of charge (SOC). Furthermore, while controlling EV participation in frequency modulation, EV aggregators (EVA) must also meet their charging needs.

[0101] The charging power of the electric vehicle aggregator EVA is obtained as follows:

[0102]

[0103] Where: is the charging power of the kth vehicle in the electric vehicle cluster n, is the charging power of the nth electric vehicle cluster, N is the total number of electric vehicle clusters, and the charging power of multiple electric vehicle clusters is superimposed to obtain the charging power p of the electric vehicle aggregator EVA EVA (t); K n is the number of electric vehicles owned by electric vehicle cluster n.

[0104] Regarding the uncertain driving and parking behaviors of electric vehicles during commuting, based on the U.S. Department of Transportation's survey of vehicle travel across the United States, it was found that the departure time and arrival time of car users conform to the truncated normal distribution. The entry time, departure time, and initial SOC of electric vehicles are simulated by truncated Gaussian distribution, as shown in the following formula:

[0105]

[0106] Where: is the access time of the kth vehicle in the electric vehicle cluster n, is the departure time of the kth vehicle in the electric vehicle cluster n, is the initial SOC of the kth vehicle in the electric vehicle cluster n; T s n is the lower limit of the charging time of electric vehicles in electric vehicle cluster n; T e n is the upper limit of the charging time of electric vehicles in electric vehicle cluster n; The upper limit of the SOC safety range for electric vehicle cluster n is to prevent deep discharge of electric vehicles and reflect user driving behavior; The lower limit of the SOC safety range for electric vehicle cluster n is to prevent deep discharge of electric vehicles and reflect user driving behavior; is the mean of the Gaussian distribution of the access time, departure time and initial SOC of the electric vehicle cluster n; is the variance of the Gaussian distribution of the access time, departure time and initial SOC of the electric vehicle cluster n.

[0107] When charging, electric vehicles participate in grid frequency regulation. Their demand of charging (DOC) is calculated using the following formula:

[0108]

[0109]

[0110] Where: The minimum charging requirement for the electric vehicle cluster n; The maximum charging demand for the electric vehicle cluster n; represents the minimum SOC value set to prevent the k-th electric vehicle in the electric vehicle cluster n from overcharging; B is the maximum SOC value set to prevent the k-th electric vehicle in the electric vehicle cluster n from overcharging; nk is the capacity of the kth electric vehicle in the electric vehicle cluster n; u is the charging efficiency; Δt is the time interval;

[0111] The net charging power of the electric vehicle aggregator EVA is obtained as follows:

[0112]

[0113] Where: is the net charging power of electric vehicle cluster n; Plan power for charging; is the upward frequency modulation power of electric vehicle cluster n; is the downward frequency modulation power of electric vehicle cluster n.

[0114] 2.3 Constraints

[0115] Electric vehicle aggregators (EVAs) must first meet the charging needs of users. This requires EV aggregators (EVAs) to control the DOC of electric vehicles to the value required by users both when determining charging plans in the day-ahead market and when actually charging in the real-time market:

[0116]

[0117]

[0118] The EV aggregator (EVA) quotes the remaining capacity based on the day-ahead charging plan. Therefore, the sum of the day-ahead charging plan and the offered capacity should not exceed the EV aggregator's maximum charging power at the current moment. Furthermore, the present invention employs a one-way energy transfer model between the EV aggregator (EVA) and the grid, leading to the following constraint:

[0119]

[0120] Where: is the maximum charging power that the k-th vehicle in electric vehicle cluster n can provide to electric vehicle cluster n, The capacity provided for electric vehicle cluster n during period t;

[0121] After the electric vehicle aggregator EVA participates in the frequency modulation market and provides frequency modulation capacity, when the market calls on this capacity, the maximum call amount shall not exceed the capacity provided by the electric vehicle aggregator EVA:

[0122]

[0123] Considering that the system frequency is a fixed value at each moment, that is, there can only be one of the requirements for upward frequency modulation and downward frequency modulation at each moment, it is necessary to constrain its frequency modulation state at each moment. The constraint is constructed using the big M method as follows:

[0124]

[0125] Where: and is an auxiliary binary variable; M is a penalty factor.

[0126] Equations (14) to (16) reflect three frequency modulation situations. Based on the real-time status of the SCADA system frequency and the frequency modulation signal issued by the independent system operator ISO, the following judgments are made on the three frequency modulation situations:

[0127] ① If the regional control error ACE of the SCADA system is less than 0, the ISO sends a positive frequency modulation signal, instructing the electric vehicle aggregator EVA to operate according to formula (14) and provide upward frequency modulation power, that is, reduce charging power to help the grid restore frequency;

[0128] ② If the regional control error ACE of the SCADA system is greater than 0, the ISO sends a negative frequency modulation signal, instructing the electric vehicle aggregator EVA to operate according to formula (15) and provide downward frequency modulation power, that is, increase the charging power to meet the power demand of the power grid;

[0129] ③ The regional control error ACE of the SCADA system is equal to 0, indicating that the SCADA system frequency is stable and there is no frequency modulation requirement. In this case, the electric vehicle aggregator EVA does not need to adjust its charging power and maintains the current charging state according to formula (16);

[0130] The above frequency modulation situations adopt the large M method as shown in Equations (22) to (24) to construct constraints to ensure that at any time, the electric vehicle aggregator EVA can only be in one of the above three frequency modulation situations.

[0131] The electric vehicle aggregator EVA model is optimized and trained with the objective function through the collected historical data set to obtain the optimized electric vehicle aggregator EVA model.

[0132] The historical data set required by the objective function includes the following:

[0133] a. Electric vehicle cluster information of the electric vehicle aggregator EVA: including the number of electric vehicles, models, charging power, battery capacity, etc. of each electric vehicle cluster, as shown in Table 2.

[0134] b. Price data of the electricity market: including the price of the day-ahead energy market and the real-time market price. These prices change over time and affect the calculation of charging costs, as shown in Table 1.

[0135] c. Electric vehicle behavior data: including the electric vehicle's access time, departure time, and initial state of charge (SOC). These data usually follow a specific statistical distribution, as shown in formulas (10) to (12).

[0136] d. Frequency modulation signal and performance score: Electric vehicle aggregators (EVAs) adjust charging power based on the frequency modulation signal, and the performance score affects the settlement of frequency modulation revenue.

[0137] e. Charging requirements of electric vehicles: the minimum and maximum charging requirements of electric vehicles, as well as the upper and lower limits of SOC.

[0138] f. Capacity and mileage prices in the frequency modulation market: used to calculate the revenue of frequency modulation services.

[0139] g. System frequency deviation: used to simulate the real-time status of the power grid to determine the frequency regulation requirements.

[0140] h. Control strategy parameters of electric vehicle aggregator EVA: including frequency modulation power proportional coefficient, etc.

[0141] 3 Simulation Model

[0142] In the day-ahead peak load regulation market, the charging cost of the electric vehicle aggregator EVA is minimized to optimize and obtain the charging plan power. In the real-time frequency modulation market, electric vehicle aggregator EVA Provides FM power for reference response FM signal The regulation power of electric vehicle cluster n in response to the system given frequency modulation signal The frequency modulation capacity limit cannot be exceeded, namely:

[0143]

[0144] In the PJM market, according to the provisions of FERC Order 755, it adopts pay for performance. PJM evaluates the performance of frequency regulation resources based on the actual frequency regulation effect. The performance score indicates the tracking effect of the frequency regulation resource on the frequency regulation signal and affects the settlement of frequency regulation revenue. Resources with high scores will obtain higher revenue.

[0145] When the load changes, the frequency deviation cannot be eliminated by relying solely on the speed regulator, that is, the primary frequency regulation. In this case, the system's secondary frequency regulation is required. In a single-area power system, the cumulative difference regulation method is to adjust the frequency based on the accumulated value of the system frequency deviation. The regulation principle of the frequency regulation unit is shown in the following formula:

[0146] CΔP r +∫Δfdt=0 (26);

[0147] Where: ΔP r is the output change of the frequency-modulating unit; Δf is the frequency deviation of the system; C is the frequency-modulating power proportional coefficient.

[0148] A model for electric vehicle aggregators (EVAs) participating in secondary frequency regulation of the system is built in Simulink. The model fully considers the charging needs of electric vehicles and the constraints of the capacity of the electric vehicle aggregator (EVA) on the frequency regulation power, which is more in line with the actual situation of electric vehicles participating in secondary frequency regulation. Indicator signals can be added to the system to observe the effect of electric vehicles participating in the system frequency regulation. For electric vehicles participating in grid frequency regulation, the specific process of their state transition is shown in Figure 2 .

[0149] In electric vehicle control, information is often transmitted through communication networks, and network-induced communication delays are unavoidable. From the time a signal is sent to the time it is received by the electric vehicle, it passes through various dispatch centers. Communication delays are divided into propagation delay, exchange delay, and queuing delay. To fully reflect the actual charging schedule of electric vehicles, a delay element is added to the model, with the communication delay time being τ.

[0150] From this we can get Figure 3 The EVA equivalent model for electric vehicle aggregators is shown. Figure 3 middle: k is the time required for an electric vehicle in electric vehicle cluster n to receive the FM signal and for its charging or discharging power to reach a certain percentage (e.g., 63.2%). EV is the corresponding coefficient of the relevant indicator signal; ΔP ref is the reference power related to the indicator signal; S is the reference capacity; is the maximum energy of the electric vehicle cluster n in period t, which is related to the number of vehicles connected in each period; define the energy surplus or shortage index k1 of the electric vehicle aggregator EVA in period t, which is used to determine whether the electric vehicle aggregator EVA has the ability to respond to the frequency modulation signal. The expression of k1 is as follows:

[0151]

[0152] Among them, D n (t) is the energy of the electric vehicle aggregator EVA during period t.

[0153] When k1≥0, the electric vehicle aggregator EVA is no longer able to respond to the frequency modulation signal, and the frequency modulation power is 0; when k1<0, the electric vehicle aggregator EVA can Provide FM power under restrictions, and make judgments and provide FM power according to the three FM situations mentioned above.

[0154] The total energy storage model of electric vehicle aggregator EVA is as follows Figure 4 As shown in the figure, the primary task of the electric vehicle aggregator EVA is to control the charging of electric vehicles to meet the travel needs of users. In the day-ahead peak-shaving market, the charging plan power is optimized with the goal of maximizing benefits through the guidance of energy prices. In the real-time frequency modulation market, electric vehicle aggregator EVA Provides FM power for reference response FM signal Therefore, the net charging power of electric vehicles is given by and It consists of two parts, namely:

[0155]

[0156] When establishing the charging model of the electric vehicle aggregator EVA, the energy accumulation injected into the electric vehicle aggregator EVA includes two parts: the initial energy that the electric vehicle has when it is connected and charging energy for electric vehicles When the time and initial power of each electric vehicle in the cluster is determined when it is connected to the charging station, the existing initial energy can be directly obtained. By charging energy The calculation formula is used to obtain the total energy storage model of the electric vehicle aggregator EVA. The total energy storage model of the electric vehicle aggregator EVA uses the energy D of the electric vehicle aggregator EVA in each period n (t) characterization;

[0157] Among them, charging energy The calculation formula is:

[0158]

[0159] The energy D of the electric vehicle aggregator EVA at each time period n (t) is:

[0160]

[0161] In summary, we can get the following Figure 4 The total energy storage model of the electric vehicle aggregator EVA is shown.

[0162] The present invention designs a real-time automatic power generation control AGC model of the electric vehicle aggregator EVA through equations (25) to (29). The aggregator uses the designed model to quickly regulate the entire electric vehicle cluster, achieve good control effects, and improve performance scores.

[0163] 4 Case Analysis

[0164] Since the scale of the electric vehicle aggregator EVA used in this invention is small, it is not enough to affect the market price. The electric vehicle aggregator EVA real-time automatic power generation control AGC model field RegD frequency modulation indication signal and market price data on January 25, 2020, RegD frequency modulation indication signal see Figure 5 , electricity market price see Figure 6 And Table 1.

[0165] Table 1 PJM market prices on January 25, 2020

[0166]

[0167] In this example, the electric vehicle aggregator EVA owns two electric vehicle clusters: an office area electric vehicle cluster that provides frequency regulation capacity from 07:00 to 17:00, and a home area electric vehicle cluster that provides frequency regulation capacity from 19:00 to 05:00 the next day. The block diagram of the electric vehicle cluster managed by the electric vehicle aggregator EVA participating in the secondary frequency regulation model of the power grid is shown in Figure 1 The parameters within the electric vehicle cluster are shown in Table 2. We equate electric vehicles with battery energy storage, and determine the performance score of the electric vehicle aggregator EVA to be 0.95 based on the performance score of battery energy storage.

[0168] Table 2 EVA parameters of electric vehicle aggregators

[0169]

[0170] To fully compare the economic benefits of electric vehicles participating in the power market and leveraging their load-frequency regulation characteristics, this paper sets up three cases: 1) direct charging of electric vehicles, that is, they are not managed by the electric vehicle aggregator (EVA) and do not consider the current overall system frequency; 2) electric vehicles only participate in the day-ahead energy market, that is, they are managed by the electric vehicle aggregator (EVA) and participate in the peak regulation of the power system; 3) electric vehicles participate in the day-ahead energy market and the frequency regulation market, that is, they are managed by the electric vehicle aggregator (EVA) and participate in both the peak regulation and frequency regulation of the power system.

[0171] Figure 7 The charging power of the three examples is shown in Figure 1. Under the influence of price, the charging power curves are very different. In Example 1, the electric vehicle is connected to the grid and continuously charged at maximum power until the charging demand is met. Examples 2 and 3 will optimize the charging power at each moment under the guidance of price. Because Example 3 considers both peak shaving and frequency regulation, the charging power is quite different from Example 2 which only considers peak shaving. In the end, all three charging methods will meet the charging requirements (i.e., DOC). Figure 8 shown. Figure 9 This is the charging power optimization result in Example 3. Price has a significant impact on charging power. For example, from 3:00 PM to 4:00 PM, as the price increases, the EV aggregator EVA increases the power and the charging power decreases. As the price decreases, the EV aggregator EVA decreases the power and the charging power increases.

[0172] In this example, EV aggregators (EVAs) participate in grid frequency regulation from 7:00 AM to 5:00 PM and from 7:00 PM to 5:00 AM the next day, but not at other times. EVs, with their rapid response, offer better frequency regulation than traditional thermal power units. Figure 10 and Figure 11 Shows the actual frequency modulation effect of electric vehicle aggregator EVA. Figure 10 As can be seen, frequency fluctuations are more effectively suppressed during periods when EVAs participate in frequency regulation than during periods when EVAs do not, and significant positive deviations in system frequency are largely avoided. This paper employs a one-way transmission mode, preventing electric vehicles from feeding power into the grid. Therefore, when negative system frequency deviations occur, conventional generators primarily handle upward frequency regulation. However, as the figure shows, negative deviations remain significant after frequency regulation, unlike positive deviations, which can be reduced to almost zero. Therefore, grids without EVAs experience significant positive and negative deviations in system frequency regulation; however, grids with EVAs experience positive deviations that can be reduced to zero. Figure 11 This is a partial enlargement of the frequency modulation effect. It can be seen that the overshoot of the electric vehicle's response to the frequency signal is relatively small, and the frequency modulation effect is good.

[0173] Table 3 Cost of different examples

[0174]

[0175] Table 4 Cost of Example 3

[0176]

[0177] Charging costs vary across different examples. As shown in Tables 3 and 4, Example 1 has the highest cost, while Example 3 has the lowest. When the EV aggregator EVA participates only in the energy market in Example 2, Table 3 shows that the charging cost is $977.8. When the EV aggregator EVA participates in both the energy market and the frequency regulation market in Example 3, Table 4 shows that the charging cost is $998.9. This indicates that reserving frequency regulation capacity when participating in multi-commodity electricity market transactions by the EV aggregator EVA results in an additional $21.1 in energy market charging costs. However, the market compensates the EV aggregator EVA for both capacity and mileage. Therefore, overall, the cost of Example 3 is still lower than that of Example 2, demonstrating the economic benefits of EV aggregator EVA's participation in the frequency regulation market.

[0178] The present invention uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only intended to help understand the method and core concept of the present invention. At the same time, those skilled in the art will find that the specific implementation methods and application scopes may vary based on the concept of the present invention. In summary, the contents of this specification should not be understood as limiting the present invention.

Claims

1. The electric vehicle aggregator (EVA) participates in the day-ahead energy market and the frequency regulation ancillary services market through a collaborative charging optimization strategy, which is characterized by: The following steps are involved: S1. Perform day-ahead energy market settlement; In the day-ahead energy market, electric vehicle aggregators (EVAs) formulate charging plans for electric vehicles for the next day, guided by the day-ahead energy price. The day-ahead energy market settlement aims to minimize the charging costs of EV aggregators (EVAs). S2. Conduct real-time market settlement; In the real-time operation phase, electric vehicles participate in power system frequency regulation; S3. Settlements in the frequency modulation market; the electric vehicle aggregator EVA's revenue from the frequency modulation market includes capacity revenue and mileage revenue; S4. Establish an optimization model; electric vehicle aggregators (EVAs) aggregate electric vehicles and centrally regulate them, leveraging economies of scale to generate revenue.

2. The coordinated charging optimization strategy for electric vehicle aggregators (EVAs) participating in the day-ahead energy market and frequency regulation ancillary service market according to claim 1 is characterized by: In step S1, the charging cost is calculated by the following formula: Where: F Chr Charging costs for electric vehicle aggregator EVA; Chr (t) is the day-ahead price in period t; p Chr (t) is the charging power during period t; T is the total scheduling period.

3. The coordinated charging optimization strategy for electric vehicle aggregators (EVAs) participating in the day-ahead energy market and frequency regulation ancillary service market according to claim 1 is characterized by: Step S2 is specifically as follows: S21. The independent system operator (ISO) obtains frequency and voltage through a data acquisition and monitoring system (DAMS). The system also calculates the regional control error (RCE) and uses it as the input to a filter. After the RCE error passes through the filter, a frequency modulation signal is generated and sent to the load service provider. S22. The load service provider receives the frequency regulation signal from the independent system operator, receives the frequency regulation capacity response signal reported by the frequency regulation resources it manages, and sends the frequency regulation power signal to the electric vehicle aggregator (EVA) and other frequency regulation units it manages; S23. The electric vehicle aggregator (EVA) receives the frequency modulation power signal from the load service provider and sends a charging power distribution control signal by controlling the driving signal transmitted to the power electronic converter by the charging pile control unit under its management; S24. The charging pile receives the charging power distribution control signal from the electric vehicle aggregator EVA and controls the charging power of the electric vehicles connected to the charging pile, ultimately achieving the purpose of charging and frequency modulation.

4. The coordinated charging optimization strategy for electric vehicle aggregators (EVAs) participating in the day-ahead energy market and frequency regulation ancillary service market according to claim 1 is characterized by: In step S2, the electric vehicle aggregator EVA in the real-time market will bring about changes in charging power due to participation in system frequency regulation. The energy benefit obtained from the change is represented by the cost of the electric vehicle cluster frequency regulation power: Where: F RP Cost of frequency regulation for electric vehicle clusters; RT (t) is the real-time market price in period t; p RD (t) is the downward frequency modulation power; p RU (t) is the upward frequency modulation power.

5. The coordinated charging optimization strategy for electric vehicle aggregators (EVAs) participating in the day-ahead energy market and frequency regulation ancillary service market according to claim 1 is characterized by: In step S3, the calculation formula is: p RM (t)=p RC (t)M(t) (6); Where: F RC The capacity revenue of electric vehicle aggregator EVA; F RM EVA mileage income for electric vehicle aggregators; C (t) is the capacity price in period t; r M (t) is the mileage price in period t; p RC (t) is the capacity provided by the electric vehicle aggregator EVA during period t; p RM (t) is the frequency modulation mileage provided by the electric vehicle aggregator EVA during period t; M(t) is the frequency modulation mileage; n' is the number of frequency modulation signals in the scheduling period; A Reg,t (i) is the frequency modulation indicator signal given by the market during period t; EVA The performance score of the electric vehicle aggregator EVA indicates how well the frequency modulation resources track the frequency modulation signal and affects the settlement of frequency modulation revenue. Resources with higher scores receive higher revenue.

6. The coordinated charging optimization strategy for electric vehicle aggregators (EVAs) participating in the day-ahead energy market and frequency regulation ancillary service market according to claim 1 is characterized by: Step S4 is specifically as follows: S41. Assume that the electric vehicle aggregator EVA contains N clusters, and use the superscript n to represent the number of the n-th electric vehicle cluster. Establish the objective function with the goal of minimizing cost as follows: Where: Cost of charging the nth electric vehicle cluster; The cost of frequency regulation for the nth electric vehicle cluster; is the EVA capacity revenue of the EV aggregator for the nth EV cluster; is the mileage revenue of the EV aggregator EVA of the nth EV cluster; S42. When controlling electric vehicles to participate in frequency modulation, the electric vehicle aggregator EVA also meets the charging needs of the electric vehicles. The charging power of the electric vehicle aggregator EVA is obtained by the following formula: Where: is the charging power of the kth vehicle in the electric vehicle cluster n, is the charging power of the nth electric vehicle cluster, N is the total number of electric vehicle clusters, and the charging power of multiple electric vehicle clusters is superimposed to obtain the charging power p of the electric vehicle aggregator EVA EVA (t); K n is the number of electric vehicles owned by electric vehicle cluster n; S43. The connection time, the connection time, and the initial battery state of charge (SOC) of the electric vehicle are simulated by a truncated Gaussian distribution. The truncated Gaussian distribution simulation is shown as follows: Where: is the access time of the kth vehicle in the electric vehicle cluster n, is the departure time of the kth vehicle in the electric vehicle cluster n, is the initial SOC of the kth vehicle in the electric vehicle cluster n; T s n is the lower limit of the charging time of electric vehicles in electric vehicle cluster n; T e n is the upper limit of the charging time of electric vehicles in electric vehicle cluster n; The upper limit of the SOC safety range for electric vehicle cluster n is to prevent deep discharge of electric vehicles and reflect user driving behavior; The lower limit of the SOC safety range for electric vehicle cluster n is to prevent deep discharge of electric vehicles and reflect user driving behavior; is the mean of the Gaussian distribution of the access time, departure time and initial SOC of the electric vehicle cluster n; is the variance of the Gaussian distribution of the access time, departure time and initial SOC of the electric vehicle cluster n; S45. When electric vehicles are charging and participating in grid frequency regulation, their charging demand is calculated using the following formula: Where: The minimum charging requirement for the electric vehicle cluster n; The maximum charging demand for the electric vehicle cluster n; represents the minimum SOC value set to prevent the k-th electric vehicle in the electric vehicle cluster n from overcharging; B is the maximum SOC value set to prevent the k-th electric vehicle in the electric vehicle cluster n from overcharging; nk is the capacity of the kth electric vehicle in electric vehicle cluster n; u is the charging efficiency; Δt is the time interval.

7. The coordinated charging optimization strategy for electric vehicle aggregators (EVAs) participating in the day-ahead energy market and frequency regulation ancillary service market according to claim 6 is characterized by: The net charging power of the electric vehicle aggregator EVA is obtained as follows: Where: is the net charging power of electric vehicle cluster n; Plan power for charging; is the upward frequency modulation power of electric vehicle cluster n; is the downward frequency modulation power of electric vehicle cluster n.

8. The coordinated charging optimization strategy for electric vehicle aggregators (EVAs) participating in the day-ahead energy market and frequency regulation ancillary service market according to claim 7 is characterized by: Electric vehicle aggregator EVA Provides FM power for reference response FM signal Specifically: equal Downward FM power minus Modulate power upwards.

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