An electric vehicle charging power scheduling analysis method and device

Abstract

The application discloses a kind of electric vehicle charging power scheduling analysis method and device, belong to energy internet technical field, this method is by contract design, construct the demand response strategy of electric vehicle user and aggregator jointly participate, make user satisfy the power index of self charging demand, and provide auxiliary power supply service for power grid;Formulate the charging price and discharging price in each time period;Make electric vehicle user charge in the period of low power grid load, discharge in the period of high power grid load;Build simulation experiment model, the variable parameter according to which real-time scheduling and power distribution;Aggregator is adjusted according to the variable parameter after optimization, combines the real-time frequency modulation signal of power grid and the charging state of electric vehicle, and adjusts the charging power of each electric vehicle user;By adjusting the charging power of electric vehicle user, meet the frequency modulation demand of power grid.The application is based on the margin of electric vehicle to charging power distribution scheduling, realizes real-time fast solution, satisfies charging and participates demand response.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of energy internet, and particularly relates to a power scheduling analysis method and device for electric vehicle charging. BACKGROUND

[0002] Demand response refers to the adjustment of load demand by changing the power consumption behavior of users according to the balance between supply and demand and the stability of the power grid. As an important part of the new power system, flexible load has the ability of demand side response, and therefore can play an important role in orderly power consumption as a deployment resource. Rapid and accurate excavation of demand response potential to ensure safe and effective operation of the power grid to alleviate power supply pressure is an important work goal for city companies to provide power supply services.

[0003] In recent years, the rapid development of electric vehicles has had a huge impact on the power system, especially in terms of demand response. The power battery used by electric vehicles is controllable and can feed power back to the grid, and can be used as a distributed energy storage unit. Through the charging and discharging behavior of electric vehicles, load demand can be adjusted to achieve demand response. This feature makes electric vehicles have great potential to serve the power grid system as flexible resources. According to statistics, as of 2022, the market share of new energy vehicles in China reached 1310 million, of which the proportion of pure electric vehicles was 79.77%, with a market share of 1045 million. Assuming that the battery capacity of each electric vehicle is 50kWh, there will be more than 655 million kWh of power battery capacity. China is expected to have nearly 100 million electric vehicles by 2030, so there will be more than 490 million kWh of power battery capacity. Electric vehicle load is flexible and can respond quickly, which can offset the power fluctuations caused by large-scale integration of new energy into the grid, allowing the grid to operate safely and stably.

[0004] At present, the penetration rate of renewable energy generated by wind and photovoltaic power in China is increasing, resulting in a growing problem of lack of flexible resources. The volatility and uncertainty of wind and photovoltaic power generation cause great pressure on grid peak shaving and power supply safety, and the electricity market mechanism has not been fully established. The marketization mechanism of electricity price is not mature, the charging power optimization for demand response and the compensation mechanism for EV users still lack a mature operation framework, and the demand side response resources have not been actively mobilized. With the continuous increase in the number of electric vehicles in China, the disorderly integration of electric vehicles into the grid will cause the undesirable effect of "peak on peak". Therefore, it is necessary to reasonably arrange electric vehicles (EVs) to participate in demand response in an orderly manner.

[0005] Therefore, how to design a charging power scheduling method for electric vehicle users and aggregators to participate in demand response has become a problem to be solved. SUMMARY

[0006] To this end, the application provides an electric vehicle charging power scheduling analysis method and device, which can realize the immediate charging and walking of users, facilitate the joint optimization of electric quantity / capacity, and set the electric quantity margin parameter of electric vehicles under charging demand, so as to allocate and schedule the charging power based on the margin of each electric vehicle, realize real-time and rapid solution, and meet the charging and participation in demand response.

[0007] To achieve the above purpose, the application provides the following technical scheme: an electric vehicle charging power scheduling analysis method, comprising:

[0008] Through contract design, a demand response strategy in which electric vehicle users and aggregators jointly participate is constructed; the demand response strategy makes the electric vehicle users meet the power index of their own charging demand and provides auxiliary power supply services for the power grid;

[0009] According to the load condition and frequency modulation demand of the power grid, charging and discharging prices in a specified time period are formulated; through the charging and discharging prices in the specified time period, the electric vehicle users charge in a period of low power grid load and discharge in a period of high power grid load;

[0010] Through planning the charging and discharging period and formulating the charging and discharging prices in the specified time period, a simulation experiment model is constructed; the simulation experiment model optimizes variable parameters on which the aggregator performs real-time scheduling and power distribution;

[0011] The aggregator adjusts the charging power of each electric vehicle user according to the optimized variable parameters, in combination with the real-time frequency modulation signal of the power grid and the charging state of the electric vehicle user; the adjustment of the charging power of the electric vehicle user meets the frequency modulation demand of the power grid.

[0012] As a preferred scheme of the electric vehicle charging power scheduling analysis method, it further comprises:

[0013] According to the participation degree and contribution of the electric vehicle user in the frequency modulation service, a benefit distribution strategy of the aggregator and the electric vehicle user is constructed; the electric vehicle user and the aggregator distribute the benefits obtained by participating in the frequency modulation of the power grid through the benefit distribution strategy.

[0014] As a preferred scheme of the electric vehicle charging power scheduling analysis method, the step of constructing a demand response strategy in which electric vehicle users and aggregators jointly participate through contract design comprises:

[0015] A contract mode between the electric vehicle user and the aggregator is constructed, and through the contract mode, the electric vehicle user participates in frequency modulation of the power grid under the premise of meeting own charging demand;

[0016] A margin evaluation strategy is constructed taking actual charging demand of the electric vehicle user as a premise;

[0017] Through the margin evaluation strategy, a real-time stage power allocation problem is linearized to obtain a solution in real-time scheduling, and marginal cost and benefit accounting in the settlement stage is completed.

[0018] As an optimal scheme of the electric vehicle charging power scheduling analysis method, the variable parameters on which the aggregator optimizes real-time scheduling and power allocation through the simulation experiment model include: charging power of the aggregator in each period, uplink capacity reported by the aggregator, and downlink capacity reported by the aggregator;

[0019] The relationship function of the charging power of the aggregator, the uplink capacity reported by the aggregator, and the downlink capacity reported by the aggregator is:

[0020]

[0021] In the formula, n i is the average number of charging electric vehicles EV in the i period; n j is the average number of charging electric vehicles EV in the j period; max{n i}, max{n j} are maximum possible values of feasible region constraints on optimization parameters; P max is the maximum charging power of the electric vehicle battery; P charge,i is the charging power of the aggregator in each period; R up,i is the uplink capacity reported by the aggregator; R down,i is the downlink capacity reported by the aggregator.

[0022] As an optimal scheme of the electric vehicle charging power scheduling analysis method, in the process of distributing the benefits obtained by the electric vehicle user and the aggregator through the benefit distribution strategy for participating in frequency modulation of the power grid:

[0023] The benefit of the aggregator is:

[0024] B AO,i = α (Y R,i + Y M,i ) - βS de,charge,i

[0025] In the formula, B AO,i is the benefit of the aggregator; α is the profit sharing ratio of frequency modulation; S de,charge,iis the power shortage of the electric vehicle relative to the guaranteed charging power promised to the owner after the time period; β is a penalty coefficient; Y R,i is the capacity benefit; Y M,i is the frequency modulation mileage benefit;

[0026] The benefit of the electric vehicle user is:

[0027] Q i,j = -c i,j +y i,j +βS de,charge,i

[0028] In the formula, Q i,j is the total cost of the electric vehicle due to charging in the time period i; c i,j is the charging cost of the electric vehicle in the time period i; y i,j is the frequency modulation benefit.

[0029] The application also provides an electric vehicle charging power scheduling analysis processing device, which adopts the electric vehicle charging power scheduling analysis method and comprises:

[0030] A demand response strategy participation construction module is configured to construct a demand response strategy participated by electric vehicle users and aggregators through contract design; the demand response strategy is configured to make the electric vehicle users meet the power index of their own charging demand and provide auxiliary power supply services for the power grid;

[0031] A charging and discharging price making module is configured to make charging and discharging prices in a specified time period according to the load condition and frequency modulation demand of the power grid; the charging and discharging prices in the specified time period are configured to make the electric vehicle users charge in a time period with low power grid load and discharge in a time period with high power grid load;

[0032] A simulation experiment model construction and application module is configured to construct a simulation experiment model through planning charging and discharging time periods and making the charging and discharging prices in the specified time period; the simulation experiment model is configured to optimize variable parameters on which the aggregators make real-time scheduling and power distribution;

[0033] An electric vehicle charging power adjustment module is configured to adjust the charging power of each electric vehicle user according to the optimized variable parameters, in combination with real-time frequency modulation signals of the power grid and charging states of the electric vehicle users; the adjustment of the charging power of the electric vehicle users is configured to meet the frequency modulation demand of the power grid.

[0034] As an optimal scheme of the electric vehicle charging power scheduling analysis processing device, the device further comprises:

[0035] A benefit distribution module is configured to construct a benefit distribution strategy between the aggregator and the electric vehicle user according to the participation degree and contribution of the electric vehicle user in the frequency regulation service, and the electric vehicle user and the aggregator distribute the benefits obtained by participating in the grid frequency regulation through the benefit distribution strategy.

[0036] As an optimal solution of the electric vehicle charging power scheduling analysis processing device, in the participation demand response strategy construction module, the step of constructing the demand response strategy participated by the electric vehicle user and the aggregator through contract design includes:

[0037] The contract mode between the electric vehicle user and the aggregator is constructed, and the electric vehicle user participates in the grid frequency regulation under the premise of meeting the own charging demand through the contract mode.

[0038] The actual charging demand of the electric vehicle user is taken as a premise to construct a margin evaluation strategy.

[0039] Through the margin evaluation strategy, the power allocation problem in the real-time stage is linearized to obtain the solution in the real-time scheduling, and the marginal cost and benefit accounting in the settlement stage is completed.

[0040] As an optimal solution of the electric vehicle charging power scheduling analysis processing device, in the simulation experiment model construction and application module, the variable parameters on which the aggregator bases for real-time scheduling and power allocation are optimized through the simulation experiment model, including the charging power of the aggregator in each period, the uplink capacity reported by the aggregator, and the downlink capacity reported by the aggregator.

[0041] The relationship function of the charging power of the aggregator, the uplink capacity reported by the aggregator, and the downlink capacity reported by the aggregator is:

[0042]

[0043] In the formula, n i is the average number of charging electric vehicles EV in the i period; n j is the average number of charging electric vehicles EV in the j period; max{n i}, max{n j} are the maximum possible values of the feasible region constraints of the optimization parameters; P max is the maximum charging power of the electric vehicle battery; P charge,i is the charging power of the aggregator in each period; R up,i is the uplink capacity reported by the aggregator; R down,i is the downlink capacity reported by the aggregator.

[0044] As a kind of electric vehicle charging power scheduling analysis processing device preferred scheme, in the income distribution module, the electric vehicle user and the aggregator are distributed by the income distribution strategy in the process of the income obtained by participating in frequency modulation of power grid:

[0045] The income of the aggregator is:

[0046] B AO,i = α (Y R,i + Y M,i )- βS de,charge,i

[0047] In the formula, B AO,i The income of the aggregator;α is the proportion of frequency modulation income;S de,charge,i The power shortage relative to the minimum guaranteed charging power promised to the car owner after the end of the period;β is the penalty coefficient;Y R,i Capacity income;Y M,i Frequency modulation mileage income;

[0048] The income of the electric vehicle user is:

[0049] Q i,j =- c i,j + y i,j + βS de,charge,i

[0050] In the formula, Q i,j The total cost of electric vehicle charging in period i;c i,j The charging cost of electric vehicle in period i;y i,j Frequency modulation income.

[0051] This invention has the following advantages: Through contract design, a demand response strategy involving both electric vehicle users and aggregators is constructed; this strategy enables electric vehicle users to meet their own charging power requirements and provides auxiliary power supply services to the power grid; charging and discharging prices are set for a specified time period based on the power grid load and frequency regulation requirements; these prices ensure that electric vehicle users charge during periods of low grid load and discharge during periods of high grid load; a simulation model is constructed by planning charging and discharging periods and setting the charging and discharging prices for the specified time periods; the simulation model optimizes the variable parameters used by the aggregator for real-time scheduling and power allocation; the aggregator adjusts the charging power of each electric vehicle user based on the optimized variable parameters, combined with the real-time frequency regulation signal of the power grid and the charging status of the electric vehicle users; this adjustment of the charging power of electric vehicle users meets the frequency regulation requirements of the power grid. A revenue sharing strategy between the aggregator and the electric vehicle users is constructed based on the degree of participation and contribution of electric vehicle users in the frequency regulation service; the electric vehicle users and the aggregator share the revenue obtained from participating in power grid frequency regulation through this strategy. This invention analyzes the positive impacts of electric vehicles (EVs) participating in demand response and integrating into the power grid. It also elucidates different pathways for EV participation in demand response and argues that EVs should participate as load aggregators. This approach facilitates information transmission and more effective control over the orderly charging and discharging behavior of EVs, while addressing the drawbacks of individual EV participation. This invention proposes an optimal charging and discharging strategy for EV participation in demand response, constructs a framework for EV participation as load aggregators, and designs a trading market mechanism. By planning charging and discharging periods and setting charging and discharging prices, this invention guides EVs to engage in orderly charging and discharging behavior through price-based demand-side response, aiming to achieve peak shaving and valley filling and minimize EV user costs. This invention discusses the design of user contracts for charging needs based on the positive impact of electric vehicles participating in demand response, and studies the aggregator operation model that integrates charging, backup optimization, real-time power allocation, and revenue settlement under contract constraints. It proposes an operational framework for electric vehicle users and aggregators to participate in demand response in a market environment. The proposed contract model between electric vehicle users and aggregators enables users to charge and leave immediately, facilitating joint optimization of power / capacity. Furthermore, the proposed power margin parameter for electric vehicles under charging demand can allocate and schedule charging power based on the margin of each electric vehicle, achieving real-time and rapid solution to meet both charging and demand response needs. Attached Figure Description

[0052] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are only exemplary, and for those skilled in the art, other drawings can be obtained from the provided drawings without creative labor.

[0053] Figure 1 A flowchart of an electric vehicle charging power scheduling analysis method provided in embodiment 1 of the present application;

[0054] Figure 2 A flowchart of priority ranking of response items in an electric vehicle charging power scheduling analysis method provided in embodiment 1 of the present application;

[0055] Figure 3 A schematic diagram of aggregator day-ahead optimization results in a possible embodiment provided in embodiment 1 of the present application;

[0056] Figure 4 A schematic diagram of EV charging real-time scheduling results in a possible embodiment provided in embodiment 1 of the present application;

[0057] Figure 5 A schematic diagram of aggregator real-time scheduling results in a possible embodiment provided in embodiment 1 of the present application;

[0058] Figure 6 A schematic diagram of average charging power in a possible embodiment provided in embodiment 1 of the present application;

[0059] Figure 7 A schematic diagram of average income of a single EV in a possible embodiment provided in embodiment 1 of the present application;

[0060] Figure 8 An architecture diagram of an electric vehicle charging power scheduling analysis processing device provided in embodiment 2 of the present application. DETAILED DESCRIPTION

[0061] The embodiments of the present application will be described below by specific embodiments. Those skilled in the art can easily understand other advantages and effects of the present application from the content disclosed in the present specification. Obviously, the described embodiments are part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0062] Embodiment 1

[0063] Reference Figure 1The embodiment 1 of the present application provides an engine health monitoring method based on an OSA-CBM architecture, comprising the following steps:

[0064] S1, through contract design, a demand response strategy participated by electric vehicle users and aggregators is constructed; through the demand response strategy, the electric vehicle users meet the power index of their own charging demand, and provide auxiliary power supply service for the power grid;

[0065] S2, according to the load condition and frequency modulation demand of the power grid, a charging price and a discharging price in a specified time period are formulated; through the charging price and the discharging price in the specified time period, the electric vehicle users charge in the period when the load of the power grid is low, and discharge in the period when the load of the power grid is high;

[0066] S3, through planning of charging and discharging periods and the formulated charging price and discharging price in the specified time period, a simulation experiment model is constructed; through the simulation experiment model, variable parameters on which the aggregator performs real-time scheduling and power distribution are optimized;

[0067] S4, the aggregator adjusts the charging power of each electric vehicle user according to the optimized variable parameters, in combination with the real-time frequency modulation signal of the power grid and the charging state of the electric vehicle user; the frequency modulation demand of the power grid is met through the adjustment of the charging power of the electric vehicle user;

[0068] S5, according to the participation degree and contribution of the electric vehicle user in the frequency modulation service, a benefit distribution strategy of the aggregator and the electric vehicle user is constructed; the electric vehicle user and the aggregator distribute the benefits obtained by participating in the frequency modulation of the power grid through the benefit distribution strategy.

[0069] In the embodiment, in step S1, the step of constructing a demand response strategy participated by electric vehicle users and aggregators through contract design comprises:

[0070] A contract mode between the electric vehicle user and the aggregator is constructed, and through the contract mode, the electric vehicle user participates in the frequency modulation of the power grid on the premise of meeting its own charging demand;

[0071] The actual charging demand of the electric vehicle user is taken as a premise to construct a margin evaluation strategy;

[0072] Through the margin evaluation strategy, the power distribution problem in the real-time stage is linearized to obtain a solution in real-time scheduling, and marginal cost and benefit accounting in the settlement stage is completed.

[0073] Specifically, the electric vehicle user and the aggregator jointly participate in the demand response strategy, considering the charging behavior of the electric vehicle and the frequency regulation demand of the power grid, and the charging demand and participation willingness of the user. Through contract design, the user is allowed to meet the charging demand while providing auxiliary services for the power grid.

[0074] For the aggregator operator (AO) responsible for managing the EV cluster, the operation framework for centralized control of EV participation in frequency regulation auxiliary services and income generation should include three parts: first, optimization of charging capacity and standby capacity in the day-ahead (or intra-day) capacity trading stage; second, real-time allocation and scheduling of EV charging power; and third, settlement of charging costs and frequency regulation income.

[0075] Under the prior art, there are the following difficulties in realizing EV aggregator participation in frequency regulation: first, there is no general contract mechanism, and the inherent uncertainty of user vehicle use makes it unrealistic to report off-grid time every time charging, and not reporting time also faces the problem of random capacity. Second, the coupling problem of electric quantity / capacity, due to the existence of charging demand and frequency regulation demand, a large amount of power needs to be controlled at the same time, so that the decision needs to balance between charging / frequency regulation, resulting in unclear and non-unique optimization objectives, and involving many variables, which is difficult to quickly solve. Third, the settlement problem after EV charging is completed, how to fairly allocate the electric quantity cost and frequency regulation income traded in the market based on the given contract and power curve.

[0076] To solve the above difficulties, the present application proposes an operation framework for EV aggregator participation in frequency regulation auxiliary services in a market environment. First, a contract mode between EV users and the aggregator is proposed, and a margin evaluation method based on actual charging demand under the contract mode is proposed. The margin is used as the core index, which can linearize the power allocation problem in the real-time stage, facilitate the solution in the real-time scheduling, and calculate the marginal cost / benefit in the settlement stage.

[0077] In this embodiment, in step S2, according to the load condition and frequency regulation demand of the power grid, the charging and discharging prices in different time periods are formulated, so as to guide the user to charge in the period when the load of the power grid is low and to discharge in the period when the load is high through the price signal.

[0078] Specifically, as the cluster EV aggregator operator (Aggregator Operator, AO), the AO will represent all possible EVs participating in frequency modulation, report the standby capacity of each period to the system, and be responsible for the charging power scheduling of all charging piles in the real-time response stage, and settle the charging cost and frequency modulation income with each EV. In this AO-led operation mode, the AO will participate in frequency modulation standby to obtain income by signing a contract with the EV owner under the premise of guaranteeing the charging demand. In the present application, the EV charging adopts the "charge and go" mode, and the AO must promise the owner that before the battery is fully charged, the pre-agreed minimum power (denoted as S base ) should be guaranteed every period. In terms of benefit distribution, the charging cost is shared by the owners with charging records, and the income from frequency modulation is shared by the AO and the owners.

[0079] Considering the different levels of willingness of users to participate, the charging contract is refined into two modes, which are referred to as mode A and mode B when modeling different modes.

[0080] Mode A: charging priority mode. In this mode, the minimum power constraint is always maintained, and if the battery is fully charged before going off-grid, it automatically exits the frequency modulation and maintains the power unchanged.

[0081] Mode B: allow discharging mode. Set the satisfactory power (generally the maximum power multiplied by a coefficient close to 1, denoted as χ), and use the minimum power as the constraint when the satisfactory power is not reached. When the charging is between the satisfactory power and the maximum power, the minimum power constraint is removed, allowing discharging, but maintaining the level not lower than the satisfactory power.

[0082] The AO will act as a proxy for a cluster of electric vehicles to purchase electricity for charging in the energy market, report standby in the ancillary service market, and control the charging power of each electric vehicle to meet the charging demand and frequency modulation demand in real-time operation. At the same time, the AO also acts as an independent interest subject to participate in the income distribution of the aggregator to maintain the operation of the aggregator, and improve the management level and profitability of the aggregator under the drive of income distribution.

[0083] Let the charging power of the aggregator per period be P charge,i , which is the reference power reported to the system, and the frequency modulation service will adjust the charging power up and down based on this power; the uplink and downlink capacity reported are R up,i , R down,i , where subscript i represents the i-th period. The aggregator needs to pay the energy cost C E,i to purchase electricity, the capacity income Y R,i obtained by providing standby, and the mileage income Y M,i of the real-time response frequency; the definitions are as follows:

[0084] CE,i =π E,i P charge,i t period (1)

[0085] Y R,i =π Rup,i R up,i +π Rdown,i R down,i (2)

[0086] Y M,i =π M,i k i M i (3)

[0087] Where: π E,i π Rup,i π Rdown,i These are the energy market price (in yuan / (kW·h)) and the uplink and downlink reserve capacity prices (in yuan / kW); t period This indicates the duration of a standby period. k i M i These represent the accuracy and mileage during real-time response to the frequency modulation signal, respectively, and are related to the system's frequency modulation power requirement P for the aggregator. IR,T and the frequency modulation power P of the aggregator response RR,T related.

[0088] Based on user demand under the charging contract, this section will define a real-time margin parameter for a single EV to constrain its power curve to ensure user demand, and also serve as the basis for real-time power scheduling and revenue distribution.

[0089] First, define the real-time minimum battery level S. EVbase,j,t For the j-th EV, if it disconnects from the grid at time t, its total battery capacity must not be less than S. EVbase,j,t Then S EVbase,j,t The calculation needs to be discussed based on the current power level. Equations (4) and (5) are S under mode A and mode B, respectively. EVbase,j,t .

[0090] S EVbase,j,t =min{S initial,j,t +S base T j,t / Δt,S max}(4)

[0091] S EVbase,j,t =min{S initial,j,t +S base T j,t / Δt,χS max}(5)

[0092] Then, under the constraint of electricity demand, the real-time margin γ of EV at time point t can be calculated as j,t The electricity S at this time is defined as EV,j,t The electricity S at this time is defined as EVbase,j,t The excess electricity:

[0093] γ j,t = S EV,j,t -S EVbase,j,t (6)

[0094] In particular, in mode B, when the electricity is greater than the satisfactory electricity and the power needs to be reduced, the EV allows discharging, and at this time γ j,t+1 Should be:

[0095]

[0096] First, in real-time scheduling, the margin should not be less than 0, and in addition, the model will use the margin as the basis for revenue allocation and real-time scheduling.

[0097] In this embodiment, in step S3, the variable parameters on which the aggregator performs real-time scheduling and power allocation according to the simulation experiment model are optimized, including: the charging power of the aggregator in each period, the uplink capacity reported by the aggregator, and the downlink capacity reported by the aggregator.

[0098] The relationship function of the charging power of the aggregator, the uplink capacity reported by the aggregator, and the downlink capacity reported by the aggregator is:

[0099]

[0100] In the formula, n i is the average number of charging electric vehicles EV at i period; n j is the average number of charging electric vehicles EV at j period; max{n i}, max{n j} are the maximum possible values of the feasible region constraints of the optimization parameters; P max is the maximum charging power of the electric vehicle battery; P charge,i is the aggregator charging power per period; R up,i is the uplink capacity reported by the aggregator; R down,i is the downlink capacity reported by the aggregator.

[0101] Specifically, the simulation experiment model formulates the electric vehicle charging and discharging price with the goal of "peak load shifting" and the least cost of electric vehicle users. The experimental results are used to verify the effectiveness of the proposed method and provide a reference for practical application.

[0102] The simulation experiment model provides an evaluation method of actual benefits under a specific index framework: AO will define its own interests B in formula (4) AO,i Maximize energy purchase and capacity reporting as the goal. The variable parameters are P charge,i , R up,i , and R down,i . P charge,i is the charging power of each period for aggregators; R up,i is the uplink capacity reported by the aggregator; R down,i is the downlink capacity reported by the aggregator.

[0103] Where Y M ,i and S de，charge,i need to be settled after real-time operation, which is an uncertain quantity in day-ahead optimization, so the random variable needs to be extracted and decoupled from the optimization variable to solve the optimization problem. Based on the historical data of the frequency modulation signal and the charging EV, the uncertain scenario set is given, denoted as ρ k k, and F k,i (P charge,i , R up,i , R down,i ) is the aggregator's revenue B AO,i as a function of the variable parameters [P charge,i , R up,i , R down,i ].

[0104] Specifically, the function calculation process is as follows:

[0105] Based on the frequency modulation signal δ up,t , δ down,t without standby capacity information, the random variables m up,i and m down,i are defined for the standby period as the minimum time scale for the calculation of the mileage M i :

[0106]

[0107] The calculation of these two quantities is only related to the frequency modulation signal values δ up,t , δ down,t as coefficients, and t-1 represents a time point before time point t. The reason for the two cases respectively is that considering that in reality, at any time, these two quantities cannot be non-zero at the same time, and the uplink and downlink capacities may be different, so when the signs of the frequency modulation signals of two adjacent time points are different (the second case in the formula), they need to be calculated respectively.

[0108] At this time, the mileage M is:

[0109] Mi = m up,i R up,i + m down,i R down,i (9)

[0110] Redefine random variable s up,i and s down,i for estimating the charging energy float caused by frequency modulation:

[0111]

[0112] Meanwhile, for the estimation accuracy k i :

[0113]

[0114] Then the period aggregator needs to charge more or less energy float S r,i and the total charging energy of the aggregator S charge,i respectively:

[0115] S r,i = s up,i R up,i + s down,i R down,i

[0116] S charge,i = P charge,i t period + S r,i (12)

[0117] Note that AO pre-informs and promises to charge each EV S base as the bottom line (when not full), then under the scheduling plan [P charge,i , R up,i , R down,i ], the adjustable energy space of the aggregator at time period i can be represented as S a,up,i , S a,down,i . In mode A, the adjustable energy space can be represented by the following formula:

[0118]

[0119] S a,down,i = n i S base - P charge,i t period (13)

[0120] where S a,up,i represents the uplink energy space, that is, on the premise that the EV battery is not overcharged, the original charging power P c harge,i how much more power can be charged on the basis of the original charging power P a,down,i denotes the downlink power space, i.e., on the premise that the power charged at the end of the period is not less than the guaranteed power χ charge,i how much less power can be charged on the basis of the original charging power P max denotes the maximum power of the EV battery; n i denotes the average number of charging EVs in the period; S initial,i,j denotes the initial power of the jth EV at the beginning of the charging in period i.

[0121] In mode B, since the power of the EV exceeds the satisfactory power χ max , the aggregator can obtain additional downlink adjustable space, so formula (13) is modified to formula (14):

[0122]

[0123]

[0124] The reason why the EV aggregator cannot fully respond to the frequency modulation comes from the power limit, so the cumulative error of the frequency modulation in a period can be estimated by the difference between S a,up,i , S a,down,i and S r,i , so as to combine formulas (11)-(14):

[0125]

[0126] So far, the uncertain quantity in the calculation of B AO,i is analyzed as random parameters [δ up,t , δ down,t , s up,i , s down.i , n i , S initial,i ](the first four are related to the frequency modulation signal, and the last two are related to the number of charging EVs) and optimization variables [P charge,i , R up,i , R down,i ]. Based on historical data, the joint probability distribution of [δ up,t , δ down,t , s up,i , s down.i ] and [n i , S initial,i ] can be obtained, and then K random parameter scenarios are generated, and the probability of the kth scenario is denoted as ρ k .

[0127] Therefore, the optimization objective will be to maximize the expected value of the revenue minus the risk cost CCVaR :

[0128]

[0129] where n i is the average number of charging EVs at time period i; n j is the average number of charging EVs at time period j; max{n i}, max{n j} are the maximum possible values of the feasible region constraints for the optimization parameters; P max is the maximum charging power of the EV battery.

[0130] In this embodiment, in step S4, the aggregator dynamically adjusts the charging power of each EV according to the real-time frequency modulation signal of the power grid and the charging status of the EV, to respond to the frequency modulation demand of the power grid.

[0131] In each cycle, according to the determined [P charge,i , R up,i , R down,i ] after optimization, the AO will take the power P charge,i as the basis to respond to the frequency modulation power demand P IR,t sent by the system to the AO, so that at each time point t, no matter how many EVs are charging, the total charging power of the aggregator is P total,t .

[0132]

[0133] Therefore, in the real-time control of the AO, the charging power needs to be allocated to each EV.

[0134] Let the number of charging EVs at time point t be W t ; the charging power of the jth EV be p j,t ; the real-time power be S EV,j,t ; and the charging time of this EV at this time be T j,t ; then the operation constraints of a single EV can be listed as follows:

[0135]

[0136] where μ represents the battery charging efficiency; and Δt represents the time interval of the frequency modulation signal.

[0137] For convenience of representation, in the present application, all the powers involved are the absorbed powers from the power grid, and the actual charging power of the battery needs to be multiplied by the charging efficiency.

[0138] Then for the jth EV on the grid, at time point t, the maximum power that can be adjusted upward and downward is defined by the following formula.

[0139]

[0140] For each EV, its maximum power does not exceed P max , and its minimum power does not fall below 0, while ensuring that if the EV is to be off-grid at the next time instant, its state of charge is not below S EVbase,j,t It is emphasized that in the definition of S , since the charging efficiency factor is 1 / μ in the charging scenario and μ in the discharging scenario, if we follow the treatment of (19) as an equation with absolute value operation, the optimization problem will be non-convex and thus intractable or slow to solve. Therefore, the constraints in (20) and (21) are relaxed to the pure charging scenario, i.e., no discharging before reaching the satisfactory state of charge. In particular, in Mode B, when the state of charge is greater than the satisfactory state of charge and the power needs to be reduced, the EV is allowed to discharge, and the minimum power is:

[0141] when S EV,j,t > χS max & P IR,t < 0:

[0142]

[0143] Thus, by accumulating the power constraints of each EV, the upper and lower bounds of the aggregated power of the aggregator

[0144]

[0145]

[0146] Then defines the response power capability of the aggregator, and thus the AO can assess whether P IR,t can be fully met. If indicates that the aggregator has the ability to respond to the power adjustment; if the aggregator will certainly be unable to fully respond to the power adjustment, and will adjust the power with the maximum capability.

[0147]

[0148] In combination with (18), after P RR,t is determined, the total charging power of the aggregator is the sum of the charging powers of all EVs.

[0149]

[0150] At each time instant t, the AO needs to allocate the power P total,t to the total number of W tThe goal is to meet the charging needs of each EV. First, the power constraint for each EV at this moment is defined as follows:

[0151]

[0152] Meeting this constraint ensures that each EV can meet its charging needs at that moment.

[0153] Therefore, the principle of AO power allocation is: to optimize p j,t This aims to maximize the minimum EV margin among all EVs at the next time step, thereby preventing some EVs from having excessively low charging levels. Therefore, the optimization objective should be determined by the optimization variable p. j,t The next time step yields all EV margins γ j,t+1 The minimum margin in.

[0154] Let the minimum margin be γ m i n The optimization problem can then be written as

[0155] maxγ min,t+1

[0156]

[0157] γ min,t+1 ≤γ j,t+1

[0158] Where i represents the i-th time period of the day, during which the frequency-modulated signal δ is received. t The set T consists of all times t. i R up,i R down,i This indicates the spare capacity that was used during this period.

[0159] As mentioned earlier, the constraints in this optimization model vary depending on the current battery level and operating mode of each EV. The applicable constraints for each EV are as follows: Figure 2 As shown, it is represented in the form of a decision tree. After the constraints are determined, it is imported into the optimization model for solution.

[0160] In this embodiment, in step S5, revenue is distributed between the aggregator and the electric vehicle users based on the degree of participation and contribution of electric vehicles in the frequency regulation service. The revenue distribution mechanism should be fair and reasonable, and able to incentivize users to actively participate in the frequency regulation service.

[0161] According to the aforementioned revenue distribution strategy, the aggregator's revenue is:

[0162] The AO will maximize its own benefits in energy purchase and capacity reporting. The energy cost of charging for the aggregator is borne by all the vehicle owners, while the total benefit of participating in frequency modulation is distributed among all the vehicle owners and the AO, but the AO needs to ensure the minimum power for each EV. Therefore, in period i, the benefit of the aggregator should be the frequency modulation benefit distribution minus the compensation for the vehicle owners due to insufficient charging:

[0163] B AO,i = α(Y R,i + Y M,i )- βS de,charge,i (28)

[0164] In the formula, B AO,i is the benefit of the aggregator; α is the distribution ratio of frequency modulation benefit; S de,charge,i is the power shortage relative to the minimum charging power promised to the vehicle owners after the end of the period; β is the penalty coefficient; Y R,i is the capacity benefit; and Y M,i is the frequency modulation mileage benefit.

[0165] The benefit of the electric vehicle user is:

[0166] After day-ahead optimization, the total energy cost C E,i of the aggregator and the capacity benefit Y R,i can be determined, and after the end of real-time operation, the system can settle the frequency modulation mileage benefit Y M,i for the aggregator. For the EVs with charging records, the charging cost in period i can be recorded as c i，j , and the frequency modulation benefit can be recorded as y i,j . After deducting the benefit allocated to the AO, the total energy cost and the total frequency modulation benefit of all the EVs are given by formula (29), where x i represents the total number of EVs with charging records in the period.

[0167]

[0168] Therefore, for a single EV, the total fee (positive value for benefit, negative value for cost) Q i,j in period i due to charging includes the energy cost due to charging, the benefit due to frequency modulation service, and the compensation from the AO in case of insufficient charging, as shown in the following formula:

[0169] Q i,j = -c i,j +y i,j + βS de,charge,i (30)

[0170] Therefore, as the aggregator manager, the AO needs to fairly distribute the total cost and the total benefit to each EV, and calculate c i,j and yi,j Unlike the settlement between the system and the aggregator, the total frequency regulation mileage of the aggregator can be directly calculated based on the purchased charging power; but within the aggregator, from the perspective of a single EV, since it only accepts the charging power allocated by the AO, there is no explicit boundary between the reference power for planned charging and the power for frequency regulation, so it is also impossible to give a clear frequency regulation mileage. Therefore, within the aggregator, the settlement of the AO to each EV should be treated as a cooperative game problem, based on the Shapley value theory, considering the energy resource occupation of the EV in energy cost allocation and the frequency regulation contribution of the EV in frequency regulation income, so as to fairly allocate the energy cost and frequency regulation income. The basic equation of the Shapley value of the energy cost and frequency regulation income of the jth EV is:

[0171]

[0172] In the formula, X i represents a set composed of all x i EVs; Γ represents a non-empty subset of X i that does not contain the jth EV; v(Γ) is a utility function with the set Γ as the independent variable. The essence of formula (31) is to calculate the marginal cost or marginal income of the EV, i.e. the difference between the cost or income when the EV is charged and when the EV is not charged.

[0173] After obtaining the Shapley value, the total cost or total income is allocated in proportion, that is:

[0174]

[0175] In the formula, and respectively represent the Shapley value of the jth EV in period i with respect to the charging cost and the frequency regulation income.

[0176] Formula (31) expresses the general case in cooperative game allocation, i.e. the marginal effects of the participants may be coupled with each other, and do not have monotonic additivity, so the Shapley value can be obtained only by traversing all non-empty subsets Γ and calculating their marginal effects. For an aggregator involving scheduling problems and containing a large number of EVs, the calculation amount of the Shapley value derived by traversing all EV combinations is too large, so the calculation method will be given in combination with the characteristics of the EVs in the following.

[0177] Shapley value calculation of charging cost:

[0178] Since the AO promises the charging power per unit time in the charging contract with the EV, for each EV connected to the network for charging (charging time T total,j ), the AO needs to ensure that S base T total,jt period The marginal cost of the jth EV is the amount of electricity that it needs to provide to the grid without incurring additional cost to the additional AO, thus the marginal cost of the jth EV is S base , which is a constant value, thus the marginal cost is proportional to the on-grid time. Since the charging of each EV does not interfere with each other, the charging demand of different EVs is monotonically additive, thus the equation (31) can be simplified as equation (33); the charging cost of each EV can be proportionally allocated according to its marginal cost.

[0179]

[0180] Shapley value calculation of frequency regulation revenue:

[0181] The frequency regulation revenue is divided into capacity revenue Y R,i and mileage revenue Y M,i . The frequency regulation revenue of a single EV y i,j is also divided into capacity revenue y R,i,j and mileage revenue y M,i,j .

[0182] y i,j = y R,i,j + y M,i,j (34)

[0183] The capacity revenue is determined in advance, in the real-time stage, the more the number of charging EVs, the more the actual available capacity, thus this part of the revenue can be directly based on the charging time distribution of the period, similar to equation (33), as shown in equation (35);

[0184]

[0185] And for the mileage revenue, the mileage revenue Y M,t at each time t can be split out, and each Y M,t is allocated

[0186] Y M,t = π M,i k i | P IR,t - P IR,t-1 | (36)

[0187] But since there is no reference power for a single EV, thus it is also impossible to determine its frequency regulation mileage, thus the frequency regulation revenue can also be allocated by the margin. Based on the definition of the margin of each EV in the scheduling, the essence of the marginal effect of the jth EV on the frequency regulation revenue is that when the EV participates in the frequency regulation, it shares the change amount of the margin of the remaining EVs by changing its own margin of electricity. Let the charging power of the jth EV at time t be p j,t , and the margin change between time t and time t+1 be:

[0188] Δγ j,t+1 = γ j,t+1 - γ j,t (37)

[0189] Based on the basic definition of contribution based on Shapley value, and the relationship between AO and EV in the present application should be: first, the frequency adjustment contribution of EV with unchanged margin is 0. Second, when upward adjustment is needed, the margin change is positive, so the contribution is positive, and the margin change is negative, so the contribution is negative. Third, when downward adjustment is needed, the margin change is negative, so the contribution is positive, and the margin change is positive, so the contribution is negative.

[0190] For the entire aggregator, it is obvious that the total margin change of the entire aggregator is the same sign as the frequency adjustment demand: when upward adjustment, the total margin becomes larger, and when downward adjustment, the total margin becomes smaller. Therefore, γ j,t As a contribution degree, the proportional distribution can meet the above conditions.

[0191]

[0192] Therefore, for the jth EV, the mileage benefit accumulated in period i is divided as shown in equation (38).

[0193] In this embodiment, the aggregator periodically evaluates the effect of the scheduling strategy and the benefit distribution mechanism, and optimizes and adjusts according to the evaluation results to improve the scheduling efficiency and user satisfaction.

[0194] In one possible embodiment, the specific examples are provided as follows:

[0195] The used charging EV historical data comes from a charging station in a city in southern China, containing 350 charging piles; the frequency adjustment signal and market price data come from PJM. The maximum power of EV is 30kW.h, and the guaranteed power per hour in the present application is 3kW.h (i.e. the power under uniform charging is 3kW). The satisfied power allowed to discharge under mode B is 0.9 times of the maximum power. The simulation time is 24h, and the length of each standby period is 1h according to the PJM market rules.

[0196] The simulation results under mode A and mode B are given respectively and compared and analyzed:

[0197] Referring to Figure 3 , the day-ahead optimization results are given, and in Figure 3In the middle, a) is the upper / lower spare capacity; b) is the amount of electricity purchased for charging per hour. Obviously, the aggregator with EV as the main body is more inclined to provide upward capacity, on the one hand, the EV itself has charging demand, which limits the space for downward charging power; on the other hand, providing upward power also meets the charging demand, which can reduce the cost. Therefore, the EV aggregator has stronger upward reserve capacity. At the same time, by comparing the optimization results of mode A and mode B, it can be seen that the EV with higher power in mode B allows discharging, and the aggregator can obtain more downward space. Considering that the more electricity purchased, the more downward adjustment space and the less upward adjustment space, mode B can increase the downward capacity while reducing the demand for electricity purchase, thereby reporting more upward capacity. Therefore, the upward / downward capacity reported under mode B is larger, and the electricity purchase is less, which not only improves the frequency regulation benefit, but also reduces the electricity cost.

[0198] Referring to Figure 4 , the power change of each EV in the 10:00-18:00 period in real-time scheduling is given, and each gray line represents the power change curve of an EV during charging, and the EVs that reach the full charge under mode A and the satisfied power but still offline under mode B are marked with red curves. It can be seen that in order to respond to frequency regulation, the charging power of each EV is fluctuating, but it is stably increasing under the constraint of the guaranteed power, which verifies the effectiveness of the application of the defined margin in scheduling. Obviously, the power of the EV under mode A does not change after being fully charged, and the power of the EV under mode B also responds to frequency regulation after exceeding the satisfied power, adjusts the charging power, and even has a discharging condition, but ensures that the power is not less than the satisfied power.

[0199] Referring to Figure 5 , the power change of the aggregator as a whole in the same period (10:00-18:00) is given. It can be seen that the frequency regulation error under mode A is slightly larger than that under mode B because the margin under mode A is larger than that under mode B. In addition, since the aggregator reports a lower downward reserve capacity, the total power of the aggregator will not be negative, i.e. the power will not be sent to the grid in the reverse direction.

[0200] In order to study the charging condition and cost and benefit of the EV owner under this mode, Figure 6 and Figure 7 give the average value change of a single EV from the time of entering the network to charging. Figure 6 The charging power change under mode A and mode B is given respectively. Figure 7 is the total income settled with the AO at a certain time if a single EV goes offline from the time of entering the network to charging, assuming that the conditions of other EVs remain unchanged. It is worth emphasizing that Figure 6 and Figure 7All the EVs are aligned at the time of entering the network, and the average value is taken. The longer the time in the network, the fewer the EV samples still in the network, and the weaker the reference value. Figure 6 The gray auxiliary line in the figure is the uniform charging power to ensure the bottom power. It can be seen that the charging power can be higher than the bottom power when the EV leaves the network at any time, verifying the ability of the contract mechanism and the scheduling model to ensure user charging power. (When charging for a long time, the EV has reached the desired power or is fully charged, so the power will not increase steadily, and therefore the red and blue curves are lower than the bottom power auxiliary line at about 7h. However, this does not mean that the charging demand of some EVs is not met.) As time goes on, the average power in mode A will be maintained at a high level because the EVs in mode A are fully charged.

[0201] According to formula (6) and formula (30), Figure 7 The income curve of a single EV consists of three parts: charging cost, capacity income, and mileage income (and potential compensation for insufficient charging). The two modes have little difference at the beginning of charging, and both have to pay the cost and increase it. This is because of the constraint of the bottom power. When entering the network, the EV must charge a certain amount of power to ensure the margin, so the contribution to frequency regulation may be negative. After adding the charging cost, the car owner always needs to pay the charging fee at the beginning of charging, resulting in negative income. As the charging proceeds, there is a surplus margin, so it can participate in frequency regulation, and therefore the income can be quickly converted from negative to positive. Then, as the power of part of the EVs approaches full charge, the income curves of the two modes start to be completely different, mainly due to the difference in the sharing system: in mode A, the EVs that are fully charged will exit the frequency regulation and no longer generate capacity income and frequency regulation income, but due to the occupation of the charging station, the charging cost will continue to grow, so the total income will continue to decrease and even become negative again; in mode B, the EVs will continue to provide frequency regulation above the desired power after reaching the desired power, although the available power interval is small (between the desired power and the full charge), which makes the participation ratio low, but the income will still gradually increase.

[0202] Through one possible embodiment, the effectiveness of the day-ahead and real-time scheduling model proposed by the application is verified. The decision of charging and standby reporting in day-ahead scheduling, the allocation of charging power in real-time scheduling, and the standby and real-time response of frequency modulation are ensured on the premise of EV charging power. On this basis, from the perspective of cooperative game, the cost and benefit sharing mechanism between the aggregator administrator and each participating EV is proposed, and the charging and benefit of a single EV under two contract modes are given. The allocation mechanism can give more benefits to the EV participating in frequency modulation under the premise of ensuring fairness, thereby improving the enthusiasm of users participating in frequency modulation service. Mode A and mode B can offset the charging cost and make a profit. Under mode A, users tend to charge, and long-term charging can maintain a higher power under this mode. However, after being fully charged, the user cannot participate in frequency modulation and occupies the charging pile, so the frequency modulation benefit will decrease rapidly. Under mode B, if the user does not go offline for a long time, the power can be maintained within a certain interval to respond to frequency modulation, so that the benefit can still slowly increase, but the benefit is not as good as normal charging. The essential reason for the large difference in benefits under the two modes is that the charging cost allocation in the application does not use the traditional contract-based power charging mode, but a charging pile occupancy time-based (equation (29)) charging mode, and the capacity benefit (equation (31)) part is also based on the length of time participating in frequency modulation. Therefore, from the user's perspective, mode A heavily punishes the behavior of not going offline after being fully charged, which can encourage users to go offline in time, but the relatively low benefit may make users return to the traditional charging mode of not participating in frequency modulation and only paying for power. Mode B encourages users to participate in frequency modulation through stable growth of benefits, but users lack the motivation to go offline in time, which may occupy the charging opportunities of later users. In order to encourage both frequency modulation participation and timely going offline, a compromise between mode A and mode B can be adopted by assigning weights.

[0203] The application constructs an operation framework of electric vehicle aggregators participating in frequency modulation auxiliary service under the guidance of an aggregator manager, defines a power margin based on charging demand, and applies the margin to day-ahead scheduling, real-time scheduling, and benefit sharing, thereby simplifying the coupling problem of power consumption and frequency modulation into a single variable problem based on the margin, achieving the maximum benefit expectation in day-ahead charging / standby optimization, achieving fast scheduling that meets charging demand and frequency modulation demand in real-time scheduling, and achieving fair distribution based on the contribution of the margin in settlement.

[0204] Based on this framework, two frequency modulation participation mechanisms are proposed, the effectiveness is verified through simulation analysis, the charging and benefit expectation from the perspective of EV users is given, and the following conclusions can be drawn:

[0205] First, the effectiveness of the proposed model is verified by simulation scenarios: in the market where the upper and lower reserves are reported separately, the cluster EV has stronger ability to provide upper reserve; and by reasonably optimizing the purchase of electricity and the reported capacity, the user's charging demand can be met.

[0206] Second, participating in frequency regulation can provide users with profits, and allowing partial discharge when the EV user has sufficient electricity can significantly improve profitability, but even if discharging is not allowed (mode A), the EV aggregator has the ability to respond to frequency regulation and create benefits for users.

[0207] Third, the proposed margin variable can be used as the core basis for scheduling and settlement. Through the settlement of the user's income, the value guidance function with social benefits such as "encouraging participation in frequency regulation" and "encouraging timely off-grid" is achieved.

[0208] In summary, the application designs a contract, constructs a demand response strategy in which an electric vehicle user and an aggregator participate together; the demand response strategy makes the electric vehicle user meet a power index of self charging demand and provides auxiliary power supply service for a power grid; according to a load condition and a frequency modulation demand of the power grid, charging price and discharging price in a specified time period are formulated; the charging price and the discharging price in the specified time period make the electric vehicle user charge in a period in which the load of the power grid is low and discharge in a period in which the load of the power grid is high; a simulation experiment model is constructed through planning of charging and discharging periods and the charging price and the discharging price in the specified time period; the simulation experiment model optimizes variable parameters on which the aggregator performs real-time scheduling and power distribution; the aggregator adjusts charging power of each electric vehicle user according to the optimized variable parameters, in combination with a real-time frequency modulation signal of the power grid and a charging state of the electric vehicle user; the adjustment of the charging power of the electric vehicle user meets the frequency modulation demand of the power grid; a benefit distribution strategy of the aggregator and the electric vehicle user is constructed according to a participation degree and contribution of the electric vehicle user in frequency modulation service; the electric vehicle user and the aggregator distribute benefits obtained by participating in frequency modulation of the power grid through the benefit distribution strategy. The application analyzes positive influences of the electric vehicle participating in demand response and entering the power grid, expounds different ways of the electric vehicle participating in demand response, and considers that the electric vehicle should participate in demand response in the form of a load aggregator, which is more beneficial to information transmission and more effective for orderly charging and discharging behavior of the electric vehicle, and solves disadvantages of the electric vehicle participating in demand response in the form of an individual. The application proposes an optimal charging and discharging strategy for the electric vehicle participating in demand response, constructs a thought of the electric vehicle participating in demand response in the form of the load aggregator, and designs a mechanism of a transaction market. The application plans charging and discharging periods and formulates charging and discharging prices, takes price-type demand side response as a guide, takes peak clipping and valley filling and minimum cost of the electric vehicle as targets, formulates charging and discharging prices of the electric vehicle, and guides orderly charging and discharging behavior of the electric vehicle. The application discusses a user contract design facing charging demand of the electric vehicle participating in demand response, and studies an aggregator operation model integrating charging, standby optimization, real-time power distribution and benefit settlement under the contract constraint, proposes an operation framework of the electric vehicle user and the aggregator participating in demand response in a market environment, and proposes a contract mode between the electric vehicle user and the aggregator, which can realize immediate charging and walking of the user, facilitates joint optimization of electric quantity / capacity, and realizes real-time and rapid solution of the electric quantity based on the margin of each electric vehicle, so as to meet charging and participation in demand response.

[0209] It should be noted that the method of the embodiments of the present disclosure can be executed by a single device, such as a computer or a server, etc. The method of the embodiments can also be applied to a distributed scenario, and be completed by multiple devices cooperating with each other. In the case of such a distributed scenario, one of the multiple devices can only execute one or more steps in the method of the embodiments of the present disclosure, and the multiple devices can interact with each other to complete the method.

[0210] It should be noted that some embodiments of the present disclosure have been described above. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in a different order than the order described above and still achieve desirable results. Additionally, the processes depicted in the figures do not necessarily require the particular order shown, or sequential order, to achieve the desired results. In certain implementations, multitasking and parallel processing can be advantageous.

[0211] Embodiment 2

[0212] Referring to Figure 8 Embodiment 2 of the present disclosure also provides an electric vehicle charging power scheduling analysis processing device, which adopts the electric vehicle charging power scheduling analysis method of Embodiment 1 described above, and comprises:

[0213] A demand response strategy construction module 001 is configured to construct a demand response strategy in which electric vehicle users and aggregators jointly participate through contract design; and make the electric vehicle users meet the power index of their own charging demand and provide auxiliary power supply services for the power grid through the demand response strategy.

[0214] A charging and discharging price making module 002 is configured to make charging and discharging prices in a specified time period according to the load condition and frequency modulation demand of the power grid; and make the electric vehicle users charge in a period of low power grid load and discharge in a period of high power grid load through the charging and discharging prices in the specified time period.

[0215] A simulation experiment model construction and application module 003 is configured to construct a simulation experiment model through planning of charging and discharging time periods and the charging and discharging prices in the specified time period; and optimize variable parameters on which the aggregators make real-time scheduling and power distribution through the simulation experiment model.

[0216] An electric vehicle charging power adjustment module 004 is configured to adjust the charging power of each electric vehicle user according to the optimized variable parameters, in combination with the real-time frequency modulation signal of the power grid and the charging state of the electric vehicle user, by the aggregators; and meet the frequency modulation demand of the power grid through the adjustment of the charging power of the electric vehicle user.

[0217] In this embodiment, the method further comprises:

[0218] The benefit distribution module 005 is configured to construct a benefit distribution strategy of the aggregator and the electric vehicle user according to the participation degree and contribution of the electric vehicle user in the frequency modulation service, and the electric vehicle user and the aggregator distribute the benefits obtained by participating in the grid frequency modulation through the benefit distribution strategy.

[0219] In this embodiment, in the participation demand response strategy construction module 001, the step of constructing the demand response strategy participated in by the electric vehicle user and the aggregator through contract design in the participation demand response strategy construction module includes:

[0220] The contract mode between the electric vehicle user and the aggregator is constructed, and the electric vehicle user participates in the grid frequency modulation under the premise of meeting the own charging demand through the contract mode;

[0221] The actual charging demand of the electric vehicle user is taken as a premise to construct a margin evaluation strategy;

[0222] Through the margin evaluation strategy, the power allocation problem in the real-time stage is linearized to obtain the solution in the real-time scheduling, and the marginal cost and benefit accounting in the settlement stage is completed.

[0223] In this embodiment, in the simulation experiment model construction and application module 003, the variable parameters on which the aggregator bases for real-time scheduling and power allocation are optimized through the simulation experiment model, including the charging power of the aggregator in each period, the uplink capacity reported by the aggregator, and the downlink capacity reported by the aggregator.

[0224] The relationship function of the charging power of the aggregator, the uplink capacity reported by the aggregator, and the downlink capacity reported by the aggregator is:

[0225]

[0226] In the formula, n i is the average number of charging electric vehicles EV in the i period; n j is the average number of charging electric vehicles EV in the j period; max{n i}, max{n j} are the maximum possible values of the feasible region constraints of the optimization parameters; P max is the maximum charging power of the electric vehicle battery; P charge,i is the charging power of the aggregator in each period; R up,i is the uplink capacity reported by the aggregator; R down,i is the downlink capacity reported by the aggregator.

[0227] In the embodiment, in the income distribution module 005, the electric vehicle user and the aggregator distribute the income obtained by participating in the frequency modulation of the power grid through the income distribution strategy:

[0228] The income of the aggregator is:

[0229] B AO,i = α (Y R,i + Y M,i ) - βS de,charge,i (40)

[0230] In the formula, B AO,i is the income of the aggregator; α is the proportion of frequency modulation income; S de,charge,i is the power shortage relative to the guaranteed charging power promised to the vehicle owner after the end of the period; β is the penalty coefficient; Y R,i is the capacity income; Y M,i is the frequency modulation mileage income.

[0231] The income of the electric vehicle user is:

[0232] Q i,j = -c i,j + y i,j + βS de,charge,i (41)

[0233] In the formula, Q i，j is the total cost of the electric vehicle due to charging in period i; c i，j is the charging cost of the electric vehicle in period i; y i，j is the frequency modulation income.

[0234] It should be noted that the information interaction and execution process between the modules of the above device are based on the same concept as the method embodiment in Embodiment 1 of the application, and the technical effects brought by them are the same as those of the method embodiment of the application. For specific content, refer to the description in the foregoing method embodiment of the application. Here, it will not be repeated.

[0235] Embodiment 3

[0236] Embodiment 3 of the application provides a non-transitory computer readable storage medium, the computer readable storage medium stores a program code of an electric vehicle charging power scheduling analysis method, and the program code includes instructions for executing an electric vehicle charging power scheduling analysis method of embodiment 1 or any possible implementation manner thereof.

[0237] The computer-readable storage medium can be any available media or a set of one or more available media that is accessible by a computer, a server, a data center, etc. data storage device integrated. The available media can be a magnetic medium, (for example, a floppy disk, a hard disk, a magnetic tape), an optical medium (for example, a DVD), or a semiconductor medium (for example, a solid state disk (Solid State Disk, SSD)), etc.

[0238] Embodiment 4

[0239] Embodiment 4 of the present application provides an electronic device, comprising: a memory and a processor;

[0240] The processor and the memory complete the communication between each other through the bus; the memory stores program instructions executable by the processor, and the processor calling the program instructions can execute the electric vehicle charging power scheduling analysis method of embodiment 1 or any possible implementation manner thereof.

[0241] Specifically, the processor can be implemented by hardware or software. When implemented by hardware, the processor can be a logic circuit, an integrated circuit, etc. When implemented by software, the processor can be a general-purpose processor, which realizes by reading software codes stored in the memory. The memory can be integrated in the processor or exist independently outside the processor.

[0242] In the above embodiments, all or part of them can be realized by software, hardware, firmware or any combination thereof. When realized by software, all or part of them can be realized in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions can be transferred from one website site, computer, server or data center to another website site, computer, server or data center through wired (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (such as infrared, wireless, microwave, etc.) mode.

[0243] It should be apparent to those skilled in the art that the modules or steps of the application described above can be implemented with a general purpose computing device, which can be centralized on a single computing device or distributed across a network of multiple computing devices, and optionally implemented with program code executable by a computing device, which can be stored in a storage device and executed by a computing device, and in some cases, the steps shown or described can be performed in a different order than shown or described, or made into individual integrated circuit modules or multiple modules or steps made into a single integrated circuit module. Thus, the application is not limited to any particular combination of hardware and software.

[0244] While the application has been described in detail and with reference to specific embodiments thereof, it will be apparent to one skilled in the art that various modifications or changes can be made therein without departing from the spirit and scope of the application. Accordingly, it is intended that all such modifications and changes be included within the scope of the application as claimed.

Claims

1.An electric vehicle charging power scheduling analysis processing apparatus, characterized by, The method comprises the following steps: a participation demand response strategy construction module is used to construct a demand response strategy participated in by electric vehicle users and aggregators through contract design; the electric vehicle users meet their own power indicators for charging demand through the demand response strategy, and provide auxiliary power supply services for the power grid; a charging and discharging price setting module is used to set charging and discharging prices in a specified time period according to load conditions and frequency modulation demands of the power grid; the charging and discharging prices in the specified time period are used to make the electric vehicle users charge in a time period with low load of the power grid and discharge in a time period with high load of the power grid; a simulation experiment model construction and application module is used to construct a simulation experiment model through planning of charging and discharging time periods and the set charging and discharging prices in the specified time period; variable parameters on which the aggregator bases for real-time scheduling and power distribution are optimized through the simulation experiment model; an electric vehicle charging power adjustment module is used to adjust charging power of each electric vehicle user according to the optimized variable parameters, real-time frequency modulation signals of the power grid and charging states of the electric vehicle users; the frequency modulation demands of the power grid are met through adjustment of the charging power of the electric vehicle users; the method further comprises the following steps: a benefit distribution module is used to construct a benefit distribution strategy of the aggregator and the electric vehicle users according to participation degrees and contributions of the electric vehicle users in frequency modulation services; the benefit distribution strategy is used to distribute benefits obtained by the electric vehicle users and the aggregator in participation in frequency modulation of the power grid; in the participation demand response strategy construction module, the step of constructing the demand response strategy participated in by the electric vehicle users and the aggregator through contract design comprises the following steps: a contract mode between the electric vehicle users and the aggregator is constructed; the contract mode is used to make the electric vehicle users participate in frequency modulation of the power grid on the premise of meeting their own charging demands; the actual charging demands of the electric vehicle users are taken as a premise to construct a margin evaluation strategy: a real-time margin parameter is defined for a single electric vehicle to constrain a power curve of the electric vehicle and ensure user demands, and the real-time margin parameter is also used as a basis for real-time power scheduling and benefit distribution; the power allocation problem in a real-time stage is linearized through the margin evaluation strategy to obtain a solution in real-time scheduling and complete marginal cost and benefit accounting in a settlement stage; in the simulation experiment model construction and application module, the variable parameters on which the aggregator bases for real-time scheduling and power distribution include: charging power of the aggregator in each time period, uplink capacity reported by the aggregator and downlink capacity reported by the aggregator; a relationship function of the charging power of the aggregator, the uplink capacity reported by the aggregator and the downlink capacity reported by the aggregator is: ； where n i is the average number of charging electric vehicles (EVs) at time i; n j is the average number of charging electric vehicles (EVs) at time j; max{n i}, max{n j} are the maximum possible values of the feasible region constraints for the optimization parameters; P max is the maximum charging power of an electric vehicle battery; P charge,i is the aggregated merchant charging power per time period; R up,i is the uplink capacity reported by the aggregator; R down,i is the downlink capacity reported by the aggregator; in the benefit distribution module, in the process of distributing the benefits obtained by the electric vehicle users and the aggregator in participation in frequency modulation of the power grid through the benefit distribution strategy: the benefit of the aggregator is: B AO，i = a(Y R，i + Y M，i )- βS de_charge，i In the formula, B AO,i is the revenue of the aggregator; a is the sharing ratio of the frequency modulation revenue; S de_charge,i is the shortage of the electricity quantity relative to the guaranteed charging electricity quantity promised to the vehicle owner after the end of the period; β is the penalty coefficient; Y R,i is the capacity revenue; Y M,i is the frequency modulation mileage revenue; the benefit of the electric vehicle users is: Q i，j = -c i，j + y i，j + βS de_chage，i ； ； ； ； where Q i,j is the total cost of the EV for charging in period i; c i,j is the charging cost of the EV in period i; y i,j is the frequency regulation benefit, C E,i is the energy cost of the aggregator fleet, T total,j is the charging time; y R,i,j is the capacity benefit of the individual EV frequency regulation benefit; y M,i,j is the mileage benefit of the individual EV frequency regulation benefit; Y R,i is the capacity benefit of the aggregator fleet; Y M，t is the settlement of the frequency regulation mileage benefit to the aggregator; is the margin change; where the EV frequency regulation contribution is 0 when the margin is unchanged; the margin change is positive when the margin needs to be adjusted upward, and the margin change is positive; the margin change is negative when the margin needs to be adjusted downward, and the margin change is negative. Under the constraint of electricity demand, the real-time margin γ of EV at time point t is j,t The electricity amount S at this time is defined EV,j,t The electricity amount S at this time is defined EVbase,j,t The electricity amount S at this time is defined EVbase,j,t The electricity amount S at this time is defined 。

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