Power distribution network electric vehicle joint low-carbon scheduling method and system

Abstract

The invention discloses a power distribution network electric vehicle combined low-carbon scheduling method and system, and is used for solving the technical problem of overhigh scheduling cost caused by the existing power distribution network electric vehicle combined scheduling method. The method comprises the following steps: setting a first constraint condition by taking the operation cost minimization of the electric vehicle as an optimization target, and constructing a lower-layer model by taking the aggregator data of the electric vehicle as a decision variable; taking the minimum carbon perception operation cost of the power distribution network as an optimization target, setting a second constraint condition, taking power distribution system operator data corresponding to the power distribution network as a decision variable, and constructing an upper layer model; based on the initial electric vehicle aggregator data, performing joint optimization solution on the upper layer model and the lower layer model, and outputting target power distribution system operator data and target electric vehicle aggregator data; and taking the target power distribution system operator data and the target electric vehicle aggregator data as a power distribution network electric vehicle joint low-carbon scheduling scheme.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network optimization dispatching, and in particular to a distribution network electric vehicle joint low-carbon dispatching method and system. Background Art

[0002] As the main carrier of distributed renewable energy, the distribution network needs to further improve its flexible adjustment capabilities to cope with the uncertainty of renewable energy and load. In this context, the active distribution network integrates distributed energy such as photovoltaic, wind power, small gas generators, energy storage systems and electric vehicle charging facilities to achieve two-way power flow, active scheduling and optimal utilization of distributed resources, and improve the flexibility, reliability and economy of the system. With the popularization of electric vehicles, the effective use of their charging flexibility in active distribution networks has received widespread attention. Carbon-aware collaboration between active distribution networks and large-scale electric vehicles under source-load uncertainty can improve the economy of the system and reduce carbon emissions, thereby helping to achieve the goal of carbon neutrality. However, the coordinated optimization scheduling of active distribution networks and electric vehicles also faces some problems.

[0003] First, the uncertainty of renewable energy on the power generation side may lead to unstable operation of the active distribution network. It is very important to effectively coordinate and manage the charging of electric vehicles to improve the stability of the active distribution network. Secondly, due to the uncertainty of electric vehicle charging behavior and its large number, directly dispatching each electric vehicle will bring a huge computing burden to the distribution system operator (DSO). It is necessary to design an electric vehicle aggregator (EVA) to utilize the flexibility of large-scale electric vehicles. With the green transformation of the power system, the effective coordinated optimization dispatch of active distribution networks and large-scale electric vehicles will become increasingly important.

[0004] Most of the existing methods for joint dispatching of electric vehicles in distribution networks design pricing strategies for bidding between distribution system operators and load service entities, or construct a two-tier model between distribution system operators and virtual power plants, in order to optimize the pricing of energy and ancillary services or optimize the pricing of active and reactive power. However, these methods only consider economic dispatch, and the dispatching means are too single, resulting in insufficient carbon emission capacity and excessive dispatching costs. Summary of the invention

[0005] The present invention provides a distribution network electric vehicle joint low-carbon dispatching method and system, which are used to solve the technical problem that the existing distribution network electric vehicle joint dispatching method leads to excessive dispatching cost.

[0006] A first aspect of the present invention provides a method for joint low-carbon dispatching of electric vehicles in a distribution network, comprising:

[0007] In response to the dispatch request, the first constraint is set with the minimum operation cost of electric vehicles as the optimization goal, and the lower-level model is constructed with the electric vehicle aggregator data as the decision variable;

[0008] Taking the minimization of the carbon-aware operating cost of the distribution network as the optimization goal, setting the second constraint condition, taking the distribution system operator data corresponding to the distribution network as the decision variable, and constructing the upper model;

[0009] Based on the initial electric vehicle aggregator data, jointly optimizing and solving the upper model and the lower model, and outputting target distribution system operator data and target electric vehicle aggregator data;

[0010] The target distribution system operator data and the target electric vehicle aggregator data are used as a joint low-carbon dispatching plan for distribution network electric vehicles.

[0011] Optionally, based on the initial electric vehicle aggregator data, jointly optimizing and solving the upper model and the lower model, and outputting target distribution system operator data and target electric vehicle aggregator data, comprises:

[0012] Optimizing and solving the upper model according to the initial electric vehicle aggregator data, and outputting the distribution system operator data at the current moment;

[0013] Calculate the average carbon intensity based on the power generation of distributed generation units in the distribution system operator data at the current moment;

[0014] Based on the average carbon intensity, the lower model is optimized and solved, the electric vehicle aggregator data at the current moment is output, and the number of iterations is counted in real time;

[0015] Calculate a scalar value according to the electric vehicle aggregator data at the current moment and the electric vehicle aggregator data at the historical moment;

[0016] When the scalar value is less than a preset scalar threshold or the number of iterations is greater than a preset iteration threshold, the distribution system operator data at the current moment is used as the target distribution system operator data, and the electric vehicle aggregator data at the current moment is used as the target electric vehicle aggregator data.

[0017] Optionally, the first constraint condition includes a total charging energy balance constraint of the electric vehicle aggregator, an incentive price constraint of the electric vehicle aggregator, and a spare capacity constraint of the electric vehicle aggregator.

[0018] Optionally, the second constraint condition includes power balance constraint, distributed power generation unit power constraint, energy storage device energy power constraint, and up and down adjustment of standby power constraint.

[0019] Optionally, the total charging energy balance constraint of the electric vehicle aggregator includes:

[0020] The balance constraints between energy and power of electric vehicle aggregators are:

[0021]

[0022] in, The total energy provided by the jth EV aggregator accumulated at time t; The total energy provided by the jth EV aggregator accumulated at time t-1; is the average charging efficiency of EVs by EV aggregators; is the total charging power actually provided to the electric vehicles by the jth electric vehicle aggregator at time t-1; is the length of the time interval;

[0023] The total charging demand constraint of the electric vehicle fleet in one day is:

[0024]

[0025] in, is the total energy provided by the jth EV aggregator in one day; is the total energy required by the j-th electric vehicle fleet in one day.

[0026] Optionally, the incentive price constraint of the electric vehicle aggregator is specifically:

[0027]

[0028] in, is the upper bound of the total energy or total power required by the j-th electric vehicle fleet; is the lower bound of the total energy or total power required by the j-th electric vehicle fleet; For incentive price The corresponding first convex function; is the energy or power actually provided by the jth electric vehicle aggregator at time t; M is a constant; For incentive price The corresponding second convex function; is the first binary variable; is the second binary variable; is the upper bound of the incentive price; is the incentive price of the jth EV aggregator in period t; is the lower bound of the incentive price.

[0029] Optionally, the spare capacity constraint of the electric vehicle aggregator is specifically:

[0030]

[0031]

[0032] in, is the downregulated reserve power of the jth EV aggregator at time t; is the power actually provided by the jth EV aggregator at time t; is the upper bound of the power that the jth EV aggregator can provide at time t under demand response; is the upper bound of the energy that the jth EV aggregator can provide at time t under demand response; is the energy actually provided by the jth EV aggregator at time t; is the average charging efficiency of EVs by EV aggregators; is the length of the time interval; is the up-regulated reserve power of the j-th EV aggregator at time t; is the lower bound of the power that the jth EV aggregator can provide at time t under demand response; is the lower bound of the energy that the j-th EV aggregator can provide at time t under demand response.

[0033] Optionally, the power balance constraint includes:

[0034] The linear power flow constraints of the distribution network are as follows:

[0035]

[0036]

[0037] in, is the actual active power on the mn line; is the susceptance between node m and node n; is the phase angle of node m at time t; is the phase angle of node n at time t; is the admittance between node m and node n; is the voltage amplitude of node m at time t; is the voltage amplitude of node m at time t;

[0038] Node power balance constraints, specifically:

[0039]

[0040]

[0041] in, is the net active power flow of node m into the system at time t, in MW; is the total number of nodes in the active distribution network; From node m to other The sum of active power flows of nodes; is the active power output by the generator at node m; is the active power of wind power generation at node m at time t; is the net load active power at node m; is the active power consumed by the charging station at node m; is the net reactive power flow into the system at node m, in MVar; From node m to other The sum of reactive power flows of nodes; is the reactive power output by the generator at node m; is the wind power reactive power at node m at time t; is the net load reactive power at node m.

[0042] Optionally, the power constraint condition of the distributed generation unit is specifically:

[0043]

[0044]

[0045] in, is the active power output of distributed generation unit i at time t; is the down-regulated reserve power of distributed generation unit i at time t; is the lower limit of active power generation of distributed generation unit i; is the up-regulated reserve power of distributed generation unit i at time t; is the upper limit of active power generation of distributed generation unit i; is the total number of distributed generation units; is the lower limit of reactive power generation of distributed generation unit i; is the reactive power output of distributed generation unit i at time t; is the lower limit of reactive power generation of distributed generation unit i.

[0046] Optionally, the energy power constraint of the energy storage device is specifically:

[0047]

[0048]

[0049]

[0050]

[0051]

[0052] in, is the energy of energy storage device k at time t+1; is the energy of energy storage device k at time t; is the charging efficiency of energy storage device k; is the charging power of energy storage device k at time t; is the discharge power of energy storage device k at time t; is the discharge efficiency of energy storage device k; is the time interval; is a 0 / 1 variable, indicating that the energy storage device k can only be in one of the charging or discharging states at time t; The charging power of energy storage device k is subject to the maximum charging power of the device; is the maximum energy capacity of energy storage device k; The discharge power of the energy storage device k is subject to the maximum discharge power of the device; is the minimum energy limit of energy storage device k; is the energy of energy storage device k at time T, indicating the energy of energy storage device k that needs to be restored to its initial state after the entire scheduling cycle ends.

[0053] Optionally, the adjusting up and down of the reserve power constraint is specifically:

[0054]

[0055] in, is the participation factor of distributed generation unit i at time t; is the scaling factor for the confidence level; is the load fluctuation error covariance matrix; is the renewable energy fluctuation error covariance matrix; , To increase the standby power, To reduce the reserve power, To adjust the reserve power up and down; is the total number of distributed generation units.

[0056] Optionally, the electric vehicle aggregator data includes average carbon intensity, charging power of electric vehicle aggregators, downward reserve power provided by electric vehicle aggregators, upward reserve power provided by electric vehicle aggregators, and incentive price; the lower layer model is specifically:

[0057]

[0058] in, is the cost function of the jth charging station; T is the total number of planned time periods; is the time-of-use electricity price in time period t, that is, the basic charging price of the power grid for charging; aggregator data for electric vehicles; is the carbon price, in $ / kg, used to price carbon emissions; is the average carbon intensity, which indicates the carbon emissions per unit of charging power, in kg / kWh; is the charging power of the jth EV aggregator in time period t; hiked prices to spare for EV aggregators; The upregulated reserve power provided to the jth EV aggregator in time period t; reduced prices for backup of EV aggregators; The down-regulated reserve power provided to the j-th EV aggregator in time period t; is the user response probability of the jth EV aggregator in time period t; is the maximum response time of the user of the jth EV aggregator in time period t; is the incentive price in time period t, which is used to encourage users to participate in demand response.

[0059] Optionally, the distribution system operator data includes the charging power of the energy storage device, the discharging power of the energy storage device, the generating power of the distributed generator, the upward reserve power provided by the distributed generator, and the downward reserve power provided by the distributed generator; the upper layer model is specifically:

[0060]

[0061]

[0062]

[0063] in, is the total operating cost of the distribution system operator; T is the length of the scheduling time horizon; is the total number of distributed generation units, that is, the set of distributed generators, representing all distributed energy units participating in power generation in the distribution network; is the distributed generation cost with carbon emission penalty for distributed generator i; Cost of purchasing electricity from the main grid and reserve costs for distribution system operators; It is a collection of energy storage devices, representing all batteries or energy storage units involved in the charging and discharging operations; is the unit charging and discharging cost of the energy storage device; is the charging power of energy storage device k; is the discharge power of energy storage device k; is the power generation cost coefficient of distributed generator i; is the carbon price, reflecting the unit cost of carbon emissions, in $ / kg; is the carbon intensity of distributed generator i, reflecting the carbon emissions per unit power generation; is the power generation of distributed generator i at time t; The cost coefficient for providing the reserve increase to distributed generator i; The upregulated reserve power provided to distributed generator i; The cost coefficient for providing down-reserve to distributed generator i; The down-regulated reserve power provided to distributed generator i; The market price of electricity when the main grid sells power to the distribution grid, in $ / MW; is the carbon intensity of main grid electricity, in kg / MWh; is the electricity purchased by the distribution system operator from the main grid at time t; The market price at which the main grid sells the increased reserve power to the distribution system operator; The upward reserve power provided by the main grid to the distribution system operator at time t; The market price at which the main grid sells reserve power to distribution system operators; It is the downward reserve power provided by the main grid to the distribution system operator at time t.

[0064] A second aspect of the present invention provides a distribution network electric vehicle joint low-carbon dispatching system, comprising:

[0065] A first building module is used to respond to the dispatch request, take the minimum electric vehicle operating cost as the optimization goal, set the first constraint condition, take the electric vehicle aggregator data as the decision variable, and build the lower model;

[0066] A second construction module is used to set a second constraint condition with the minimum carbon-aware operating cost of the distribution network as the optimization goal, and to construct an upper-level model with the distribution system operator data corresponding to the distribution network as the decision variable;

[0067] A solution module, configured to jointly optimize and solve the upper model and the lower model based on the initial electric vehicle aggregator data, and output target distribution system operator data and target electric vehicle aggregator data;

[0068] The output scheme module is used to use the target distribution system operator data and the target electric vehicle aggregator data as a joint low-carbon dispatch scheme for distribution network electric vehicles.

[0069] It can be seen from the above technical solutions that the present invention has the following advantages:

[0070] The above scheme of the present invention provides a method for joint low-carbon dispatching of electric vehicles in a distribution network. When joint low-carbon dispatching of electric vehicles in a distribution network is required, first, the minimum operating cost of the electric vehicle is taken as the optimization goal, the first constraint condition is set, and the electric vehicle aggregator data is taken as the decision variable to construct a lower model; then, the carbon-aware operating cost of the distribution network is minimized as the optimization goal, the second constraint condition is set, and the distribution system operator data corresponding to the distribution network is taken as the decision variable to construct an upper model; based on the initial electric vehicle aggregator data, the upper model and the lower model are jointly optimized and solved, and the target distribution system operator data and the target electric vehicle aggregator data are output; finally, the target distribution system operator data and the target electric vehicle aggregator data are used as a joint low-carbon dispatching scheme for electric vehicles in the distribution network; based on the above scheme, the present invention takes the minimum carbon-aware operating cost of the distribution network as the optimization goal and the minimum operating cost of the electric vehicle as the optimization goal, combines the decision variables and the constraints, constructs the upper model and the lower model and solves them, and outputs the process of the joint low-carbon dispatching scheme for electric vehicles in the distribution network. Compared with the existing dispatching method, the present invention takes into account the carbon-aware coordination between the distribution system operator and the electric vehicle aggregator, thereby reducing carbon emissions and further reducing the dispatching cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. 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 creative labor.

[0072] Figure 1 A flowchart of the steps of a method for joint low-carbon dispatching of electric vehicles in a distribution network provided in Embodiment 1 of the present invention;

[0073] Figure 2 A schematic diagram of the process of the method for joint low-carbon dispatching of electric vehicles in a distribution network provided in the first embodiment of the present invention;

[0074] Figure 3 This is a structural block diagram of a distribution network electric vehicle joint low-carbon dispatching system provided in Embodiment 2 of the present invention. DETAILED DESCRIPTION

[0075] The embodiment of the present invention provides a distribution network electric vehicle joint low-carbon dispatching method and system, which are used to solve the technical problem that the existing distribution network electric vehicle joint dispatching method leads to excessive dispatching costs.

[0076] In order to make the purpose, features and advantages of the present invention more obvious and easy to understand, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described below 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 creative work are within the scope of protection of the present invention.

[0077] See also Figure 1 , Figure 1 A flowchart of the steps of a method for joint low-carbon dispatching of electric vehicles in a distribution network provided in Embodiment 1 of the present invention.

[0078] The present invention provides a method for joint low-carbon dispatching of electric vehicles in a distribution network, comprising:

[0079] Step 101, responding to a dispatch request, taking the minimization of electric vehicle operating costs as the optimization goal, setting a first constraint condition, taking electric vehicle aggregator data as a decision variable, and constructing a lower-level model.

[0080] The first constraints include the total charging energy balance constraint of the electric vehicle aggregator, the incentive price constraint of the electric vehicle aggregator, and the spare capacity constraint of the electric vehicle aggregator.

[0081] The electric vehicle aggregator data includes average carbon intensity, charging power of electric vehicle aggregators, downward reserve power provided by electric vehicle aggregators, upward reserve power provided by electric vehicle aggregators, incentive price, and total charging energy of electric vehicle aggregators. Among them, the total charging energy of electric vehicle aggregators and the power of electric vehicle aggregators are coupled.

[0082] It should be noted that due to the randomness of the charging behavior of electric vehicle users, it is difficult to predict the time distribution of a single electric vehicle. In order to effectively deal with the uncertainty of electric vehicles, an aggregated electric vehicle aggregator model is established for the electric vehicle fleet. The electric vehicle aggregator is a coordination entity for charging management and scheduling, rather than an actual energy storage. Based on the single electric vehicle (EV) model, the total energy and power boundaries of the electric vehicle group are derived, thereby establishing an electric vehicle aggregator model based on power and energy boundaries, that is, through the energy boundary, power boundary and energy update formula, the electric vehicle aggregator model is established, which is specifically expressed as:

[0083] (1)

[0084] in, is the minimum total energy required by the electric vehicle fleet at time t; is the lower limit of energy demand of individual electric vehicle i; N is the total number of electric vehicles; is the actual total energy provided by the EV aggregator to the EV fleet at time t; is the upper limit of energy demand of individual electric vehicle i; is the maximum total energy required by the electric vehicle fleet at time t; is the minimum total charging power required by the electric vehicle fleet at time t; is the lower limit of the individual power demand of electric vehicle i at time t; is the total charging power actually provided by the EV aggregator to the EV at time t; is the upper limit of individual power demand of electric vehicle i at time t; is the maximum total charging power required by the electric vehicle fleet at time t; is the actual total energy provided by the EV aggregator to the EV fleet at time t+1; is the average charging efficiency of EVs by EV aggregators; is the length of the time interval.

[0085] It is worth mentioning that the first constraint in formula (1) requires that the total energy that the electric vehicle aggregator can provide at time t is Must be within the fleet's needs and to ensure that the energy provided meets the demand without exceeding the maximum acceptance capacity of the fleet; and They represent the minimum total charging power and maximum total charging power required by the electric vehicle fleet at time t, which are the lower limits of the individual power requirements of all electric vehicles i at time t. and upper limit The second constraint in formula (1) requires that the total charging power that the electric vehicle aggregator can provide at time t is Must be within the fleet's required power range and to ensure that the charging rate meets the demand without exceeding the vehicle's charging power tolerance; the third formula in formula (1) is the energy update formula, which indicates the total energy increase provided by the electric vehicle aggregator to the fleet during the entire time period, reflecting the state of the electric vehicle aggregator continuously providing services.

[0086] Further, according to formula (1), electric vehicles can be aggregated through electric vehicle aggregators to provide charging flexibility for active distribution networks. However, without sufficient incentives, electric vehicle users may not participate in coordinated charging. Therefore, it is necessary to establish a responsiveness model for electric vehicle users and develop appropriate demand response plans.

[0087] First, the expected responsiveness of EV users to the demand response scheme can be expressed as the incentive price A linear function of:

[0088] (2)

[0089] in, is the user's ideal expected response probability; is the maximum response probability of the user, and its value is less than 100%; is the actual incentive price; For The corresponding maximum excitation.

[0090] In practical applications, Determined by the willingness of electric vehicle users to participate in demand response. If the willingness of electric vehicle users to participate in demand response is higher, then Will be lower.

[0091] Secondly, due to the different sensitivities of users to incentive prices, their responsiveness is uncertain. This invention is based on the consumer psychological model and uses triangular membership functions to model The maximum deviation of user responsiveness is defined as:

[0092] (3)

[0093] in, The maximum deviation of the user's response describes the deviation between the user's response behavior to the incentive price and the ideal expected response. It indicates the possible fluctuation range between the user's actual response and the expected value under different incentive levels. is a constant coefficient greater than zero, used to adjust the size of the deviation; is the actual incentive price; For The corresponding maximum incentive; for The turning point from increase to decrease; It is a constant coefficient greater than zero, used to adjust the size of the deviation.

[0094] Therefore, taking into account the uncertainty, the actual responsiveness of the user can be expressed as the random variable To describe:

[0095] (4)

[0096] (5)

[0097] (6)

[0098] Where, formulas (5) and (6) define the random variable The mean and standard deviation of the driver's responsiveness are calculated to quantify the degree of deviation in the driver's response to the incentive.

[0099] In formula (4), represents the actual response probability of the driver to the stimulus at time t, is the incentive price at time t ($ / h); this is the economic incentive given in the demand response program to attract users to participate in the response. is a random variable, which indicates the uncertainty factor of the user's response to the incentive price, reflecting the individual differences of users to the same incentive price. For example, some users may be more sensitive, while others may be less sensitive. Formula (4) shows that the actual responsiveness of the user is By incentive price and the random uncertainty factor, i.e., the random variable Joint decision.

[0100] In formula (5), is a random variable The expected value of describes the average uncertainty response deviation of all user responses. The expected value of is equal to the maximum response probability and the maximum incentive price ratio.

[0101] In formula (6), is a random variable The standard deviation describes the fluctuation range of user response behavior, that is, the degree of uncertainty of user response. is a constant, a positive deviation factor, used to amplify or reduce the fluctuation range of user response uncertainty. It is the inflection point value of the incentive price, indicating the critical point where the user's sensitivity to the incentive price changes from increasing to decreasing. is the maximum value of the incentive price, indicating the maximum incentive level that can be provided in the demand response program. Formula (6) defines when the incentive price Below the inflection point When the incentive price is , the volatility of user response behavior is large, and the standard deviation increases with the increase of incentive price; when the incentive price is Above the inflection point , the uncertainty of user response gradually decreases, and the standard deviation decreases as the incentive price increases.

[0102] The responsiveness of electric vehicle users in demand response affects the available energy and power range of electric vehicle aggregators. On the other hand, the charging rates of different electric vehicles vary significantly due to differences in charging time. An hourly responsiveness incentive program is used to coordinate electric vehicles with different responsiveness. Specifically, electric vehicle users can adjust the charging speed according to the hourly responsiveness during their charging process. ($ / h) in response to demand response programs, where Represents the incentive for hourly demand response.

[0103] For example, define ∈Z is the maximum response time between electric vehicles in time period t, then the total incentive for electric vehicles participating in demand response in time period t is At the same time, the energy boundary and power boundary of the EV aggregator can be obtained by The linear function is modeled as follows:

[0104] (7)

[0105] (8)

[0106] in, is the maximum energy boundary that the EV aggregator is to provide to EVs under the demand response program, is the maximum total energy required by the electric vehicle fleet at time t; is the minimum energy boundary that the EV aggregator must provide to EVs under the DR program, which represents the minimum energy demand that the EV aggregator must meet in the DR program. represents the actual response probability of the driver to the stimulus at time t, is the minimum total energy required by the electric vehicle fleet at time t. Formula (7) shows that under the demand response plan, the maximum energy boundary of the electric vehicle aggregator is consistent with the maximum energy demand of the fleet; the minimum energy boundary that the electric vehicle aggregator must provide under demand response is based on the user's response probability Adjustment, the higher the responsiveness, the closer the minimum total energy the system needs to provide is to the minimum demand of the electric fleet .

[0107] In formula (8), is the maximum total power that the EV aggregator is to provide to the fleet under the demand response program, is the non-coordinated charging power, is the maximum total charging power required by the electric vehicle fleet at time t. Between and The degree is affected by the probability of demand response The higher the user response probability, the closer the maximum power demand of the electric vehicle aggregator is to . is the minimum total power that the EV aggregator must provide to the fleet under the demand response program. The higher the responsiveness, the closer the EV aggregator's minimum power requirement is to , is the minimum total charging power required by the electric vehicle fleet at time t. Based on the above foundation, the present invention establishes an electric vehicle aggregation model that considers the uncertainty in user demand response.

[0108] Furthermore, the EV aggregator interacts with the DSO to take carbon-related costs into account, thereby participating in carbon-aware demand response and minimizing its operating costs (EV operating costs) based on the node carbon emission intensity and prices published by the DSO. At the same time, the EV aggregator needs to set the best incentive price for users participating in the demand response program. Therefore, the objective function of the EV aggregator includes: i) charging and carbon emission costs, ii) backup service revenue, and iii) incentive costs for EV users.

[0109] Based on the above foundation, taking into account the uncertainty of electric vehicle aggregator optimization and user response, the objective function of the j-th electric vehicle aggregator, that is, the lower model, can be obtained as follows:

[0110] (9)

[0111] in, is the cost function of the jth charging station; T is the total number of planned time periods; is the time-of-use electricity price in time period t, that is, the basic charging price of the power grid for charging; is the carbon price, in $ / kg, used to price carbon emissions; is the average carbon intensity, which indicates the carbon emissions per unit of charging power, in kg / kWh; is the charging power of the jth EV aggregator in time period t; hiked prices to spare for EV aggregators; The upregulated reserve power provided to the jth EV aggregator in time period t; reduced prices for backup of EV aggregators; The down-regulated reserve power provided to the j-th EV aggregator in time period t; is the user response probability of the jth EV aggregator in time period t; is the maximum response time of the user of the jth EV aggregator in time period t; is the incentive price in time period t, which is used to encourage users to participate in demand response.

[0112] It is worth mentioning that , is the electric vehicle aggregator data, representing the decision variables of the electric vehicle aggregator, all t ,all .

[0113] Furthermore, for the first constraint, first, the balance constraint between the energy and power of the electric vehicle aggregator is set as:

[0114] (10)

[0115] in, The total energy provided by the jth EV aggregator accumulated at time t; The total energy provided by the jth EV aggregator accumulated at time t-1; is the average charging efficiency of EVs by EV aggregators; is the total charging power actually provided to the electric vehicles by the jth electric vehicle aggregator at time t-1; is the length of the time interval;

[0116] It is worth mentioning that the balance constraint between the energy and power of the EV aggregator is also the energy update formula, which indicates the increase in the total energy provided by the EV aggregator to the fleet over the entire time period, reflecting the status of the EV aggregator continuously providing services.

[0117] At the same time, the total charging demand of the electric vehicle fleet in one day should be met, that is, the total charging demand constraint of the electric vehicle fleet in one day, which is specifically expressed as follows:

[0118] (11)

[0119] in, is the total energy provided by the jth EV aggregator in one day; is the total energy required by the j-th electric vehicle fleet in one day.

[0120] In addition, the power and energy range under user response uncertainty is modeled by the following distributed robust chance constraints:

[0121] (12)

[0122] (13)

[0123] in, is the upper bound of the energy that the jth EV aggregator can provide at time t under demand response, is the energy actually provided by the j-th EV aggregator at time t. is the lower bound of the energy that the jth EV aggregator can provide at time t under demand response, is the probability of default, express The distribution belongs to The chance constraint on the right side of Equation (12) ensures that the driver's response uncertainty The energy actually provided by the electric vehicle aggregator Not less than the energy lower bound The probability is no less than .

[0124] is the upper bound of the power that the jth EV aggregator can provide at time t under demand response, is the power actually provided by the j-th EV aggregator at time t. is the lower bound of the power that the jth EV aggregator can provide at time t under demand response, is the probability of default, express The distribution belongs to the family The chance constraint on the right side of formula (13) ensures that the driver's response uncertainty The actual power provided by the EV aggregator Not less than the power lower limit The probability is no less than .

[0125] Furthermore, in order to make the distributed robustness opportunity constraints in (12) and (13) in a tractable form, let X∈{P,E} be a power or energy variable. The distributed robustness opportunity constraints in (12) and (13) can be transformed into the following mixed integer programming, namely the incentive price constraint for the electric vehicle aggregator, specifically:

[0126] (14)

[0127] in, is the upper bound of the total energy or total power required by the j-th electric vehicle fleet; is the lower bound of the total energy or total power required by the j-th electric vehicle fleet; For incentive price The corresponding first convex function is used to describe the uncertainty impact under different user response levels; is the energy or power actually provided by the jth electric vehicle aggregator at time t; M is a constant, a sufficiently large constant; For incentive price The corresponding second convex function; is the first binary variable, is the second binary variable, used to indicate Located in different intervals. Less than or equal to hour, is equal to 1, the first constraint takes effect. Greater than hour, is equal to 1, the second constraint takes effect. It is the cut-off point for incentive price; It is the upper limit of the incentive price, which is used to define the effective range; is the incentive price of the jth EV aggregator in period t; It is the lower bound of the incentive price and is used to define the effective range.

[0128] In order to transform the probability conditions in formulas (12) and (13) into linear constraints, the difference between the upper and lower limits is introduced To measure the fluctuation range of variables under uncertainty. Because under the influence of incentive prices, the range of power or energy changes is usually within this upper and lower limits; use Multiplying by a convex function and , the nonlinear chance constraint problem can be approximated as a linear problem, which is convenient for solving using mixed integer programming. This linear approximation can ensure flexible adjustment under different uncertain conditions; After that, there is no need to directly deal with complex probability distributions , but is transformed into a linear constraint within a given range, making the problem easier to solve.

[0129] Therefore, the convex function and constant is defined as follows:

[0130] (15)

[0131] (16)

[0132] (17)

[0133] (18)

[0134] (19)

[0135] in, is the incentive price of the jth EV aggregator in period t, is the coefficient associated with the incentive price boundary, ϵ1 is the tolerated default probability in the opportunity constraint, is the maximum response probability of the user, and its value is less than 100%; is with The corresponding maximum excitation is is the demarcation point of the incentive price. is the coefficient of variation associated with the fluctuation of user responses.

[0136] Formula (15) defines the convex function when the incentive price is lower than the inflection point value, that is, The corresponding first convex function , the first term describes the user's sensitivity to the incentive price, which increases with the square of the incentive price; the second term combines the responsiveness and the linear part of the incentive price adjustment function. Formula (16) defines the incentive price in Upper bound of function interval , which represents the function The scope of application of is controlled by the inflection point value and responsiveness of the incentive price. Formula (17) defines the parameters It is related to the inflection point value and the maximum incentive price. Formula (18) defines the convex function when the incentive price is higher than the inflection point value, that is, The corresponding second convex function , describes the user response adjustment when the incentive price is high, and is inversely proportional to the incentive price. Formula (19) defines the incentive price at Lower bound of function interval , ensuring that the function The definition range is adapted to user responsiveness.

[0137] Through convex function and Describe the changes in user responsiveness in different incentive price ranges in different segments. When using function, the responsiveness gradually increases; when the incentive price is higher ,use Function, responsiveness tends to saturate or decrease. The design of piecewise function helps to model the complex behavior of users more accurately in optimization.

[0138] Finally, the spare capacity of the EV aggregator is modeled, namely the spare capacity constraint of the EV aggregator, which is:

[0139] (20)

[0140] (twenty one)

[0141] in, is the downregulated reserve power of the jth EV aggregator at time t; is the power actually provided by the jth EV aggregator at time t; is the upper bound of the power that the jth EV aggregator can provide at time t under demand response; is the upper bound of the energy that the jth EV aggregator can provide at time t under demand response; is the energy actually provided by the jth EV aggregator at time t; is the average charging efficiency of EVs by EV aggregators; is the length of the time interval; is the up-regulated reserve power of the j-th EV aggregator at time t; is the lower bound of the power that the jth EV aggregator can provide at time t under demand response; is the lower bound of the energy that the j-th EV aggregator can provide at time t under demand response.

[0142] It is worth mentioning that the down-regulated reserve power refers to the excess power that the electric vehicle aggregator can consume for the grid when the load of the grid suddenly decreases or there is an over-generation situation. The first half of formula (20) requires that the sum of the down-regulated reserve power and the actual charging power of the j-th electric vehicle aggregator at time t should not be higher than the maximum total charging power that the j-th electric vehicle fleet can receive under demand response; the second half of formula (20) constrains the total power to be limited by the upper bound of future energy.

[0143] Furthermore, the upward reserve power means that when the load of the power grid suddenly increases or there is a reduction in power generation, the electric vehicle aggregator can reduce the power consumed. The first half of formula (21) requires that the actual charging power of the j-th electric vehicle aggregator at time t minus the upward reserve power should not be lower than the minimum total charging power that the j-th electric vehicle fleet can receive under demand response; the second half of formula (21) constrains the total power to be limited by the lower bound of future energy.

[0144] In an embodiment of the present invention, when it is necessary to carry out joint low-carbon dispatch of electric vehicles in the distribution network, the minimization of the operating cost of electric vehicles is taken as the optimization goal, the first constraint condition is set, the electric vehicle aggregator data is used as the decision variable, and the lower-level model is constructed.

[0145] Step 102: Taking minimizing the carbon-aware operating cost of the distribution network as the optimization goal, setting the second constraint condition, taking the distribution system operator data corresponding to the distribution network as the decision variable, and constructing the upper-level model.

[0146] The second constraint conditions include power balance constraint, distributed generation unit power constraint, energy storage device energy power constraint, and up and down adjustment of reserve power constraint.

[0147] The distribution system operator data includes the charging power of the energy storage equipment, the discharging power of the energy storage equipment, the generation power of the distributed generators, the upward reserve power provided by the distributed generators, and the downward reserve power provided by the distributed generators.

[0148] It should be noted that the goal of the distribution system operator is to minimize the carbon-aware operating cost of the active distribution network, including: i) the cost of distributed generation with carbon emission penalties, ii) the cost of the distribution system operator purchasing energy and backup from the main grid, iii) the degradation cost of the energy storage battery, and taking into account the carbon-aware dispatch of the distribution system operator with net load uncertainty, the upper model can be obtained, specifically:

[0149] (twenty two)

[0150] (twenty three)

[0151] (twenty four)

[0152] in, is the total operating cost of the distribution system operator; T is the length of the scheduling time horizon; is the total number of distributed generation units, that is, the set of distributed generators, representing all distributed energy units participating in power generation in the distribution network; is the distributed generation cost with carbon emission penalty for distributed generator i; Cost of purchasing electricity from the main grid and reserve costs for distribution system operators; It is a collection of energy storage devices, representing all batteries or energy storage units involved in the charging and discharging operations; is the unit charging and discharging cost of the energy storage device; is the charging power of energy storage device k; is the discharge power of energy storage device k; is the power generation cost coefficient of distributed generator i; is the carbon price, reflecting the unit cost of carbon emissions, in $ / kg; is the carbon intensity of distributed generator i, reflecting the carbon emissions per unit power generation; is the power generation of distributed generator i at time t; The cost coefficient for providing the reserve increase to distributed generator i; The upregulated reserve power provided to distributed generator i; The cost coefficient for providing down-reserve to distributed generator i; The down-regulated reserve power provided to distributed generator i; The market price of electricity when the main grid sells power to the distribution grid, in $ / MW; is the carbon intensity of main grid electricity, in kg / MWh; is the electricity purchased by the distribution system operator from the main grid at time t; The market price at which the main grid sells the increased reserve power to the distribution system operator; The upward reserve power provided by the main grid to the distribution system operator at time t; The market price at which the main grid sells reserve power to distribution system operators; It is the downward reserve power provided by the main grid to the distribution system operator at time t.

[0153] It is worth mentioning that , is the distribution system operator data, representing the decision variables of the distribution system operator, .

[0154] Furthermore, in formula (22), the total operating cost of the distribution system operator is , the goal is to minimize it; distributed generation cost with carbon emission penalty for distributed generator i , which is calculated by formula (23); the power purchase cost and reserve cost of the distribution system operator from the main grid , which is specifically calculated by formula (24). Formula (23) calculates the power generation cost of the i-th distributed generator with carbon emission penalty; formula (24) calculates the power purchase cost and the backup power cost, and considers the impact of low-carbon targets on costs. is the carbon price, expressed in $ / kg, reflecting the cost to be paid for each unit of carbon emissions.

[0155] Furthermore, for the constraint condition of the active distribution network, i.e., the second constraint condition, firstly, the linearized power flow model of the distribution network, i.e., the linearized power flow constraint of the distribution network, is set, specifically:

[0156] (25)

[0157] (26)

[0158] in, is the actual active power on the mn line; is the susceptance between node m and node n; is the phase angle of node m at time t; is the phase angle of node n at time t; is the admittance between node m and node n; is the voltage amplitude of node m at time t; is the voltage amplitude of node m at time t.

[0159] It is worth mentioning that formula (25) describes the active power from node m to node n at time t. Formula (26) describes the reactive power from node m to node n at time t.

[0160] Then, the power balance of each node, that is, the node power balance constraint, is:

[0161] (27)

[0162] (28)

[0163] in, is the net active power flow of node m into the system at time t, in MW; is the total number of nodes in the active distribution network; From node m to other The sum of active power flows of nodes; is the active power output by the generator at node m; is the active power of wind power generation at node m at time t; is the net load active power at node m; is the active power consumed by the charging station at node m; is the net reactive power flow into the system at node m, in MVar; From node m to other The sum of reactive power flows of nodes; is the reactive power output by the generator at node m; is the wind power reactive power at node m at time t; is the net load reactive power at node m.

[0164] It is worth mentioning that formula (27) describes the active power balance of node m at time t, and formula (28) describes the reactive power balance of node m at time t.

[0165] At the same time, the power flow should be guaranteed within the rated value:

[0166] (29)

[0167] Wherein, formula (29) constrains the active power and reactive power transmitted between lines m and n to be within the rated range, is the actual active power on the mn line, It is the maximum active power transmission capability of the line. is the reactive power on the actual mn line, It is the maximum reactive power transmission capability of the line.

[0168] Furthermore, the distributed generation unit is a conventional coal-fired unit. Only the economic dispatch without start-stop constraints is considered, and the power constraint conditions of the distributed generation unit can be obtained, which are:

[0169] (30)

[0170] (31)

[0171] in, is the active power output of distributed generation unit i at time t; is the down-regulated reserve power of distributed generation unit i at time t; is the lower limit of active power generation of distributed generation unit i; is the up-regulated reserve power of distributed generation unit i at time t; is the upper limit of active power generation of distributed generation unit i; is the total number of distributed generation units; is the lower limit of reactive power generation of distributed generation unit i; is the reactive power output of distributed generation unit i at time t; is the lower limit of reactive power generation of distributed generation unit i.

[0172] It is worth mentioning that equations (30) and (31) describe the constraints on the active and reactive power of distributed generation units.

[0173] For the energy storage device unit, the following constraints are considered, namely, the energy power constraints of the energy storage device, specifically:

[0174] (32)

[0175] (33)

[0176] (34)

[0177] (35)

[0178] (36)

[0179] in, is the energy of energy storage device k at time t+1; is the energy of energy storage device k at time t; is the charging efficiency of energy storage device k; is the charging power of energy storage device k at time t; is the discharge power of energy storage device k at time t; is the discharge efficiency of energy storage device k; is the time interval; is a 0 / 1 variable, indicating that the energy storage device k can only be in one of the charging or discharging states at time t; The charging power of energy storage device k is subject to the maximum charging power of the device; is the maximum energy capacity of energy storage device k; The discharge power of the energy storage device k is subject to the maximum discharge power of the device; is the minimum energy limit of energy storage device k; is the energy of energy storage device k at time T, indicating the energy of energy storage device k that needs to be restored to its initial state after the entire scheduling cycle ends.

[0180] It is worth mentioning that formula (32) constrains the energy change of the energy storage device. The remaining energy of the energy storage device k at the next time t+1 is equal to the current energy plus the increased energy brought by the charging power, minus the energy consumed by the discharging power. The energy change is affected by the charging and discharging efficiency.

[0181] Furthermore, formula (33) constrains the charging power of energy storage device k to be subject to the maximum charging power of the device and the remaining chargeable capacity. Formula (34) constrains the discharge power of energy storage device k to be limited by the maximum discharge power of the device and the current available energy limit. Formula (35) constrains the energy storage device k to have a minimum capacity at any time t. and maximum capacity Formula (36) constrains the energy storage device k to be restored to its initial state after the entire scheduling cycle ends.

[0182] In order to compensate for the fluctuations of load and renewable energy, the net load of node i at time t is first defined as follows:

[0183] (37)

[0184] in, and are the load and expected power of renewable energy at node i, and are the corresponding random forecast errors respectively. Then, the total forecast error of net load is: To offset the net load uncertainty, the reserve of distributed generation units needs to be activated and is modeled by the following distributed robust opportunity constraint:

[0185] (38)

[0186] (39)

[0187] in, is the participation factor of distributed generation unit i at time t, indicating its contribution to the total net load forecast error ratio, satisfying . and is the reserve power increase and reserve power decrease of the ith distributed generation unit at time t. ϵ2 is the allowed default probability. express The distribution belongs to ,in Contains all distributions with the same mean and variance, defined as follows:

[0188] (40)

[0189] in, The mean of is 0, indicating that the prediction errors are balanced in positive and negative directions. , are the variances of load forecast error and renewable energy forecast error, respectively, The variance of .

[0190] Furthermore, the distributed robustness constraints in formulas (38) and (39) can be restated as the following convex programming under fuzzy sets, that is, the up and down adjustment of the reserve power constraints, specifically:

[0191] (41)

[0192] in, is the participation factor of distributed generation unit i at time t, indicating its contribution to the total net load forecast error ratio, satisfying ; is the permissible probability of default, is the scaling factor of the confidence level, which is derived from the mathematical derivation of the distributed robustness chance constraint and is used to balance the uncertainty and feasibility of the constraint; is the load fluctuation error covariance matrix, which represents the load forecast error at time t The distribution characteristics of are often used to describe the correlation between loads; is the renewable energy fluctuation error covariance matrix, describing the renewable energy generation fluctuation error at time t The distribution characteristics of , To increase the standby power, To reduce the reserve power, To adjust the reserve power up and down; is the total number of distributed generation units.

[0193] In the embodiment of the present invention, the minimization of the carbon-aware operating cost of the distribution network is taken as the optimization goal, the second constraint condition is set, and the distribution system operator data corresponding to the distribution network is taken as the decision variable to construct the upper model.

[0194] Step 103: Based on the initial electric vehicle aggregator data, the upper model and the lower model are jointly optimized and solved to output the target distribution system operator data and the target electric vehicle aggregator data.

[0195] Specifically, step 103 may include the following sub-steps S31-S35:

[0196] Step S31, optimizing and solving the upper model according to the initial electric vehicle aggregator data, and outputting the distribution system operator data at the current moment;

[0197] The initial electric vehicle aggregator data is initialized electric vehicle aggregator data.

[0198] Step S32, calculating the average carbon intensity based on the power generation of distributed generation units in the distribution system operator data at the current moment;

[0199] Step S33: Based on the average carbon intensity, optimize and solve the lower model, output the electric vehicle aggregator data at the current moment, and count the number of iterations in real time;

[0200] It should be noted that the carbon emission intensity of nodes close to renewable energy units is lower, while the carbon emission intensity of nodes close to fuel distributed generators is higher. In order to avoid the imbalance of carbon emission responsibilities, the present invention adopts the average carbon intensity, which is calculated as follows:

[0201] (42)

[0202] in, is the average carbon intensity (ACI), which represents the average carbon emissions per unit power output of the active distribution network at time t; is the carbon intensity of distributed generation unit i, which represents the carbon emissions generated per unit power output; is the power generation of distributed generation unit i at time t, that is, the power generation of distributed generation unit; The carbon intensity of electricity purchased by the distribution system operator from the upstream grid, which represents the carbon emissions per unit power of electricity provided by the upstream grid; is the power purchased by the distribution system operator from the upper power grid at time t; is a collection of distributed generation units; A collection of renewable energy power generation units; is the power generation power of renewable energy power generation unit j at time t.

[0203] It is worth mentioning that the total carbon emissions are divided by the total power to obtain the average carbon intensity. The distribution system operator will then send it to the EV aggregator for carbon-aware demand response in the down-level optimization.

[0204] Step S34, calculating a scalar value according to the electric vehicle aggregator data at the current moment and the electric vehicle aggregator data at the historical moment;

[0205] Step S35: When the scalar value is less than the preset scalar threshold or the number of iterations is greater than the preset iteration threshold, the distribution system operator data at the current moment is used as the target distribution system operator data, and the electric vehicle aggregator data at the current moment is used as the target electric vehicle aggregator data.

[0206] The electric vehicle aggregator data at a historical moment is the electric vehicle aggregator data at the previous moment.

[0207] It should be noted that, referring to Table 1, the present invention uses a two-layer coordinated iterative method to solve the upper model and the lower model. In the proposed coordinated model, is a highly nonlinear function of distributed generation, which makes the optimization of EV aggregators difficult to handle. To address this problem, the operators at the upper and lower levels are designed to gradually solve their respective sub-problems by exchanging distribution system operator data and EV aggregator data to achieve equilibrium. The overall process of the iterative method proposed in this paper is summarized in Table 1.

[0208] Table 1 Iterative method of two-layer coordination

[0209]

[0210] Further, when the scalar value is greater than or equal to the preset scalar threshold and the number of iterations is less than or equal to the preset iteration threshold, the electric vehicle aggregator data at the current moment is used as the new initial electric vehicle aggregator data, and the process jumps to execute step S31, outputs the new distribution system operator data at the current moment, thereby calculating the new average carbon intensity, and then obtaining the new electric vehicle aggregator data at the current moment, until the scalar value is less than the preset scalar threshold or the number of iterations is greater than the preset iteration threshold, the distribution system operator data at the current moment determined when the scalar value is less than the preset scalar threshold or the number of iterations is greater than the preset iteration threshold is used as the target distribution system operator data, and the electric vehicle aggregator data at the current moment determined when the scalar value is less than the preset scalar threshold or the number of iterations is greater than the preset iteration threshold is used as the target electric vehicle aggregator data.

[0211] For example, see Figure 2 After the establishment of the upper-level problem (upper-level model) and the lower-level problem (lower-level model), the upper-level problem and the lower-level problem are coordinated at two levels based on Algorithm 1 (iterative method of two-level coordination) proposed in the present invention. Through cyclic iteration, the strategy is gradually adjusted to obtain the optimal solution of the DSO and EVA system, that is, the target distribution system operator data and the target electric vehicle aggregator data.

[0212] In an embodiment of the present invention, based on the initial electric vehicle aggregator data, the upper model and the lower model are jointly optimized and solved to output the target distribution system operator data and the target electric vehicle aggregator data.

[0213] Step 104: Use the target distribution system operator data and the target electric vehicle aggregator data as a distribution network electric vehicle joint low-carbon dispatching plan.

[0214] It should be noted that based on the solved target distribution system operator data and target electric vehicle aggregator data, the operating cost of electric vehicles and the carbon-aware operating cost of the distribution network can be minimized, thereby completing the joint low-carbon scheduling of the distribution network and electric vehicles.

[0215] In an embodiment of the present invention, the target distribution system operator data and the target electric vehicle aggregator data are used as a joint low-carbon dispatching scheme for electric vehicles in the distribution network.

[0216] As a comparison of technical effects, it can be combined with existing technologies for reference. At present, the design schemes of electric vehicle aggregators mainly include electric vehicle aggregator models based on power and energy boundaries, electric vehicle aggregator models based on travel chains, robust electric vehicle aggregator models based on historical data, and accurate electric vehicle aggregator models based on complete binary trees. The existing methods related to the effective coordinated optimization scheduling of active distribution networks and large-scale electric vehicles model the optimization problem as a two-level planning between distribution system operators and electric vehicle aggregators, and then apply mathematical programming with equilibrium constraints. In order to optimize the pricing of active and reactive power, the existing work designs a two-level model between distribution system operators and virtual power plants. In order to obtain the pricing of energy and ancillary services, a pricing strategy for bidding between distribution system operators and load service entities is designed. In terms of carbon-aware collaboration, the existing methods incorporate the carbon emission factors of generators into the formulation of the objective function and combine them with the carbon trading mechanism. In order to realize the carbon emission accounting on the power generation side and the demand side, the carbon emission flow model is introduced to calculate and analyze the dynamic carbon emissions in the power grid. Based on the carbon emission flow model, a carbon-aware energy management model that can stimulate the carbon emission reduction potential on the demand side is proposed. Based on the node carbon intensity released from the upper layer, a two-tier demand response model for low carbon is formulated.

[0217] Existing technical solutions for electric vehicle aggregators have ignored the interaction between active distribution networks and charging stations, and there is a lack of coordination between the charging demand of charging stations and the power supply of active distribution networks. Existing work on two-level optimization of active distribution networks and large-scale electric vehicles ignores the fact that electric vehicle users may refuse to participate in the optimal scheduling of electric vehicle aggregators due to reduced charging satisfaction. In terms of power supply and demand balance, charging stations may not consider the user's response willingness, which will lead to excessive scheduling requirements for electric vehicle users during peak or trough periods of power, which may make scheduling in active distribution networks infeasible. On the other hand, traditional economic scheduling of active distribution networks fails to meet the low-carbon requirements of power systems, and carbon-aware operation methods are increasingly valued by researchers. Existing methods that incorporate carbon emission factors into the objective function and combine them with carbon trading mechanisms ignore the potential for reducing carbon emissions from the perspective of flexible loads. Few studies have explored the carbon-aware optimal coordination between active distribution networks and large-scale electric vehicles under multiple uncertainties.

[0218] In view of the above problems, the present invention proposes a method for joint low-carbon dispatch of electric vehicles in distribution networks. Taking into account the shortcomings of existing methods, the present invention proposes a two-layer model for carbon emissions that takes into account the uncertainty of multi-source loads for active distribution networks containing multiple charging stations. At the upper level, the distribution system operator minimizes the carbon emission-related operating costs of the active distribution network under the uncertainty of renewable energy. At the lower level, the electric vehicle aggregator optimizes the charging of electric vehicles based on the carbon intensity published by the distribution system operator and sets the optimal incentive for the response uncertainty of electric vehicle users. The multi-source load uncertainty is modeled by distributed robust opportunity constraints, and its tractable reconstruction is derived. In addition, a heuristic iterative algorithm (an iterative method for two-layer coordination) is designed to efficiently solve the established two-layer model, thereby ensuring the economy of the system, computational efficiency, and effectiveness in reducing carbon emissions. At the same time, unlike the traditional two-layer optimization model that only considers economic dispatch, the present invention considers the carbon-aware coordination between the distribution system operator and multiple electric vehicle aggregators. In addition, through the distributed robust opportunity constraint modeling, the uncertainty of distributed energy in the active distribution network and the user responsiveness in multiple charging stations are fully considered. This method can model the uncertainty on the network side and the agent side without the need for a specific probability distribution form. The present invention also considers the responsiveness uncertainty of individuals in terms of their willingness to participate in demand response, designs appropriate incentives, and explores the impact of user willingness on the responsiveness of electric vehicle aggregators. Compared with the existing electric vehicle aggregator model, the proposed model establishes the relationship between individual responsiveness and demand response incentives based on the consumer psychological model, which is more practical and can quantify the responsiveness of large-scale electric vehicles.

[0219] In summary, the present invention can achieve the low-carbon, economical, stable and flexible nature of the system, and solve the problem of low-carbon and flexible utilization of a large number of distributed energy sources of electric vehicles. The active distribution network and large-scale electric vehicle collaborative optimization scheduling method proposed in the present invention integrates and manages large-scale scattered electric vehicles through electric vehicle aggregators by constructing a two-layer model based on carbon emissions. The upper-layer distribution system operator minimizes the carbon emission-related operating costs of the active distribution network, and the lower-layer electric vehicle aggregator optimizes the charging of electric vehicles based on the carbon intensity released by the distribution system operator, and sets the best incentives for the response uncertainty of electric vehicle users.

[0220] Compared with the prior art, the present invention aims to achieve efficient utilization of flexibility resources and collaborative optimization of complex power systems. By constructing a distributed robust opportunity constraint model of user response uncertainty, a large number of flexibility resources are effectively aggregated and converted into an equivalent scheduling model, thereby reducing the requirements for communication and data processing and avoiding the "curse of dimensionality" problem. In order to fully consider the impact of user response and distributed energy uncertainty, opportunity constraints are used to perform robustness constraints on demand response and backup resources to ensure the reliability and flexibility of the system. In addition, the present invention proposes a linearization method based on a distributed robust model, which converts complex uncertainty optimization problems into convex optimization problems that can be directly solved, so that the optimization model can be quickly and efficiently solved by existing solvers, while taking into account the economy and robustness of the system.

[0221] Compared with the traditional optimization framework, the present invention has significant advantages in the following aspects: the collaborative optimization of DSO and multiple EVAs is achieved through a two-layer collaborative mechanism; the carbon emission intensity is introduced into the objective function, which solves the problem of uneven distribution of carbon responsibilities among different nodes in the distributed network; and the flexible scheduling of the energy storage system is introduced, combined with the dynamic allocation of charging and discharging constraints and backup resources, which greatly improves the overall economic benefits and carbon emission reduction capabilities of the system. The present invention effectively responds to the challenges of random response behavior of users and fluctuations in renewable energy, while improving the solution efficiency with innovative optimization algorithms, significantly reducing the scheduling costs of large-scale complex systems.

[0222] In an embodiment of the present invention, the present invention provides a method for joint low-carbon dispatching of electric vehicles in a distribution network. When joint low-carbon dispatching of electric vehicles in a distribution network is required, first, the minimum operating cost of electric vehicles is taken as the optimization goal, the first constraint condition is set, and the electric vehicle aggregator data is taken as the decision variable to construct a lower model; then, the carbon-aware operating cost of the distribution network is minimized as the optimization goal, the second constraint condition is set, and the distribution system operator data corresponding to the distribution network is taken as the decision variable to construct an upper model; based on the initial electric vehicle aggregator data, the upper model and the lower model are jointly optimized and solved, and the target distribution system operator data and the target electric vehicle aggregator data are output; finally, the target distribution system operator data and the target electric vehicle aggregator data are used as a joint low-carbon dispatching scheme for electric vehicles in the distribution network; based on the above scheme, the present invention takes the minimum carbon-aware operating cost of the distribution network as the optimization goal and the minimum operating cost of electric vehicles as the optimization goal, combines the decision variables and the constraints, constructs the upper model and the lower model and solves them, and outputs the process of the joint low-carbon dispatching scheme for electric vehicles in the distribution network. Compared with the existing dispatching method, the present invention takes into account the carbon-aware coordination between the distribution system operator and the electric vehicle aggregator, thereby reducing carbon emissions and reducing the dispatching cost.

[0223] See also Figure 3 , Figure 3This is a structural block diagram of a distribution network electric vehicle joint low-carbon dispatching system provided in Embodiment 2 of the present invention.

[0224] The present invention provides a distribution network electric vehicle joint low-carbon dispatching system, comprising:

[0225] The first construction module 301 is used to respond to the dispatch request, take the minimum electric vehicle operating cost as the optimization goal, set the first constraint condition, take the electric vehicle aggregator data as the decision variable, and construct the lower model;

[0226] The second construction module 302 is used to set the second constraint condition with the minimum carbon-aware operation cost of the distribution network as the optimization target, and to construct the upper model with the distribution system operator data corresponding to the distribution network as the decision variable;

[0227] A solution module 303, used for jointly optimizing and solving the upper model and the lower model based on the initial electric vehicle aggregator data, and outputting target distribution system operator data and target electric vehicle aggregator data;

[0228] The output scheme module 304 is used to use the target distribution system operator data and the target electric vehicle aggregator data as a distribution network electric vehicle joint low-carbon dispatch scheme.

[0229] Furthermore, the solution module 303 is specifically used for:

[0230] Optimize and solve the upper model based on the initial electric vehicle aggregator data, and output the distribution system operator data at the current moment;

[0231] Calculate the average carbon intensity based on the distributed generation unit power generation power in the distribution system operator data at the current moment;

[0232] Based on the average carbon intensity, the lower-level model is optimized and solved, the current electric vehicle aggregator data is output, and the number of iterations is counted in real time;

[0233] Calculate a scalar value based on the electric vehicle aggregator data at the current moment and the electric vehicle aggregator data at the historical moment;

[0234] When the scalar value is less than a preset scalar threshold or the number of iterations is greater than a preset iteration threshold, the distribution system operator data at the current moment is used as the target distribution system operator data, and the electric vehicle aggregator data at the current moment is used as the target electric vehicle aggregator data.

[0235] Furthermore, the first constraint condition includes a total charging energy balance constraint of the electric vehicle aggregator, an incentive price constraint of the electric vehicle aggregator, and a spare capacity constraint of the electric vehicle aggregator.

[0236] Furthermore, the second constraint condition includes power balance constraint, distributed generation unit power constraint, energy storage device energy power constraint, and up and down adjustment of standby power constraint.

[0237] Furthermore, the total charging energy balance constraints of the electric vehicle aggregator include:

[0238] The balance constraints between energy and power of electric vehicle aggregators are:

[0239]

[0240] in, The total energy provided by the jth EV aggregator accumulated at time t; The total energy provided by the jth EV aggregator accumulated at time t-1; is the average charging efficiency of EVs by EV aggregators; is the total charging power actually provided to the electric vehicles by the jth electric vehicle aggregator at time t-1; is the length of the time interval;

[0241] The total charging demand constraint of the electric vehicle fleet in one day is:

[0242]

[0243] in, is the total energy provided by the jth EV aggregator in one day; is the total energy required by the j-th electric vehicle fleet in one day.

[0244] Furthermore, the incentive price constraints for electric vehicle aggregators are as follows:

[0245]

[0246] in, is the upper bound of the total energy or total power required by the j-th electric vehicle fleet; is the lower bound of the total energy or total power required by the j-th electric vehicle fleet; For incentive price The corresponding first convex function; is the energy or power actually provided by the jth electric vehicle aggregator at time t; M is a constant; For incentive price The corresponding second convex function; is the first binary variable; is the second binary variable; is the upper bound of the incentive price; is the incentive price of the jth EV aggregator in period t; is the lower bound of the incentive price.

[0247] Furthermore, the spare capacity constraint of the electric vehicle aggregator is:

[0248]

[0249]

[0250] in, is the downregulated reserve power of the jth EV aggregator at time t; is the power actually provided by the jth EV aggregator at time t; is the upper bound of the power that the jth EV aggregator can provide at time t under demand response; is the upper bound of the energy that the jth EV aggregator can provide at time t under demand response; is the energy actually provided by the jth EV aggregator at time t; is the average charging efficiency of EVs by EV aggregators; is the length of the time interval; is the up-regulated reserve power of the j-th EV aggregator at time t; is the lower bound of the power that the jth EV aggregator can provide at time t under demand response; is the lower bound of the energy that the j-th EV aggregator can provide at time t under demand response.

[0251] Furthermore, the power balance constraints include:

[0252] The linear power flow constraints of the distribution network are as follows:

[0253]

[0254]

[0255] in, is the actual active power on the mn line; is the susceptance between node m and node n; is the phase angle of node m at time t; is the phase angle of node n at time t; is the admittance between node m and node n; is the voltage amplitude of node m at time t; is the voltage amplitude of node m at time t;

[0256] Node power balance constraints, specifically:

[0257]

[0258]

[0259] in, is the net active power flow of node m into the system at time t, in MW; is the total number of nodes in the active distribution network; From node m to other The sum of active power flows of nodes; is the active power output by the generator at node m; is the active power of wind power generation at node m at time t; is the net load active power at node m; is the active power consumed by the charging station at node m; is the net reactive power flow into the system at node m, in MVar; From node m to other The sum of reactive power flows of nodes; is the reactive power output by the generator at node m; is the wind power reactive power at node m at time t; is the net load reactive power at node m.

[0260] Furthermore, the power constraints of distributed generation units are specifically:

[0261]

[0262]

[0263] in, is the active power output of distributed generation unit i at time t; is the down-regulated reserve power of distributed generation unit i at time t; is the lower limit of active power generation of distributed generation unit i; is the up-regulated reserve power of distributed generation unit i at time t; is the upper limit of active power generation of distributed generation unit i; is the total number of distributed generation units; is the lower limit of reactive power generation of distributed generation unit i; is the reactive power output of distributed generation unit i at time t; is the lower limit of reactive power generation of distributed generation unit i.

[0264] Furthermore, the energy power constraints of the energy storage device are as follows:

[0265]

[0266]

[0267]

[0268]

[0269]

[0270] in, is the energy of energy storage device k at time t+1; is the energy of energy storage device k at time t; is the charging efficiency of energy storage device k; is the charging power of energy storage device k at time t; is the discharge power of energy storage device k at time t; is the discharge efficiency of energy storage device k; is the time interval; is a 0 / 1 variable, indicating that the energy storage device k can only be in one of the charging or discharging states at time t; The charging power of energy storage device k is subject to the maximum charging power of the device; is the maximum energy capacity of energy storage device k; The discharge power of the energy storage device k is subject to the maximum discharge power of the device; is the minimum energy limit of energy storage device k; is the energy of energy storage device k at time T, indicating the energy of energy storage device k that needs to be restored to its initial state after the entire scheduling cycle ends.

[0271] Furthermore, the reserve power constraint is adjusted up and down, specifically:

[0272]

[0273] in, is the participation factor of distributed generation unit i at time t; is the scaling factor for the confidence level; is the load fluctuation error covariance matrix; is the renewable energy fluctuation error covariance matrix; , To increase the standby power, To reduce the reserve power, To adjust the reserve power up and down; is the total number of distributed generation units.

[0274] Further, the electric vehicle aggregator data includes average carbon intensity, charging power of electric vehicle aggregators, down-regulated reserve power provided by electric vehicle aggregators, up-regulated reserve power provided by electric vehicle aggregators, and incentive price; the lower-level model is specifically:

[0275]

[0276] in, is the cost function of the jth charging station; T is the total number of planned time periods; is the time-of-use electricity price in time period t, that is, the basic charging price of the power grid for charging; aggregator data for electric vehicles; is the carbon price, in $ / kg, used to price carbon emissions; is the average carbon intensity, which indicates the carbon emissions per unit of charging power, in kg / kWh; is the charging power of the jth EV aggregator in time period t; hiked prices to spare for EV aggregators; The upregulated reserve power provided to the jth EV aggregator in time period t; reduced prices for backup of EV aggregators; The down-regulated reserve power provided to the j-th EV aggregator in time period t; is the user response probability of the jth EV aggregator in time period t; is the maximum response time of the user of the jth EV aggregator in time period t; is the incentive price in time period t, which is used to encourage users to participate in demand response.

[0277] Furthermore, the distribution system operator data includes the charging power of the energy storage device, the discharging power of the energy storage device, the generating power of the distributed generator, the upward reserve power provided by the distributed generator, and the downward reserve power provided by the distributed generator; the upper model is specifically:

[0278]

[0279]

[0280]

[0281] in, is the total operating cost of the distribution system operator; T is the length of the scheduling time horizon; is the total number of distributed generation units, that is, the set of distributed generators, representing all distributed energy units participating in power generation in the distribution network; is the distributed generation cost with carbon emission penalty for distributed generator i; Cost of purchasing electricity from the main grid and reserve costs for distribution system operators; It is a collection of energy storage devices, representing all batteries or energy storage units involved in the charging and discharging operations; is the unit charging and discharging cost of the energy storage device; is the charging power of energy storage device k; is the discharge power of energy storage device k; is the power generation cost coefficient of distributed generator i; is the carbon price, reflecting the unit cost of carbon emissions, in $ / kg; is the carbon intensity of distributed generator i, reflecting the carbon emissions per unit power generation; is the power generation of distributed generator i at time t; The cost coefficient for providing the reserve increase to distributed generator i; The upregulated reserve power provided to distributed generator i; The cost coefficient for providing down-reserve to distributed generator i; The down-regulated reserve power provided to distributed generator i; The market price of electricity when the main grid sells power to the distribution grid, in $ / MW; is the carbon intensity of main grid electricity, in kg / MWh; is the electricity purchased by the distribution system operator from the main grid at time t; The market price at which the main grid sells the increased reserve power to the distribution system operator; The upward reserve power provided by the main grid to the distribution system operator at time t; The market price at which the main grid sells reserve power to distribution system operators; It is the downward reserve power provided by the main grid to the distribution system operator at time t.

[0282] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-described system and module can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0283] In the several embodiments provided in the present application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are only schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0284] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0285] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for joint low-carbon dispatching of electric vehicles in a distribution network, characterized in that: include: In response to the dispatch request, the first constraint is set with the minimum operation cost of electric vehicles as the optimization goal, and the lower-level model is constructed with the electric vehicle aggregator data as the decision variable; Taking the minimization of the carbon-aware operating cost of the distribution network as the optimization goal, setting the second constraint condition, taking the distribution system operator data corresponding to the distribution network as the decision variable, and constructing the upper model; Based on the initial electric vehicle aggregator data, jointly optimizing and solving the upper model and the lower model, and outputting target distribution system operator data and target electric vehicle aggregator data; The target distribution system operator data and the target electric vehicle aggregator data are used as a joint low-carbon dispatching plan for distribution network electric vehicles.

2. The method for joint low-carbon dispatching of electric vehicles in distribution networks according to claim 1, characterized in that: The method of jointly optimizing and solving the upper model and the lower model based on the initial electric vehicle aggregator data and outputting target distribution system operator data and target electric vehicle aggregator data includes: Optimizing and solving the upper model according to the initial electric vehicle aggregator data, and outputting the distribution system operator data at the current moment; Calculate the average carbon intensity based on the power generation of distributed generation units in the distribution system operator data at the current moment; Based on the average carbon intensity, the lower model is optimized and solved, the electric vehicle aggregator data at the current moment is output, and the number of iterations is counted in real time; Calculate a scalar value according to the electric vehicle aggregator data at the current moment and the electric vehicle aggregator data at the historical moment; When the scalar value is less than a preset scalar threshold or the number of iterations is greater than a preset iteration threshold, the distribution system operator data at the current moment is used as the target distribution system operator data, and the electric vehicle aggregator data at the current moment is used as the target electric vehicle aggregator data.

3. The method for joint low-carbon dispatching of electric vehicles in distribution networks according to claim 1, characterized in that: The first constraint conditions include the total charging energy balance constraint of the electric vehicle aggregator, the incentive price constraint of the electric vehicle aggregator, and the spare capacity constraint of the electric vehicle aggregator.

4. The method for joint low-carbon dispatching of electric vehicles in distribution networks according to claim 1, characterized in that: The second constraint conditions include power balance constraint, distributed generation unit power constraint, energy storage device energy power constraint, and up and down adjustment of standby power constraint.

5. The method for joint low-carbon dispatching of electric vehicles in distribution networks according to claim 3 is characterized in that: The total charging energy balance constraints of the electric vehicle aggregator include: The balance constraints between energy and power of electric vehicle aggregators are: in, The total energy provided by the jth EV aggregator accumulated at time t; The total energy provided by the jth EV aggregator accumulated at time t-1; is the average charging efficiency of EVs by EV aggregators; is the total charging power actually provided to the electric vehicles by the jth electric vehicle aggregator at time t-1; is the length of the time interval; The total charging demand constraint of the electric vehicle fleet in one day is: in, is the total energy provided by the jth EV aggregator in one day; is the total energy required by the j-th electric vehicle fleet in one day.

6. The method for joint low-carbon dispatching of electric vehicles in distribution networks according to claim 3 is characterized in that: The incentive price constraints for electric vehicle aggregators are specifically: in, is the upper bound of the total energy or total power required by the j-th electric vehicle fleet; is the lower bound of the total energy or total power required by the j-th electric vehicle fleet; For incentive price The corresponding first convex function; is the energy or power actually provided by the jth electric vehicle aggregator at time t; M is a constant; For incentive price The corresponding second convex function; is the first binary variable; is the second binary variable; is the upper bound of the incentive price; is the incentive price of the jth EV aggregator in period t; is the lower bound of the incentive price.

7. The method for joint low-carbon dispatching of electric vehicles in distribution networks according to claim 3, characterized in that: The spare capacity constraint of the electric vehicle aggregator is specifically: in, is the downregulated reserve power of the jth EV aggregator at time t; is the power actually provided by the jth EV aggregator at time t; is the upper bound of the power that the jth EV aggregator can provide at time t under demand response; is the upper bound of the energy that the jth EV aggregator can provide at time t under demand response; is the energy actually provided by the jth EV aggregator at time t; is the average charging efficiency of EVs by EV aggregators; is the length of the time interval; is the up-regulated reserve power of the j-th EV aggregator at time t; is the lower bound of the power that the jth EV aggregator can provide at time t under demand response; is the lower bound of the energy that the j-th EV aggregator can provide at time t under demand response.

8. The method for joint low-carbon dispatching of electric vehicles in distribution networks according to claim 4, characterized in that: The power balance constraint includes: The linear power flow constraints of the distribution network are as follows: in, is the actual active power on the mn line; is the susceptance between node m and node n; is the phase angle of node m at time t; is the phase angle of node n at time t; is the admittance between node m and node n; is the voltage amplitude of node m at time t; is the voltage amplitude of node m at time t; Node power balance constraints, specifically: in, is the net active power flow of node m into the system at time t, in MW; is the total number of nodes in the active distribution network; From node m to other The sum of active power flows of nodes; is the active power output by the generator at node m; is the active power of wind power generation at node m at time t; is the net load active power at node m; is the active power consumed by the charging station at node m; is the net reactive power flow into the system at node m, in MVar; From node m to other The sum of reactive power flows of nodes; is the reactive power output by the generator at node m; is the wind power reactive power at node m at time t; is the net load reactive power at node m.

9. The method for joint low-carbon dispatching of electric vehicles in a distribution network according to claim 4, characterized in that: The power constraint conditions of the distributed generation units are specifically: in, is the active power output of distributed generation unit i at time t; is the down-regulated reserve power of distributed generation unit i at time t; is the lower limit of active power generation of distributed generation unit i; is the up-regulated reserve power of distributed generation unit i at time t; is the upper limit of active power generation of distributed generation unit i; is the total number of distributed generation units; is the lower limit of reactive power generation of distributed generation unit i; is the reactive power output of distributed generation unit i at time t; is the lower limit of reactive power generation of distributed generation unit i.

10. The method for joint low-carbon dispatching of electric vehicles in distribution networks according to claim 4, characterized in that: The energy power constraint of the energy storage device is specifically: in, is the energy of energy storage device k at time t+1; is the energy of energy storage device k at time t; is the charging efficiency of energy storage device k; is the charging power of energy storage device k at time t; is the discharge power of energy storage device k at time t; is the discharge efficiency of energy storage device k; is the time interval; is a 0 / 1 variable, indicating that the energy storage device k can only be in one of the charging or discharging states at time t; The charging power of energy storage device k is subject to the maximum charging power of the device; is the maximum energy capacity of energy storage device k; The discharge power of the energy storage device k is subject to the maximum discharge power of the device; is the minimum energy limit of energy storage device k; is the energy of energy storage device k at time T, indicating the energy of energy storage device k that needs to be restored to its initial state after the entire scheduling cycle ends.

11. The method for joint low-carbon dispatching of electric vehicles in distribution networks according to claim 4, characterized in that: The above-mentioned adjustment of the reserve power constraint is specifically as follows: in, is the participation factor of distributed generation unit i at time t; is the scaling factor for the confidence level; is the load fluctuation error covariance matrix; is the renewable energy fluctuation error covariance matrix; , To increase the standby power, To reduce the reserve power, To adjust the reserve power up and down; is the total number of distributed generation units.

12. The method for joint low-carbon dispatching of electric vehicles in distribution networks according to claim 1, characterized in that: The electric vehicle aggregator data includes average carbon intensity, charging power of electric vehicle aggregators, downward reserve power provided by electric vehicle aggregators, upward reserve power provided by electric vehicle aggregators, and incentive price; the lower-level model is specifically: in, is the cost function of the jth charging station; T is the total number of planned time periods; is the time-of-use electricity price in time period t, that is, the basic charging price of the power grid for charging; aggregator data for electric vehicles; is the carbon price, in $ / kg, used to price carbon emissions; is the average carbon intensity, which indicates the carbon emissions per unit of charging power, in kg / kWh; is the charging power of the jth EV aggregator in time period t; hiked prices to spare for EV aggregators; The upregulated reserve power provided to the jth EV aggregator in time period t; reduced prices for backup of EV aggregators; The down-regulated reserve power provided to the j-th EV aggregator in time period t; is the user response probability of the jth EV aggregator in time period t; is the maximum response time of the user of the jth EV aggregator in time period t; is the incentive price in time period t, which is used to encourage users to participate in demand response.

13. The method for joint low-carbon dispatching of electric vehicles in distribution networks according to claim 1, characterized in that: The distribution system operator data includes the charging power of the energy storage device, the discharging power of the energy storage device, the generating power of the distributed generator, the upward reserve power provided by the distributed generator, and the downward reserve power provided by the distributed generator; the upper layer model is specifically: in, is the total operating cost of the distribution system operator; T is the length of the scheduling time horizon; is the total number of distributed generation units, that is, the set of distributed generators, representing all distributed energy units participating in power generation in the distribution network; is the distributed generation cost with carbon emission penalty for distributed generator i; Cost of purchasing electricity from the main grid and reserve costs for distribution system operators; It is a collection of energy storage devices, representing all batteries or energy storage units involved in the charging and discharging operations; is the unit charging and discharging cost of the energy storage device; is the charging power of energy storage device k; is the discharge power of energy storage device k; is the power generation cost coefficient of distributed generator i; is the carbon price, reflecting the unit cost of carbon emissions, in $ / kg; is the carbon intensity of distributed generator i, reflecting the carbon emissions per unit power generation; is the power generation of distributed generator i at time t; The cost coefficient for providing the reserve increase to distributed generator i; The upregulated reserve power provided to distributed generator i; The cost coefficient for providing down-reserve to distributed generator i; The down-regulated reserve power provided to distributed generator i; The market price of electricity when the main grid sells power to the distribution grid, in $ / MW; is the carbon intensity of main grid electricity, in kg / MWh; is the electricity purchased by the distribution system operator from the main grid at time t; The market price at which the main grid sells the increased reserve power to the distribution system operator; The upward reserve power provided by the main grid to the distribution system operator at time t; The market price at which the main grid sells reserve power to distribution system operators; It is the downward reserve power provided by the main grid to the distribution system operator at time t.

14. A distribution network electric vehicle joint low-carbon dispatching system, characterized in that: include: A first building module is used to respond to the dispatch request, take the minimum electric vehicle operating cost as the optimization goal, set the first constraint condition, take the electric vehicle aggregator data as the decision variable, and build the lower model; A second construction module is used to set a second constraint condition with the minimum carbon-aware operating cost of the distribution network as the optimization goal, and to construct an upper-level model with the distribution system operator data corresponding to the distribution network as the decision variable; A solution module, configured to jointly optimize and solve the upper model and the lower model based on the initial electric vehicle aggregator data, and output target distribution system operator data and target electric vehicle aggregator data; The output scheme module is used to use the target distribution system operator data and the target electric vehicle aggregator data as a joint low-carbon dispatch scheme for distribution network electric vehicles.

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