Electric Vehicle Cluster Charging Regulation Method in Power Market

Abstract

The present invention discloses a method for regulating the charging of an electric vehicle cluster in a power market. The method includes: predicting the degree of regulation willingness of the vehicle owner based on the time abundance and incentive satisfaction of the vehicle owner's charging time, and obtaining the electric vehicle cluster to be regulated according to the degree of regulation willingness; determining the state-of-charge boundary and charge-discharge power boundary of the electric vehicle cluster in the period to be regulated; constructing a two-objective function based on the vehicle owner side and the load aggregator side, and constructing a constraint function of the two-objective function based on the state-of-charge boundary and the charging power boundary; determining the charge-discharge power of the electric vehicle cluster in the period to be regulated based on the two-objective function, so as to improve the stability of the power grid while ensuring the benefits of both the vehicle owner and the load aggregator.

Description

Technical Field

[0001] The present invention belongs to the technical field of electric vehicle charging control, and in particular relates to a method for controlling charging of electric vehicle clusters in a power market. Background Art

[0002] As the number of electric vehicles increases, the centralized charging of a large number of electric vehicles increases the demand for electricity, which poses a challenge to the stability of the power grid. Load aggregators can achieve market-based transactions and promote the development of the power market by centrally managing and optimizing the distribution of customer-side power loads. They can coordinate and control electric vehicles in their jurisdiction and participate in power market transactions to reduce the impact of increased power demand on the stability of the power grid.

[0003] In the prior art, when charging and regulating electric vehicles in the same area, the optimization goal is single, which sometimes damages the interests of car owners. In addition, when regulating, the user's wishes are not taken into consideration, and the accuracy of the regulation plan is insufficient.

[0004] Therefore, how to improve the stability of the power grid while ensuring the benefits of car owners and load aggregators is a technical problem that needs to be solved by those skilled in the art. Summary of the invention

[0005] The purpose of the present invention is to solve the technical problem in the prior art that the stability of the power grid cannot be improved while ensuring the benefits of vehicle owners and load aggregators at the same time.

[0006] To achieve the above technical objectives, on the one hand, the present invention provides a method for regulating and controlling charging of electric vehicle clusters in a power market, the method comprising:

[0007] Predicting the degree of the owner's willingness to regulate based on the owner's charging time margin and incentive satisfaction, and acquiring the electric vehicle cluster to be regulated according to the degree of the willingness to regulate;

[0008] Determine the state of charge boundary and charge and discharge power boundary of the electric vehicle cluster in the period to be regulated, wherein the state of charge boundary is specifically the sum of the upper and lower boundaries of the state of charge of all the electric vehicles to be regulated in the electric vehicle cluster at each moment in the period to be regulated, and the charging power boundary is specifically the sum of the upper and lower boundaries of the charging power of all the electric vehicles to be regulated in the electric vehicle cluster at each moment in the period to be regulated;

[0009] Constructing a dual objective function based on the vehicle owner and the load aggregator, and constructing a constraint function of the dual objective function based on the state of charge boundary and the charging power boundary;

[0010] The charging and discharging power of the electric vehicle cluster within the period to be regulated is determined based on the dual objective function.

[0011] Furthermore, the time adequacy T i is specifically determined by the following formula:

[0012] T i,par = T i,dep - T i,arr

[0013]

[0014]

[0015] In the formula, T i,par is the arrival time of the i-th electric vehicle at the charging station, T i,dep is the departure time of the i-th electric vehicle from the charging station, T i,arr is the parking duration of the i-th electric vehicle, SOC i,exp is the expected state of charge of the i-th electric vehicle, SOC i,arr is the initial state of charge of the i-th electric vehicle when it is included in the electric vehicle cluster, C i is the total battery capacity of the i-th electric vehicle, η i is the charging efficiency, is the rated charging power of the i-th electric vehicle, T i,min is the minimum charging time of the i-th electric vehicle.

[0016] Furthermore, the incentive satisfaction I i is specifically determined by the following formula:

[0017]

[0018] In the formula, I actual is the actual incentive given by the load aggregator, I i,exp is the expected incentive of the i-th vehicle owner.

[0019] Furthermore, the state of charge boundary is specifically expressed by the following formula:

[0020]

[0021]

[0022]

[0023]

[0024] In the formula, is the state of charge boundary, n q is the number of electric vehicles to be regulated in the electric vehicle cluster, SOC i,minis the minimum state of charge of the i-th electric vehicle, t is the current time, t arr,i is the time when the i-th electric vehicle is connected to the charging station, t dep,i is the time when the i-th electric vehicle disconnects from the charging station, ε is the step function, SOC i,max is the maximum state of charge of the i-th electric vehicle, is the rated charging power of the i-th electric vehicle, is the rated discharging power of the i-th electric vehicle, SOC i,exp is the expected state of charge of the i-th electric vehicle, C i is the total battery capacity of the i-th electric vehicle, SOC i,arr is the initial state of charge when the i-th electric vehicle is included in the electric vehicle cluster.

[0025] Furthermore, the charging power boundary is specifically expressed by the following formula:

[0026]

[0027] In the formula, is the charging and discharging power boundary, n q is the number of electric vehicles in the electric vehicle cluster, t is the current time, t arr,i is the time when the i-th electric vehicle is connected to the charging station, t dep,i is the time when the i-th electric vehicle disconnects from the charging station, ε is the step function, is the rated charging power of the i-th electric vehicle, is the rated discharging power of the i-th electric vehicle.

[0028] Furthermore, the bi-objective function is specifically shown by the following formula:

[0029] f1 = maxP EVA = R DR + R grid + R EV

[0030]

[0031]

[0032]

[0033]

[0034] In the formula, f1 is to maximize the daily revenue of the load aggregator, f2 is to maximize the satisfaction of the vehicle owners, P EVA is the daily revenue of the load aggregator, R DR is the demand response subsidy, Rgrid is the difference between electricity purchase and sale in the electricity market, R EV is the charging service income value of electric vehicles, T is the time period to be regulated, S DR is the unit price of demand response, is the charging power of the electric vehicle cluster at time t, is the discharging power of the electric vehicle cluster at time t, is the unit price of electricity sale, is the unit price of electricity purchase, Δt is the time interval, is the cost discount provided by the load aggregator to the owners of electric vehicles in the electric vehicle cluster, is the charging service unit price, is the unit price of discharging cost, C user is the owner satisfaction, is the base load of the electric vehicle cluster at time t, is the cost expected by the owner, is the state of charge of the i-th electric vehicle when it arrives at the charging facility.

[0035] Furthermore, the constraint functions specifically include charging and discharging power constraints and state of charge constraints, which are shown as follows:

[0036]

[0037]

[0038]

[0039]

[0040]

[0041] In the formula, is the minimum charging power of the electric vehicle cluster, is the charging power of the electric vehicle cluster at time t, is the maximum charging power of the electric vehicle cluster, is the minimum discharging power of the electric vehicle cluster, is the discharging power of the electric vehicle cluster at time t, is the maximum discharging power of the electric vehicle cluster, E t+1 is the capacity of the electric vehicle cluster at time t + 1, E t is the capacity of the electric vehicle cluster at time t, η chr is the charging efficiency of the electric vehicle cluster, η dis is the discharging efficiency of the electric vehicle cluster, is the capacity of the electric vehicle cluster connected to the charging facility at time t, is the capacity of the electric vehicle cluster leaving the charging facility at time t, is the minimum capacity of the electric vehicle cluster, is the maximum capacity of the electric vehicle cluster.

[0042] Further, determining the charging and discharging power of the electric vehicle cluster within the to-be-regulated period based on the dual-objective function specifically includes:

[0043] Solving the dual-objective function to obtain the optimal solution;

[0044] Adjusting the charging and discharging power of the electric vehicle cluster within the to-be-regulated period according to the optimal solution.

[0045] A method for regulating the charging of an electric vehicle cluster in a power market provided by the present invention, compared with the prior art, this method predicts the degree of regulation willingness value of the vehicle owner based on the time abundance and incentive satisfaction of the vehicle owner's charging time, and obtains the electric vehicle cluster to be regulated according to the degree of regulation willingness value; determines the state of charge boundary and the charging and discharging power boundary of the electric vehicle cluster in the to-be-regulated period, the state of charge boundary is specifically the sum of the upper and lower boundaries of the state of charge of all to-be-regulated electric vehicles in the electric vehicle cluster at each moment in the to-be-regulated period, and the charging power boundary is specifically the sum of the upper and lower boundaries of the charging power of all to-be-regulated electric vehicles in the electric vehicle cluster at each moment in the to-be-regulated period; constructs a dual-objective function based on the vehicle owner side and the load aggregator side, and constructs a constraint function of the dual-objective function based on the state of charge boundary and the charging power boundary; determines the charging and discharging power of the electric vehicle cluster within the to-be-regulated period based on the dual-objective function, achieving the improvement of the stability of the power grid while ensuring the benefits of both the vehicle owner and the load aggregator. Description of the Drawings

[0046] In order to more clearly illustrate the technical solutions in the embodiments of this specification or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments described in this specification. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0047] Figure 1 The flowchart of the method for regulating the charging of an electric vehicle cluster in a power market provided by the embodiment of this specification is shown;

[0048] Figure 2 The structural schematic diagram of the device for regulating the charging of an electric vehicle cluster in a power market provided by the embodiment of this specification is shown. Detailed Embodiments

[0049] To enable those of ordinary skill in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without making creative efforts belong to the scope of protection of this application.

[0050] As Figure 1 shown is a schematic flowchart of a method for regulating the charging of an electric vehicle cluster in a power market provided in an embodiment of this specification. Although this specification provides the method operation steps or device structures shown in the following embodiments or drawings, based on routine or without creative efforts, more or fewer operation steps or module units may be included in the method or device. In steps or structures where there is no necessary causal relationship logically, the execution order of these steps or the module structure of the device is not limited to the execution order or module structure shown in the embodiments or drawings of this specification. When the method or module structure is applied to an actual device, server or terminal product, it can be executed sequentially or in parallel according to the method or module structure shown in the embodiments or drawings (for example, in an environment of parallel processors or multi-threaded processing, and even including an implementation environment of distributed processing and server clusters).

[0051] The method for regulating the charging of an electric vehicle cluster in a power market provided in an embodiment of this specification can be applied to terminal devices such as clients and servers. As Figure 1 shown, the method specifically includes the following steps:

[0052] Step S101: Predict the degree of regulation willingness of the vehicle owner based on the time adequacy and incentive satisfaction of the vehicle owner's charging time, and obtain the electric vehicle cluster to be regulated according to the degree of regulation willingness value.

[0053] Specifically, the current number of electric vehicles is large, and the traditional regulation of electric vehicles fails to consider the willingness of vehicle owners. However, this application first considers the adjustment willingness of electric vehicle owners, aggregates the adjustable electric vehicle clusters, and forms the adjustable capacity of the clusters. First, the degree of regulation willingness value of the vehicle owner is predicted based on the time adequacy and incentive satisfaction of the vehicle owner's charging time, and the electric vehicles to be regulated are determined according to this degree of regulation willingness value, so as to obtain the electric vehicle cluster to be regulated. All electric vehicles in this application support V2G technology and have the function of discharging.

[0054] In fact, the vehicle owner has two options: normal charging and orderly charging and discharging. When the vehicle owner selects normal charging, the electric vehicle will immediately charge at the rated power. An electric vehicle under normal charging is regarded as an uncontrollable load. If the second charging method, i.e., the orderly charging and discharging option, is selected, the user can set the departure time and the expected SOC (State of Charge). Then, the above-mentioned regulation willingness degree value is calculated. The EVA (Electric Vehicle Aggregator) will adjust the charging and discharging power of the electric vehicle to reduce the load fluctuation amplitude and obtain better economic benefits.

[0055] In the embodiment of the present application, whether to list an electric vehicle as a vehicle to be regulated is specifically determined according to the time abundance and incentive satisfaction of the vehicle owner. Specifically, the regulation willingness degree value corresponding to the vehicle owner is determined according to the time abundance and incentive satisfaction. When the regulation willingness degree value is greater than the preset threshold, the vehicle owner's instruction is determined as an orderly charging and discharging instruction, and this electric vehicle is included in the electric vehicle cluster to be regulated.

[0056] Before calculating the adjustable capacity of the electric vehicle cluster, the willingness of the vehicle owner to charge and discharge orderly needs to be considered. Generally, the willingness of electric vehicle owners to participate in demand response is related to two factors: time abundance and incentive satisfaction. Through the comprehensive evaluation of these two factors, they finally decide whether to participate in orderly charging and discharging, that is, whether the regulation willingness degree value is greater than the preset threshold. The specific process is as follows:

[0057] If the user selects normal charging, the time to reach the expected SOC is the minimum charging time. If the parking duration of the electric vehicle is greater than the minimum parking duration, the user will be more willing to participate in demand response. The time abundance factor can be calculated by the following formula:

[0058] T i,par =T i,dep -T i,arr

[0059]

[0060]

[0061] In the formula, T i,par is the arrival time of the i-th electric vehicle at the charging station, T i,dep is the departure time of the i-th electric vehicle from the charging station, T i,arr is the parking duration of the i-th electric vehicle, SOC i,exp is the expected state of charge of the i-th electric vehicle, SOC i,arr is the initial state of charge of the i-th electric vehicle when it is included in the electric vehicle cluster, C iis the total battery capacity of the i-th electric vehicle, and η i is the charging efficiency, is the rated charging power of the i-th electric vehicle, and T i,min is the minimum charging time of the i-th electric vehicle.

[0062] If T i < 1, the probability of electric vehicle users participating in demand response is very low. On the contrary, the larger the value of T i is, the stronger the willingness of electric vehicle users to participate in demand response.

[0063] Another factor is the satisfaction degree of electric vehicle users with the incentive mechanism, which is related to the price incentive determined by EVA and the expected incentive level of EV users. The calculation of incentive satisfaction is as follows:

[0064]

[0065] Among them, I actual is the actual incentive given by EVA, and I i,exp is the expected incentive level of the i-th electric vehicle user. Similar to the time adequacy, if I i < 1, the user is less likely to participate in orderly charging and discharging. On the contrary, the larger the value of I i is, the stronger the willingness of electric vehicle users to participate in orderly charging and discharging.

[0066] The probability of electric vehicle users participating in orderly charging and discharging is between 0 and 1, and is strongly related to the time adequacy and incentive satisfaction. The sigmoid function is used to map the relationship between the two factors and the probability of user willingness, as follows:

[0067]

[0068]

[0069] Among them, p i,1 and p i,2 are respectively the willingness of electric vehicle users to participate in orderly charging and discharging under T i and I i The coefficients of the function are α1, α2, β1, β2 (α1 = 4, α2 = 6, β1 = 0.6, β2 = 0.3).

[0070] In order to determine the willingness degree of electric vehicle users to participate in demand response, it is necessary to comprehensively consider the influence of time adequacy and incentive satisfaction. Therefore, this study uses the entropy weight method to calculate the weights of the two factors. The process of weight determination is as follows:

[0071] (1) Data normalization: The influence degree of each factor needs to be normalized. In this method, it is represented by the probability of user willingness, which is a value in [0,1], so data normalization is not required.

[0072] (2) Information entropy calculation: Calculate the information entropy of the j-th factor.

[0073]

[0074]

[0075] Among them, p ij is the willingness of the i-th electric vehicle user to participate in demand response when affected by the j-th factor, n is the number of electric vehicles, and r ij is a transition variable.

[0076] (3) Final weight calculation: After obtaining the information entropy, the final weights ω j of the two factors can be determined as follows:

[0077]

[0078] Among them, m is the number of factors.

[0079] After determining the weights of the two factors in the previous part, the comprehensive willingness degree of electric vehicle users can be obtained by the following formula:

[0080] p i = ω1p i,1 + ω2p i,2

[0081] If the total probability is greater than 0.8, it can be regarded as a clear willingness to participate in orderly charging and discharging, and an orderly charging and discharging instruction is determined, that is, the corresponding electric vehicle can be included in the electric vehicle cluster to be regulated.

[0082] Specifically, in the charging and discharging mode of SOC and charging and discharging power within the set limit set by the user, a single electric vehicle should meet the following SOC boundary constraints, that is, the first state of charge boundary constraints:

[0083] SOC i,min ≤ SOC i,t ≤ SOC i,max t arr,i ≤ t ≤ t dep,i

[0084]

[0085]

[0086]

[0087] Wherein, SOC i,min and SOC i,max are respectively the minimum and maximum values of the SOC, i.e., the state of charge, of the i-th electric vehicle, and t arr,i is the time when the i-th electric vehicle connects to the charging station, and t dep,i is the time when the i-th electric vehicle disconnects from the charging station. and are respectively the rated charging and discharging powers of the i-th electric vehicle; C i is the total battery capacity of the i-th electric vehicle, and SOC i,exp is the expected SOC of the i-th electric vehicle.

[0088] Meanwhile, the charging and discharging power of the electric vehicle should also be within the following range, i.e., satisfy the first charging and discharging power boundary constraint:

[0089]

[0090]

[0091] Wherein, μ i,t is the charging state of the i-th electric vehicle. If the electric vehicle is charging, μ i,t = 1, otherwise, μ i,t = 0.

[0092] Step S102, determine the state of charge boundary and the charging and discharging power boundary of the electric vehicle cluster in the to-be-regulated period. The state of charge boundary is specifically the sum of the upper and lower boundaries of the state of charge of all to-be-regulated electric vehicles in the electric vehicle cluster at each moment in the to-be-regulated period, and the charging power boundary is specifically the sum of the upper and lower boundaries of the charging power of all to-be-regulated electric vehicles in the electric vehicle cluster at each moment in the to-be-regulated period.

[0093] Specifically, based on the electric vehicle cluster, sum the upper and lower boundaries of a single electric vehicle, including the state of charge and the charging power, so as to obtain the state of charge boundary and the charging power boundary of the entire electric vehicle cluster. The state of charge boundary and the charging power boundary are also the dispatchable capabilities of the electric vehicle cluster.

[0094] The state of charge boundary is specifically represented by the following formula:

[0095]

[0096]

[0097]

[0098]

[0099] In the formula, is the state of charge boundary, n q is the number of electric vehicles to be regulated in the electric vehicle cluster, SOC i,min is the minimum state of charge of the i-th electric vehicle, t is the current time, t arr,i is the time when the i-th electric vehicle connects to the charging station, t dep,i is the time when the i-th electric vehicle disconnects from the charging station, ε is the step function, SOC i,max is the maximum state of charge of the i-th electric vehicle, is the rated charging power of the i-th electric vehicle, is the rated discharging power of the i-th electric vehicle, SOC i,exp is the expected state of charge of the i-th electric vehicle, C i is the total battery capacity of the i-th electric vehicle, SOC i,arr is the initial state of charge of the i-th electric vehicle when it is included in the electric vehicle cluster.

[0100] The charging power boundary is specifically expressed by the following formula:

[0101]

[0102] In the formula, is the charge and discharge power boundary, n q is the number of electric vehicles to be regulated in the electric vehicle cluster, t is the current time, t arr,i is the time when the i-th electric vehicle connects to the charging station, t dep,i is the time when the i-th electric vehicle disconnects from the charging station, ε is the step function, is the rated charging power of the i-th electric vehicle, is the rated discharging power of the i-th electric vehicle.

[0103] Step S103: Construct a two-objective function based on the vehicle owner side and the load aggregator side, and construct a constraint function of the two-objective function based on the state of charge boundary and the charging power boundary.

[0104] Specifically, the EVA (Load Aggregator) can control the electric vehicle cluster, enabling it to compete in the electricity spot market. It can not only obtain economic profits from the spot market, reduce the charging costs of electric vehicle users, but also reduce the load volatility and the impact on the power grid. The electricity market trading varieties that the EVA can participate in include demand response trading and electricity spot trading. (1) Demand response. The power trading center issues an invitation one day before the demand response period, and the EVA responds to the invitation according to the planned demand response capacity. Based on the difference between the actual operating load and the base load, the EVA will receive corresponding economic subsidies. (2) Electricity spot trading. The EVA integrates the controllable electric vehicle load to purchase and sell electricity in the electricity market. Market participants report the electricity purchase and sale volume curves in the day-ahead trading market and adjust the actual operation in the real-time market.

[0105] Then, a two-objective optimization and control model for the electric vehicle cluster in the electricity market is constructed. Two optimization objectives are set: maximizing the daily profit of the EVA and maximizing the satisfaction of electric vehicle users. The decision variables are the demand response volume and the charge and discharge power of the electric vehicle cluster in each time period.

[0106] The daily profit of the EVA is the difference between the daily income and the daily cost. Through the electricity market, the EVA can obtain demand response subsidies and electricity sales revenue. In addition, the charging fees for electric vehicle users are also another source of income for the EVA. The costs of the EVA mainly come from market electricity purchases and discharge compensation for users.

[0107] Generally, electric vehicle owners participating in orderly charge and discharge have two requirements: achieving the maximum charging capacity as much as possible and reducing the charging cost as much as possible. Therefore, the second objective is to maximize the satisfaction of electric vehicle owners. The cost that can be saved is one of the most important factors in improving the owner's satisfaction. Another factor is the expectation of the charging capacity. When an electric vehicle leaves the charging station, the optimal off-grid SOC is 100%. However, in the orderly charge and discharge mode, electric vehicle owners usually agree with the EVA on the minimum off-grid SOC expectation to ensure flexibility. The closer the off-grid SOC is to 100%, the more satisfied the owner is.

[0108] Therefore, a two-objective function based on the vehicle owner and the load aggregator is constructed, and the two-objective function is specifically shown by the following formula:

[0109] f1 = maxP EVA = R DR + R grid + R EV

[0110]

[0111]

[0112]

[0113]

[0114] In the formula, f1 is the maximum daily revenue of the load aggregator, f2 is the maximum satisfaction of vehicle owners, P EVA is the daily revenue of the load aggregator, R DR is the demand response subsidy, R grid is the difference between the electricity purchase and sale in the electricity market, R EV is the charging service income value of electric vehicles, T is the time period to be regulated, S DR is the unit price of demand response, is the charging power of the electric vehicle cluster at time t, is the discharging power of the electric vehicle cluster at time t, is the unit price of selling electricity, is the unit price of purchasing electricity, Δt is the time interval, is the cost discount provided by the load aggregator for vehicle owners in the electric vehicle cluster, is the unit price of charging service, is the unit price of discharging cost, C user is the satisfaction of vehicle owners, is the base load of the electric vehicle cluster at time t, is the cost expected by vehicle owners, is the state of charge of the i-th electric vehicle when it arrives at the charging facility.

[0115] However, the bi-objective function needs to be solved under the constraint of dispatchable capacity, that is, the constraint function of the bi-objective function is constructed based on the state-of-charge boundary and the charging power boundary.

[0116] The constraint function specifically includes charging and discharging power constraints and state-of-charge constraints, which are shown as follows:

[0117]

[0118]

[0119]

[0120]

[0121]

[0122] In the formula, is the minimum charging power of the electric vehicle cluster, is the charging power of the electric vehicle cluster at time t, is the maximum charging power of the electric vehicle cluster, is the minimum discharge power of the electric vehicle cluster, is the discharge power of the electric vehicle cluster at time t, is the maximum discharge power of the electric vehicle cluster, E t+1 is the electric vehicle cluster capacity at time t + 1, E t is the electric vehicle cluster capacity at time t, η chr is the charging efficiency of the electric vehicle cluster, η dis is the discharge efficiency of the electric vehicle cluster, is the electric vehicle cluster capacity connected to the charging facility at time t, is the electric vehicle cluster capacity leaving the charging facility at time t, is the minimum capacity of the electric vehicle cluster, is the maximum capacity of the electric vehicle cluster.

[0123] Step S104: Determine the charging and discharging power of the electric vehicle cluster during the period to be regulated based on the dual-objective function.

[0124] The determining the charging and discharging power of the electric vehicle cluster during the period to be regulated based on the dual-objective function specifically includes:

[0125] Solve the dual-objective function to obtain the optimal solution;

[0126] Adjust the charging and discharging power of the electric vehicle cluster during the period to be regulated according to the optimal solution.

[0127] Specifically, when determining the dual-objective function and its constraint function, the NSGA-II algorithm is used for solution. In this algorithm, the solutions of each generation are grouped into multiple layers according to the domination relationship, and the individuals in the same layer are sorted according to the crowding distance. For individuals that do not meet the constraint conditions, penalties will be imposed on the objective function. After iteration, the Pareto optimal solution is obtained, which can provide the maximum regulation ability while maximizing the benefits of the vehicle owners and the load aggregators, ensuring the stability of the power grid.

[0128] Based on the above-mentioned electric vehicle cluster charging regulation method in the power market, one or more embodiments of this specification also provide a platform and a terminal for electric vehicle cluster charging regulation in the power market. The platform or terminal may include devices, software, modules, plugins, servers, clients, etc. that use the method described in the embodiments of this specification, and devices that combine necessary implementation hardware. Based on the same innovative concept, the systems in one or more embodiments provided by the embodiments of this specification are as described in the following embodiments. Since the implementation schemes for the systems to solve problems are similar to the method, the implementation of the specific systems in the embodiments of this specification may refer to the implementation of the foregoing method, and the repeated parts will not be elaborated here. The term "unit" or "module" used hereinafter may be a combination of software and / or hardware that can achieve a predetermined function. Although the systems described in the following embodiments are preferably implemented in software, implementation in hardware or a combination of software and hardware is also possible and contemplated.

[0129] Specifically, Figure 2 is a schematic diagram of the module structure of an embodiment of the electric vehicle cluster charging regulation device provided in this specification. As Figure 2 shown, the electric vehicle cluster charging regulation device provided in this specification includes:

[0130] An acquisition module 201, configured to predict the degree value of the regulation willingness of the vehicle owner based on the time sufficiency and incentive satisfaction of the vehicle owner's charging time, and obtain the electric vehicle cluster to be regulated according to the degree value of the regulation willingness;

[0131] A determination module 202, configured to determine the state of charge boundary and the charge and discharge power boundary of the electric vehicle cluster in the period to be regulated. The state of charge boundary is specifically the sum of the upper and lower boundaries of the state of charge of all the electric vehicles to be regulated in the electric vehicle cluster at each moment in the period to be regulated, and the charging power boundary is specifically the sum of the upper and lower boundaries of the charging power of all the electric vehicles to be regulated in the electric vehicle cluster at each moment in the period to be regulated;

[0132] A construction module 203, configured to construct a dual-objective function based on the vehicle owner side and the load aggregator side, and construct a constraint function of the dual-objective function based on the state of charge boundary and the charging power boundary;

[0133] A regulation module 204, configured to determine the charge and discharge power of the electric vehicle cluster in the period to be regulated based on the dual-objective function.

[0134] It should be noted that the above-mentioned system may also include other implementation manners according to the description of the corresponding method embodiments. The specific implementation manners may refer to the description of the corresponding method embodiments above, and will not be elaborated one by one here.

[0135] The method or device described in the above embodiments provided in this specification can implement business logic through a computer program and record it on a storage medium. The storage medium can be read and executed by a computer to achieve the effects of the solutions described in the embodiments of this specification, such as:

[0136] Predict the degree of regulation willingness of the vehicle owner based on the time adequacy and incentive satisfaction of the vehicle owner's charging time, and obtain an electric vehicle cluster to be regulated according to the degree of regulation willingness value;

[0137] Determine the state of charge boundary and charge and discharge power boundary of the electric vehicle cluster in the period to be regulated. The state of charge boundary is specifically the sum of the upper and lower boundaries of the state of charge of all electric vehicles to be regulated in the electric vehicle cluster at each moment within the period to be regulated. The charging power boundary is specifically the sum of the upper and lower boundaries of the charging power of all electric vehicles to be regulated in the electric vehicle cluster at each moment within the period to be regulated;

[0138] Construct a dual-objective function based on the vehicle owner side and the load aggregator side, and construct a constraint function of the dual-objective function based on the state of charge boundary and the charging power boundary;

[0139] Determine the charge and discharge power of the electric vehicle cluster within the period to be regulated based on the dual-objective function.

[0140] The storage medium may include a physical device for storing information. Usually, information is digitized and then stored in a medium using electrical, magnetic, or optical methods. The storage medium may include: devices that store information using electrical energy, such as various memories, such as RAM, ROM, etc.; devices that store information using magnetic energy, such as hard disks, floppy disks, magnetic tapes, magnetic core memories, magnetic bubble memories, USB flash drives; devices that store information using optical methods, such as CDs or DVDs. Of course, there are other types of readable storage media, such as quantum memories, graphene memories, and so on.

[0141] The embodiments of this specification are not limited to those that must conform to industry communication standards, standard computer resource data update and data storage rules, or the situations described in one or more embodiments of this specification. Some industry standards or implementation schemes slightly modified based on the implementation described in a custom manner or embodiment can also achieve the same, equivalent, or similar, or predictable implementation effects after deformation as the above embodiments. The embodiments obtained by applying these modified or deformed data acquisition, storage, judgment, processing methods, etc. still fall within the scope of the optional implementation schemes of the embodiments of this specification.

[0142] The controller can be implemented in any suitable manner. For example, the controller can take the form of, for example, a microprocessor or a processor and a computer-readable medium storing computer-readable program code (such as software or firmware) executable by the (micro)processor, logic gates, switches, application specific integrated circuits (ASICs), programmable logic controllers, and embedded microcontrollers. Examples of the controller include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicon Labs C8051F320. The memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art also know that in addition to implementing the controller in the form of pure computer-readable program code, it is entirely possible to logically program the method steps to enable the controller to be implemented in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, and embedded microcontrollers to achieve the same function. Therefore, such a controller can be considered a hardware component, and the devices included therein for implementing various functions can also be regarded as the structures within the hardware component. Or even, the devices for implementing various functions can be regarded as either software modules for implementing the method or structures within the hardware component.

[0143] The device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or plugins can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the couplings or direct couplings or communication connections shown or discussed with each other can be through some interfaces. The indirect couplings or communication connections of the devices or units can be in electrical, mechanical, or other forms.

[0144] These computer program instructions can also be loaded onto a computer or other programmable resource data update device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 a process or multiple processes and / or blocks Figure 1 a block or multiple blocks.

[0145] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other, and the key point of each embodiment is to illustrate the differences from other embodiments. In particular, for system embodiments, since they are basically similar to method embodiments, the description is relatively simple, and for the relevant parts, reference can be made to the description of the method embodiments. In the description of this specification, the description referring to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of this specification. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples

[0146] Those of ordinary skill in the art will realize that the embodiments described herein are for helping the reader understand the principles of the present invention, and it should be understood that the protection scope of the present invention is not limited to such specific statements and embodiments. Those of ordinary skill in the art can make various other specific deformations and combinations that do not depart from the essence of the present invention based on these technical revelations disclosed in the present invention, and these deformations and combinations are still within the protection scope of the present invention

Claims

1. A method for regulating the charging of an electric vehicle cluster in a power market, characterized in that, The method includes: Predicting the degree of regulation willingness of the vehicle owner based on the time adequacy and incentive satisfaction of the vehicle owner's charging time, and obtaining an electric vehicle cluster to be regulated according to the degree of regulation willingness; Determining the state of charge boundary and charge and discharge power boundary of the electric vehicle cluster in the regulation period to be regulated. The state of charge boundary is specifically the sum of the upper and lower boundaries of the state of charge of all electric vehicles to be regulated in the electric vehicle cluster at each moment in the regulation period to be regulated, and the charge and discharge power boundary is specifically the sum of the upper and lower boundaries of the charge and discharge power of all electric vehicles to be regulated in the electric vehicle cluster at each moment in the regulation period to be regulated; Constructing a dual-objective function based on the vehicle owner side and the load aggregator, and constructing a constraint function of the dual-objective function based on the state of charge boundary and the charge and discharge power boundary; Determining the charge and discharge power of the electric vehicle cluster in the regulation period to be regulated based on the dual-objective function; wherein, the time adequacy is specifically determined by the following formula: ， ， ， Wherein, is the arrival time of the i-th electric vehicle at the charging station, is the departure time of the i-th electric vehicle from the charging station, is the parking duration of the i-th electric vehicle, is the expected state of charge of the i-th electric vehicle, is the initial state of charge of the i-th electric vehicle when it is included in the electric vehicle cluster, is the total battery capacity of the i-th electric vehicle, is the charging efficiency, is the rated charging power of the i-th electric vehicle, is the minimum charging time of the i-th electric vehicle; Among them, the incentive satisfaction is specifically determined by the following formula: ， In the formula, is the actual incentive given by the load aggregator, is the expected incentive for the i-th vehicle owner; Among them, the dual-objective function is specifically shown by the following formula: ， ， ， ， ， In the formula, is to maximize the daily revenue of the load aggregator, is to maximize the satisfaction of vehicle owners, is the daily revenue of the load aggregator, is the demand response subsidy, is the difference between the electricity purchase and sale in the power market, is the charging service income value of the electric vehicle, is the time period to be regulated, is the unit price of demand response, is the charging power of the electric vehicle cluster at time t, is the discharging power of the electric vehicle cluster at time t, is the unit price of electricity sold, is the unit price of electricity purchased, is the time interval, is the cost discount provided by the load aggregator for the vehicle owners in the electric vehicle cluster, is the unit price of charging service, is the unit price of discharging cost, is the satisfaction of vehicle owners, is the base load of the electric vehicle cluster at time t, is the cost expected by the vehicle owner, is the state of charge of the i-th electric vehicle when it arrives at the charging facility.

2. The method for regulating the charging of an electric vehicle cluster in a power market according to claim 1, characterized in that, The state of charge boundary is specifically represented by the following formula: ， ， ， ， In the formula, is the state of charge boundary, is the number of electric vehicles to be regulated in the electric vehicle cluster, is the minimum state of charge of the i-th electric vehicle, t is the current time, is the connection time of the i-th electric vehicle to the charging station, is the disconnection time of the i-th electric vehicle from the charging station, is the step function, is the maximum state of charge of the i-th electric vehicle, is the rated charging power of the i-th electric vehicle, is the rated discharging power of the i-th electric vehicle, is the expected state of charge of the i-th electric vehicle, is the total battery capacity of the i-th electric vehicle, is the initial state of charge of the i-th electric vehicle when it is included in the electric vehicle cluster.

3. The method for regulating the charging of an electric vehicle cluster in a power market according to claim 1, characterized in that, The charge and discharge power boundary is specifically represented by the following formula: ， Wherein, is the charging and discharging power boundary, is the number of electric vehicles to be regulated in the electric vehicle cluster, t is the current time, is the connection time of the i-th electric vehicle to the charging station, is the disconnection time of the i-th electric vehicle from the charging station, is the step function, is the rated charging power of the i-th electric vehicle, is the rated discharging power of the i-th electric vehicle.

4. The method for regulating the charging of an electric vehicle cluster in a power market according to claim 1, characterized in that, The constraint function specifically includes a charge and discharge power constraint and a state of charge constraint, which are shown by the following formulas respectively: ， ， ， ， ， Wherein, is the minimum charging power of the electric vehicle cluster, is the charging power of the electric vehicle cluster at time t, is the maximum charging power of the electric vehicle cluster, is the minimum discharging power of the electric vehicle cluster, is the discharging power of the electric vehicle cluster at time t, is the maximum discharging power of the electric vehicle cluster, is the electric vehicle cluster capacity at time t + 1, is the electric vehicle cluster capacity at time t, is the charging efficiency of the electric vehicle cluster, is the discharging efficiency of the electric vehicle cluster, is the electric vehicle cluster capacity connected to the charging facility at time t, is the electric vehicle cluster capacity leaving the charging facility at time t, is the minimum capacity of the electric vehicle cluster, is the maximum capacity of the electric vehicle cluster.

5. The method for regulating the charging of an electric vehicle cluster in a power market according to claim 1, characterized in that, Determining the charge and discharge power of the electric vehicle cluster in the regulation period to be regulated based on the dual-objective function specifically includes: Solving the dual-objective function to obtain an optimal solution; Adjusting the charge and discharge power of the electric vehicle cluster in the regulation period to be regulated according to the optimal solution.

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