Cooperative scheduling method and system considering vehicles, stations and energy

Abstract

The invention provides a cooperative scheduling method and system considering vehicles, stations and energy, and relates to the technical field of new energy, and the method comprises the steps: obtaining statistical data, and building a mathematical model corresponding to the charging load of an electric vehicle according to the statistical data; establishing a regional integrated energy system master-slave game model taking an integrated energy system operator as a leader and an energy supplier, a charging station operator and an electric vehicle aggregator as followers; a genetic algorithm is utilized to initialize and update the electricity purchase price and the slow charging price of an integrated energy system operator, a regional integrated energy system master-slave game model is solved, and an energy supplier and an electric vehicle aggregator respond to the dynamic electricity price and optimize the power supply and the electricity utilization power of the energy supplier and the electric vehicle aggregator. The benefit of each subject in the regional integrated energy system is considered, the new energy consumption degree can be effectively improved, the benefit of each party of the integrated energy system can be balanced, and the scheduling potential of the electric vehicle in the power system can be fully exerted.

Description

Technical Field

[0001] The present disclosure relates to the field of new energy technology, and in particular to a coordinated scheduling method and system considering vehicles, stations, and energy. Background Art

[0002] Electric vehicles are vehicles that use electricity to drive. They are considered to be a means of transportation that achieves sustainable development and does not damage the ecological environment. They have been widely used. As a low-carbon, green and environmentally friendly means of transportation, electric vehicles will increase the burden on the power grid when they are connected to the grid on a large scale and charged in an unordered manner.

[0003] Among the current studies on the optimal scheduling of electric vehicles, the more representative research results are mostly carried out from the two perspectives of electricity price regulation and car owner demand preferences, without comprehensively considering the interests of multiple parties involved in power generation, power sales and power purchases, resulting in unsatisfactory regulation effects.

[0004] Therefore, how to schedule multiple parties has become one of the technical problems that need to be solved urgently at this stage. Summary of the invention

[0005] In order to solve the above technical problems, the present disclosure provides a coordinated scheduling method and system considering vehicles, stations and energy, which is used to perform scheduling for multiple parties and give full play to the scheduling potential of electric vehicles in the power system.

[0006] In a first aspect, the present disclosure provides a coordinated scheduling method considering vehicles, stations, and energy, including:

[0007] Obtaining statistical data, and establishing a mathematical model corresponding to the electric vehicle charging load based on the statistical data;

[0008] Establish a regional integrated energy system master-slave game model with integrated energy system operators as leaders and energy suppliers, charging station operators and electric vehicle aggregators as followers;

[0009] Genetic algorithm initialization is used to update the electricity purchase price and slow charging price of the integrated energy system operator, and the master-slave game model of the regional integrated energy system is solved. The energy supplier and the electric vehicle aggregator respond to dynamic electricity prices and optimize their own power supply and power consumption.

[0010] Optionally, where:

[0011] The mathematical model corresponding to the electric vehicle charging load is established based on the statistical data, including:

[0012] Classifying the electric vehicles according to the owner's demand preferences, the categories include: constant power charging electric vehicles, variable power charging electric vehicles and flexible scheduling electric vehicles;

[0013] For the constant power charging electric vehicle,

[0014] and The electric vehicle charging constraints are:

[0015]

[0016] Among them, N ev,1 represents a set of constant power charging electric vehicles;

[0017] t in,i Indicates the time when the electric vehicle joins the network;

[0018] P 1,i (t) represents the charging power of the constant power charging electric vehicle;

[0019] Indicates the maximum charging power of the electric vehicle;

[0020] SOC i (t) represents the state of charge of the ith electric vehicle in period t;

[0021] SOC i,in represents the state of charge of the i-th electric vehicle when it enters the grid;

[0022] SOC i,ex represents the expected state of charge of the i-th electric vehicle when it is off-grid;

[0023] η c Indicates the charging efficiency of the electric vehicle;

[0024] E i Indicates the battery capacity of the electric vehicle;

[0025] For the variable power charging electric vehicle,

[0026] and The charging constraints of the electric vehicle are:

[0027]

[0028] Among them, N ev,2 represents a set of variable power charging electric vehicles;

[0029] P 2,i (t) represents the charging power of the constant power charging electric vehicle;

[0030] SOC i,th Indicates the charge state of the i-th variable power charging electric vehicle when it can participate in demand response;

[0031] For the flexibility in dispatching electric vehicles,

[0032] and The charging constraints of the electric vehicle are:

[0033]

[0034] Among them, N ev,3 represents a set of flexible dispatching electric vehicles;

[0035] P 3,i (t) represents the charging power of the flexible scheduling electric vehicle;

[0036] represents the maximum discharge power of the electric vehicle;

[0037] η d represents the discharge efficiency of the electric vehicle;

[0038] The state of charge SOC of the electric vehicle when it is off-grid i,dep The constraints are:

[0039] SOC i,ex ≤SOC i,dep ≤1;

[0040] The electric vehicle is online for a period of time t on,i The constraints are:

[0041]

[0042] Among them, t onmin,i Indicates the shortest online time of the electric vehicle;

[0043] The constraints of the electric vehicle charging duration are:

[0044] t sta,i +t on,min,i ≤t ran,i ≤T,

[0045] Among them, t sta,i Indicates the charging start time of the electric vehicle;

[0046] t ran,i represents the random dwell time of the electric vehicle;

[0047] T represents the total number of time periods.

[0048] Optionally, where:

[0049] The master-slave game model of a regional integrated energy system is established with the integrated energy system operator as the leader and energy suppliers, charging station operators and electric vehicle aggregators as followers, including:

[0050] Models are established for the integrated energy system operator, the energy supplier-charging station operator alliance, the electric vehicle aggregator and the user aggregator respectively.

[0051] Optionally, where:

[0052] A model is established for the integrated energy system operator, including:

[0053] Calculate the net profit of the integrated energy system operator and obtain its maximum profit:

[0054]

[0055] in,

[0056]

[0057] In the formula, F IESO represents the net revenue of the operator of said integrated energy system;

[0058] represents the revenue from selling energy to the user aggregator in period t;

[0059] represents the revenue from selling energy to the electric vehicle aggregator in period t;

[0060] and Respectively represent the total power charged to the constant power charging electric vehicle and the variable power charging electric vehicle in the t period;

[0061] Indicates the total charging and discharging power of the flexible electric vehicles in the t period;

[0062] C b,t represents the cost of purchasing energy from the energy supplier in period t;

[0063] P wt,t , P pv,t and P csp,t They represent the optimized output power of wind turbines, photovoltaic and solar thermal equipment at the tth period in a day respectively;

[0064] η csp,e Indicates the efficiency of converting the output of solar thermal equipment into electrical energy;

[0065] E ess,t Indicates the amount of energy stored in the energy storage device at time period t;

[0066] η ess Indicates the charge and discharge loss rate of energy storage equipment;

[0067] They represent the charging power and discharging power of the energy storage device in period t respectively;

[0068] and They respectively represent the electricity purchase price and slow charging price for the tth period of a day set by the integrated energy system operator;

[0069] represents the power selling price of the power grid in the tth period of a day;

[0070] They respectively represent the electricity load value of the user aggregator in the tth period of a day.

[0071] Optionally, where:

[0072] A model is established for the energy supplier-charging station operator alliance, including:

[0073] The energy storage device stores electricity E ess,t , expressed in mathematical formula as:

[0074]

[0075] Among them, E ess,t-1 Indicates the amount of energy stored in the energy storage device during period t-1;

[0076] Generate an objective function for the charging station operator to dynamically adjust the service fee according to the output value of the energy supplier:

[0077]

[0078] in,

[0079] F s,t =C b,t ,

[0080]

[0081] C pun,t =λ e,pun ·(P wt,max -P wt,t +P pv,max -P pv,t ),

[0082] Where:

[0083] F es-cso represents the total revenue of the energy supplier-charging station operator alliance during 24 hours of the day;

[0084] F s,t represents the energy sales revenue of the energy supplier in period t;

[0085] represents the service fee income of the charging station operator in period t;

[0086] C op,t represents the operation and maintenance cost of the equipment in the energy supplier in the t period;

[0087] represents the number of electric vehicles of each type, and j represents the type of electric vehicles;

[0088] represents the basic value of service fee for the jth electric vehicle under different strategies;

[0089] represents the subsidized electricity price for the jth type of electric vehicle in the tth period of a day;

[0090] κ op,wt , κ op,pv , κ op,csp , κ op,ees Respectively represent the operation and maintenance costs per unit power output of wind turbines, photovoltaic and solar thermal equipment;

[0091] C pun,t It represents the penalty cost for unabsorbed wind and solar energy;

[0092] λ e,pun It represents the penalty electricity price for curtailing wind and solar power;

[0093] P wt,max and P pv,max Represent the maximum output values ​​of wind power and photovoltaic power respectively.

[0094] Optionally, where:

[0095] Build a model for the electric vehicle aggregator, including:

[0096] Generate the dynamically adjusted objective function of the EV aggregator:

[0097]

[0098] Among them, C eva,t represents the electricity cost of the electric vehicle aggregator in period t.

[0099] Optionally, where:

[0100] Building a model for the electric vehicle aggregator also includes:

[0101] The relationship model between the service fee subsidy electricity price and the user response rate is established based on Stevens' law:

[0102]

[0103] in,

[0104] K j represents the response constant of the j-th type of electric vehicle user;

[0105] and They represent the initial subsidized electricity price and the maximum subsidized electricity price for the j-th type of electric vehicle user respectively;

[0106] θ is an exponential and is always greater than 1;

[0107] Subsidized electricity price The corresponding response rate of the electric vehicle users in the jth category;

[0108] η j,max represents the maximum response rate of the electric vehicle users in the jth category.

[0109] Optionally, where:

[0110] A model is established for the user aggregator, including:

[0111] Calculate the electricity purchase cost C of the user aggregator ua :

[0112]

[0113] in, Represents the user's electricity load in the tth period.

[0114] Optionally, where:

[0115] The master-slave game model G of the regional integrated energy system is:

[0116]

[0117] Among them, N represents the game participants; and They represent the strategy of the leader integrated energy system operator, the strategy of the follower energy supplier-charging station operator alliance, and the strategy of the follower electric vehicle aggregator, respectively; F ieso 、F es-cso and F eva They represent the benefits of the integrated energy system operator, the benefits of the energy supplier-charging station operator alliance, and the benefits of the electric vehicle aggregator, respectively.

[0118] In the second aspect, based on the same inventive concept, the present disclosure also provides a coordinated dispatching system that takes vehicles, stations, and energy into consideration, and uses the coordinated dispatching method that takes vehicles, stations, and energy into consideration as described in the first aspect to dispatch the power system.

[0119] Compared with the prior art, the technical solution provided by the embodiments of the present disclosure has the following advantages:

[0120] The present disclosure provides a coordinated dispatching method and system considering vehicles, stations, and energy, wherein the coordinated dispatching method includes: obtaining statistical data, and establishing a mathematical model corresponding to the electric vehicle charging load based on the statistical data; establishing a regional integrated energy system master-slave game model with the integrated energy system operator as the leader, and energy suppliers, charging station operators, and electric vehicle aggregators as followers; using genetic algorithm initialization, updating the purchase price of electricity and slow charging price of the integrated energy system operator, solving the regional integrated energy system master-slave game model, and energy suppliers and electric vehicle aggregators responding to dynamic electricity prices to optimize their own power supply and power consumption. The coordinated dispatching method considering vehicles, stations, and energy provided by the present disclosure takes into account the interests of various subjects in the regional integrated energy system, is conducive to effectively improving the absorption rate of new energy, balancing the interests of all parties in the integrated energy system, and is conducive to giving full play to the dispatching potential of electric vehicles in the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0121] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0122] 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, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0123] Figure 1 The figure shows a flow chart of a coordinated scheduling method considering vehicles, stations and energy provided by an embodiment of the present disclosure;

[0124] Figure 2 The figure shows a schematic diagram of the architecture of a coordinated scheduling method considering vehicles, stations and energy provided by an embodiment of the present disclosure;

[0125] Figure 3 The figure shows a schematic diagram of the proportion of each interval of the initial charging state of charge of a slow-charging electric vehicle provided by an embodiment of the present disclosure;

[0126] Figure 4 The figure shows a probability distribution curve diagram of the state of charge of a slow-charging electric vehicle provided by an embodiment of the present disclosure;

[0127] Figure 5 The figure shows a schematic diagram of the proportion of slow charging demand of various types of electric vehicles in different time periods provided by an embodiment of the present disclosure;

[0128] Figure 6 The figure shows a schematic diagram of the number of various types of electric vehicles in different time periods provided by an embodiment of the present disclosure;

[0129] Figure 7 Shown is a schematic diagram of electric energy variation intervals under three types of electric vehicle charging modes provided by an embodiment of the present disclosure;

[0130] Figure 8 Shown is a curve diagram of the response rate of a variable power charging electric vehicle and a flexible scheduling electric vehicle as a function of the subsidized electricity price provided by an embodiment of the present disclosure;

[0131] Fig. 9 The figure shows a flow chart of solving a master-slave game model of a regional integrated energy system provided by an embodiment of the present disclosure;

[0132] Fig.10 Shown is a total electric load, wind power and photovoltaic output prediction curve provided by an embodiment of the present disclosure;

[0133] Fig.11 The figure shows a schematic diagram of the dispatching of a regional integrated energy system in a fourth strategy provided by an embodiment of the present disclosure;

[0134] Fig.12 The figure shows a schematic diagram of the comparison between the output of new energy equipment and the electric load value in a fourth strategy provided by an embodiment of the present disclosure;

[0135] Fig.13 The figure shows a schematic diagram of electricity purchase price for an integrated energy system operator provided by an embodiment of the present disclosure;

[0136] Fig.14 The figure shows a schematic diagram of the number of electric vehicles of various types in each time period in a fourth strategy provided by an embodiment of the present disclosure;

[0137] Fig.15 The figure shows a schematic diagram of charging and discharging power of various types of electric vehicles in various time periods in a fourth strategy provided by an embodiment of the present disclosure;

[0138] Fig.16 The figure shows a schematic diagram of a dynamic service fee subsidy electricity price for electric vehicles in a fourth strategy provided by an embodiment of the present disclosure;

[0139] Fig.17 The figure shows a schematic diagram of a dynamic slow charging price for an electric vehicle in a fourth strategy provided by an embodiment of the present disclosure;

[0140] Fig.18The figure shows a schematic diagram comparing the electric load after optimization with that before optimization according to different strategies provided by an embodiment of the present disclosure;

[0141] Fig.19 Shown is a schematic diagram of recording the charging and discharging process of three types of electric vehicles in a fourth strategy provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0142] In order to more clearly understand the above-mentioned objectives, features and advantages of the present disclosure, the scheme of the present disclosure will be further described below. It should be noted that the embodiments of the present disclosure and the features in the embodiments can be combined with each other without conflict.

[0143] In the following description, many specific details are set forth to facilitate a full understanding of the present disclosure, but the present disclosure may also be implemented in other ways different from those described herein; it is obvious that the embodiments in the specification are only part of the embodiments of the present disclosure, rather than all of the embodiments.

[0144] Figure 1 The figure is a flow chart of a coordinated scheduling method considering vehicles, stations and energy provided by an embodiment of the present disclosure. Please refer to Figure 1 The present disclosure provides a coordinated scheduling method considering vehicles, stations, and energy, including:

[0145] Step S01, obtaining statistical data, and establishing a mathematical model corresponding to the electric vehicle charging load according to the statistical data;

[0146] Step S02: Establish a regional integrated energy system master-slave game model with the integrated energy system operator as the leader and energy suppliers, charging station operators and electric vehicle aggregators as followers;

[0147] Step S03: Initialize using genetic algorithm, update the electricity purchase price and slow charging price of the integrated energy system operator, solve the master-slave game model of the regional integrated energy system, and energy suppliers and electric vehicle aggregators respond to dynamic electricity prices to optimize their own power supply and power consumption.

[0148] It should be noted that the Regional Integrated Energy System (RIES), as an innovative energy management model, provides ideas for reducing the burden on the power grid by integrating regional distributed energy, loads, energy storage, electric vehicles and other resources. The regional integrated energy system includes multiple entities such as the Integrated Energy System Operator (IESO), Energy Supplier (ES), Charging Station Operators (CSO) and Electric Vehicle Aggregator (EVA). This disclosure regards the transaction process between the various entities in the regional integrated energy system as a game. In order to improve the utilization rate of new energy in the regional integrated energy system and coordinate the interests of various power generation, storage, sales and power consumption entities, a coordinated scheduling method considering vehicles, stations and energy is proposed.

[0149] Specifically, the collaborative scheduling method considering vehicles, stations, and energy provided by the present disclosure includes but is not limited to steps S01 to S03.

[0150] In step S01, statistical data is obtained, and a mathematical model corresponding to the electric vehicle charging load is established based on the statistical data to characterize the initial state of charge of the electric vehicle, the number at each moment, and the charging and discharging process of each type of electric vehicle.

[0151] Figure 2 The figure shows a schematic diagram of the architecture of a coordinated scheduling method considering vehicles, stations and energy provided by an embodiment of the present disclosure. Please refer to Figure 1 and Figure 2In step S02, a regional integrated energy system master-slave game model is established with the integrated energy system operator IESO as the leader, the energy supplier ES, the charging station operator CSO, and the electric vehicle aggregator EVA as followers, and the transaction process between the various subjects in the regional integrated energy system is regarded as a game. On the one hand, the integrated energy system operator IESO sets the purchase price of energy supplier ES and the charging price of electric vehicle aggregator EVA based on load demand and with the goal of maximizing its own interests. It sets dynamic electricity prices on the basis of ensuring that the purchase price is lower than the time-of-use electricity price of the power grid to ensure its maximum benefits. In the process of energy trading, the integrated energy system operator IESO needs to bear the risks brought by price fluctuations and imbalance between supply and demand. On the other hand, as the power generation subject and electricity consumption subject of the integrated energy system, the energy supplier ES and the electric vehicle aggregator EVA respectively optimize their own power supply and power consumption by responding to the dynamic electricity prices set by the integrated energy system operator IESO, and flexibly adjust the energy supply and energy consumption plans, thereby ensuring the maximization of their own benefits. The integrated energy system operator IESO forms a one-master-many-slave game relationship with the energy supplier ES, the charging station operator CSO and the electric vehicle aggregator EVA, in which the integrated energy system operator IESO is the leader, and the energy supplier ES, the charging station operator CSO and the electric vehicle aggregator EVA are followers. The present disclosure provides an optional implementation method in which the energy supplier ES and the charging station operator CSO form an energy supplier-charging station operator alliance ES-CSO, and there is also an interactive relationship between the energy supplier-charging station operator alliance ES-CSO and the electric vehicle aggregator EVA. The energy supplier-charging station operator alliance ES-CSO optimizes the output value of new energy based on the maximum new energy consumption and the maximum charging station revenue as the optimization goal to formulate a dynamic service fee and pass it to the lower layer. The charging station operator CSO gives electric vehicle EV owners full autonomy to choose, and electric vehicle EV owners comprehensively consider the service fee and their own wishes to determine the electric vehicle EV response type.

[0152] Please continue to refer to Figure 1 and Figure 2 In step S03, the master-slave game model of the regional integrated energy system is initialized using a genetic algorithm to update the electricity purchase price and slow charging price of the integrated energy system operator IESO; the master-slave game model of the regional integrated energy system is solved, and the energy supplier ES and the electric vehicle aggregator EVA respond to dynamic electricity prices to optimize their own power supply and power consumption.

[0153] The coordinated dispatching method provided by the present invention, which takes into account vehicles, stations, and energy, takes into account the interests of various entities in the regional integrated energy system, is conducive to effectively improving the new energy consumption rate, balancing the interests of all parties in the integrated energy system, and is conducive to giving full play to the dispatching potential of electric vehicles EV in the power system.

[0154] Please continue to refer to Figure 1 and Figure 2 The present disclosure provides an optional implementation mode, in step S01, a mathematical model corresponding to the charging load of an electric vehicle EV is established according to statistical data, including:

[0155] Electric vehicles (EVs) are classified according to the owner's demand preferences, including: constant power charging electric vehicles, variable power charging electric vehicles and flexible scheduling electric vehicles;

[0156] For constant power charging of electric vehicles, and The charging constraints for electric vehicles (EVs) are:

[0157]

[0158] Among them, N ev,1 represents the set of electric vehicles charged at constant power; t in,i represents the time when the electric vehicle EV enters the grid; P 1,i (t) represents the charging power of the electric vehicle with constant power charging; Indicates the maximum charging power of electric vehicles EV; SOC i (t) represents the state of charge of the i-th electric vehicle EV at time period t; SOC i,in It indicates the state of charge of the i-th electric vehicle EV when it enters the grid; SOC i,ex represents the desired state of charge of the i-th electric vehicle EV when it is off-grid; η c Indicates the charging efficiency of electric vehicles EV; E i Indicates the battery capacity of an electric vehicle EV.

[0159] For variable power charging electric vehicles, and The charging constraints of electric vehicles EV are:

[0160]

[0161] Among them, N ev,2 represents the set of variable power charging electric vehicles; P 2,i (t) represents the charging power of the electric vehicle with constant power charging; SOC i,th It represents the state of charge of the i-th variable power charging electric vehicle when it can participate in demand response.

[0162] For flexible dispatch of electric vehicles, and The charging constraints of electric vehicles EV are:

[0163]

[0164] Among them, N ev,3 represents the set of flexible dispatching electric vehicles; P 3,i (t) represents the charging power of flexible scheduling electric vehicles; represents the maximum discharge power of the electric vehicle EV; η d Indicates the discharge efficiency of the electric vehicle EV.

[0165] State of charge (SOC) of electric vehicles (EV) when they are off-grid i,dep The constraints are:

[0166] SOC i,ex ≤SOC i,dep ≤1(4).

[0167] Electric vehicle EV online time t on,i The constraints are:

[0168]

[0169] Among them, t onmin,i Indicates the shortest time that an electric vehicle EV stays online.

[0170] The constraints for the charging duration of electric vehicles EV are:

[0171] t sta,i +t on,min,i ≤t ran,i ≤T(6),

[0172] Among them, t sta,i Indicates the charging start time of the electric vehicle EV; t ran,i represents the random residence time of the electric vehicle EV; T represents the total number of time periods.

[0173] Specifically, in step S01, statistical data needs to be obtained. In practical applications, the actual situation needs to be considered. Figure 3 The figure shows a schematic diagram of the proportion of each interval of the initial charging state of charge of a slow-charging electric vehicle provided by an embodiment of the present disclosure. Figure 4 The figure shows a probability distribution curve of the state of charge of a slow-charging electric vehicle provided by the embodiment of the present disclosure. Please refer to Figure 1 to Figure 4 The present disclosure provides an optional implementation method, which is to obtain the initial charge state of private electric vehicles (EVs) in my country during slow charging in a certain year according to statistical literature. Figure 3 As shown, based on the statistical results, a probability density function is selected to describe the initial charging state of charge f soc (x), as shown in formula (7), its probability distribution curve can be found in Figure 4 .

[0174]

[0175] Among them, μ soc =38.78; δ soc =20; x is the state of charge in the interval [0,100]. Figure 5 The figure shows a schematic diagram of the proportion of slow charging demand of various types of electric vehicles in different time periods provided by an embodiment of the present disclosure. Figure 6 The figure shows a schematic diagram of the number of various types of electric vehicles in different time periods provided by the embodiment of the present disclosure. Please refer to Figure 1 to Figure 6 The disclosed embodiment uses real statistical results instead of probability density functions to describe the proportion of the starting charging time of each type of electric vehicle EV. For specific proportions of the starting time, please refer to Figure 5 Considering the traffic conditions of two different charging stations at different time periods, the number of slow-charging EVs of different types at different time periods is set. For the number of EVs of different types at different time periods, please refer to Figure 6 .

[0176] The present disclosure establishes a mathematical model corresponding to the charging load of electric vehicles (EVs) based on statistical data. The three elements of charging load modeling are the starting charging time, charging power, and charging duration. The starting charging time has been obtained based on actual data. Based on actual conditions, the present disclosure sets the maximum slow charging power of the i-th electric vehicle EV to At the same time, slow-charging electric vehicles EV are divided into three categories according to the owner's demand preferences: constant power charging electric vehicles, variable power charging electric vehicles, and flexible scheduling electric vehicles. Assume that the remaining power in the i-th electric vehicle EV when it is connected to the charging station is E sta,i , until the state of charge reaches the desired charge E ex,i Just leave the charging station, during this process, the charging start time is t sta,i , the charging end time is t dep,i , the latest charging time is t last,i Constant power charging: The charging power of electric vehicles is kept at the rated power from the time they are connected to the charging station, that is, the maximum charging power of slow charging. Variable power charging The charging power of electric vehicles is maintained according to the needs of the power grid. In the interval, when the state of charge reaches the dispatchable threshold E cdthr,i When the state of charge reaches the dispatchable threshold E cdthr,i When the power is reduced and discharged to the grid, the discharge power is The specific parameter settings are shown in Table 1.

[0177] Table 1 Basic parameters of electric vehicles

[0178]

[0179]

[0180] The three different types of charging vehicles represent three different charging needs: the constant power charging method means that the car owner wants to minimize the loss of the charging battery and the shortest charging time; the variable power charging method means that the car owner wants to earn a certain response subsidy without excessively depleting the battery life; the flexible scheduling method means that the user expects to obtain the highest subsidy income to reduce the charging cost. Figure 7 The figure shows a schematic diagram of the energy variation range under three types of electric vehicle charging modes provided by the embodiment of the present disclosure. Please refer to Figure 7 , Figure 7 The figure shows the charging and discharging operation ranges of three types of electric vehicles (EV). During the same time period, vehicles coming to each charging station can decide which type of charging mode the vehicle belongs to by responding to the service fee set by the charging station. The number set N of three types of electric vehicles (EV) is determined based on the dynamic service fee. ev,1 、N ev,2 、N ev,3 After that, i is used to represent each electric vehicle EV, and the specific modeling is shown in formulas (1) to (6).

[0181] It should be noted that for the EV charging duration constraint, the present disclosure uses one day, i.e. 24 hours, as the total time scale. On the basis that the EV meets the minimum access time, a random stay time is set to calculate the total access time. The random access time satisfies formula (6). For vehicles connected to the grid in the first 19 hours, their stay time satisfies formula (8):

[0182] t on,i =t ran,i -t sta,i -t on，min,i (8);

[0183] For vehicles connected to the grid between 20:00 and 24:00, in order to reduce the complexity of the model, the grid access characteristics of these electric vehicles EVs are uniformly considered to continue until 5:00 the next day.

[0184] The embodiment of the present disclosure fully considers the demand preferences of car owners when establishing the mathematical model corresponding to the charging load of electric vehicles (EV), which is conducive to effectively stimulating the enthusiasm of electric vehicle (EV) users to participate in demand response and achieve optimization of resource allocation.

[0185] Please refer to Figure 1 and Figure 2 The present disclosure provides an optional implementation mode, in step S02, a regional integrated energy system master-slave game model is established with the integrated energy system operator IESO as the leader, the energy supplier ES, the charging station operator CSO and the electric vehicle aggregator EVA as followers, including:

[0186] Models are established for the integrated energy system operator IESO, the energy supplier-charging station operator alliance ES-CSO, the electric vehicle aggregator EVA and the user aggregator UA respectively.

[0187] Specifically, in step S02, a master-slave game model of a regional integrated energy system is established. In this embodiment, models are established for the integrated energy system operator IESO, the energy supplier-charging station operator ES-CSO, the electric vehicle aggregator EVA, and the user aggregator UA. In this way, by coordinating the output value of the energy supplier ES's new energy equipment in each time period and combining the charging and discharging power of the electric vehicle EV in each time period, it is beneficial to improve the absorption rate of wind and solar new energy. As a demand-side resource, the electric vehicle EV is beneficial to balance the supply and demand relationship and reduce the phenomenon of wind and solar power abandonment by charging during the peak period of wind and solar power output and discharging during the off-peak period.

[0188] For further information, please refer to Figure 1 and Figure 2 In an optional embodiment of the present disclosure, a model is established for an integrated energy system operator IESO, including:

[0189] Calculate the net profit of the integrated energy system operator IESO and obtain its maximum profit:

[0190]

[0191] in,

[0192]

[0193]

[0194] In the formula, F IESO represents the net income of the integrated energy system operator IESO; represents the revenue from selling energy to the user aggregator UA in period t; represents the revenue from selling energy to the electric vehicle aggregator EVA in period t; and They represent the total power charged to the constant power charging electric vehicle and the variable power charging electric vehicle in the t period respectively; Indicates the total charging and discharging power of electric vehicles in the t period to be dispatched flexibly; C b,t represents the cost of purchasing energy from energy suppliers in period t; P wt,t , P pv,t and P csp,t Respectively represent the optimized output power of wind turbine, photovoltaic and solar thermal equipment in the tth period of a day; η csp,e Indicates the efficiency of converting the output of solar thermal equipment into electrical energy; Eess,t Represents the amount of energy stored in the energy storage device during the t period; η ess Indicates the charge and discharge loss rate of energy storage equipment; They represent the charging power and discharging power of the energy storage device in period t respectively; and They represent the electricity purchase price and slow charging price for the tth period of a day set by the integrated energy system operator; represents the power selling price of the power grid in the tth period of a day; They respectively represent the electricity load value of the user aggregator in the tth period of a day.

[0195] Specifically, the integrated energy system operator IESO sets the electricity purchase price and the slow charging price for electric vehicles EV according to the load demand, the charging power of the electric vehicle aggregator EVA, and the output plan fed back by the energy supplier-charging station operator alliance EC-CSO. At the same time, when the supply and demand within the system are not cleared, the optimization goal is to maximize the profit from the perspective of the grid participation. IESO represents the net profit of the integrated energy system operator IESO, then the maximum profit of the integrated energy system operator IESO is maxF IESO , calculated by formula (9), where the revenue from selling energy to the user aggregator UA in period t is The revenue from selling energy to the electric vehicle aggregator EVA in period t and the cost C of purchasing energy from the energy supplier ES in period t b,t Calculate using formulas (10) to (12) respectively. In order to avoid direct transactions between the energy supplier-charging station operator alliance ES-CSO and the electric vehicle aggregator EVA and the power grid, the buying price of the integrated energy system operator IESO should be slightly higher than the duration and the selling price should be slightly lower than the duration price, which needs to meet the constraints of formula (13).

[0196] Please continue to refer to Figure 1 and Figure 2 In an optional implementation of the present disclosure, a model is established for an energy supplier-charging station operator alliance ES-CSO, including:

[0197] The energy storage device stores electricity E ess,t , expressed in mathematical formula as:

[0198]

[0199] Among them, E ess,t-1 Indicates the amount of energy stored in the energy storage device during period t-1;

[0200] Generate the objective function for charging station operators to dynamically adjust service fees based on the output value of energy suppliers:

[0201]

[0202] in,

[0203] F s,t =C b,t (16)

[0204]

[0205] C pun,t =λ e,pun ·(P wt,max -P wt,t +P pv,max -P pv,t )(19),

[0206] Where:

[0207] F es-cso represents the total revenue of the ES-CSO alliance in 24 hours a day; F s,t represents the energy supplier's revenue from energy sales in period t; represents the service fee income of the charging station operator in period t; C op,t represents the operation and maintenance cost of the equipment in ES during period t; represents the number of electric vehicles of each type, and j represents the type of electric vehicles; represents the basic value of service fee for the jth electric vehicle under different strategies; represents the subsidized electricity price for the jth type of electric vehicle in the tth period of a day; κ op,wt , κ op,pv , κ op,csp , κ op,ees Respectively represent the operation and maintenance costs of wind turbines, photovoltaic and solar thermal equipment per unit power output; C pun,t represents the penalty cost for unabsorbed wind and solar energy; e,pun P represents the penalty electricity price for wind and solar power abandonment; wt,max and P pv,max Represent the maximum output values ​​of wind power and photovoltaic power respectively.

[0208] Specifically, the energy supplier ES contains wind power, photovoltaic, solar thermal and energy storage equipment. Since the heat storage characteristics of solar thermal ensure the energy utilization rate, the penalty cost is only for the wind and photovoltaic equipment abandonment phenomenon. On the energy supply side, based on the power purchase price given by the integrated energy system operator IESO, the output value of the new energy equipment within the energy supplier ES is optimized with the goal of maximizing profits. The charging station corresponding to the charging station operator CSO can be considered to be invested and built by the energy supplier ES. The two together form an interest alliance. The charging station operator CSO must meet the minimum penalty cost of new energy in the energy supplier ES while maximizing its own interests. Based on the quotation of the integrated energy system operator IESO, the energy supplier ES optimizes the output of each device in each time period to obtain the optimal profit. At the same time. The charging station operator CSO dynamically adjusts the service fee according to the output value of the energy supplier ES. Its objective function is formula (15). In formula (15), the energy supplier ES energy sales revenue F in the tth period s,t Please refer to formula (16) for the calculation method; the service fee income of the charging station operator CSO in period t Please refer to formula (17) for the calculation method; The operation and maintenance cost C of the equipment in the energy supplier ES in the t period op,t Please refer to formula (18) for the calculation method; the penalty cost C for unabsorbed wind and solar energy pun,t Please refer to formula (19) for the calculation method. represents the number of electric vehicles EV of each category, j represents the category of electric vehicles EV, and the ratio of the number of electric vehicles EV of the jth category to the total number of electric vehicles EV changes with the change of the dynamic service fee and satisfies Stevens' law, and the maximum ratio is 0.35.

[0209] In an optional embodiment of the present disclosure, in order to compare the operating results of the integrated energy system in different scenarios, four strategies are set respectively, Strategy 1 includes: each operator and aggregator in the regional integrated energy system responds to the time-of-use pricing of the power grid; Strategy 2 includes: when the service fee adjustment capability is not considered, each operator and aggregator in the regional integrated energy system responds to the dynamic electricity price based on the master-slave game; Strategy 3 includes: on the basis of Strategy 2, the electric vehicle aggregator EVA responds to the dynamic charging electricity price and service fee without considering Stevens' law; Strategy 4 includes: on the basis of Strategy 2, the electric vehicle aggregator EVA responds to the dynamic charging electricity price, and all electric vehicles EV respond to the service fee based on Stevens' law. Basic value of service fee for the jth electric vehicle EV under Strategy n It can be selected by formula (20).

[0210]

[0211] It should be noted that when building a model for the energy supplier-charging station operator alliance ES-CSO, it is also necessary to consider the constraints that each power generation and heating equipment needs to meet. Each device within the energy supplier ES needs to meet the following constraints:

[0212] 1) New energy equipment operation constraints:

[0213]

[0214] 2) Energy storage equipment operation constraints:

[0215]

[0216] 3) Constraints on starting and stopping solar thermal equipment:

[0217]

[0218] 4) Electric power balance constraints:

[0219]

[0220] They are the charging and discharging state variables of the energy storage device during period t, Otherwise it is discharge; n max P is the maximum number of charge and discharge times throughout the day; ees,min , P ees,max are the minimum and maximum values ​​of the charging (or discharging) power of the energy storage device; E ees,min 、E ees,max E are the minimum and maximum storage capacity of the energy storage device respectively; ees,1 、E ees,T are the storage capacity of the energy storage device at the initial and final moments of the charging (or discharging) cycle; D is the start / stop state indicator variable; σ csp is a binary variable, indicating the start and stop status of the CSP equipment; R is the start range variable; T on is the minimum continuous start time; P csp,max , P csp,t-1 are the maximum output value of the CSP station and the output value of the period t-1, P csp,down , P csp,up It is the maximum downward and upward climbing power of the CSP equipment.

[0221] Please continue to refer to Figure 1 and Figure 2 In an optional embodiment of the present disclosure, a model is established for an electric vehicle aggregator EVA, including:

[0222] Generate the objective function for the dynamic adjustment of the electric vehicle aggregator EVA:

[0223]

[0224] Among them, C eva,t represents the electricity cost of the electric vehicle aggregator EVA in period t.

[0225] Specifically, the present disclosure mainly conducts research on slow-charging electric vehicles (EV). The electric vehicle aggregator EVA optimizes the number of variable-power charging electric vehicles and flexible-dispatch electric vehicles and the charging and discharging power based on the charging price and service fee given by the charging station operator CSO and the charging preferences of electric vehicle EV owners. The present disclosure sets a subsidized electricity price for variable-power charging electric vehicles and flexible-dispatch electric vehicles. Exemplarily, the highest subsidized electricity price for variable-power charging electric vehicles and flexible-dispatch electric vehicles is 0.2 yuan / kWh and 0.3 yuan / kWh, and the lowest is 0.05 yuan / kWh. In particular, the present disclosure sets the subsidized electricity price for flexible-dispatch electric vehicles to feed electricity to the grid as That is, the EV discharge subsidy is consistent with the charging electricity price. Considering that the charging cost and service fee paid by the EV aggregator EVA are minimized, the objective function of the EV aggregator EVA's dynamic adjustment can be found in formula (25).

[0226] Please continue to refer to Figure 1 and Figure 2 , in an optional embodiment of the present disclosure,

[0227] Modeling for electric vehicle aggregators EVA also includes:

[0228] The relationship model between the service fee subsidy electricity price and the user response rate is established based on Stevens' law:

[0229]

[0230] in,

[0231] K j represents the response constant of the j-th type of electric vehicle EV user; and They represent the initial subsidized electricity price and the maximum subsidized electricity price for the jth type of electric vehicle EV user respectively; θ>1 is an index; Subsidized electricity price The corresponding response rate of the j-th electric vehicle EV user; η j,max represents the maximum response rate of the j-th electric vehicle EV user.

[0232] Specifically, the present disclosure establishes a relationship model between the service fee subsidy electricity price and the user response rate based on Stevens' law. It should be noted that Stevens' law holds that the magnitude of the sensation is proportional to the square of the stimulus, that is, the psychological quantity is a power function of the physical quantity. Based on Stevens' law, a response rate model of owners of various types of electric vehicles (EVs) is constructed, please refer to formula (26). Figure 8 The figure shows a curve diagram of the response rate of a variable power charging electric vehicle and a flexible dispatching electric vehicle as a function of the subsidized electricity price provided by an embodiment of the present disclosure. Please refer to Figure 8 , Figure 8 The relationship curve between the response rate of each type of electric vehicle EV owner and the subsidized electricity price is shown in Figure 1. The second type of electric vehicle represents variable power charging electric vehicles, the third type of electric vehicle represents flexible scheduling electric vehicles, and the third type of electric vehicle represents flexible scheduling electric vehicles. Figure 8 It can be seen that the maximum subsidized electricity prices for variable power charging electric vehicles and flexible dispatch electric vehicles are 0.2 yuan / kWh and 0.3 yuan / kWh respectively. It should be noted that this disclosure is only described by this example and is not limited to this.

[0233] Please refer to Figure 1 and Figure 2 In an optional implementation of the present disclosure, establishing a master-slave game model for a regional integrated energy system further includes establishing a model for a user aggregator UA, and establishing a model for a user aggregator UA includes:

[0234] Calculate the electricity purchase cost C of the user aggregator UA ua :

[0235]

[0236] in, Represents the user's electricity load in the tth period.

[0237] Specifically, considering the low power level of community users, large-scale direct participation will increase the pressure on regional power grid power supply. This paper aggregates small and medium-sized users through user aggregators UA to represent decentralized users in market transactions. In order to better compare the demand response potential of electric vehicles EV under different strategies, this paper does not consider the dispatchability of user power loads. Please refer to formula (27) to calculate the power purchase cost of user aggregators UA.

[0238] The coordinated scheduling method considering vehicles, stations and energy provided by the present disclosure fully considers the interests of various subjects in the regional integrated energy system. The interactive variables between the integrated energy system operator IESO and the lower-level subjects (energy supplier-charging station operator alliance ES-CSO, electric vehicle aggregator EVA and user aggregator UA) are the purchase price of electricity, slow charging price, purchase and sale of electricity and electric vehicle EV charging electricity. When the price set by the integrated energy system operator IESO is too high or too low, the lower-level operators and aggregators dynamically adjust their own purchase and sale of electricity, thereby indirectly affecting the income of the integrated energy system operator IESO; conversely, the integrated energy system operator IESO will also re-formulate the pricing strategy according to the purchase and sale of electricity of each subject until the optimal pricing strategy is found. In the transaction process, there is a conflict of benefits between the upper layer (integrated energy system operator IESO) and the lower layer (energy supplier-charging station operator alliance ES-CSO, electric vehicle aggregator EVA), and the decision-making of both parties has a sequence, so it can be regarded as satisfying the Stackelberg game model of one master and multiple slaves. The present disclosure provides an optional implementation method: the master-slave game model G of the regional integrated energy system is expressed as:

[0239]

[0240] Among them, N represents the game participants; and They represent the strategy of the leader Integrated Energy System Operator IESO, the strategy of the follower Energy Supplier-Charging Station Operator Alliance ES-CSO, and the strategy of the follower Electric Vehicle Aggregator EVA; F ieso 、F es-cso and F eva They represent the revenue of the integrated energy system operator IESO, the revenue of the energy supplier-charging station operator alliance ES-CSO, and the revenue of the electric vehicle aggregator EVA respectively.

[0241] It should be noted that the Stackelberg game model, also known as the Stackelberg model, is a game model used in economics to analyze the competitive relationship between leaders and followers in the market. When the leader accepts the optimal response feedback from all followers and no longer changes its own strategy, the game reaches the Stackelberg equilibrium. For the regional integrated energy system master-slave game model proposed in this disclosure, the equilibrium solution must satisfy:

[0242]

[0243] in, and They represent the optimal strategy of the integrated energy system operator IESO, the optimal strategy of the energy supplier-charging station operator alliance ES-CSO, and the optimal strategy of the electric vehicle aggregator EVA respectively.

[0244] For the Stackelberg game model, the following three conditions must be met to prove that its equilibrium solution exists and is unique: first, the strategy spaces of the leader and the follower are both non-empty compact convex sets; second, for a given leader strategy set, the optimal solution of all followers exists and is unique; third, for a given follower strategy set, the leader's optimal solution exists and is unique. The reasons why the equilibrium solution of the master-slave game model of the regional integrated energy system provided by the present disclosure exists and is unique are as follows:

[0245] (1) According to the Stackelberg model, the optimal strategy of the leader integrated energy system operator IESO must satisfy equations (9) to (13), the optimal strategy of the follower energy supplier-charging station operator alliance ES-CSO must satisfy equations (16) to (19), and the optimal strategy of the follower electric vehicle aggregator EVA must satisfy equation (25). Therefore, the strategy set of each participant is a non-empty compact convex set.

[0246] (2) First, the objective function of the integrated energy system operator IESO is calculated with respect to P wt,t , P pv,t , P csp,t The first-order partial derivative and setting it equal to 0 gives:

[0247]

[0248] Secondly, the objective function of the electric vehicle aggregator EVA is calculated The first-order partial derivative of and setting its first-order partial derivative equal to 0 can be obtained:

[0249]

[0250] From formula (31), it can be seen that when the purchase price of electricity changes, the output of new energy equipment and the income of the energy supplier-charging station operator alliance ES-CSO, the dispatch value of the three types of electric vehicles EV and the income of the electric vehicle aggregator EVA are linearly related. Therefore, no matter what the value is, when the purchase price of the integrated energy system operator IESO is given, the income of the energy supplier-charging station operator alliance ES-CSO and the electric vehicle aggregator EVA has a unique optimal solution. The optimized strategy of the follower is

[0251] (3) Optimizing the follower's strategy Substitute into the objective function of the leader integrated energy system operator IESO, and get the objective function about The value of is unique. Therefore, when the strategy set of the follower is given, the optimal solution of the leader exists and is unique.

[0252] Genetic algorithm is used for initialization, and the electricity purchase price and slow charging price of the integrated energy system operator IESO are updated. The master-slave game model of the regional integrated energy system is solved. The energy supplier ES and the electric vehicle aggregator EVA respond to dynamic electricity prices and optimize their own power supply and power consumption. Fig. 9 FIG. 1 is a flow chart of solving a master-slave game model of a regional integrated energy system provided by an embodiment of the present disclosure. Please refer to Fig. 9 , the present disclosure provides an optional implementation method of using the CPLEX solver to solve the problem. For the specific solution process, please refer to Fig. 9 .

[0253] Please refer to Figure 1 and Figure 2 , With respect to the coordinated dispatching method considering vehicles, stations, and energy provided by the present disclosure, the present disclosure provides an optional implementation method to establish a master-slave game model of a regional integrated energy system, with reference to a certain urban area, which includes commercial buildings, community residential buildings, work area charging stations, living area charging stations and other electrical loads. Assuming that a day can be divided into T = 24 time periods, the equipment supplying power to the area includes: wind turbines, photovoltaic power stations, and solar thermal power stations; energy storage equipment includes small energy storage power stations. The output prediction values ​​of wind turbines and photovoltaic power stations are taken as the maximum output values ​​of wind turbines and photovoltaic equipment in this article. The total electrical load includes two parts: the load of electric vehicles EVs in a disordered charging state and the electrical load of users. Fig.10 The figure shows a total electric load, wind power and photovoltaic output prediction curve provided by the embodiment of the present disclosure. Fig.10 The regional time-of-use energy prices are shown in Table 2, the basic parameters of each device in the regional integrated energy system are shown in Table 3, and the parameter settings of the genetic algorithm are shown in Table 4.

[0254] Table 2 Regional time-of-use energy prices

[0255]

[0256] Table 3 Parameters of each unit of RIES

[0257] equipment Operation and maintenance costs Maximum Power Minimum output efficiency Fan WT 0.07 27881.3 1280.8 -- Photovoltaic 0.07 21384 0 -- CSP 0.06 18000 0 0.4 Energy storage power station 0.05 3500 0 0.95

[0258] Table 4 Parameter settings of genetic algorithm

[0259] parameter Numeric Number of individuals 168 Chromosome length 40 Scaling factor F 0.5 Crossover factor Cr 0.9

[0260] In order to compare the operation results of the integrated energy system under different scenarios, four strategies are set respectively. Strategy 1 includes: each operator and aggregator in the regional integrated energy system responds to the time-of-use pricing of the power grid; Strategy 2 includes: when the service fee adjustment ability is not considered, each operator and aggregator in the regional integrated energy system responds to the dynamic electricity price based on the master-slave game; Strategy 3 includes: on the basis of Strategy 2, the electric vehicle aggregator EVA responds to the dynamic charging electricity price and service fee without considering Stevens' Law; Strategy 4 includes: on the basis of Strategy 2, the electric vehicle aggregator EVA responds to the dynamic charging electricity price, and all electric vehicles EV respond to the service fee based on Stevens' Law.

[0261] This disclosure introduces the average equivalent load value, the amount of wind and solar power abandoned, the wind and solar power absorption degree, the income of each operator and aggregator, etc. as indicators to measure the advantages and disadvantages of various strategies. Among them, the average equivalent load value mainly reflects the average value of the total load after the electric load is superimposed on the output of new energy within 24 hours a day, reflecting the advantages and disadvantages of the dispatching result. The smaller the value, the better the dispatching result. The formula is expressed as formula (32) to (34). The comparison of various indicators under different strategies is shown in Table 5 (positive numbers in the table represent income and negative numbers represent costs, the same below).

[0262]

[0263]

[0264] Among them, P el is the equivalent load of a certain wind power, photovoltaic power and basic load; is the base load predicted on the day before; is the electric vehicle EV load predicted on the previous day; is the wind power output predicted on the previous day; is the photovoltaic power generation output predicted on the previous day; P av is the average value of equivalent load; is the total charging power of the electric vehicle charged at constant power during period t; is the total charging power of the electric vehicle with variable power charging during period t; The total charging and discharging power of electric vehicles is flexibly scheduled during period t.

[0265] Table 5 Comparison of indicators under different strategies

[0266]

[0267]

[0268] Under Strategy 1, the profits of energy supplier ES and integrated energy system operator IESO are far less than those of other strategies. The low profits of integrated energy system operator IESO are due to the fact that the integrated energy system operator IESO in the region does not formulate dynamic electricity prices, so it does not have the ability to earn the difference between the purchase and sale prices. In Strategy 1, it only earns the penalty fees for abandoning wind and solar power. The low profits of energy supplier ES are due to the fact that energy supplier ES directly participates in electricity market transactions at the grid-connected electricity price. At the same time, since the time-of-use electricity price cannot dynamically adjust the electricity price according to the wind and solar output to match, the system does not have the demand response capability, and the wind and solar consumption is low. Although the solar thermal power station makes up for the shortfall, the integrated energy system purchases electricity at the grid-connected electricity price at all times. Therefore, the profits of energy supplier ES are greatly reduced compared with other strategies.

[0269] Strategy 2 changes the time-of-use electricity price to a dynamic electricity price, which significantly improves the income of the integrated energy system operator IESO, the energy supplier ES and the charging station operator CSO compared with Strategy 1. Although dynamic service fees are not considered under this strategy, considering that individual electric vehicle EV owners still want to reduce charging costs by responding to dynamic slow charging prices, this embodiment sets some electric vehicles EV to respond to variable power charging electric vehicles to match the actual situation. It can be seen from Table 5 that the wind and solar power consumption of Strategy 2 is slightly improved compared with Strategy 1. This is because the second type of electric vehicles EV under Strategy 2 has a certain demand response capability, and the regulation effect is achieved by reducing (increasing) the number of charging electric vehicles EV during peak (trough) loads. Since the charging station operator CSO in Strategy 2 does not need to subsidize the electricity price for service fees to electric vehicle EV owners, the income of the charging station operator CSO is higher than that of strategies 3 and 4, but the interests of electric vehicle EV owners are also greatly sacrificed.

[0270] In strategy 3, since Stevens’ law is not used, the aggregator cannot determine the response amount and can only take the full response approach. At this time, the response ratios of variable power charging electric vehicles and flexible dispatching electric vehicles are both The maximum value is 0.35. The charging station operator CSO has to use more subsidies to reduce the service fee price to ensure full response. Therefore, the charging station operator CSO's income is too low, even lower than the strategy 1 without any dispatching ability. On the contrary, the charging cost of electric vehicle EV owners has dropped significantly. The integrated energy system operator IESO has low income in strategy 3. On the one hand, it is because the income from selling electricity to the load side has decreased. On the other hand, after more electric vehicles EV join the demand response, the wind and solar consumption increases. The energy supplier ES has increased its income from selling electricity to the integrated energy system operator IESO and paid less penalty costs for abandoning wind and solar.

[0271] It is worth noting that due to the use of Stevens' law to set dynamic service fees, the income of charging station operator CSO increased by 7,520 yuan compared with strategy 3. Under this strategy, the optimal wind and solar power consumption is guaranteed with the least variable power charging of electric vehicles and flexible scheduling of electric vehicles, further coordinating the income of integrated energy system operator IESO and energy supplier ES. At the same time, compared with strategy 2, the charging cost of electric vehicle aggregator EVA is reduced by 18,523 yuan, thus ensuring the interests of each subject in the regional integrated energy system. When charging station operator CSO does not adopt Stevens' law, the service fee determined is less than the service fee determined when Stevens' law is adopted, and the increased charging cost of electric vehicle aggregator EVA is within a reasonable range. This result shows that in most cases, the aggregator can effectively coordinate the costs and benefits of both electric vehicle aggregator EVA and charging station operator CSO by adopting Stevens' law, thereby achieving better overall benefits.

[0272] The wind and solar power consumption of strategy three increased by 2.29% and 2.06% respectively compared with strategy one and strategy two, and the wind and solar power consumption of strategy four increased by 2.15% and 1.92% respectively compared with strategy one and strategy two. This is because the latter two strategies have added flexible scheduling of electric vehicles, and their flexible scheduling characteristics make the electric vehicle EV regulation ability stronger than the strategy that only includes constant power charging electric vehicles and variable power charging electric vehicles. The lower the average value of the equivalent load, the better the scheduling effect. From strategy one to strategy three, the average value of the equivalent load decreases successively, indicating that the demand response capability increases successively. Since the variable power charging electric vehicles and flexible scheduling electric vehicles participating in the response of strategy four are less than those of strategy three, correspondingly, its scheduling capability is slightly lower, so the average value of the equivalent load is slightly smaller than that of strategy three.

[0273] The following is an analysis of the scheduling results for Strategy 4:

[0274] Fig.11 The figure shows a schematic diagram of the dispatching of a regional integrated energy system in a fourth strategy provided by an embodiment of the present disclosure. Fig.12 The figure shows a schematic diagram of the comparison between the output of new energy equipment and the electric load value in a fourth strategy provided by an embodiment of the present disclosure. Fig.13 The figure shows a schematic diagram of the electricity purchase price of an integrated energy system operator provided by the embodiment of the present disclosure. Please refer to Figures 11 to 13 For the regional integrated energy system dispatch, please refer to the regional integrated energy system dispatch under Strategy 4, the comparison between the output value of new energy and the load value, and the IESO power purchase price of the integrated energy system operator. Fig.11 , Fig.12 and Fig.13 , comprehensive analysis Fig.11 and Fig.12It can be seen that the maximum output power of wind and solar power is low during the period of 00:00-04:00 and 17:00-20:00. At this time, it is mainly supplied by the CSP station. Due to the climbing power constraint of the CSP station, it cannot provide enough power to the electric load in some periods. The insufficient power is compensated by the output of the energy storage station. In the period of 6:00-15:00, the output of wind and solar power far exceeds the electric load value. In order to ensure the maximum wind and solar power consumption, the energy storage station is charged in large quantities at this time, thereby ensuring the electrical balance of the system.

[0275] For the dispatch of electric vehicles (EV), the electric vehicles (EV) respond to charging with the minimum charging cost as the optimization goal. The present disclosure considers classifying electric vehicles (EV) according to dynamic service fees, but since most of the newly connected electric vehicles (EV) have too low initial charge states and cannot immediately reduce the charging power or participate in the discharge process, the classification results often take effect after 2-5 periods. Fig.14 The figure shows a schematic diagram of the number of electric vehicles of various types in each time period in a fourth strategy provided by an embodiment of the present disclosure. Fig.15 The figure shows a schematic diagram of charging and discharging power of various types of electric vehicles in different time periods in a fourth strategy provided by an embodiment of the present disclosure. Fig.16 The figure shows a schematic diagram of a dynamic service fee subsidy electricity price for electric vehicles in a fourth strategy provided by an embodiment of the present disclosure. Fig.17 The figure shows a schematic diagram of the dynamic slow charging price of electric vehicles in a fourth strategy provided by the embodiment of the present disclosure. Please refer to Figure 14 to Figure 17 , which shows the number, charging and discharging power, dynamic service fee subsidy electricity price, and dynamic slow charging price of each type of electric vehicle EV in each time period in Strategy 4. Comprehensive comparative analysis Figure 14 to Figure 17 , taking the 5:00 period as an example, at this time, the output of wind, solar and solar thermal is far greater than the power load demand, and a large number of electric vehicles EV are required to participate in the charging process. Through the adjustment of service fees, a total of 13 variable power charging electric vehicles and 4 flexible dispatching electric vehicles are connected to the charging station to start charging. At the same time, the variable power charging electric vehicles and flexible dispatching electric vehicles connected to the grid in the previous periods fully participate in the charging process. In the 7th period, due to the high power load demand and relatively low wind and solar output, the dynamic service fee adjustment makes only 3 variable power charging electric vehicles connected to the grid during this period, and the charging load of electric vehicles EV is reduced to a lower level. At the same time, the variable power charging electric vehicles and flexible dispatching electric vehicles connected to the grid in the previous periods reach the dispatchable threshold, which greatly reduces their own charging load and participates in vehicle-grid interaction during this period, so the overall charging load is at a low level. In the 19:00 period, power is also fed to the grid to reduce the second load peak. The peak charging period of electric vehicles EV is, for example, 01:00, 02:00, 15:00-17:00, which are periods when the wind and solar output is large or the power load is at a valley value.

[0276] Fig.18 The figure shows a comparison diagram of the electric load after optimization with that before optimization according to different strategies provided in the embodiment of the present disclosure. Please refer to Fig.18 ,Depend on Fig.18 It can be seen that the electric load curves of strategies one and two are basically the same, proving that the dispatching capacity of strategy two is limited and that the potential of electric vehicle aggregators EVA to participate in demand response has not been fully utilized; the peak-shaving and valley-filling effect of the electric load curve under strategy three is the most obvious, but there is over-regulation in some time periods (such as 4:00 and 16:00); although the dispatching capacity of electric vehicle aggregators EVA under strategy four is lower than that of strategy three, it still guarantees the maximum absorption of wind and solar power and increases the income of charging station operators CSO.

[0277] Fig.19 The figure shows a schematic diagram of recording the charging and discharging process of three types of electric vehicles in a fourth strategy provided by the embodiment of the present disclosure. Please refer to Fig.19 , which reflects the charging and discharging process of various types of electric vehicles EV in strategy 4, Fig.19 (a) corresponds to constant power charging of electric vehicles, Fig.19 (b) Strain power charging electric vehicles, Fig.19 (c) Corresponding to the flexible scheduling of electric vehicles, the horizontal axis represents 24 time periods, and the vertical axis represents the number of electric vehicles EVs in each time period. The green rectangle in the figure represents the charging process, and the red rectangle represents the discharging process. Fig.19It can be concluded that the discharge of flexible dispatching electric vehicles is mainly concentrated in the periods of 3:00, 4:00, 7:00, 9:00, 13:00, 18:00, and 19:00, and the reduction of charging power of variable power charging electric vehicles is mainly concentrated in the periods of 3:00, 4:00, 7:00, 9:00, 13:00, 15:00, 16:00, 17:00, 18:00, 19:00, and 22:00. In the period of 3:00-4:00, the wind and solar power output is insufficient and the solar thermal power station cannot supply enough electricity to RIES. Therefore, variable power charging electric vehicles and flexible dispatching electric vehicles participate in the demand response process in large quantities; in the period of 7:00 and 9:00, the wind and solar power output is relatively sufficient, but variable power charging electric vehicles and flexible dispatching electric vehicles still cut the peak load. This is mainly because the energy storage power station charges a lot in the corresponding period and consumes a lot of power generation. The electric vehicle aggregator has to maximize the reduction of its own load to achieve load balance. During the period of 15:00-19:00, the wind and solar power output is at a low point. In order to match the power generation during this period, the variable power charging electric vehicles reduce their own loads in large quantities in the first three periods, namely 15:00, 16:00, and 17:00, to meet the peak load requirements. Then, during the period of 18:00-19:00, the user's load peak occurs. At this time, the wind and solar power output is at a low point, and the output value of the solar thermal power station cannot fully meet the load shortage. Therefore, the flexible dispatch of electric vehicles in this period cooperates with the variable power charging electric vehicles to start discharging in large quantities, so that the electric load is balanced. The dispatch result reflects the correctness of strategy four.

[0278] The present invention focuses on slow-charging electric vehicles (EV) at charging stations, and establishes a one-master-multiple-slave game optimization model that couples the integrated energy system operator IESO with the energy supplier-charging station operator alliance ES-CSO and the electric vehicle aggregator EVA. With the goal of maximizing the profits of the integrated energy system operator IESO and the energy supplier-charging station operator alliance ES-CSO and minimizing the costs of the electric vehicle aggregator EVA on the electricity consumption side, dynamic charging electricity prices and dynamic service fees are formulated to guide the new energy equipment within the energy supplier ES to optimize its own output and the electric vehicle aggregator EVA to participate in vehicle-grid interaction.

[0279] The dynamic pricing mechanism based on service fee guidance and charging electricity price adjustment proposed in this disclosure fully considers the impact of user preferences and dynamic service fees on user behavior. By optimizing dynamic service fees and subsidized electricity prices, the enthusiasm of electric vehicle EV users to participate in demand response can be effectively stimulated, and resource allocation can be optimized. This disclosure introduces Stevens' law to provide a mechanism for electric vehicle aggregators EVA to flexibly adjust service fees, so that electric vehicle aggregators EVA can minimize charging costs while ensuring user satisfaction, and give full play to the huge potential of electric vehicles EV as demand-side resources in the power market. The one-master-multiple-slave game strategy adopted in this disclosure significantly improves the absorption of wind and solar new energy by coordinating the output value of energy supplier ES new energy equipment in each period and the charging and discharging power of electric vehicles EV in each period. As a demand-side resource, electric vehicles EV effectively balance the supply and demand relationship and reduce the phenomenon of wind and solar abandonment by charging during the peak period of wind and solar output and discharging during the valley period. At the same time, the dynamic electricity price formulated by the integrated energy system operator IESO through strategy four can better reflect the market supply and demand relationship and improve the flexibility and response speed of electricity purchase and sale. This not only enhances the market competitiveness of the integrated energy system operator IESO, but also increases the profits of the energy supplier-charging station operator alliance ES-CSO, while minimizing the charging costs of the electric vehicle aggregator EVA.

[0280] Based on the same inventive concept, the present disclosure also provides a coordinated dispatching system that takes vehicles, stations, and energy into consideration, and uses the coordinated dispatching method that takes vehicles, stations, and energy provided in the embodiments of the present disclosure to dispatch the power system.

[0281] It should be noted that the embodiments of the coordinated scheduling system considering vehicles, stations and energy provided in the present disclosure can refer to the embodiments of the coordinated scheduling method considering vehicles, stations and energy provided in the present disclosure, and will not be repeated here.

[0282] It can be seen from the above embodiments that the coordinated scheduling method and system considering vehicles, stations and energy provided by the present disclosure achieve at least the following beneficial effects:

[0283] The present disclosure provides a coordinated dispatching method and system considering vehicles, stations, and energy, wherein the coordinated dispatching method includes: obtaining statistical data, and establishing a mathematical model corresponding to the electric vehicle charging load based on the statistical data; establishing a regional integrated energy system master-slave game model with the integrated energy system operator as the leader, and energy suppliers, charging station operators, and electric vehicle aggregators as followers; using genetic algorithm initialization, updating the purchase price of electricity and slow charging price of the integrated energy system operator, solving the regional integrated energy system master-slave game model, and energy suppliers and electric vehicle aggregators responding to dynamic electricity prices to optimize their own power supply and power consumption. The coordinated dispatching method considering vehicles, stations, and energy provided by the present disclosure takes into account the interests of various subjects in the regional integrated energy system, is conducive to effectively improving the absorption rate of new energy, balancing the interests of all parties in the integrated energy system, and is conducive to giving full play to the dispatching potential of electric vehicles in the power system.

[0284] It should be noted that, in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0285] The above description is only a specific embodiment of the present disclosure, so that those skilled in the art can understand or implement the present disclosure. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present disclosure. Therefore, the present disclosure will not be limited to the embodiments described herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A coordinated dispatching method considering vehicles, stations and energy, characterized in that: include: Obtaining statistical data, and establishing a mathematical model corresponding to the electric vehicle charging load based on the statistical data; Establish a regional integrated energy system master-slave game model with integrated energy system operators as leaders and energy suppliers, charging station operators and electric vehicle aggregators as followers; Genetic algorithm initialization is used to update the electricity purchase price and slow charging price of the integrated energy system operator, and the master-slave game model of the regional integrated energy system is solved. The energy supplier and the electric vehicle aggregator respond to dynamic electricity prices and optimize their own power supply and power consumption.

2. The coordinated dispatching method considering vehicles, stations and energy according to claim 1 is characterized in that: The mathematical model corresponding to the electric vehicle charging load is established based on the statistical data, including: Classifying the electric vehicles according to the owner's demand preferences, the categories include: constant power charging electric vehicles, variable power charging electric vehicles and flexible scheduling electric vehicles; For the constant power charging electric vehicle, and The electric vehicle charging constraints are: Among them, N ev,1 represents a set of constant power charging electric vehicles; t in,i Indicates the time when the electric vehicle joins the network; P 1,i (t) represents the charging power of the constant power charging electric vehicle; Indicates the maximum charging power of the electric vehicle; SOC i (t) represents the state of charge of the ith electric vehicle in period t; SOC i,in represents the state of charge of the i-th electric vehicle when it enters the grid; SOC i,ex represents the expected state of charge of the i-th electric vehicle when it is off-grid; η c Indicates the charging efficiency of the electric vehicle; E i Indicates the battery capacity of the electric vehicle; For the variable power charging electric vehicle, and The charging constraints of the electric vehicle are: Among them, N ev,2 represents a set of variable power charging electric vehicles; P 2,i (t) represents the charging power of the constant power charging electric vehicle; SOC i,th Indicates the charge state of the i-th variable power charging electric vehicle when it can participate in demand response; For the flexibility in dispatching electric vehicles, and The charging constraints of the electric vehicle are: Among them, N ev,3 represents a set of flexible dispatching electric vehicles; P 3,i (t) represents the charging power of the flexible scheduling electric vehicle; Indicates the maximum discharge power of the electric vehicle; η d represents the discharge efficiency of the electric vehicle; The state of charge SOC of the electric vehicle when it is off-grid i,dep The constraints are: SOC i,ex ≤SOC i,dep ≤1; The electric vehicle is online for a period of time t on,i The constraints are: Among them, t onmin,i Indicates the shortest online time of the electric vehicle; The constraints of the electric vehicle charging duration are: t sta,i +t on,min,i ≤t ran,i ≤T, Among them, t sta,i Indicates the charging start time of the electric vehicle; t ran,i represents the random dwell time of the electric vehicle; T represents the total number of time periods.

3. The coordinated dispatching method considering vehicles, stations and energy according to claim 2 is characterized in that: The master-slave game model of a regional integrated energy system is established with the integrated energy system operator as the leader and energy suppliers, charging station operators and electric vehicle aggregators as followers, including: Models are established for the integrated energy system operator, the energy supplier-charging station operator alliance, the electric vehicle aggregator and the user aggregator respectively.

4. The coordinated dispatching method considering vehicles, stations and energy according to claim 3 is characterized in that: A model is established for the integrated energy system operator, including: Calculate the net profit of the integrated energy system operator and obtain its maximum profit: in, In the formula, F IESO represents the net revenue of the operator of said integrated energy system; represents the revenue from selling energy to the user aggregator in period t; represents the revenue from selling energy to the electric vehicle aggregator in period t; and Respectively represent the total power charged to the constant power charging electric vehicle and the variable power charging electric vehicle in the t period; Indicates the total charging and discharging power of the flexible electric vehicle during the t period; C b,t represents the cost of purchasing energy from the energy supplier in period t; P wt,t , P pv,t and P csp,t They represent the optimized output power of wind turbines, photovoltaic and solar thermal equipment at the tth period in a day respectively; η csp,e Indicates the efficiency of converting the output of solar thermal equipment into electrical energy; E ess,t Indicates the amount of energy stored in the energy storage device at time period t; η ess Indicates the charge and discharge loss rate of energy storage equipment; They represent the charging power and discharging power of the energy storage device in period t respectively; and They respectively represent the electricity purchase price and slow charging price for the tth period of a day set by the integrated energy system operator; represents the power selling price of the power grid in the tth period of a day; They respectively represent the electricity load value of the user aggregator in the tth period of a day.

5. The coordinated dispatching method considering vehicles, stations and energy according to claim 4 is characterized in that: A model is established for the energy supplier-charging station operator alliance, including: The energy storage device stores electricity E ess,t , expressed in mathematical formula: Among them, E ess,t-1 Indicates the amount of energy stored in the energy storage device during period t-1; Generate an objective function for the charging station operator to dynamically adjust the service fee according to the output value of the energy supplier: in, F s,t =C b,t , C pun,t =λ e,pun ·(P wt,max -P wt,t +P pv,max -P pv,t ), Where: F es-cso represents the total revenue of the energy supplier-charging station operator alliance during 24 hours of the day; F s,t represents the energy sales revenue of the energy supplier in period t; represents the service fee income of the charging station operator in period t; C op,t represents the operation and maintenance cost of the equipment in the energy supplier in the t period; represents the number of electric vehicles of each type, and j represents the type of electric vehicles; represents the basic value of service fee for the jth electric vehicle under different strategies; represents the subsidized electricity price for the jth type of electric vehicle in the tth period of a day; κ op,wt , κ op,pv , κ op,csp , κ op,ees Respectively represent the operation and maintenance costs per unit power output of wind turbines, photovoltaic and solar thermal equipment; C pun,t It represents the penalty cost for unabsorbed wind and solar energy; λ e,pun It represents the penalty electricity price for curtailing wind and solar power; P wt,max and P pv,max Represent the maximum output values ​​of wind power and photovoltaic power respectively.

6. The coordinated dispatching method considering vehicles, stations and energy according to claim 5 is characterized in that: Build a model for the electric vehicle aggregator, including: Generate the dynamically adjusted objective function of the EV aggregator: Among them, C eva,t represents the electricity cost of the electric vehicle aggregator in period t.

7. The coordinated dispatching method considering vehicles, stations and energy according to claim 6 is characterized in that: Building a model for the electric vehicle aggregator also includes: The relationship model between the service fee subsidy electricity price and the user response rate is established based on Stevens' law: in, K j represents the response constant of the j-th type of electric vehicle user; and They represent the initial subsidized electricity price and the maximum subsidized electricity price for the j-th type of electric vehicle user respectively; θ is an exponential and is always greater than 1; Subsidized electricity price The corresponding response rate of the electric vehicle users in the jth category; η j,max represents the maximum response rate of the electric vehicle users in the jth category.

8. The coordinated dispatching method considering vehicles, stations and energy according to claim 7 is characterized in that: A model is established for the user aggregator, including: Calculate the electricity purchase cost C of the user aggregator ua : in, Represents the user's electricity load in the tth period.

9. The coordinated dispatching method considering vehicles, stations and energy according to claim 8 is characterized in that: The master-slave game model G of the regional integrated energy system is: Among them, N represents the game participants; and They represent the strategy of the leader integrated energy system operator, the strategy of the follower energy supplier-charging station operator alliance, and the strategy of the follower electric vehicle aggregator, respectively; F ieso 、F es-cso and F eva They represent the benefits of the integrated energy system operator, the benefits of the energy supplier-charging station operator alliance, and the benefits of the electric vehicle aggregator, respectively.

10. A coordinated dispatching system considering vehicles, stations and energy, characterized in that: The power system is dispatched by using the coordinated dispatching method considering vehicles, stations and energy as described in any one of claims 1 to 9.

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