Dynamic charging and discharging scheduling method and device for electric vehicle, medium and product

Abstract

The invention discloses an electric vehicle dynamic charging and discharging scheduling method and device, a medium and a product, and relates to the technical field of electric power, and the method comprises the steps: constructing an electric power scheduling device model and an electric vehicle disordered charging model; based on the power dispatching equipment model and the disordered charging model of the electric vehicle, constructing a dynamic charging and discharging optimization model of the electric vehicle; constructing an electric vehicle carbon transaction model; based on the electric vehicle dynamic charging and discharging optimization model and the electric vehicle carbon transaction model, taking the maximum income of the electric vehicle participating in charging and discharging as a target to construct an electric vehicle dynamic charging and discharging scheduling model; constructing constraint conditions based on the power dispatching equipment model; and obtaining a dynamic charging and discharging scheduling scheme of the electric vehicle by adopting the dynamic charging and discharging scheduling model of the electric vehicle and combining the constraint conditions. By obtaining the dynamic charging and discharging scheduling scheme of the electric vehicle, effective scheduling of dynamic charging and discharging of the electric vehicle can be achieved, effective balance of power grid loads is achieved, and then the energy utilization efficiency is improved.

Description

Technical Field

[0001] The present application relates to the field of electric power technology, and in particular to a method, device, medium and product for dynamic charging and discharging scheduling of electric vehicles. Background Art

[0002] In recent years, with the continuous reduction of fossil fuel reserves and the improvement of environmental protection awareness, the investment in renewable energy has also continued to increase. However, due to the intermittent and volatile nature of renewable energy generation, the stable operation of the power grid has always troubled researchers and practitioners in the field of power technology after large-scale access to the power grid by renewable energy generation equipment. In addition, although the rapid popularization of electric vehicles has made important contributions to reducing carbon emissions in the transportation sector, the uncertainty of their charging behavior has further exacerbated the load fluctuations of the power grid, bringing new challenges to the balance of power supply and demand.

[0003] As an important part of modern energy management, user-side energy storage systems have many significant advantages. User-side energy storage systems can reduce power loss and increase users' energy self-sufficiency, enhance power supply reliability, and effectively reduce electricity costs by storing electricity when electricity prices are low and using it to reduce peak loads during peak hours. In addition, user-side energy storage makes it possible to participate in grid demand response, supports microgrid operation, and helps reduce carbon emissions.

[0004] Electric vehicles are not only important means of transportation, but also potential user-side mobile energy storage units. The flexible charging and discharging capabilities of electric vehicles can play an important role in the power system. These characteristics can improve the stability and flexibility of the overall power grid and promote the development of energy systems in a cleaner, more efficient and reliable direction.

[0005] However, how to effectively motivate electric vehicles to participate in the balance of power supply and demand is still a difficult problem to be solved. Traditional incentive mechanisms often fail to achieve the desired effect due to the lack of attractiveness for electric vehicles to participate in the balance of power supply and demand and the lack of practical considerations for the balance of power supply and demand, resulting in low energy efficiency. Summary of the invention

[0006] The purpose of this application is to provide a method, device, medium and product for dynamic charging and discharging scheduling of electric vehicles, which can realize effective scheduling of dynamic charging and discharging of electric vehicles, achieve effective balance of power grid load, and thus improve energy utilization efficiency.

[0007] To achieve the above objectives, this application provides the following solutions:

[0008] In a first aspect, the present application provides a method for dynamic charging and discharging scheduling of an electric vehicle, comprising:

[0009] Constructing a power dispatching equipment model; the power dispatching equipment model includes: a wind power generation equipment model, a photovoltaic power generation equipment model, a gas turbine power generation equipment model, a distribution network connection model and an electric vehicle battery model;

[0010] Acquiring usage data of the electric vehicle; the usage data includes daily mileage of the electric vehicle and cruising range of the electric vehicle;

[0011] constructing an electric vehicle disorderly charging model based on the usage data;

[0012] Based on the power dispatching equipment model and the electric vehicle disorderly charging model, a dynamic charging and discharging optimization model for electric vehicles is constructed;

[0013] Obtain carbon emission quotas for electric vehicles;

[0014] Building an electric vehicle carbon trading model based on the electric vehicle carbon emission quota;

[0015] Based on the electric vehicle dynamic charging and discharging optimization model and the electric vehicle carbon trading model, a dynamic charging and discharging scheduling model for electric vehicles is constructed with the goal of maximizing the benefits of electric vehicles participating in charging and discharging;

[0016] Constructing constraint conditions based on the electric power dispatching equipment model;

[0017] The electric vehicle dynamic charging and discharging scheduling model is adopted and combined with the constraint conditions to obtain an electric vehicle dynamic charging and discharging scheduling plan.

[0018] Optionally, based on the power dispatching equipment model and the electric vehicle disorderly charging model, a dynamic charging and discharging optimization model for electric vehicles is constructed, including:

[0019] Based on the power dispatching equipment model and the electric vehicle disorderly charging model, construct a power system power shortage model;

[0020] According to the power system power shortage model, a difference function model between the power system power shortage and the maximum power reserve of the electric vehicle is constructed;

[0021] A dynamic charging and discharging optimization model for electric vehicles is constructed based on the power system power shortage model and the difference function model.

[0022] Optionally, the power system power shortage model is expressed as:

[0023]

[0024] In the formula, is the power shortage of the power system, is the basic electrical load of the power system, Disorderly charging load for electric vehicles, is the wind power generation power, is the photovoltaic power generation power, P gt,max is the maximum output power of the gas turbine.

[0025] Optionally, the basic electrical load of the power system The function model is expressed as:

[0026]

[0027] In the formula, is the wind power generation power, is the photovoltaic power generation power, The power generated by the gas turbine is Output power of the distribution network interconnection line, and are the discharge power and charging power of the electric vehicle respectively.

[0028] Optionally, the electric vehicle carbon trading model is expressed as:

[0029]

[0030] In the formula, I ev represents carbon trading income, N ev is the carbon emission quota of the electric vehicle, ε is the basic price of carbon trading, ω is the step size of the carbon emission interval, and υ is the increase in the carbon trading price.

[0031] Optionally, the constraint conditions include interconnection line constraints, wind turbine generator set operation constraints, photovoltaic generator set operation constraints, gas turbine operation constraints and electric vehicle battery operation constraints.

[0032] Optionally, the electric vehicle dynamic charging and discharging scheduling model is adopted and combined with the constraint conditions to obtain an electric vehicle dynamic charging and discharging scheduling scheme, including:

[0033] The electric vehicle dynamic charging and discharging scheduling model is adopted, combined with the constraint conditions, and the electric vehicle dynamic charging and discharging scheduling model is solved by using a solution model to obtain the electric vehicle dynamic charging and discharging scheduling plan;

[0034] The solution model is expressed as:

[0035]

[0036] Where f(x) is the dynamic charging and discharging scheduling model of electric vehicles; g(x) is the constraint condition; x i is the variable to be solved; x min and x max The variables x to be solved arei The minimum and maximum values ​​of x j is a state variable.

[0037] In a second aspect, the present application provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of any one of the above-mentioned methods for dynamic charging and discharging scheduling of electric vehicles.

[0038] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any one of the above-mentioned methods for dynamic charging and discharging scheduling of electric vehicles.

[0039] In a fourth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of any of the above-mentioned methods for dynamic charging and discharging scheduling of electric vehicles.

[0040] According to the specific embodiments provided in this application, this application has the following technical effects:

[0041] The present application provides a method, device, medium and product for dynamic charging and discharging scheduling of electric vehicles. By constructing a power dispatching device model, various power resource operation models are obtained to form the framework of the distribution network; by constructing an electric vehicle disorderly charging model, and based on the power dispatching device model and the electric vehicle disorderly charging model, an electric vehicle dynamic charging and discharging optimization model is constructed, and a dynamic charging and discharging price of electric vehicles is formulated to incentivize electric vehicles to participate in charging and discharging scheduling; by constructing an electric vehicle carbon trading model based on electric vehicle carbon emission quotas, electric vehicle carbon trading income is obtained, which further incentivizes electric vehicles to participate in charging and discharging scheduling, thereby enabling effective scheduling of dynamic charging and discharging of electric vehicles; based on the electric vehicle dynamic charging and discharging optimization model and the electric vehicle carbon trading model, a dynamic charging and discharging scheduling model for electric vehicles is constructed with the goal of maximizing the benefits of electric vehicles participating in charging and discharging, and the dynamic charging and discharging scheduling model for electric vehicles is adopted. In combination with constraint conditions, a dynamic charging and discharging scheduling plan for electric vehicles is obtained to achieve effective balance of power grid load, thereby improving energy utilization efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0043] Figure 1This is a flowchart of the steps of a method for dynamic charging and discharging scheduling of an electric vehicle in one embodiment of the present application;

[0044] Figure 2 A microgrid topology diagram provided in an embodiment of the present application;

[0045] Figure 3 This is a diagram showing the optimization scheduling results of electric vehicle charging and discharging power under scenario 1 in one embodiment of the present application;

[0046] Figure 4 This is a diagram showing the optimization scheduling results of electric vehicle charging and discharging power under scenario 2 in one embodiment of the present application;

[0047] Figure 5 This is a diagram showing the optimization scheduling results of electric vehicle charging and discharging power under scenario 3 in one embodiment of the present application;

[0048] Figure 6 This is a diagram showing the optimization scheduling results of electric vehicle charging and discharging power under scenario 4 in one embodiment of the present application;

[0049] Figure 7 A comparison chart of the benefits and costs of electric vehicle charging and discharging scheduling in four scenarios provided in an embodiment of the present application;

[0050] Figure 8 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0051] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0052] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0053] In an exemplary embodiment, a method for dynamic charging and discharging scheduling of electric vehicles is provided. The method is executed by a computer device, and specifically can be executed by a computer device such as a terminal or a server alone, or can be executed by a terminal and a server together. Figure 1 As shown, the method includes:

[0054] Step 1: Construct a power dispatching equipment model. The power dispatching equipment model includes: wind power generation equipment model, photovoltaic power generation equipment model, gas turbine power generation equipment model, distribution network connection model and electric vehicle battery model.

[0055] According to the types and types of power resources, and considering the characteristics of resource equipment operation mechanism, various power resource equipment operation models are established. These models are established based on actual operation data and equipment characteristics to ensure the accuracy and operability of the models.

[0056] For example, wind power generation is a renewable energy technology that uses wind power to convert kinetic energy into electrical energy. The wind drives the wind rotor to rotate, driving the generator to convert mechanical energy into electrical energy, which is then regulated by the transformer and transmitted to the power grid. The electrical power output of wind power generation is mainly related to the wind speed.

[0057] The wind power generation equipment model is expressed as:

[0058] In the formula, is the output power of the fan at time t, v t is the wind speed at the fan hub height, P wt.n is the rated output power of the fan, v n 、v in 、v to They are rated wind speed, cut-in wind speed, and cut-out wind speed respectively.

[0059] Photovoltaic power generation is a renewable energy technology that converts solar energy directly into electrical energy. Through the photovoltaic effect, solar energy is converted into electrical energy, thus providing a clean source of electricity.

[0060] The photovoltaic power generation equipment model is expressed as:

[0061] In the formula, is the photovoltaic output power at time t, P STC is the rated output power under standard conditions, Q STC is the rated irradiance under standard conditions, Q T is the actual light radiation at time t, τ is the power temperature coefficient, T c is the temperature at time t, T n Rated temperature under standard conditions.

[0062] Gas turbine power generation is a way of generating electricity by burning natural gas and other fuels using gas turbines as core equipment. The specific process includes the fuel and compressed air mixed and burned in the combustion chamber, producing high-temperature and high-pressure gas to drive the turbine to rotate, and the mechanical energy of the turbine then drives the generator to generate electricity.

[0063] The gas turbine power generation equipment model is expressed as:

[0064] In the formula, is the gas turbine output power at time t, η gt represents the gas turbine power output efficiency, Represents the natural gas consumed by the gas turbine at time t.

[0065] The distribution network interconnection model is expressed as:

[0066] In the formula, is the grid interaction power at time t, η tr is the transformer coefficient, is the electric energy output by the distribution network at time t.

[0067] The electric vehicle battery model is expressed as:

[0068]

[0069] In the formula, is the remaining power of the electric vehicle at time t, δ ev is the electric vehicle battery self-consumption coefficient, η ev,chr and η ev,dis are the charging and discharging efficiency of electric vehicles, and are the charging and discharging power of the electric vehicle at time t respectively.

[0070] These power generation equipment models and distribution network connection models together constitute the framework of the distribution network, such as Figure 2 As shown in the figure, the microgrid topology structure diagram is divided into the supply side and the user side. The supply side includes wind power generation, photovoltaic power generation, gas turbines and distribution networks; the user side includes conventional electrical loads (i.e., the basic electrical load of the power system) and electric vehicle loads. By clarifying the framework model of the supply side, the decision-making and optimization process in the dynamic charging and discharging scheduling of electric vehicles can be supported. Among them, electric vehicles can be used as a mobile energy storage system to help stabilize the power grid, especially in the case of unstable renewable energy (such as the intermittent nature of wind and solar energy), to provide reliable power supply and achieve a balance between power supply and demand.

[0071] Step 2: Obtain the usage data of electric vehicles. The usage data includes the daily mileage of electric vehicles and the cruising range of electric vehicles. A disorderly charging model of electric vehicles is constructed based on the usage data.

[0072] The factors that affect the charging load of electric vehicles mainly include: the number of electric vehicles, charging power, the start time of charging and the end time of charging. Without considering the long-distance driving of electric vehicles, since the average daily driving mileage of electric vehicles is much smaller than the maximum driving mileage of electric vehicle power batteries, only one slow charge per day can meet the travel needs of the day.

[0073] For example, assuming that electric vehicle users have the same driving habits as ordinary fuel vehicle users, the data on the usage patterns of electric vehicles is processed to obtain a daily mileage distribution that follows a log-normal distribution. The probability density function of the daily mileage of electric vehicles is:

[0074]

[0075] In the formula, μ s is the mean value, which is 3.2; s is the variance, and its value is 0.88.

[0076] Assuming that electric vehicles are fully charged before each trip, after finding the daily trip probability distribution of electric vehicles, the probability distribution of the starting time of disorderly charging of electric vehicles can be found. The return time of electric vehicles follows a normal distribution. The probability density function of the return time of electric vehicles is:

[0077]

[0078] In the formula, μ s The value is 17.6, σ s The value is 3.4.

[0079] Assuming that there is no correlation between the charging start time, charging end time and daily mileage of each electric vehicle, and that they are independent of each other, the charging start time, charging end time and daily mileage of m electric vehicles are obtained according to probability distribution.

[0080] Then the remaining power (SOC) of each electric vehicle after driving and charging is:

[0081]

[0082]

[0083] The charging loads are superimposed to obtain the large-scale charging load of electric vehicles:

[0084]

[0085] Where, t 0 ,t 1 and t 2 are the driving start time, end time and charging start time of the i-th electric vehicle, respectively, and d i and D i is the daily mileage and cruising range of the i-th electric vehicle, Δt is the charging time, η ev is the charging efficiency, C i is the battery capacity of the i-th electric car, is the charging power of the i-th electric vehicle at time t, is the charging load of the electric vehicle cluster at time t.

[0086] According to the established electric vehicle disorderly charging load model, the Monte Carlo method is used to extract the electric vehicle starting charging time and charging power, calculate the charging duration, and simulate the electric vehicle disorderly charging load model to obtain the electric vehicle disorderly charging load power within 24 hours a day.

[0087] Step 3: Based on the power dispatching equipment model and the electric vehicle disorderly charging model, a dynamic charging and discharging optimization model for electric vehicles is constructed.

[0088] Based on the power dispatching equipment model and the disordered charging model of electric vehicles, a power system power shortage model is constructed to obtain the power shortage of the power system, thereby constructing a difference function model between the power shortage of the power system and the maximum power reserve of electric vehicles. Based on the power system power shortage model and the difference function model, a dynamic charging and discharging optimization model for electric vehicles is constructed, and a dynamic charging and discharging price for electric vehicles is formulated to encourage electric vehicles to participate in charging and discharging scheduling.

[0089] For example, the power shortage of the power system is mainly calculated based on the difference between the power load and the sum of photovoltaic power generation, wind power generation and gas turbine power generation.

[0090] The power shortage model of the power system is expressed as:

[0091] In the formula, is the power shortage of the power system at time t, and are the basic electric load of the power system and the disorderly charging load of electric vehicles at time t, is the wind power generation at time t, is the photovoltaic power generation power at time t, P gt,max is the maximum output power of the gas turbine.

[0092] Among them, the basic electric load of the power system The function model is expressed as:

[0093]

[0094] In the formula, is the wind power generation power, is the photovoltaic power generation power, The power generated by the gas turbine is Output power of the distribution network interconnection line, and are the discharge power and charging power of the electric vehicle respectively.

[0095] According to the power system power shortage model, a difference function model between the power system power shortage and the maximum power reserve of electric vehicles is constructed.

[0096] The difference function model is expressed as:

[0097] In the formula, P is the difference between the power shortage of the power system and the maximum power reserve of the electric vehicle at time t. ev,max is the maximum charging power of the electric vehicle; s is the electric energy reserve coefficient of the electric vehicle, that is, the proportion of the maximum charging power of the electric vehicle at time t that can be used for electric energy reserve.

[0098] Based on the constructed power system power shortage model and difference function model, a dynamic charging and discharging optimization model for electric vehicles can be constructed, so as to formulate dynamic charging and discharging prices for electric vehicles to encourage electric vehicles to participate in charging and discharging scheduling. The dynamic charging and discharging optimization model for electric vehicles is divided into the following cases:

[0099] 1) When there is a power shortage in the power system, that is, When , the dynamic charging and discharging optimization model of electric vehicles is expressed as:

[0100]

[0101] 2) When there is no power shortage in the power system, that is, When , the dynamic charging and discharging optimization model of electric vehicles is expressed as:

[0102]

[0103] In the formula, and They are the dynamic time-of-use electricity prices for electric vehicle charging and discharging, and These are the conventional time-of-use electricity prices for electric vehicle charging and discharging.

[0104] Step 4: Obtain electric vehicle carbon emission quotas. Build an electric vehicle carbon trading model based on electric vehicle carbon emission quotas.

[0105] Since electric vehicles can effectively reduce carbon emissions by replacing fuel vehicles, the carbon emissions reduced by replacing fuel vehicles with electric vehicles can be compensated to electric vehicles through carbon trading, thereby further increasing the travel rate of electric vehicles. In order to compare the carbon emissions of electric vehicles and fuel vehicles at the same driving distance, the following method can be used to calculate and allocate carbon emission quotas.

[0106] For example, the carbon emission quota obtained by electric vehicle travel is:

[0107]

[0108] In the formula, Carbon emission quotas obtained for driving electric vehicles instead of fuel vehicles; The amount of electricity consumed by electric vehicles; run The electric energy consumption coefficient per kilometer for electric vehicles is 0.1084kW·h / km; The carbon emission coefficient per kilometer for fuel vehicles is 242.18g / km.

[0109] Electric vehicles participate in the regulation of power system load supply and demand, and their discharge is mainly used for power load. Assuming that the discharge of electric vehicles mainly replaces the output of coal-fired units, the carbon emission quota obtained by the discharge of electric vehicles is:

[0110]

[0111] In the formula, Carbon emission quotas obtained for discharging electric vehicles, is the discharge amount of the electric vehicle at time t, κ 1 is the carbon emission calculation coefficient of coal-fired units.

[0112] Then the electric vehicle carbon emission quota N ev It is expressed as:

[0113]

[0114] According to the carbon emission quota of electric vehicles, a step-by-step model is used to represent the carbon trading model of electric vehicles to calculate the carbon trading income obtained by electric vehicles. The carbon trading model of electric vehicles is expressed as:

[0115]

[0116] In the formula, I ev represents carbon trading income, N ev is the carbon emission quota of electric vehicles, ε is the basic price of carbon trading, ω is the step size of carbon emission interval, and v is the increase of carbon trading price.

[0117] Step 5: Based on the dynamic charging and discharging optimization model of electric vehicles and the electric vehicle carbon trading model, a dynamic charging and discharging scheduling model for electric vehicles is constructed with the goal of maximizing the benefits of electric vehicles participating in charging and discharging.

[0118] For example, the dynamic charging and discharging scheduling model for electric vehicles is an objective function model that aims to maximize the benefits of electric vehicles participating in charging and discharging. The dynamic charging and discharging scheduling model for electric vehicles is expressed as:

[0119]

[0120] In the formula, F evlaThe benefits of electric vehicles participating in charging and discharging, and are the utility function value of the electric vehicle load aggregator at time t and the benefits obtained by electric vehicle discharge, and are the charging cost and battery loss cost of the electric vehicle load aggregator at time t, I ev Carbon trading revenue for electric vehicles.

[0121] Among them, the function model of each benefit or cost function in the dynamic charging and discharging scheduling model of electric vehicles is as follows:

[0122] 1) The utility function model of electric vehicle load aggregator is expressed as:

[0123]

[0124] 2) The profit model obtained by discharging electric vehicles is expressed as:

[0125]

[0126] 3) The charging cost function model of electric vehicle load aggregators is expressed as:

[0127]

[0128] 4) The battery loss cost function model of electric vehicle load aggregators is expressed as:

[0129]

[0130] In the formula, w ev and v ev is the preference coefficient of electric vehicle energy consumption, and are the discharge price and charging price of electric vehicles at time t, L et is the charge-discharge cycle life of electric vehicle batteries, E s is the battery capacity of electric vehicles, C bat The purchase cost of electric vehicle batteries.

[0131] Step 6: Construct constraints based on the power dispatching equipment model.

[0132] The constraints include interconnection line constraints, wind turbine operation constraints, photovoltaic generator operation constraints, gas turbine operation constraints and electric vehicle battery operation constraints.

[0133] For example, the tie line constraint is expressed as:

[0134] In the formula, and are the minimum and maximum power of the interconnection line at time t, is the grid interaction power at time t.

[0135] The wind turbine operating constraints are expressed as:

[0136] In the formula, is the output power of the fan at time t, P wt.n is the rated output power of the fan.

[0137] The operation constraints of the photovoltaic generator set are expressed as:

[0138] In the formula, is the photovoltaic output power at time t, P pv.n is the rated output power of the photovoltaic generator set.

[0139] The gas turbine operating constraints are expressed as:

[0140] In the formula, and are the minimum and maximum output power of the gas turbine, is the gas turbine output power at time t.

[0141] The electric vehicle battery operation constraints are expressed as:

[0142] In the formula, is the remaining power of the electric vehicle at time t; and are the minimum and maximum remaining power of the electric vehicle respectively; μ ev is the charging and discharging state of the electric vehicle. ev =1 indicates discharge, when μ ev =0 means charging; and are the maximum charging and discharging power of electric vehicles, respectively; and are the charging and discharging power of the electric vehicle at time t respectively.

[0143] Step 7, using the electric vehicle dynamic charging and discharging scheduling model and combining the constraints to obtain the electric vehicle dynamic charging and discharging scheduling plan.

[0144] For example, a dynamic charging and discharging scheduling model of electric vehicles is adopted, and the constraint conditions are combined, and the dynamic charging and discharging scheduling model of electric vehicles is solved by using a solution model to obtain a dynamic charging and discharging scheduling plan of electric vehicles. The dynamic charging and discharging scheduling model of electric vehicles can be solved by using the solution model by calling the YALMIP toolbox and CPLEX solver in the commercial software MATLAB.

[0145] The solution model is expressed as:

[0146]

[0147] Where f(x) is the dynamic charging and discharging scheduling model of electric vehicles; g(x) is the constraint condition; x i is the variable to be solved; x min and x max The variables x to be solved are i The minimum and maximum values ​​of x j is a state variable.

[0148] In another exemplary embodiment of the present application, in order to verify the effectiveness of the dynamic charging and discharging scheduling method for electric vehicles in the present application, the present application sets up 4 scenarios for comparative analysis, among which scenario 4 is a specific scenario of the dynamic charging and discharging scheduling method for electric vehicles proposed in the present application, and the remaining scenarios are comparative scenarios.

[0149] The four scenarios are described as follows:

[0150] Scenario 1: Consider a fixed electricity price for charging and discharging traditional electric vehicles.

[0151] Scenario 2: Consider the charging and discharging electricity prices of electric vehicles that are dynamically adjusted based on the balance of supply and demand.

[0152] Scenario 3: Further consider electric vehicle carbon trading compensation based on Scenario 2.

[0153] Scenario 4: Further consider the battery loss of electric vehicles based on Scenario 3.

[0154] The optimization results of electric vehicle charging and discharging power under scenario 1 are as follows: Figure 3 shown. Figure 33-a represents the optimization result of electric vehicle charging power, and 3-b represents the optimization result of electric vehicle discharging power. In this scenario, due to the lack of consideration of the dynamic adjustment of electricity prices, electric vehicle users are less motivated to discharge during the peak electricity period (18:00-22:00), and the discharge volume only accounts for 3.3% of the charging volume. Although most electric vehicle users choose to charge during low electricity price periods, even if they discharge when the electricity price is the highest, electric vehicle users are unlikely to obtain significant benefits, so they are not willing to participate in the charging and discharging scheduling of electric vehicles. Due to the lack of response to real-time changes in electricity supply and demand, the flexibility and efficiency of the strategy are limited, and the benefits of electric vehicles participating in charging and discharging cannot be fully improved.

[0155] The optimization results of electric vehicle charging and discharging power under scenario 2 are as follows: Figure 4 shown. Figure 4 4-a represents the optimization result of the charging power of electric vehicles, and 4-b represents the optimization result of the discharging power of electric vehicles. In this scenario, a dynamic price adjustment mechanism is adopted to cope with the predicted power shortage. Most electric vehicle users tend to charge at noon when the electricity price is low and wind and photovoltaic power generation are sufficient. Compared with scenario 1, scenario 2 significantly increases the charging and discharging prices during 18:00 to 22:00, and the discharging price is almost twice the charging price at noon, thus providing a strong economic incentive for users to actively discharge during this period to participate in load regulation. The electricity price remains unchanged during other periods, indicating that there may be a problem of insufficient power supply from 18:00 to 22:00. Under this strategy, the proportion of user discharge to charging increased to 48.22%. By flexibly adjusting the electricity price to reflect real-time supply and demand changes, the charging and discharging behavior of electric vehicles can respond more efficiently to the actual power market conditions. This mechanism not only improves the utilization efficiency of energy, but also effectively balances the supply and demand of electricity through economic incentives, optimizes the charging and discharging strategies of users, and thus improves the overall economic benefits. Compared with scenario 1, the charging and discharging mode of scenario 2 shows greater flexibility.

[0156] The optimization results of electric vehicle charging and discharging power under scenario 3 are as follows: Figure 5 shown. Figure 55-a represents the optimization result of the charging power of electric vehicles, and 5-b represents the optimization result of the discharging power of electric vehicles. In this scenario, the combination of the dynamic electricity price adjustment mechanism and the carbon trading mechanism not only stimulates the enthusiasm of electric vehicle users to participate in the charging and discharging scheduling for discharging, but also further encourages electric vehicles to replace fuel vehicles for daily travel. When electric vehicles are driving, it is equivalent to reducing the carbon emissions of fuel vehicles, and when participating in the charging and discharging scheduling for discharging, it reduces the demand for power generation from coal-fired units. Therefore, electric vehicles can obtain compensation through carbon trading during the charging and discharging process, which not only makes up for the economic losses caused by electricity price fluctuations, but also provides additional benefits for electric vehicle users. Compared with scenario 2, scenario 3 brings more significant economic benefits to electric vehicle users, and the stimulus effect is more obvious. The proportion of electric vehicle users' discharge to charging volume further increases to 63.09%. By using this mechanism, electric vehicles can not only flexibly respond to the dynamic needs of the power grid, but also obtain additional incentives in the carbon trading market. Such dual incentives significantly improve the comprehensive benefits of electric vehicles, achieve a win-win situation of economic and environmental benefits, and promote the widespread application of electric vehicles and the green transformation of the power grid.

[0157] The optimization results of electric vehicle charging and discharging power under scenario 4 are as follows: Figure 6 shown. Figure 6 6-a represents the optimization result of the charging power of electric vehicles, and 6-b represents the optimization result of the discharging power of electric vehicles. Although scenario 3 is significant in terms of economic benefits and incentives, it ignores an important factor: the battery loss of electric vehicles. Battery loss is a matter of great concern to electric vehicle users when participating in the charging and discharging scheduling of electric vehicles. Therefore, scenario 4 takes this factor into special consideration. In this context, the proportion of electric vehicle users' discharge to charging volume drops to 51.64%, a decrease of 11.45% compared with scenario 3. However, this proportion is still 48.34% and 3.42% higher than that of scenario 1 and scenario 2, respectively. It is worth noting that scenarios 1, 2, and 3 do not consider the battery loss of electric vehicles, which makes the rationality of the results questionable. By further considering the battery loss in scenario 4, the optimization results of electric vehicle charging and discharging are more practical and provide a more reasonable solution.

[0158] Comparison of the benefits and costs of electric vehicle load aggregators participating in electric vehicle charging and discharging scheduling under different scenarios Figure 7 As shown in the figure, when comparing these four scenarios comprehensively, we can find that:

[0159] The optimization of scenario 1 mainly relies on fixed electricity price periods, but lacks flexible response to dynamic adjustments in electricity supply and demand.

[0160] Scenario 2 makes the charging and discharging behavior of electric vehicles more flexible and responsive by dynamically adjusting electricity prices, thereby improving economic benefits.

[0161] Scenario 3 not only combines dynamic electricity price adjustment, but also introduces a carbon trading mechanism into the optimization strategy, so that it can maximize economic benefits while taking into account environmental benefits, showing the best comprehensive effect. However, scenario 3 ignores battery loss, which may bury economic risks in the long-term application of the strategy. In practice, electric vehicle users usually pay much attention to the loss of batteries caused by discharge.

[0162] Scenario 4 further considers the battery loss cost based on Scenario 3, providing more comprehensive benefits and cost control compared to the previous scenarios.

[0163] In general, Scenario 4 becomes an ideal strategy model for optimizing the management of electric vehicle charging and discharging scheduling due to its comprehensive and coordinated cost management and electric vehicle interaction mechanism, providing the best benefits for electric vehicles to participate in charging and discharging scheduling.

[0164] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 8 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data related to the electric vehicle charging and discharging scheduling plan. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a dynamic charging and discharging scheduling method for electric vehicles is implemented.

[0165] Those skilled in the art will understand that Figure 8The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components. In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0166] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0167] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0168] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0169] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0170] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0171] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0172] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application; at the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A dynamic charging and discharging scheduling method for electric vehicles, characterized in that: The electric vehicle dynamic charging and discharging scheduling method comprises: Constructing a power dispatching equipment model; the power dispatching equipment model includes: a wind power generation equipment model, a photovoltaic power generation equipment model, a gas turbine power generation equipment model, a distribution network connection model and an electric vehicle battery model; Acquiring usage data of the electric vehicle; the usage data includes daily mileage of the electric vehicle and cruising range of the electric vehicle; constructing an electric vehicle disorderly charging model based on the usage data; Based on the power dispatching equipment model and the electric vehicle disorderly charging model, a dynamic charging and discharging optimization model for electric vehicles is constructed; Obtain carbon emission quotas for electric vehicles; Building an electric vehicle carbon trading model based on the electric vehicle carbon emission quota; Based on the electric vehicle dynamic charging and discharging optimization model and the electric vehicle carbon trading model, a dynamic charging and discharging scheduling model for electric vehicles is constructed with the goal of maximizing the benefits of electric vehicles participating in charging and discharging; Constructing constraint conditions based on the electric power dispatching equipment model; The electric vehicle dynamic charging and discharging scheduling model is adopted and combined with the constraint conditions to obtain an electric vehicle dynamic charging and discharging scheduling plan.

2. The electric vehicle dynamic charging and discharging scheduling method according to claim 1, characterized in that: Based on the power dispatching equipment model and the electric vehicle disorderly charging model, a dynamic charging and discharging optimization model for electric vehicles is constructed, including: Based on the power dispatching equipment model and the electric vehicle disorderly charging model, construct a power system power shortage model; According to the power system power shortage model, a difference function model between the power system power shortage and the maximum power reserve of the electric vehicle is constructed; A dynamic charging and discharging optimization model for electric vehicles is constructed based on the power system power shortage model and the difference function model.

3. The dynamic charging and discharging scheduling method for electric vehicles according to claim 2 is characterized in that: The power system power shortage model is expressed as: In the formula, is the power shortage of the power system, is the basic electrical load of the power system, Disorderly charging load for electric vehicles, is the wind power generation power, is the photovoltaic power generation power, P gt,max is the maximum output power of the gas turbine.

4. The method for dynamic charging and discharging scheduling of electric vehicles according to claim 3, characterized in that: The basic electric load of the power system The function model is expressed as: In the formula, is the wind power generation power, is the photovoltaic power generation power, The power generated by the gas turbine is Output power of the distribution network interconnection line, and are the discharge power and charging power of the electric vehicle respectively.

5. The electric vehicle dynamic charging and discharging scheduling method according to claim 1, characterized in that: The electric vehicle carbon trading model is expressed as: In the formula, I ev represents carbon trading income, N ev is the carbon emission quota of the electric vehicle, ε is the basic price of carbon trading, ω is the step size of the carbon emission interval, and υ is the increase in the carbon trading price.

6. The method for dynamic charging and discharging scheduling of electric vehicles according to claim 1, characterized in that: The constraints include interconnection line constraints, wind turbine operation constraints, photovoltaic power generation unit operation constraints, gas turbine operation constraints and electric vehicle battery operation constraints.

7. The electric vehicle dynamic charging and discharging scheduling method according to claim 1, characterized in that: The electric vehicle dynamic charging and discharging scheduling model is adopted and combined with the constraint conditions to obtain an electric vehicle dynamic charging and discharging scheduling scheme, including: The electric vehicle dynamic charging and discharging scheduling model is adopted, combined with the constraint conditions, and the electric vehicle dynamic charging and discharging scheduling model is solved by using a solution model to obtain the electric vehicle dynamic charging and discharging scheduling plan; The solution model is expressed as: Where f(x) is the dynamic charging and discharging scheduling model of electric vehicles; g(x) is the constraint condition; x i is the variable to be solved; x min and x max The variables x to be solved are i The minimum and maximum values ​​of x j is a state variable.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the dynamic charging and discharging scheduling method for electric vehicles according to any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the electric vehicle dynamic charging and discharging scheduling method described in any one of claims 1 to 7 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the electric vehicle dynamic charging and discharging scheduling method described in any one of claims 1 to 7 is implemented.

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