A method for evaluating carbon reduction potential of urban vehicle network interaction

Abstract

The application designs a kind of urban vehicle network interaction carbon reduction potential evaluation method, comprising: step S1, according to the interaction characteristics of urban electric vehicle and power grid, the behavior characteristic model of electric vehicle in urban power grid is established to carry out disorder charging, participate in orderly charging and participate in vehicle network interaction;Step S2, considering the steady-state secondary coal consumption characteristics, deep peak regulation characteristics and transient variable load additional coal consumption characteristics of thermal power unit, a high-precision coal consumption characteristic model of thermal power unit is established;Step S3, based on unit combination model, considering the uncertainty of new energy, taking the optimal time window system economy expectation optimal as the objective function, the urban vehicle network interaction carbon reduction potential evaluation model framework is established;Step S4, combined with electric vehicle model and high-precision coal consumption characteristics of thermal power unit, the urban vehicle network interaction carbon reduction potential evaluation model is established based on the framework, and the urban power grid vehicle network interaction carbon reduction potential is evaluated.Compared with the prior art, the application has higher accuracy.

Description

Technical Field

[0001] This invention relates to the field of carbon emission reduction in power systems, and in particular to a method for assessing the carbon reduction potential of urban vehicle-grid interaction that takes into account the high-precision coal consumption characteristics of thermal power units. Background Technology

[0002] Assessing the carbon reduction potential of urban vehicle-grid interaction is part of the field of power system carbon reduction research; therefore, evaluating the carbon reduction potential of grid carbon reduction technologies is necessary. This assessment can provide strong data support and high-quality decision-making basis for the low-carbon and green development of cities.

[0003] In recent years, distributed mobile energy storage, represented by electric vehicles, has seen rapid development. The vast number of electric vehicles in the future can serve as a highly influential and flexible resource, providing multifaceted support to the power grid. Through the rapidly developing vehicle-to-grid (V2G) interaction technologies, the uncertainties brought about by a high proportion of renewable energy can be mitigated. Therefore, discovering and exploring the carbon reduction potential of urban power grids through V2G participation has gradually become a research hotspot. Against this backdrop, assessment methods for the carbon reduction potential of urban V2G interaction are particularly important. Summary of the Invention

[0004] In view of this, this invention proposes a method for assessing the carbon reduction potential of urban vehicle-grid interaction considering the high-precision coal consumption characteristics of thermal power units, in order to solve the problem in existing technologies that cannot accurately assess the carbon reduction potential of urban vehicle-grid interaction based on the high-precision coal consumption characteristics of thermal power units. This method aims to fully utilize electric vehicles in vehicle-grid interaction, providing grid flexibility and promoting the consumption of new energy sources, thereby achieving more efficient and cleaner energy utilization.

[0005] "Vehicle-to-grid interaction" refers to the interaction between electric vehicles and the power grid. This interaction means that electric vehicles can charge and discharge according to the grid's needs, thereby providing ancillary services to the grid, such as peak shaving and frequency regulation. Simultaneously, electric vehicles can also charge from the grid to meet user needs. This interaction can optimize the operation of the power system, improve its stability, and provide better services to users.

[0006] "Promoting the consumption of new energy sources" refers to promoting the consumption and absorption of new energy sources through various means. New energy sources refer to renewable energy sources such as solar and wind power. Because the supply of these energy sources is unstable, measures are needed to balance supply and demand and ensure the stable operation of the power system. Through vehicle-to-grid (V2G) interaction, the energy storage function of electric vehicles can be better utilized to balance the supply and demand of the power system and promote the consumption and absorption of new energy sources. This helps reduce carbon emissions and promotes the development of clean energy.

[0007] The specific technical solution of this invention is as follows:

[0008] A method for assessing the carbon reduction potential of urban vehicle-to-grid (V2G) interaction includes the following steps:

[0009] Step S1: Based on the interaction characteristics between urban electric vehicles and the power grid, establish a behavioral characteristic model of electric vehicles in the urban power grid that involves disordered charging, participation in orderly charging, and participation in vehicle-grid interaction.

[0010] Step S2: Considering the steady-state secondary coal consumption characteristics, deep peak-shaving characteristics, and transient load-related additional coal consumption characteristics of thermal power units, establish a high-precision coal consumption characteristic model for thermal power units.

[0011] Step S3: Based on the unit combination model and considering the uncertainty of new energy sources, establish a framework for assessing the carbon reduction potential of urban vehicle-grid interaction with the objective function of optimizing the expected economic performance of the system within the optimization time window.

[0012] Step S4: Combining the electric vehicle model with the high-precision coal consumption characteristics of thermal power units, establish an assessment model for the carbon reduction potential of urban vehicle-grid interaction based on the framework, considering two scenarios: active vehicle-grid interaction and all disordered charging, to assess the carbon reduction potential of urban power grid vehicle-grid interaction.

[0013] Furthermore, step S1 specifically includes:

[0014] Step S11: Collect data on the actual travel and ownership of electric vehicles in the city, and then classify all electric vehicle resources by combining the actual policies of vehicle-to-grid interaction in the city and the vehicle situation. The classified sets include the set of vehicles charging in an unordered manner, the set of vehicles participating in orderly charging, and the set of vehicles participating in vehicle-to-grid interaction.

[0015] Step S12: Based on the different behavioral characteristics and operating characteristics of electric vehicles, three different models are established to describe the behavior of electric vehicles: disordered charging model, ordered charging model, and vehicle-to-grid interaction model.

[0016] Furthermore, step S2 specifically includes:

[0017] Step S21: Establish a corresponding model to describe the steady-state coal consumption characteristics of thermal power units;

[0018] Step S22: Establish a corresponding model to describe the additional coal and oil consumption of thermal power units under deep peak shaving and frequent load change conditions.

[0019] Furthermore, step S3 specifically includes:

[0020] Step S31: With the goal of optimizing system economy, define the optimization objective of the urban vehicle-grid interaction carbon reduction potential assessment model; the objective function considers multiple factors such as unit coal consumption cost, fuel consumption cost, start-up and shutdown cost, and wind and solar curtailment cost.

[0021] Step S32: Based on the unit combination model, establish a framework for assessing the carbon reduction potential of urban vehicle-grid interaction. The framework involves multiple new energy power generation scenarios, each of which includes its own constraints, including power balance constraints, system wind and solar power constraints, upper and lower limits of thermal power unit output constraints, and thermal power unit ramping constraints.

[0022] Furthermore, step S4 specifically includes:

[0023] Step S41: Analyze and collect information on electric vehicle resources, new energy data, and system load within the optimization time window. Then, based on the urban vehicle-grid interaction carbon reduction potential assessment model framework proposed in Step S3, combined with the electric vehicle-grid interaction model established in Step S1 and the high-precision coal consumption characteristic model of thermal power units established in Step S2, set the disordered charging of electric vehicles on the last day within the optimization window as the boundary, and update the constraints and parameters involved in the thermal power units participating in deep peak shaving. A linearized unit steady-state coal consumption model is used to establish a complete urban vehicle-grid interaction carbon reduction potential assessment model considering the high-precision coal consumption characteristics of thermal power units. Calculate the expected total carbon emissions of the urban power grid based on the scheduling solution.

[0024] Step S42: Set up two scenarios in the urban power grid: electric vehicles actively participating in vehicle-grid interaction and all participating in disorderly charging. Use the urban vehicle-grid interaction carbon reduction potential assessment model to solve for these two scenarios and obtain the expected total carbon emissions for each scenario. Based on the expected total carbon emissions for the two scenarios, calculate the overall assessment result of the urban vehicle-grid interaction carbon reduction potential.

[0025] Furthermore, in step S12, the disordered charging model for electric vehicles is established as follows:

[0026]

[0027] In equation (5), The expected charging amount for car i. Improve the charging efficiency of electric vehicles. This represents the maximum charging power of the charging station. It is the shortest charging time for electric vehicle i;

[0028] Equation (6) models the disordered charging power of electric vehicles. It is the charging power variable of car i in time period t. This represents the floor function. The charging time begins. To end the charging time, A collection of cars that are charging in an unordered manner.

[0029] Furthermore, in step S12, based on the characteristics of flexible loads, an orderly charging model for electric vehicles is established as follows:

[0030]

[0031] Equation (7) represents the upper and lower limits of the orderly charging power for electric vehicles. It is the charging power variable of car i in time period t. This represents the maximum charging power of the charging station. For unordered charging of electric vehicles, For the set of online time of electric vehicle i;

[0032] Equation (8) is the power constraint for orderly charging of electric vehicles. Improve the charging efficiency of electric vehicles. For the set of online time of electric vehicle i, It is the charging power variable of car i in time period t. The expected charging amount for car i. A collection of cars that are charging in an unordered manner.

[0033] Furthermore, in step S21, the steady-state coal consumption characteristics of the thermal power unit under different load rates are modeled as follows:

[0034]

[0035] The above formula is the steady-state coal consumption characteristic curve of the unit. It is the steady-state coal consumption of unit i during time period t. It is the active power output of unit i during time period t. , , These are the unit's coal consumption coefficients.

[0036] Furthermore, in step S42, based on the expected total carbon emissions from the two scenarios, the overall assessment result of the carbon reduction potential of urban vehicle-to-grid interaction is calculated using the following formula:

[0037]

[0038] in, , These represent the expected total carbon emissions from the urban power grid under scenarios of purely disorderly charging and active participation in vehicle-grid interaction. This is the overall assessment result of the carbon reduction potential of urban vehicle-to-grid interaction.

[0039] The beneficial effects of this invention are as follows:

[0040] 1. Establishing a model to describe the behavioral characteristics of electric vehicles in urban power grids, including disordered charging, orderly charging, and participation in vehicle-grid interaction, will help to accurately predict and manage the behavior of electric vehicles;

[0041] 2. Establish a high-precision coal consumption characteristic model for thermal power units, which can predict coal consumption under different load rates and provide a reference for optimizing urban power grid dispatch and energy management;

[0042] 3. Establish a framework for assessing the carbon reduction potential of urban vehicle-grid interaction, with the goal of optimizing system economics, taking into account the uncertainty of new energy sources, and optimizing the expected system economics within the time window, providing an important tool and method for assessing the carbon reduction potential of urban power grid vehicle-grid interaction;

[0043] 4. Establish a complete assessment model for the carbon reduction potential of urban vehicle-grid interaction, which can be effectively solved and the expected total carbon emissions of the urban power grid can be calculated based on the scheduling solution, providing a reference for the overall assessment of the carbon reduction potential of vehicle-grid interaction;

[0044] 5. Set up two scenarios: electric vehicles actively participating in vehicle-to-grid interaction and all participating in disorderly charging. Use models to solve for the expected total carbon emissions for each scenario, and formulate scientific emission reduction strategies based on the results. Attached Figure Description

[0045] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0046] Figure 1 This is a flowchart illustrating the method for assessing the carbon reduction potential of urban vehicle-to-grid interaction in this application.

[0047] Figure 2 This is a schematic diagram illustrating the correlation between peak shaving and coal consumption of thermal power units in this application. Detailed Implementation

[0048] To make the technical problems to be solved, the technical solutions, and the beneficial effects of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present invention and are not intended to limit the present invention.

[0049] This application proposes a method for assessing the carbon reduction potential of urban vehicle-to-grid interaction, such as... Figure 1As shown, the process includes the following steps: Step S1: Based on the interaction characteristics between urban electric vehicles and the power grid, establish behavioral characteristic models of electric vehicles engaging in disordered charging, participating in orderly charging, and participating in vehicle-grid interaction within the urban power grid; Step S2: Considering the steady-state secondary coal consumption characteristics, deep peak-shaving characteristics, and transient load-related additional coal consumption characteristics of thermal power units, establish a high-precision coal consumption characteristic model for thermal power units; Step S3: Based on the unit combination model, considering the uncertainty of new energy sources, and with the objective function of optimizing the expected economic performance of the system within the optimization time window, establish a framework for evaluating the carbon reduction potential of urban vehicle-grid interaction; Step S4: Combining the electric vehicle model and the high-precision coal consumption characteristics of thermal power units, establish an evaluation model for the carbon reduction potential of urban vehicle-grid interaction based on the framework, considering two scenarios: active vehicle-grid interaction and complete disordered charging, to evaluate the carbon reduction potential of urban power grid vehicle-grid interaction.

[0050] The specific implementation of step S1, "Based on the interaction characteristics of urban electric vehicles and the power grid, establish a behavioral characteristic model of electric vehicles engaging in disordered charging, participating in orderly charging, and participating in vehicle-grid interaction in the urban power grid," is as follows, including the following steps:

[0051] Step S11: Collect actual trips and ownership data of electric vehicles in the city. Then, based on this data, combined with the city's actual vehicle-to-grid interaction policies and vehicle conditions, classify all electric vehicle resources. The classified set includes a set of unordered charging vehicles. A collection of cars participating in orderly charging and the collection of cars participating in car network interaction . This refers to the collection of all electric vehicle resources.

[0052] Step S12: Based on the different behavioral characteristics and operating characteristics of electric vehicles, three different models are established to describe the behavior of electric vehicles. The disordered charging model describes the minimum charging time for an electric vehicle to complete its charging demand at maximum charging power; the ordered charging model describes the upper and lower limits of power and the energy constraints of an electric vehicle in the ordered charging mode; and the vehicle-grid interaction model describes the charging and discharging power constraints, charging and discharging logic constraints, and SOC constraints of an electric vehicle as a distributed mobile energy storage unit.

[0053] The disordered charging model for electric vehicles is established as follows:

[0054]

[0055] In equation (9), The expected charging amount for car i. Improve the charging efficiency of electric vehicles. This represents the maximum charging power of the charging station. It is the shortest charging time for electric vehicle i.

[0056] Equation (10) models the disordered charging power of electric vehicles. In Equation (10), It is the charging power variable of car i in time period t. This represents the floor function. The charging time begins. To end the charging time, A collection of cars that are charging in an unordered manner.

[0057] Based on the characteristics of flexible loads, the orderly charging model for electric vehicles is established as follows:

[0058] , , (11)

[0059] , (12)

[0060] Equation (11) represents the upper and lower limits of the ordered charging power for electric vehicles. In Equation (11), It is the charging power variable of car i in time period t. This represents the maximum charging power of the charging station. For unordered charging of electric vehicles, Let i be the set of online time for electric vehicle i.

[0061] Equation (12) is the energy constraint for orderly charging of electric vehicles. In Equation (12), Improve the charging efficiency of electric vehicles. For the set of online time of electric vehicle i, It is the charging power variable of car i in time period t. The expected charging amount for car i. A collection of cars that are charging in an unordered manner.

[0062] Based on the distributed mobile energy storage characteristics of electric vehicles, the following vehicle-to-grid (V2G) interaction model is established:

[0063] (13)

[0064] (14)

[0065] (15)

[0066] (16)

[0067] (17)

[0068] (18)

[0069] (19)

[0070] (20)

[0071] (twenty one)

[0072] (twenty two)

[0073] Equation (13) is the clustering constraint for electric vehicles. Due to the high complexity of V2G modeling, it is used... , , Describe the charging mode and use the k-means clustering method to... Clustering is performed for multiple charging modes, as shown in equation (13). It refers to the number of vehicles involved in charging mode s. It refers to the number of vehicles participating in the vehicle-to-everything (V2X) interaction.

[0074] Equations (14)-(15) are the upper and lower limits of the charging and discharging power of electric vehicles. In equations (14)-(15), and These are the charging and discharging power variables during time period t in charging mode s. and These represent the start and end times of charging for electric vehicles in charging mode s. and These are the zero and one variables of the charging and discharging state during time period t in charging mode s. This is the maximum discharge power of electric vehicles participating in V2G. This is the maximum charging power of the charging station.

[0075] Equation (16) is the logic constraint for charging and discharging electric vehicles.

[0076] Equations (17)-(20) represent the SOC constraints for electric vehicles, namely the initial SOC calculation formula, the SOC inter-time coupling constraint, the SOC upper and lower limit constraints, and the charging capacity constraint. In equations (17)-(20), This is the expected charging amount in charging mode s. , , , , These are the SOC during time period t in charging mode s, the initial SOC in charging mode s, the upper and lower limits of the electric vehicle battery SOC, and the expected SOC after charging. It refers to the charging efficiency of electric vehicles. It refers to the discharge efficiency of electric vehicles. It refers to the battery capacity of electric vehicles. It refers to the length of the time period.

[0077] Equation (21) is the external characteristic constraint of vehicle-to-grid interaction for electric vehicles. In equation (21), It is the load power during the charging mode period t. and These are the charging and discharging power variables during the charging mode s-time period t.

[0078] Equation (22) is the total electric vehicle charging load constraint of the system. In equation (22), It represents the total electric vehicle charging load power of the city's power grid during time period t. For unordered charging of electric vehicles, A collection of vehicles for orderly charging.

[0079] The model developed in step S12 can more accurately describe and predict the behavior and impact of electric vehicles in urban power grids. The disordered charging model can help predict the charging demand and charging time of electric vehicles; the ordered charging model can help optimize the charging process of electric vehicles and reduce the peak-valley difference in grid load; and the vehicle-grid interaction model can help utilize the energy storage characteristics of electric vehicles to balance grid load and improve energy efficiency.

[0080] In summary, step S1, by collecting and analyzing actual data on urban electric vehicles and establishing corresponding models to describe and predict the behavior and impact of electric vehicles, provides a scientific basis for the optimized management and policy formulation of urban power grids.

[0081] The specific implementation method of step S2, "considering the steady-state secondary coal consumption characteristics, deep peak-shaving characteristics, and transient load-related additional coal consumption characteristics of thermal power units, and establishing a high-precision coal consumption characteristic model for thermal power units," is as follows, including the following steps:

[0082] Step S21: The steady-state coal consumption characteristics of thermal power units were considered, such as... Figure 2 As shown, thermal power units exhibit a quadratic steady-state coal consumption characteristic curve. Since the steady-state coal consumption characteristics of a thermal power unit differ at different load rates, a corresponding model is needed to describe this characteristic. This model can accurately predict the coal consumption of a thermal power unit at different load rates. The modeling of the steady-state coal consumption characteristics of a thermal power unit at different load rates is as follows:

[0083] (twenty three)

[0084] Equation (23) is the steady-state coal consumption characteristic curve of the unit. In equation (23), It is the steady-state coal consumption of unit i during time period t. It is the active power output of unit i during time period t. , , These are the unit coal consumption coefficients, This represents the duration of the time period.

[0085] Step S22 considers the additional coal and fuel consumption of thermal power units under deep peak shaving and frequent load changes. During deep peak shaving, thermal power units will generate additional coal consumption and also consume a certain amount of fuel oil. These additional consumptions depend on the unit's operating status and conditions, therefore, a corresponding model needs to be established to describe these additional consumptions. The high-precision coal consumption characteristics model of thermal power units is as follows:

[0086] (twenty four)

[0087] (25)

[0088] (26)

[0089] (27)

[0090] (28)

[0091] Equation (24) represents the additional coal consumption of thermal power units participating in deep peak shaving. In Equation (24), This refers to the additional coal consumption for deep peak shaving of unit i during time period t. and These are the minimum technical output and the stable combustion limit output of unit i, respectively. These are the coal consumption rate coefficients for deep peak shaving and operation at minimum technical output, respectively. It is the coal consumption rate when the unit is operating at its rated power. It is the active power output of unit i during time period t. For other cases, The duration is the length of the time period. It is the additional coal consumption of thermal power unit i participating in deep peak shaving during period t.

[0092] Equation (25) represents the additional fuel consumption of thermal power units participating in oil injection depth peak shaving. In Equation (25), This refers to the additional fuel consumption for peak shaving at the oil injection depth of unit i during time period t. This is the minimum stable fuel output of unit i without oil injection. and These are the fuel injection coefficients for unit i. It is the stable combustion limit output of unit i. It is the active power output of unit i during time period t. For other cases, The duration is the length of the time period. It is the additional fuel consumption of thermal power unit i participating in oil injection depth peak shaving during the time period t.

[0093] Equation (26) represents the additional coal consumption for climbing slopes in thermal power units. In Equation (26), It is the ramp-up coal consumption factor of unit i. This refers to the additional coal consumption in thermal power units when climbing hills. It refers to the length of the time period.

[0094] Equation (27) represents the total coal consumption of a thermal power unit under start-up conditions. It is the steady-state coal consumption of thermal power unit i during time period t. It represents the total coal consumption of thermal power unit i under operating conditions during time period t.

[0095] Equation (28) represents the total fuel consumption of a thermal power unit under start-up conditions. It represents the total coal consumption of thermal power unit i under operating conditions during time period t.

[0096] In summary, step S2, by establishing steady-state coal consumption characteristic models and additional consumption models for thermal power units, and considering the calculation of additional coal consumption during ramp-up, total coal consumption, and total fuel consumption, can more accurately assess the energy consumption characteristics of thermal power units under various operating conditions. These models help to better understand and predict the energy consumption behavior of thermal power units, thereby providing a scientific basis for optimizing the operation of thermal power units and formulating energy-saving strategies.

[0097] The specific implementation method of step S3, "Based on the unit combination model, considering the uncertainty of new energy sources, and taking the optimization of the system's expected economic performance within the optimization time window as the objective function, establish a framework for assessing the carbon reduction potential of urban vehicle-grid interaction," is as follows, including the following steps:

[0098] Step S31 defines the optimization objective of the urban vehicle-grid interaction carbon reduction potential assessment model, which aims to achieve optimal system economics. The objective function considers multiple factors, including unit coal consumption cost, fuel consumption cost, start-up and shutdown cost, and wind and solar curtailment cost. To improve computational efficiency, a linearization constraint on the unit's secondary steady-state coal consumption curve is also introduced. The optimization objective of the urban vehicle-grid interaction carbon reduction potential assessment model is defined as follows:

[0099] (29)

[0100] (30)

[0101] (31)

[0102] (32)

[0103] (33)

[0104] (34)

[0105] (35)

[0106] (36)

[0107] Equation (29) is the objective function for optimizing the system's economic performance, which includes the unit's coal consumption cost, fuel consumption cost, start-up and shutdown cost, and wind and solar curtailment cost. This method uses the scenario approach to model the uncertainty of new energy sources, and the objective function is set as the expected economic performance of the system. In Equation (29), It is the start-up and shutdown cost of unit i during time period t. The cost of wind and solar power curtailment is denoted by (t), where (s) represents the variable under scenario s. This represents the probability of scenario s occurring in the new energy power generation sector. , These are coal prices and oil prices, respectively.

[0108] Equations (30)-(34) are linearization constraints on the unit's second-order steady-state coal consumption curve, used to improve computational efficiency. They are respectively the unit's dispatchable power segmentation, the calculation of the unit's coal consumption curve segment slope, the constraint on the relationship between the unit's linearized coal consumption and segmented coal consumption, the constraint on the relationship between the unit's power and segmented power, and the constraint on the upper and lower limits of segmented power. In equations (30)-(34), It is the (m+1)th boundary point of the power dispatchable domain of unit i. It is the maximum technical output of unit i. It is the maximum technical output of unit i. It represents the active power of unit i in the m-th segment of time period t. It is the slope of the m-th segment of the unit's coal consumption curve after linearization. It is a zero-to-one variable representing the start-stop state of thermal power unit i during time period t. It is the number of segments in the linearization of the quadratic coal consumption curve. These are the coal consumption cost coefficients for unit i.

[0109] Equation (35) is used for the start-up and shutdown costs of computer groups. In Equation (35), This is a zero-to-one variable representing the startup status of unit i during time period t. If the unit starts during this time period, its value is 1. This is the startup cost of unit i.

[0110] Equation (36) represents the system's cost of curtailing solar and wind power. In Equation (36), and These are the cost factors for wind curtailment and solar curtailment, respectively. and These represent the maximum and actual wind power output of the system during time period t, respectively. and These represent the maximum and actual output of the photovoltaic system during time period t.

[0111] Step S32: Based on the unit combination model, a framework for assessing the carbon reduction potential of urban vehicle-grid interaction was established. This framework involves various new energy power generation scenarios, each of which includes the following constraints: power balance constraints, system wind and solar power constraints, upper and lower limits of thermal power unit output constraints, and thermal power unit ramping constraints.

[0112] (37)

[0113] (38)

[0114] (39)

[0115] (40)

[0116] (41)

[0117] (42)

[0118] (43)

[0119] (44)

[0120] (45)

[0121] (46)

[0122] Equation (37) is the power balance constraint. In equation (37), and These are the external hydropower power during time period t and the net load power excluding electric vehicle charging load, respectively. These are the power generation, wind power, photovoltaic power, and electric vehicle power of thermal power unit i during time period t.

[0123] Equations (38) and (39) represent the system's wind power and photovoltaic power constraints, respectively. These are the maximum power output of wind power and the maximum power output of photovoltaic power, respectively.

[0124] Equation (40) represents the upper and lower limits of the output of thermal power units. These are the minimum and maximum technical outputs of thermal power unit i, respectively.

[0125] Equations (41)-(42) are the ramp-up constraints for thermal power units, taking into account the differences between the start-up and shutdown processes and the normal operation process of thermal power units. , , , These are the upward ramp rate, downward ramp rate, start-up ramp rate, and shutdown ramp rate of thermal power unit i, respectively. It refers to the length of the time period.

[0126] Equation (43) is the logical constraint of the state variables of the thermal power unit. In equation (43), It is a zero-one variable representing the shutdown status of unit i during time period t. When the unit is shut down and its output is zero during time period i, its value is 1. It is a zero-to-one variable representing the start-up state of unit i during time period t. When the unit's output changes from zero to non-zero during time period i, its value is 1.

[0127] Equations (44)-(45) are the minimum start-up and minimum shutdown time constraints for thermal power units, used to ensure the safe operation of the units. In equations, , These are the minimum continuous start-up time and minimum continuous shutdown time of unit i.

[0128] Equation (46) is the constraint on the maximum number of deep peak shaving calls within the thermal power unit scheduling cycle. In Equation (46), This is a zero-to-one variable representing the deep peak shaving state of unit i during time period t. When the unit is in deep peak shaving state, its value is 1. It is the maximum number of times deep peak shaving can be invoked to ensure safety within the i-th scheduling cycle of the unit.

[0129] In summary, step S3, by comprehensively considering various factors and constraints, can accurately assess the carbon reduction potential of urban vehicle-to-grid interaction, providing a scientific basis for formulating relevant policies and measures.

[0130] The specific implementation method of step S4, "Combining the electric vehicle model with the high-precision coal consumption characteristics of thermal power units, establishing an assessment model for the carbon reduction potential of urban vehicle-grid interaction based on the framework, considering two scenarios of active vehicle-grid interaction and all disordered charging, and assessing the carbon reduction potential of urban power grid vehicle-grid interaction," is as follows, including the following steps:

[0131] Step S41: Analyze and collect information on electric vehicle resources, new energy data, and system load within the optimization time window. Then, based on the urban vehicle-grid interaction carbon reduction potential assessment model framework proposed in Step S3, combined with the electric vehicle-grid interaction model established in Step S1 and the high-precision coal consumption characteristic model of thermal power units established in Step S2, set the disordered charging of electric vehicles on the last day within the optimization window as the boundary, and update the constraints and parameters involved in the thermal power units participating in deep peak shaving. In this process, a linearized unit steady-state coal consumption model is used to establish a complete urban vehicle-grid interaction carbon reduction potential assessment model considering the high-precision coal consumption characteristics of thermal power units. This model is a typical mixed-integer linear optimization problem, which can be solved effectively. Next, calculate the expected total carbon emissions of the urban power grid based on the scheduling solution. This is achieved by using the direct carbon emission calculation formula of thermal power units based on the fuel factor method, which considers factors such as carbon emissions from coal and oil combustion, carbon content per unit calorific value, lower heating value, carbon oxidation rate, and the relative atomic masses of CO2 and C. The calculation model is as follows:

[0132] (47)

[0133] (48)

[0134] (49)

[0135] Equations (47)-(48) are formulas for calculating direct carbon emissions from thermal power units based on the fuel factor method. In the equations, , These are the carbon emissions from coal combustion and oil combustion of unit i during time period t, respectively. , These are the unit calorific value and carbon content of thermal coal and diesel oil, respectively. , These are the lower heating values ​​of thermal coal and diesel oil, respectively. , These are the carbon oxidation rates of thermal coal and diesel combustion, respectively. These are the relative atomic masses of CO2 and C, respectively. , These are the total coal consumption and oil consumption of unit i during time period t when it is started.

[0136] Equation (49) is used to calculate the expected total carbon emissions of the urban power grid. In Equation (49), This is the expected total carbon emissions from the city's power grid. This refers to the carbon emissions generated during a single startup of unit i. This is the startup state variable of unit i during time period t. The steady-state coal consumption in the calculation is accurately calculated using a formula rather than a nonlinearization method. The carbon emissions generated by the fuel consumption of unit i during time period t.

[0137] Step S42: Two scenarios were set up: one where electric vehicles actively participate in vehicle-grid interaction and the other where all electric vehicles participate in disorderly charging. The urban vehicle-grid interaction carbon reduction potential assessment model was used to solve these two scenarios, obtaining the expected total carbon emissions for each. Based on the expected total carbon emissions for the two scenarios, the overall assessment result of the urban vehicle-grid interaction carbon reduction potential was calculated using formula (49):

[0138] (50)

[0139] In equation (50), , These represent the expected total carbon emissions from the urban power grid under scenarios of purely disorderly charging and active participation in vehicle-grid interaction. This is the overall assessment result of the carbon reduction potential of urban vehicle-to-grid interaction.

[0140] In summary, step S4 established an effective model and method for assessing the carbon reduction potential of urban vehicle-grid interaction by comprehensively considering the high-precision coal consumption characteristics of thermal power units and the vehicle-grid interaction behavior of electric vehicles. Through comparative evaluation of the two scenarios, an overall assessment result of the carbon reduction potential of urban vehicle-grid interaction was obtained. These results help us better understand and predict the carbon emissions of urban power grids, thus providing a scientific basis for formulating corresponding emission reduction strategies.

[0141] The beneficial effects of this application are as follows:

[0142] 1. Establishing a model to describe the behavioral characteristics of electric vehicles in urban power grids, including disordered charging, orderly charging, and participation in vehicle-grid interaction, will help to accurately predict and manage the behavior of electric vehicles;

[0143] 2. Establish a high-precision coal consumption characteristic model for thermal power units, which can predict coal consumption under different load rates and provide a reference for optimizing urban power grid dispatch and energy management;

[0144] 3. Establish a framework for assessing the carbon reduction potential of urban vehicle-grid interaction, with the goal of optimizing system economics, taking into account the uncertainty of new energy sources, and optimizing the expected system economics within the time window, providing an important tool and method for assessing the carbon reduction potential of urban power grid vehicle-grid interaction;

[0145] 4. Establish a complete assessment model for the carbon reduction potential of urban vehicle-grid interaction, which can be effectively solved and the expected total carbon emissions of the urban power grid can be calculated based on the scheduling solution, providing a reference for the overall assessment of the carbon reduction potential of vehicle-grid interaction;

[0146] 5. Set up two scenarios: electric vehicles actively participating in vehicle-to-grid interaction and all participating in disorderly charging. Use models to solve for the expected total carbon emissions for each scenario, and formulate scientific emission reduction strategies based on the results.

[0147] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for assessing the carbon reduction potential of urban vehicle-to-grid interaction, characterized in that, Including the following steps: Step S1: Based on the interaction characteristics between urban electric vehicles and the power grid, establish a behavioral characteristic model of electric vehicles in the urban power grid that involves disordered charging, participation in orderly charging, and participation in vehicle-grid interaction. Step S2: Considering the steady-state secondary coal consumption characteristics, deep peak-shaving characteristics, and transient load-related additional coal consumption characteristics of thermal power units, establish a high-precision coal consumption characteristic model for thermal power units; specifically including: Step S21: Establish a corresponding model to describe the steady-state coal consumption characteristics of thermal power units; Step S22: Establish a corresponding model to describe the additional coal and oil consumption of thermal power units under deep peak shaving and frequent load change conditions. Step S3: Based on the unit combination model and considering the uncertainties of new energy sources, establish a framework for assessing the carbon reduction potential of urban vehicle-grid interaction, with the objective function being the optimal expected economic performance of the system within the optimization time window; specifically including: Step S31: With optimal system economy as the goal, define the optimization objective of the urban vehicle-grid interaction carbon reduction potential assessment model; the objective function considers unit coal consumption cost, fuel consumption cost, start-up and shutdown cost, and wind and solar curtailment cost; the optimization objective of the urban vehicle-grid interaction carbon reduction potential assessment model is defined as follows: (1) （7） (8) Equation (1) is the objective function for optimizing the system's economic performance, which includes the unit's coal consumption cost, fuel consumption cost, start-up and shutdown costs, and wind and solar curtailment costs. It represents the total coal consumption of thermal power unit i under operating conditions during time period t. This refers to the total fuel consumption of thermal power unit i under operating conditions during time period t. It is the start-up and shutdown cost of unit i during time period t. The cost of wind and solar power curtailment is denoted by (t), where (s) represents the variable under scenario s. This represents the probability of scenario s occurring in the new energy power generation sector. , These are coal prices and oil prices; Equations (2)-(6) are linearization constraints on the unit's second-order steady-state coal consumption curve, used to improve computational efficiency. They are respectively: segmentation of the unit's dispatchable power, calculation of the slope of the segmented coal consumption curve, constraint on the relationship between the unit's linearized coal consumption and segmented coal consumption, constraint on the relationship between the unit's power and segmented power, and constraint on the upper and lower limits of segmented power; among them, It is the (m+1)th boundary point of the power dispatchable domain of unit i. It is the maximum technical output of unit i. It is the maximum technical output of unit i. It represents the active power of unit i in the m-th segment of time period t. It is the slope of the m-th segment of the unit's coal consumption curve after linearization. It is a zero-to-one variable representing the start-stop state of thermal power unit i during time period t. It is the number of segments in the linearization of the quadratic coal consumption curve. These are the coal consumption cost coefficients for unit i; Equation (7) is used for the start-up and shutdown costs of computer groups, where, This is a zero-to-one variable representing the startup status of unit i during time period t. If the unit starts during this time period, its value is 1. This refers to the startup cost of unit i; Equation (8) represents the system's cost of curtailing solar and wind power, where, and These are the cost factors for wind curtailment and solar curtailment, respectively. and These represent the maximum and actual wind power output of the system during time period t, respectively. and These represent the system's maximum photovoltaic output and its actual output during time period t, respectively. Step S32: Based on the unit combination model, a framework for assessing the carbon reduction potential of urban vehicle-grid interaction was established; each new energy power generation scenario includes power balance constraints, system wind power and photovoltaic power constraints, upper and lower limits of thermal power unit output constraints, and thermal power unit ramping constraints. (9) Equation (9) is the power balance constraint. and These are the external hydropower power during time period t and the net load power excluding electric vehicle charging load, respectively. These are the power generation, wind power, and photovoltaic power of thermal power unit i during time period t. The electric vehicle power is calculated from step S1; Step S4: Combining the electric vehicle model with the high-precision coal consumption characteristics of thermal power units, establish an assessment model for the carbon reduction potential of urban vehicle-grid interaction based on the framework, considering two scenarios: active vehicle-grid interaction and completely disordered charging, to assess the carbon reduction potential of urban power grid vehicle-grid interaction; specifically including: Step S41: Analyze and collect information on electric vehicle resources, new energy data, and system load within the optimization time window. Then, based on the urban vehicle-grid interaction carbon reduction potential assessment model framework proposed in Step S3, combined with the electric vehicle-grid interaction model established in Step S1 and the high-precision coal consumption characteristic model of thermal power units established in Step S2, set the disordered charging of electric vehicles on the last day within the optimization window as the boundary, and update the constraints and parameters involved in the thermal power units participating in deep peak shaving. A linearized unit steady-state coal consumption model is used to establish a complete urban vehicle-grid interaction carbon reduction potential assessment model considering the high-precision coal consumption characteristics of thermal power units. Calculate the expected total carbon emissions of the urban power grid based on the scheduling solution. Step S42: Set up two scenarios in the urban power grid: electric vehicles actively participating in vehicle-grid interaction and all participating in disorderly charging. Use the urban vehicle-grid interaction carbon reduction potential assessment model to solve for these two scenarios and obtain the expected total carbon emissions for each scenario. Based on the expected total carbon emissions for the two scenarios, calculate the overall assessment result of the urban vehicle-grid interaction carbon reduction potential.

2. The method for assessing the carbon reduction potential of urban vehicle-to-grid interaction according to claim 1, characterized in that, Step S1 specifically includes: Step S11: Collect data on the actual travel and ownership of electric vehicles in the city, and then classify all electric vehicle resources by combining the actual policies of vehicle-to-grid interaction in the city and the vehicle situation. The classified sets include the set of vehicles charging in an unordered manner, the set of vehicles participating in orderly charging, and the set of vehicles participating in vehicle-to-grid interaction. Step S12: Based on the different behavioral characteristics and operating characteristics of electric vehicles, three different models are established to describe the behavior of electric vehicles: disordered charging model, ordered charging model, and vehicle-to-grid interaction model.

3. The method for assessing the carbon reduction potential of urban vehicle-to-grid interaction according to claim 2, characterized in that, In step S12, the disordered charging model for electric vehicles is established as follows: (10) (11) In equation (10), The expected charging amount for car i. Improve the charging efficiency of electric vehicles. This represents the maximum charging power of the charging station. It is the shortest charging time for electric vehicle i; Equation (11) models the disordered charging power of electric vehicles. It is the charging power variable of car i in time period t. This represents the floor function. The charging time begins. To end the charging time, A collection of cars that are charging in an unordered manner.

4. The method for assessing the carbon reduction potential of urban vehicle-to-grid interaction according to claim 2, characterized in that, In step S12, based on the characteristics of flexible load, an orderly charging model for electric vehicles is established as follows: ， ， (12) ， (13) Equation (12) represents the upper and lower limits of the ordered charging power for electric vehicles. It is the charging power variable of car i in time period t. This represents the maximum charging power of the charging station. For the orderly collection of electric vehicles, For the set of online time of electric vehicle i; Equation (13) is the power constraint for orderly charging of electric vehicles. Improve the charging efficiency of electric vehicles. For the set of online time of electric vehicle i, It is the charging power variable of car i in time period t. The expected charging amount for car i. A collection of vehicles for orderly charging.

5. The method for assessing the carbon reduction potential of urban vehicle-to-grid interaction according to claim 3, characterized in that, In step S21, the steady-state coal consumption characteristics of thermal power units under different load rates are modeled as follows: The above formula is the steady-state coal consumption characteristic curve of the unit. It is the steady-state coal consumption of unit i during time period t. It is the active power output of unit i during time period t. , , These are the unit's coal consumption coefficients.

6. The method for assessing the carbon reduction potential of urban vehicle-to-grid interaction according to claim 5, characterized in that, In step S42, based on the expected total carbon emissions for the two scenarios, the overall assessment result of the carbon reduction potential of urban vehicle-to-grid interaction is calculated using the following formula: in, , These represent the expected total carbon emissions from the urban power grid under scenarios of purely disorderly charging and active participation in vehicle-grid interaction. This is the overall assessment result of the carbon reduction potential of urban vehicle-to-grid interaction.

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