Low-carbon scheduling method for vehicle-network integration system based on dynamic carbon trading and green certificate

Abstract

The application provides a low-carbon scheduling method based on a dynamic carbon transaction and green certificate vehicle network integration system, belongs to the field of low-carbon scheduling, and comprises the following steps: acquiring basic data parameters of the vehicle network integration system; constructing a vehicle network integration energy system comprising a cogeneration unit model, a gas boiler model, a gas turbine model and a dynamic traffic network model; constructing a dynamic carbon transaction mechanism and a dynamic green certificate transaction pricing mechanism; calculating a dynamic carbon transaction price and a dynamic green certificate transaction price through a piecewise linear carbon pricing function and a piecewise linear green certificate pricing function, respectively; and finally constructing a low-carbon scheduling objective function and a constraint function according to the vehicle network integration energy system, the basic data parameters, the dynamic carbon transaction price and the dynamic green certificate transaction price, and solving to obtain a low-carbon scheduling plan. The application solves the problem that the existing low-carbon scheduling method for the vehicle network integration system has low economy and reliability in obtaining a low-carbon scheduling decision and is difficult to achieve a carbon emission reduction target.

Description

Technical Field

[0001] This invention belongs to the field of low-carbon scheduling, and in particular relates to a low-carbon scheduling method for vehicle-grid integrated systems based on dynamic carbon trading and green certificates. Background Technology

[0002] As the scale of renewable energy power generation connected to the grid continues to expand, the synergistic optimization and multi-energy complementarity of integrated energy systems are becoming increasingly prominent. At the same time, the number of electric vehicles has increased significantly, and the flexible load characteristics of electric vehicles have become an important means to help energy transformation. However, the disorderly grid connection and charging of a large number of electric vehicles will exacerbate the peak-valley difference of the power grid and increase grid losses, resulting in unprecedented complexity and uncertainty in the operation and scheduling of integrated energy systems. Therefore, a low-carbon scheduling method is needed to assist technicians in scheduling integrated energy systems.

[0003] Currently, carbon emission trading mechanisms and demand response strategies have been initially applied to the low-carbon scheduling of vehicle-grid integrated systems. For example, some studies have introduced tiered carbon trading mechanisms into system scheduling to constrain carbon emission levels; other studies have explored the adjustment potential on the load side through demand response strategies to smooth out fluctuations in renewable energy output; in addition, a few studies have attempted to initially combine green certificate trading with carbon trading to explore the synergistic effect of market-based means in low-carbon scheduling.

[0004] However, existing technologies still have shortcomings: most carbon trading mechanisms still adopt fixed prices or tiered pricing, which cannot reflect the real-time supply and demand fluctuations in the carbon quota market and cannot accurately guide the low-carbon decision-making of vehicle-grid integration systems under different market conditions; green certificate trading and carbon trading have not yet formed a deep coupling and two-way interaction mechanism. In most schemes, electric vehicles are only regarded as independent individuals or flexible loads. From a system-wide perspective, their travel behavior and the dynamic characteristics of the transportation network are not incorporated into the energy system optimization framework, resulting in the inability to fully apply the carbon emission reduction strategies of vehicle-grid interaction technology; existing low-carbon dispatch generally adopts deterministic optimization methods, ignoring the uncertainty of wind and solar power output and load forecasting, which leads to a decrease in the reliability of the final decision and makes it difficult to play the actual low-carbon dispatch function. Summary of the Invention

[0005] To address the aforementioned shortcomings in existing technologies, the present invention provides a low-carbon scheduling method for vehicle-to-grid (V2G) systems based on dynamic carbon trading and green certificates. This method solves the problems of low economic efficiency and reliability of low-carbon scheduling decisions, which are difficult to achieve carbon emission reduction targets due to the lack of dynamic coupling between carbon trading and green certificate trading, the lack of global coordination between electric vehicles and the transportation network, and the neglect of load uncertainty in existing low-carbon scheduling methods for V2G systems.

[0006] To achieve the above objectives, the technical solution adopted by this invention is: a low-carbon scheduling method for a vehicle-to-grid integrated system based on dynamic carbon trading and green certificates, characterized by comprising the following steps: Obtain basic data parameters for the vehicle-to-everything (V2X) system; Based on the basic data parameters, models of cogeneration units, gas boilers, gas turbines, and dynamic traffic networks are established respectively, forming a vehicle-network integrated energy system. Based on the vehicle-grid integrated energy system, a dynamic carbon trading mechanism is constructed. Based on the net trading volume between carbon emission quotas and actual carbon emissions, the dynamic carbon trading price is calculated through a piecewise linear carbon pricing function. Based on the vehicle-grid integrated energy system, a dynamic green certificate trading pricing mechanism is constructed. The dynamic green certificate trading price is calculated by using a piecewise linear green certificate pricing function through the net trading volume between the number of green certificates corresponding to renewable energy power generation and the green certificate quota. Based on the vehicle-to-grid integrated energy system, basic data parameters, dynamic carbon trading prices, and dynamic green certificate trading prices, a low-carbon scheduling objective function and constraint functions are constructed. The low-carbon scheduling plan is obtained by minimizing the low-carbon scheduling objective function.

[0007] Furthermore, the expression for the combined heat and power unit model is as follows:

[0008]

[0009] in, For the power generation capacity of the combined heat and power unit, For the power generation efficiency of combined heat and power units, This refers to the natural gas consumption of combined heat and power (CHP) units. To provide heating power for combined heat and power units, To improve the heating efficiency of combined heat and power units; The expression for the gas-fired boiler model is as follows:

[0010] in, For the heating power of the gas boiler, For the thermal efficiency of gas-fired boilers; This refers to the natural gas consumption of the gas-fired boiler. The expression for the gas turbine model is as follows:

[0011] in, For the power generation of gas turbines, For gas turbine power generation efficiency; This refers to the natural gas consumption of the gas turbine.

[0012] Furthermore, the specific methods for constructing the dynamic traffic network model include: Transforming the transportation system network into a traffic map representation And define a set of origin-end point pairs, where, For the set of road nodes, A collection of road segments; Define the segment inflow, segment outflow, and segment traffic flow in the traffic map, and establish segment flow conservation constraints, node flow conservation constraints, and flow propagation constraints; The node flow conservation constraints include starting point flow conservation constraints, intermediate node flow conservation constraints, and ending point flow conservation constraints.

[0013] Furthermore, the expression for the flow conservation constraint of the aforementioned road segment is as follows:

[0014] in, For path, For origin and destination pairing, For origin and destination pairs Path between Lower section Traffic inflow For origin and destination pairs Path between Lower section Traffic outflow volume for Enter the road at any time Inflow volume, for Leave the road section at any time Outflow volume, For origin and destination pairs Path between Lower section The number of vehicles on the road For road section The number of vehicles on the road For road sections; The and satisfy:

[0015] in, For origin and destination pairs Between, via path Entering the section of road Traffic flow; The expression for the starting flow conservation constraint is as follows:

[0016] in, Starting from The set of all road segments starting from the node. for Start and end points of time The travel demand between them is a known quantity. The endpoint; The expression for the intermediate node flow conservation constraint is as follows:

[0017] in, For nodes The set of road segments as tail nodes For nodes The set of road segments that serve as the starting node, the node For path The intermediate node on; The expression for the endpoint flow conservation constraint is as follows:

[0018] in, To the end The set of all road segments for the tail node. for Time to reach the finish line Traffic flow, and satisfying:

[0019] in, As of the time of... via the path Reach the finish line The cumulative traffic, for Time via path Reach the finish line Traffic flow, ; The expression for the traffic propagation constraint is as follows:

[0020] in, For the node To the finish line The collection of road sections For the node To the finish line The section of road, For vehicles on the road section At the moment The estimated travel time.

[0021] Furthermore, the expression for the carbon emission allowance is as follows:

[0022]

[0023]

[0024]

[0025]

[0026] in, For carbon emission allowance trading volume of vehicle-to-grid integrated energy systems, Carbon emission allowances traded for purchasing electricity from the grid For the carbon emission quota trading volume of combined heat and power units, For carbon emission allowance trading volume for gas-fired boilers, For carbon emission allowance trading volume of gas turbines, Carbon emission factors of coal-fired facilities, Carbon emission factors of gas-fired facilities, For the scheduling period, For time series indexing, for Power purchased by the grid during a given time period for Power generation of cogeneration units during a given period for The heating capacity of the cogeneration unit during the specified time period. for The heating power of the gas boiler during the time period for The power generation capacity of the gas turbine during a given period.

[0027] Furthermore, the expression for the piecewise linear carbon pricing function is as follows:

[0028] in, For dynamic carbon trading prices, A price floor is set for the carbon trading mechanism. The benchmark equilibrium carbon price in the carbon market, The upper limit of the penalty carbon price corresponding to excess carbon emissions. For carbon emission allowance trading volume of vehicle-to-grid integrated energy systems, Set a lower limit for carbon emission quotas. Set a cap on carbon emission allowances.

[0029] Furthermore, the expression for the dynamic green certificate transaction pricing mechanism is as follows:

[0030]

[0031]

[0032]

[0033] in, The dynamic green certificate trading price corresponding to market entities. A price floor is preset for the green certificate trading mechanism. The benchmark equilibrium carbon price for the green certificate market. The maximum penalty certificate price corresponding to the outstanding green certificate amount. This refers to the net trading volume of quotas by market participants in the green certificate trading market. For the green certificate quotas required for integrated energy systems, The number of green certificates generated by the clean energy power generation of this integrated energy system. The unit price for green certificate transactions. For the green certificate demand quota coefficient, The conversion factor for green certificates corresponding to photovoltaic and wind power generation. For the scheduling period, for Constant electrical load power, for Wind power output at all times for Photovoltaic power generation at all times.

[0034] Furthermore, the expression for the low-carbon scheduling objective function is as follows:

[0035]

[0036]

[0037]

[0038]

[0039] in, The objective function for low-carbon scheduling is... For electricity purchase costs, For operation and maintenance costs, For carbon emission trading costs, For green certificate transaction costs, Costs of electric vehicle travel and charging. To account for the cost of wind and solar power curtailment For the min-max-min optimization problem, The variables for the first phase of optimization include the selection of energy storage charging and discharging capabilities, and the power purchased and sold between the energy system and the main grid. This index represents uncertainties, specifically the instantaneous output of wind and solar power, and the instantaneous values ​​of electrical and thermal loads. This is an uncertain set, including the output power of wind power and photovoltaic units, as well as the power of electrical and thermal loads. The variables for optimization in the second phase include unit output and electric vehicle travel options. for feasible domain, For electricity purchase price, To purchase electricity from the main grid, For natural gas purchase price, To purchase gas volume, For the first The operation and maintenance costs of this type of equipment For the first Operating power of such equipment For the first The operation and maintenance costs of energy storage devices For the first Charging power of energy storage devices For the first The discharge power of energy storage devices for t Time and Section a The number of vehicles on the road Costs are charged for each user's route segment. , For the value of time, The integral symbol is used. The penalty coefficient for curtailment of renewable energy. This refers to the quantified amount of abandoned wind and solar power.

[0040] Furthermore: the constraint functions include core operating equipment constraints, energy conservation constraints, and energy storage system constraints; The expression for the core operating equipment constraint is as follows:

[0041]

[0042] in, The lower limit of the equipment's output. This refers to the upper limit of the equipment's output. for The equipment outputs power at all times, including Equipment types include combined heat and power units, gas-fired boilers, and gas turbines. This is the lower limit of the equipment's ramp rate. This represents the upper limit of the equipment's ramp rate. for t +1 moment equipment output; The expression for the energy conservation constraint is as follows:

[0043]

[0044]

[0045]

[0046] in, for Power purchased by the power grid at any given time For electrical loads within the power grid, for The amount of energy storage charging within the power grid at any given time. for The amount of energy stored in the power grid at any given time is discharged. for Total charging amount of electric vehicles within the power grid at any given time. for Photovoltaic power generation within the power grid at any given time for Wind power generation within the power grid at any given time. For the power generation capacity of the combined heat and power unit, For the heat load within the heating system, for The heating network stores heat and charges energy at all times. for The thermal network stores and releases heat at all times. To provide heating power for combined heat and power units, For the heating power of the gas boiler, To purchase gas volume, for Natural gas storage within the network is constantly replenished with energy. for Energy is released from gas storage within the natural gas network at all times. To meet the natural gas demand within the comprehensive energy system, This refers to the natural gas consumption of the gas-fired boiler. This refers to the natural gas consumption of the gas turbine. This refers to the natural gas consumption of combined heat and power (CHP) units. For road section The number of vehicles on the road For electric vehicle battery capacity; The constraints of the energy storage system are expressed as follows:

[0047]

[0048]

[0049]

[0050]

[0051]

[0052] in, For the first The replenishment power of energy storage devices Representing the The energy replenishment operation mode of energy storage devices is defined as follows: a value of 1 indicates that the device is in energy replenishment mode, and a value of 0 indicates that the device is in energy dissipation mode. For the first The maximum power of a single replenishment / discharge of energy by a type of energy storage device. For the first Energy release capacity of energy storage devices Representing the The energy storage device's energy release operation mode is defined as follows: a value of 1 indicates that it is in energy release mode, and a value of 0 indicates that it is in energy replenishment mode. for t Time of the first Energy storage system for T Time (System terminated running status) Energy storage system For the first The replenishment power of energy storage devices For the first Rated capacity of energy storage devices Representing the The energy storage device's energy release operation mode is defined as follows: a value of 1 indicates that it is in energy release mode, and a value of 0 indicates that it is in energy replenishment mode. For the first The lower limit of the capacity of energy storage devices. For the first The upper limit of the capacity of energy storage devices.

[0053] The beneficial effects of this invention are: This invention constructs a dynamic traffic network model that incorporates the travel behavior, route selection, and charging / discharging scheduling of electric vehicles into global low-carbon optimization. This overcomes the limitations of traditional research that treats electric vehicles as independent entities or simple flexible loads. It can formulate orderly charging / discharging plans based on the actual spatiotemporal distribution of electric vehicles, alleviate the problem of widening peak-valley differences and increased grid losses caused by disorderly grid-connected charging of large-scale electric vehicles, and fully leverage the carbon emission reduction potential of electric vehicle clusters as distributed energy storage resources.

[0054] This invention constructs a dynamic carbon trading pricing mechanism, introducing a dual threshold of a carbon price floor and a penalty price for excessive carbon emissions. This enables the carbon trading equilibrium price to be linearly linked to the market's carbon quota supply and demand scale. The carbon price signal can reflect market supply and demand changes in real time and be transmitted to the output decisions of each unit. This solves the problem of lagging carbon price signals and inability to accurately guide the system's low-carbon decisions under the traditional fixed-price or tiered pricing model. It achieves the constraint of carbon emission levels while ensuring operational economics.

[0055] This invention incorporates green certificate trading and carbon trading into a unified dynamic pricing framework. Based on the isomorphic supply and demand response logic, they operate in synergy. Green certificate prices and carbon prices can be linked in real time according to the supply and demand of their respective markets, realizing the coupling and interaction of carbon trading and green certificate trading, and jointly playing a role in synergistic carbon emission reduction through carbon emission reduction and clean energy consumption. Attached Figure Description

[0056] Figure 1 A schematic diagram of a low-carbon scheduling method for a vehicle-to-grid integrated system based on dynamic carbon trading and green certificates; Figure 2 This is a schematic diagram of a dynamic carbon trading mechanism; Figure 3 This is a schematic diagram of the dynamic green certificate trading mechanism. Detailed Implementation

[0057] The specific embodiments of the present invention are described below to enable those skilled in the art to understand the present invention. However, it should be understood that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the present invention as defined and determined by the appended claims. All inventions utilizing the concept of the present invention are protected.

[0058] Example 1 like Figure 1 The diagram shows a low-carbon scheduling method for a vehicle-to-grid (V2G) integrated system based on dynamic carbon trading and green certificates, including the following steps: Obtain basic data parameters for the vehicle-to-everything (V2X) system; Based on the basic data parameters, models of cogeneration units, gas boilers, gas turbines, and dynamic traffic networks are established respectively, forming a vehicle-network integrated energy system. Based on the vehicle-grid integrated energy system, a dynamic carbon trading mechanism is constructed. Based on the net trading volume between carbon emission quotas and actual carbon emissions, the dynamic carbon trading price is calculated through a piecewise linear carbon pricing function. Based on the vehicle-grid integrated energy system, a dynamic green certificate trading pricing mechanism is constructed. The dynamic green certificate trading price is calculated by using a piecewise linear green certificate pricing function through the net trading volume between the number of green certificates corresponding to renewable energy power generation and the green certificate quota. Based on the vehicle-to-grid integrated energy system, basic data parameters, dynamic carbon trading prices, and dynamic green certificate trading prices, a low-carbon scheduling objective function and constraint functions are constructed. The low-carbon scheduling plan is obtained by minimizing the low-carbon scheduling objective function.

[0059] With the expansion of renewable energy grid connection and the growth of electric vehicle ownership, disorderly charging of electric vehicles in integrated energy systems, including electric vehicles, exacerbates the peak-valley difference in the power grid and increases grid losses, bringing complexity and uncertainty to the operation and scheduling of integrated energy systems. Most existing carbon emission trading mechanisms are fixed or tiered pricing, which cannot reflect the real-time supply and demand fluctuations in the carbon quota market and are difficult to accurately guide low-carbon decisions under different market conditions. Green certificate trading and carbon trading are difficult to deeply couple, and cannot reflect the impact of market changes on prices. Furthermore, in most low-carbon scheduling schemes, electric vehicles are only regarded as independent individuals or flexible loads, and their specific travel behaviors and the dynamic characteristics of the transportation network are not incorporated into the energy system optimization from a system-wide perspective, resulting in the carbon emission reduction potential of vehicle-grid interaction technology not being fully utilized. At the same time, existing low-carbon scheduling models generally adopt deterministic optimization methods, ignoring the uncertainty of load forecasting, which may cause the obtained low-carbon scheduling scheme to deviate from the optimal or even fail to meet safety constraints in actual operation due to forecasting bias. To address this, this invention proposes a low-carbon scheduling scheme that deeply integrates dynamic carbon trading, dynamic green certificate trading, and vehicle-to-grid interaction. This invention abandons the fixed or tiered pricing models of traditional carbon and green certificate trading, constructing a segmented linear pricing mechanism that dynamically adjusts with market supply and demand, enabling carbon and green certificate prices to respond in real-time to changes in quota trading volume. Simultaneously, this invention converts electric vehicles into energy storage resources coupled with a dynamic transportation network, representing the travel distribution of electric vehicles through a dynamic transportation network model, incorporating the charging and discharging behavior of electric vehicles into the optimization of the integrated energy system, and finally outputting a low-carbon scheduling scheme that is low-carbon, economical, and robust.

[0060] In a specific embodiment of the present invention, basic data parameters of the vehicle-grid integration system are obtained, and models of cogeneration units, gas boilers, gas turbines, and dynamic traffic networks are established to form a vehicle-grid integrated energy system. This system fully covers the entire process of energy transmission, energy conversion, energy storage, and end-user energy consumption. On the demand side, three types of energy loads are set: electrical load, thermal load, and natural gas load. The electrical load is jointly supplied by wind turbines, photovoltaic units, the upstream main grid, gas turbines, and cogeneration units. The thermal load is met by cogeneration units and gas boilers. The natural gas load is matched to demand through the natural gas pipeline network. To improve the system's energy supply flexibility, electrical energy storage, thermal energy storage, and gas energy storage devices are also integrated. The mathematical models of each core device are then described in detail, including the cogeneration unit model, gas boiler model, gas turbine model, and dynamic traffic network model. Combined heat and power (CHP) units can simultaneously produce electricity and heat, making them a highly efficient energy utilization method. The expression for the CHP unit model is as follows:

[0061]

[0062] in, For the power generation capacity of the combined heat and power unit, For the power generation efficiency of combined heat and power units, This refers to the natural gas consumption of combined heat and power (CHP) units. To provide heating power for combined heat and power units, The typical range for the heating efficiency of a combined heat and power (CHP) unit is 0.45 to 0.50. A gas-fired boiler is a device that converts the energy of natural gas into heat energy. The expression for the gas-fired boiler model is as follows:

[0063] in, For the heating power of the gas boiler, For the thermal efficiency of gas-fired boilers; This refers to the natural gas consumption of the gas-fired boiler. A gas turbine is a device that converts the energy of natural gas into electrical energy. The expression for a gas turbine model is as follows:

[0064] in, For the power generation of gas turbines, For gas turbine power generation efficiency; This refers to the natural gas consumption of the gas turbine.

[0065] In a specific implementation of this invention, a dynamic traffic network model is constructed to characterize the traffic flow of electric vehicles, and the travel time of the dynamic traffic assignment model is defined. Unlike static traffic models, dynamic travel time can be defined in multiple ways. This invention is based on the concept of instantaneous travel time, where the vehicle travels on a road segment... Above time Travel time refers to the time it takes for a vehicle to travel through a given road segment, assuming current traffic conditions remain unchanged. Above time The travel time is equal to the total travel time of all segments of the route at any given time. The sum of travel times; assuming the vehicle is on the road segment travel time Only related to vehicle ownership Relevant, namely:

[0066] So, for connecting start and end point pairs... path The travel time is as follows:

[0067] Conventional static traffic analysis is mostly based on the concept of user equilibrium, which refers to the stable flow distribution pattern formed after electric vehicles adjust their routes. However, it can only reflect the final distribution after the flow stabilizes and cannot characterize the time-varying characteristics of travel demand and road conditions. This invention needs to couple the charging and discharging behavior of electric vehicles with traffic dynamics to achieve global low-carbon scheduling. Therefore, this invention adopts dynamic traffic analysis, which focuses more on how to regulate traffic flow based on information such as the dynamic travel time of electric vehicles. Therefore, this invention uses the concept of user optimality for dynamic traffic analysis, which is defined as follows: the travel costs on each path that is being used are equal and do not exceed the travel costs of any unused path.

[0068] The specific methods for constructing the dynamic traffic network model of the present invention include: Transforming the transportation system network into a traffic map representation And define the set of origin and destination pairs. ,in, For the set of road nodes, This refers to a set of road segments; in this embodiment, subscripts are used. or To represent a certain start and end point pair, Starting from, The endpoint; Define the segment inflow, segment outflow, and segment traffic flow in the traffic map representation, and establish segment flow conservation constraints, node flow conservation constraints, and flow propagation constraints; node flow conservation constraints include origin flow conservation constraints, intermediate node flow conservation constraints, and destination flow conservation constraints. In one embodiment of the present invention, let express Enter the road at any time Inflow volume, express Leave the road section at any time Outflow volume, for Time and Section Traffic flow on the road; where flow variables and As a control variable, and If treated as a state variable, then and satisfy:

[0069] in, For origin and destination pairs Between, via path Entering the section of road Traffic flow, For origin and destination pairs Path between Lower section Traffic inflow For origin and destination pairs Path between Lower section The outflow of traffic flow satisfies the traffic flow conservation constraint of the road segment, and its expression is as follows:

[0070] in, For origin and destination pairs Path between Lower section The number of vehicles on the road For road section The number of vehicles on the road For road sections; for Time, Road Section The traffic flow on it is 0, that is:

[0071] Connecting the start and end point pairs path Above, there are several nodes, which can be divided into starting points. Intermediate nodes and the end point And it satisfies the traffic flow conservation constraint for the road segment; for intermediate nodes ,exist At this moment, we enter the intermediate node. Outflow of traffic from the road section , and leaving the intermediate node Inflow of traffic from the road section To maintain equality, the expression for the intermediate node flow conservation constraint is as follows:

[0072] in, For nodes The set of road segments as tail nodes For nodes The set of road segments that serve as the starting node, the node For path The intermediate node on; For the starting point ,set up express Start and end points of time The travel demand between them, this value is a given input parameter in the traffic assignment problem, and is a known quantity; based on satisfying the traffic flow conservation constraint, the starting point The following conditions must be met: Every moment starts from the beginning The outflow is equal to Time to enter Given the flow rates of each road segment starting from the origin, the expression for the flow conservation constraint at the origin is as follows:

[0073] in, Starting from The set of all road segments starting from the node. for Start and end points of time The travel demand between them is a known quantity. The endpoint; For the finish line ,set up for Time to reach the finish line Traffic flow, for Time via path Reach the finish line Traffic flow; and All are control variables, based on satisfying the traffic flow conservation constraint of the road segment, with the endpoint... The following conditions must be met: Time to reach the finish line The traffic is equal to All vehicles arriving at the destination at any time Given the outbound flow of the road segment, the expression for the endpoint flow conservation constraint is as follows:

[0074] in, To the end The set of all road segments for the tail node. for Time to reach the finish line Traffic flow, up to the time via the path Reach the finish line The cumulative flow is represented by state variables. This means that the variable satisfies:

[0075] in, As of the time of... via the path Reach the finish line The cumulative traffic, for Time via path Reach the finish line Traffic flow, and arrival time at the destination. The cumulative flow is 0, that is: Besides the flow conservation constraints at road segments, starting points, intermediate nodes, and ending points, based on actual conditions, all variables are non-negative, i.e.:

[0076]

[0077] The flow propagation constraint of this invention can characterize the traffic flow process in the road network over time, which is the biggest difference between dynamic and static traffic models; for path... intermediate nodes on Define subpaths For the node To the finish line The set of road segments, let For vehicles on the road section At the moment The travel time, for Time via path Entering the section of road The traffic flow meets one of the following two conditions: Traffic flow will Entering the sub-path at any time The subsequent road sections; Traffic flow will Time to reach the finish line .

[0078] remember For vehicles on the road section At the moment Given the estimated travel time, the expression for the flow propagation constraint is as follows:

[0079] in, For the node To the finish line The collection of road sections For the node To the finish line The section of road, For vehicles on the road section At the moment The estimated travel time. In dynamic traffic network models, other road network flow constraints exist, such as first-in-first-out constraints, but the flow propagation constraint in this invention stipulates that traffic entering a road segment must remain on that segment. The duration (i.e., the vehicle's travel time) is such that the first-in-first-out constraint is automatically satisfied in the dynamic traffic network model.

[0080] This invention characterizes the traffic flow of electric vehicles based on a dynamic traffic network model. It moves beyond the limitations of solving for long-term equilibrium flow distribution in conventional static traffic network models. Instead, it regulates traffic flow based on the time-varying travel information of electric vehicles, reflecting their dynamic distribution characteristics across different time periods and road segments. By coupling the dynamic traffic network model with an energy system model, this invention incorporates electric vehicle travel behavior, route selection, and charging / discharging scheduling into global low-carbon optimization. This allows low-carbon scheduling strategies to formulate orderly charging / discharging plans for electric vehicles based on their actual spatiotemporal distribution, thereby alleviating the problems of increased peak-valley differences and grid losses caused by large-scale disorderly grid-connected charging of electric vehicles. Furthermore, it fully leverages the carbon reduction capabilities of electric vehicle clusters as distributed energy storage power sources.

[0081] In a specific embodiment of this invention, compared to conventional traditional carbon trading mechanisms and tiered carbon trading mechanisms, this invention, based on classical economic supply and demand theory, constructs a dynamic carbon trading pricing mechanism that can adapt to fluctuations in the carbon market's supply and demand. This mechanism uses dynamic carbon prices as a regulator, aiming to guide market participants to implement proactive emission reduction behaviors and regulate and constrain the carbon emission control decisions of each participant. Based on a vehicle-grid integrated energy system, this invention constructs a dynamic carbon trading mechanism. Based on the net trading volume between carbon emission allowances and actual carbon emissions, a piecewise linear carbon pricing function is used to calculate the dynamic carbon trading price. The carbon emission allowances are mainly allocated using a non-compensation method, resulting in the following expression for the carbon emission allowances:

[0082]

[0083]

[0084]

[0085]

[0086] in, For carbon emission allowance trading volume of vehicle-to-grid integrated energy systems, Carbon emission allowances traded for purchasing electricity from the grid For the carbon emission quota trading volume of combined heat and power units, For carbon emission allowance trading volume for gas-fired boilers, For carbon emission allowance trading volume of gas turbines, The carbon emission factor for coal-fired facilities can be taken as 0.924 kgCO2 / kWh. The carbon emission factor for gas-fired facilities can be taken as 0.450 kgCO2 / kWh. For the scheduling period, For time series indexing, for Power purchased by the grid during a given time period for Power generation of cogeneration units during a given period for The heating capacity of the cogeneration unit during the specified time period. for The heating power of the gas boiler during the time period for The power generation capacity of the gas turbine during a given period.

[0087] like Figure 2The diagram illustrates a dynamic carbon trading mechanism. The dynamic carbon trading pricing mechanism of this invention introduces a dual price threshold: a lower limit for carbon prices and a penalty price for excess carbon emissions. Within the dual price boundary range, the equilibrium price of carbon trading is deterministically correlated with the market supply and demand scale of carbon allowances. This invention uses a linear function to characterize the coupling relationship between carbon trading prices and carbon emission allowances. The expression of the piecewise linear carbon pricing function is as follows:

[0088] in, For dynamic carbon trading prices, A price floor is set for the carbon trading mechanism. The benchmark equilibrium carbon price in the carbon market, The upper limit of the penalty carbon price corresponding to excess carbon emissions. For carbon emission allowance trading volume of vehicle-to-grid integrated energy systems, Set a lower limit for carbon emission quotas. Set a cap on carbon emission allowances.

[0089] Combination Figure 2 From the piecewise linear carbon pricing function, we can see that the inflection point... This is the dividing point between the carbon trading penalty ranges, the inflection point. This is the dividing point between the lower limit (floor price) and the inflection point of the carbon price range. Then, two types of supply and demand linear response intervals are divided; among them, The left side represents the linear range for the transfer of surplus carbon quotas by power companies. , The right side represents the linear range for power companies' carbon quota purchases. When the market demand for carbon allowances expands, the equilibrium carbon price will move along a linear range. The increasing trajectory gradually transitions to the penalty range; during this stage, the carbon price rises monotonically with the demand for carbon quotas. When the total supply of tradable carbon quotas in the market is exhausted, the carbon price is locked at a preset penalty high price. Conversely, if the supply of carbon quotas in the market expands significantly, the equilibrium carbon price will move along a linear range. The decreasing trajectory of carbon allowances approaches the floor price range, and carbon prices continue to decline as supply increases; once the supply of carbon allowances exceeds the critical threshold, carbon prices will remain at the predetermined lower limit and will no longer decrease.

[0090] This invention constructs a dynamic carbon trading pricing mechanism that adapts to the supply and demand fluctuations in the carbon market. It introduces a dual price threshold: a carbon price floor and a penalty price for excessive carbon emissions. Within the range of these two price boundaries, the equilibrium price of carbon trading is deterministically correlated with the supply and demand scale of carbon quotas in the market. When the supply of carbon quotas is abundant, the carbon price approaches the floor price range along a decreasing curve, reducing costs. When the demand for carbon quotas expands, the carbon price transitions to the penalty range along an increasing curve, forming a price penalty for excessive emissions. By optimizing the scheduling framework of the dynamic carbon trading mechanism, the carbon price signal can be transmitted to the output decisions of each unit in real time according to market supply and demand, enabling the low-carbon scheduling scheme to achieve a dynamic balance between carbon emission costs and energy supply costs. This solves the problem of lagging carbon price signals and inaccurate reflection of market supply and demand changes under the traditional pricing model.

[0091] In a specific embodiment of the present invention, green certificate trading is a market-based environmental policy tool. All transactions are conducted on a green certificate trading platform. If the number of certificates generated by the vehicle-grid integrated energy system through renewable energy power generation exceeds the quota target, the surplus certificates can be traded to generate revenue. If the number of certificates is insufficient, additional certificates must be purchased to meet the assessment targets.

[0092] This invention constructs a dynamic green certificate trading regulatory framework to achieve the synergistic operation of carbon trading mechanisms and green certificate trading mechanisms. For example... Figure 3 The diagram illustrates a dynamic green certificate trading mechanism. This mechanism shares a pricing logic with the dynamic carbon trading mechanism: when market demand for green certificates expands, the equilibrium price will monotonically rise along the linear response range, gradually transitioning to a penalty constraint range. Within this range, the price continues to rise with the increase in demand until market demand reaches a preset capacity threshold, at which point the price is locked at a fixed penalty high price. Conversely, if the market supply of green certificates continues to expand, the equilibrium price will continuously decline along the linear range, approaching the lower price limit. As supply exceeds a critical threshold, the price will stabilize at a preset floor price and no longer decrease. The expression for the dynamic green certificate trading pricing mechanism is as follows:

[0093]

[0094]

[0095]

[0096] in, The dynamic green certificate trading price corresponding to market entities. A price floor is preset for the green certificate trading mechanism. The benchmark equilibrium carbon price for the green certificate market. The maximum penalty certificate price corresponding to the outstanding green certificate amount. This refers to the net trading volume of quotas by market participants in the green certificate trading market. For the green certificate quotas required for integrated energy systems, The number of green certificates generated by the clean energy power generation of this integrated energy system. The unit price for green certificate transactions. For the green certificate demand quota coefficient, The conversion factor for green certificates corresponding to photovoltaic and wind power generation. For the scheduling period, for Constant electrical load power, for Wind power output at all times for Photovoltaic power generation at all times.

[0097] In a specific embodiment of the present invention, the present invention constructs a low-carbon scheduling objective function and constraint function based on the vehicle-grid integrated energy system, basic data parameters, dynamic carbon trading price and dynamic green certificate trading price, and obtains a low-carbon scheduling plan by minimizing the low-carbon scheduling objective function.

[0098] The expression for the low-carbon scheduling objective function is as follows:

[0099]

[0100]

[0101]

[0102]

[0103] in, The objective function for low-carbon scheduling is... For electricity purchase costs, For operation and maintenance costs, For carbon emission trading costs, For green certificate transaction costs, Costs of electric vehicle travel and charging. To account for the cost of wind and solar power curtailment For the min-max-min optimization problem, The variables for the first phase of optimization include the selection of energy storage charging and discharging capabilities, and the power purchased and sold between the energy system and the main grid. This index represents uncertainties, specifically the instantaneous output of wind and solar power, and the instantaneous values ​​of electrical and thermal loads. This is an uncertain set, including the output power of wind power and photovoltaic units, as well as the power of electrical and thermal loads. The variables for optimization in the second phase include unit output and electric vehicle travel options. for feasible domain, For electricity purchase price, To purchase electricity from the main grid, For natural gas purchase price, To purchase gas volume, For the first The operation and maintenance costs of this type of equipment For the first Operating power of such equipment For the first The operation and maintenance costs of energy storage devices For the first Charging power of energy storage devices For the first The discharge power of energy storage devices for t Time and Section a The number of vehicles on the road Costs are charged for each user's route segment. , For the value of time, The integral symbol is used. The penalty coefficient for curtailment of renewable energy. This refers to the quantified amount of abandoned wind and solar power.

[0104] The constraint functions include core operating equipment constraints, energy conservation constraints, and energy storage system constraints. Core operating equipment constraints can ensure the safe operation of the system. The output of each device must be limited to the rated operating range, and the ramp rate range of the devices is also constrained. The expression for the core operating equipment constraints is as follows:

[0105]

[0106] in, The lower limit of the equipment's output. This refers to the upper limit of the equipment's output. for The equipment outputs power at all times, including Equipment types include combined heat and power units, gas-fired boilers, and gas turbines. This is the lower limit of the equipment's ramp rate. This represents the upper limit of the equipment's ramp rate. for t +1 moment equipment output; The expression for the energy conservation constraint is as follows:

[0107]

[0108]

[0109]

[0110] in, for Power purchased by the power grid at any given time For electrical loads within the power grid, for The amount of energy storage charging within the power grid at any given time. for The amount of energy stored in the power grid at any given time is discharged. for Total charging amount of electric vehicles within the power grid at any given time. for Photovoltaic power generation within the power grid at any given time for Wind power generation within the power grid at any given time. For the power generation capacity of the combined heat and power unit, For the heat load within the heating system, for The heating network stores heat and charges energy at all times. for The thermal network stores and releases heat at all times. To provide heating power for combined heat and power units, For the heating power of the gas boiler, To purchase gas volume, for Natural gas storage within the network is constantly replenished with energy. for Energy is released from gas storage within the natural gas network at all times. To meet the natural gas demand within the comprehensive energy system, This refers to the natural gas consumption of the gas-fired boiler. This refers to the natural gas consumption of the gas turbine. This refers to the natural gas consumption of combined heat and power (CHP) units. For road section The number of vehicles on the road For electric vehicle battery capacity; The constraints of the energy storage system are expressed as follows:

[0111]

[0112]

[0113]

[0114]

[0115]

[0116] in, For the first The replenishment power of energy storage devices Representing the The energy replenishment operation mode of energy storage devices is defined as follows: a value of 1 indicates that the device is in energy replenishment mode, and a value of 0 indicates that the device is in energy dissipation mode. For the first The maximum power of a single replenishment / discharge of energy by a type of energy storage device. For the first Energy release capacity of energy storage devices Representing the The energy storage device's energy release operation mode is defined as follows: a value of 1 indicates that it is in energy release mode, and a value of 0 indicates that it is in energy replenishment mode. for t Time of the first Energy storage system for T Time (System terminated running status) Energy storage system For the first The replenishment power of energy storage devices For the first Rated capacity of energy storage devices Representing the The energy storage device's energy release operation mode is defined as follows: a value of 1 indicates that it is in energy release mode, and a value of 0 indicates that it is in energy replenishment mode. For the first The lower limit of the capacity of energy storage devices. For the first The upper limit of the capacity of energy storage devices.

[0117] The beneficial effects of this invention are as follows: This invention constructs a dynamic traffic network model that incorporates the travel behavior, route selection, and charging / discharging scheduling of electric vehicles into global low-carbon optimization. This overcomes the limitations of traditional research that treats electric vehicles as independent entities or simple flexible loads. It can formulate orderly charging / discharging plans based on the actual spatiotemporal distribution of electric vehicles, alleviate the problem of widening peak-valley differences and increased grid losses caused by disorderly grid-connected charging of large-scale electric vehicles, and fully leverage the carbon emission reduction potential of electric vehicle clusters as distributed energy storage resources.

[0118] This invention constructs a dynamic carbon trading pricing mechanism, introducing a dual threshold of a carbon price floor and a penalty price for excessive carbon emissions. This enables the carbon trading equilibrium price to be linearly linked to the market's carbon quota supply and demand scale. The carbon price signal can reflect market supply and demand changes in real time and be transmitted to the output decisions of each unit. This solves the problem of lagging carbon price signals and inability to accurately guide the system's low-carbon decisions under the traditional fixed-price or tiered pricing model. It achieves the constraint of carbon emission levels while ensuring operational economics.

[0119] This invention incorporates green certificate trading and carbon trading into a unified dynamic pricing framework. Based on the isomorphic supply and demand response logic, they operate in synergy. Green certificate prices and carbon prices can be linked in real time according to the supply and demand of their respective markets, realizing the coupling and interaction of carbon trading and green certificate trading, and jointly playing a role in synergistic carbon emission reduction through carbon emission reduction and clean energy consumption.

Claims

1. A low-carbon scheduling method for a vehicle-to-grid integrated system based on dynamic carbon trading and green certificates, characterized in that, Includes the following steps: Acquire basic data parameters for the vehicle-to-grid (V2G) system and construct a V2G energy system; A dynamic carbon trading mechanism is constructed, which calculates the dynamic carbon trading price based on the net trading volume between carbon emission allowances and actual carbon emissions through a piecewise linear carbon pricing function. A dynamic green certificate trading pricing mechanism is constructed. The dynamic green certificate trading price is calculated by using a piecewise linear green certificate pricing function based on the net trading volume between the number of green certificates corresponding to renewable energy power generation and the green certificate quota. Based on the vehicle-to-grid integrated energy system, basic data parameters, dynamic carbon trading prices, and dynamic green certificate trading prices, a low-carbon scheduling objective function and constraint functions are constructed. The low-carbon scheduling plan is obtained by minimizing the low-carbon scheduling objective function.

2. The low-carbon scheduling method for a vehicle-to-grid integrated system based on dynamic carbon trading and green certificates according to claim 1, characterized in that, The vehicle-to-grid integrated energy system includes a combined heat and power unit model, a gas boiler model, a gas turbine model, and a dynamic traffic network model. The expression for the combined heat and power unit model is as follows: in, For the power generation capacity of the combined heat and power unit, For the power generation efficiency of combined heat and power units, This refers to the natural gas consumption of combined heat and power (CHP) units. To provide heating power for combined heat and power units, To improve the heating efficiency of combined heat and power units; The expression for the gas-fired boiler model is as follows: in, For the heating power of the gas boiler, Thermal efficiency of gas-fired boilers; This refers to the natural gas consumption of the gas-fired boiler. The expression for the gas turbine model is as follows: in, For the power generation of gas turbines, For gas turbine power generation efficiency; This refers to the natural gas consumption of the gas turbine.

3. The low-carbon scheduling method for a vehicle-to-grid integrated system based on dynamic carbon trading and green certificates according to claim 1, characterized in that, The specific methods for constructing the dynamic traffic network model include: Transforming the transportation system network into a traffic map representation And define a set of origin-end point pairs, where, For the set of road nodes, A collection of road segments; Define the segment inflow, segment outflow, and segment traffic flow in the traffic map, and establish segment flow conservation constraints, node flow conservation constraints, and flow propagation constraints; The node flow conservation constraints include starting point flow conservation constraints, intermediate node flow conservation constraints, and ending point flow conservation constraints.

4. The low-carbon scheduling method for a vehicle-to-grid integrated system based on dynamic carbon trading and green certificates according to claim 3, characterized in that, The expression for the flow conservation constraint of the road segment is as follows: in, For path, For origin and destination pairing, For origin and destination pairs Path between Lower section Traffic inflow For origin and destination pairs Path between Lower section Traffic outflow volume for Enter the road at any time Inflow volume, for Leave the road section at any time Outflow volume, For origin and destination pairs Path between Lower section The number of vehicles on the road For road section The number of vehicles on the road For road sections; The and satisfy: in, For origin and destination pairs Between, via path Entering the section of road Traffic flow volume; The expression for the starting flow conservation constraint is as follows: in, Starting from The set of all road segments starting from the node. for Start and end points of time The travel demand between them is a known quantity. The endpoint; The expression for the intermediate node flow conservation constraint is as follows: in, For nodes The set of road segments as tail nodes For nodes The set of road segments that serve as the starting node, the node For path The intermediate node on; The expression for the endpoint flow conservation constraint is as follows: in, To the end The set of all road segments for the tail node. for Time to reach the finish line Traffic flow, and satisfying: in, As of the time specified via the path Reach the finish line The cumulative traffic, for Time via path Reach the finish line Traffic flow, ; The expression for the traffic propagation constraint is as follows: in, For the node To the finish line The collection of road sections For the node To the finish line The section of road, For vehicles on the road section At the moment The estimated travel time.

5. The low-carbon scheduling method for a vehicle-to-grid integrated system based on dynamic carbon trading and green certificates according to claim 1, characterized in that, The expression for the carbon emission allowance is as follows: in, For carbon emission allowance trading volume of vehicle-to-grid integrated energy systems, Carbon emission allowances traded for purchasing electricity from the grid For the carbon emission quota trading volume of combined heat and power units, For carbon emission allowance trading volume for gas-fired boilers, For carbon emission allowance trading volume of gas turbines, Carbon emission factors of coal-fired facilities, Carbon emission factors of gas-fired facilities, For the scheduling period, For time series indexing, for Power purchased by the grid during a given time period for Power generation of cogeneration units during a given period for The heating capacity of the cogeneration unit during the specified time period. for The heating power of the gas boiler during the time period for The power generation capacity of the gas turbine during a given period.

6. The low-carbon scheduling method for a vehicle-to-grid integrated system based on dynamic carbon trading and green certificates according to claim 1, characterized in that, The expression for the piecewise linear carbon pricing function is as follows: in, For dynamic carbon trading prices, A price floor is set for the carbon trading mechanism. The benchmark equilibrium carbon price in the carbon market The upper limit of the penalty carbon price corresponding to excess carbon emissions. For carbon emission allowance trading volume of vehicle-to-grid integrated energy systems, Set a lower limit for carbon emission quotas. Set a cap on carbon emission quotas.

7. The low-carbon scheduling method for a vehicle-to-grid integrated system based on dynamic carbon trading and green certificates according to claim 1, characterized in that, The expression for the dynamic green certificate transaction pricing mechanism is as follows: in, The dynamic green certificate trading price corresponding to market entities. A price floor is preset for the green certificate trading mechanism. The benchmark equilibrium carbon price for the green certificate market. The maximum penalty certificate price corresponding to the outstanding green certificate amount. This refers to the net trading volume of quotas by market participants in the green certificate trading market. For the green certificate quotas required for integrated energy systems, The number of green certificates generated by the clean energy power generation of this integrated energy system. The unit price for green certificate transactions. For the green certificate demand quota coefficient, The conversion factor for green certificates corresponding to photovoltaic and wind power generation. For the scheduling period, for Constant electrical load power, for Wind power output at all times for Photovoltaic power generation at all times.

8. The low-carbon scheduling method for a vehicle-to-grid integrated system based on dynamic carbon trading and green certificates according to claim 1, characterized in that, The expression for the low-carbon scheduling objective function is as follows: in, The objective function for low-carbon scheduling is... For electricity purchase costs, For operation and maintenance costs, For carbon emission trading costs, For green certificate transaction costs, Costs of electric vehicle travel and charging. To account for the cost of wind and solar power curtailment For the min-max-min optimization problem, The variables for the first phase of optimization include the selection of energy storage charging and discharging capabilities, and the power purchased and sold between the energy system and the main grid. This index represents uncertainties, specifically the instantaneous output of wind and solar power, and the instantaneous values ​​of electrical and thermal loads. This is an uncertain set, including the output power of wind power and photovoltaic units, as well as the power of electrical and thermal loads. The variables for optimization in the second phase include unit output and electric vehicle travel options. for feasible domain, For electricity purchase price, To purchase electricity from the main grid, For natural gas purchase price, To purchase gas volume, For the first The operation and maintenance costs of this type of equipment For the first Operating power of such equipment For the first The operation and maintenance costs of energy storage devices For the first Charging power of energy storage devices For the first The discharge power of energy storage devices for t Time and Section a The number of vehicles on the road Costs are charged for each user's route segment. , For the value of time, The integral symbol is used. The penalty coefficient for curtailment of renewable energy. This refers to the quantified amount of abandoned wind and solar power.

9. The low-carbon scheduling method for a vehicle-to-grid integrated system based on dynamic carbon trading and green certificates according to claim 1, characterized in that, The constraint functions include core operating equipment constraints, energy conservation constraints, and energy storage system constraints. The expression for the core operating equipment constraint is as follows: in, The lower limit of the equipment's output. This refers to the upper limit of the equipment's output. for The equipment outputs power at all times, including Equipment types include combined heat and power units, gas-fired boilers, and gas turbines. This is the lower limit of the equipment's ramp rate. This represents the upper limit of the equipment's ramp rate. for t +1 moment equipment output; The expression for the energy conservation constraint is as follows: in, for Power purchased by the power grid at any given time For electrical loads within the power grid, for The amount of energy storage charging within the power grid at any given time. for The amount of energy stored in the power grid at any given time is discharged. for Total charging amount of electric vehicles within the power grid at any given time. for Photovoltaic power generation within the power grid at any given time for Wind power generation within the power grid at any given time. For the power generation capacity of the combined heat and power unit, For the heat load within the heating system, for The heating network stores heat and charges energy at all times. for The thermal network stores and releases heat at all times. To provide heating power for combined heat and power units, For the heating power of the gas boiler, To purchase gas volume, for The natural gas network is constantly replenishing its storage capacity. for Energy is released from gas storage within the natural gas network at all times. To meet the natural gas demand within the comprehensive energy system, This refers to the natural gas consumption of the gas-fired boiler. This refers to the natural gas consumption of the gas turbine. This refers to the natural gas consumption of combined heat and power (CHP) units. For road section The number of vehicles on the road This refers to the battery capacity of electric vehicles.

10. The low-carbon scheduling method for a vehicle-to-grid integrated system based on dynamic carbon trading and green certificates according to claim 9, characterized in that, The constraints of the energy storage system are expressed as follows: in, For the first The replenishment power of energy storage devices Representing the The energy replenishment operation mode of energy storage devices is defined as follows: a value of 1 indicates that the device is in energy replenishment mode, and a value of 0 indicates that the device is in energy dissipation mode. For the first The maximum power of a single replenishment / discharge of energy by a type of energy storage device. For the first Energy release capacity of energy storage devices Representing the The energy storage device's energy release operation mode is defined as follows: a value of 1 indicates that it is in energy release mode, and a value of 0 indicates that it is in energy replenishment mode. for t Time of the first Energy storage system for T Time (System terminated running status) Energy storage system For the first The replenishment power of energy storage devices For the first Rated capacity of energy storage devices Representing the The energy storage device's energy release operation mode is defined as follows: a value of 1 indicates that it is in energy release mode, and a value of 0 indicates that it is in energy replenishment mode. For the first The lower limit of the capacity of energy storage devices. For the first The upper limit of the capacity of energy storage devices.

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