Power-traffic coupling network collaborative optimization method based on dynamic carbon emission factors
By constructing a two-level optimization model and performing linearization, the problem of coordinated scheduling of carbon emissions from electric vehicles and fuel vehicles in the power-transportation coupled network was solved, achieving optimal coordinated scheduling of economy and emission reduction, and improving the computational efficiency and scheduling accuracy of the power-transportation network.
Patent Information
- Application Number
- CN202511828119.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-05
- Publication Date
- 2026-03-17
AI Technical Summary
Existing research has failed to effectively combine the carbon emissions of electric vehicles and gasoline vehicles, resulting in a conflict between the economics and emission reduction scheduling of the electric-transportation coupling network. Furthermore, the existing pricing mechanism has failed to fully consider the emission reduction benefits brought about by the large-scale promotion of EVs and the continuous emission problems of GVs.
A two-level optimization model based on dynamic carbon emission factors is constructed. Through equivalent reconstruction and linearization, it is transformed into a mixed integer quadratic constrained programming problem. The upper and lower level models are solved alternately and the coupling variables are updated until the equilibrium state of the power-transportation coupled network is reached, thereby realizing the coordinated scheduling of user balance and distribution network optimization.
It achieves global collaborative optimization of individual user rationality and the economic and safe operation of the power distribution network in the transportation network, resolves the goal conflict when the power and transportation networks are optimized independently, finds the optimal balance point between economic cost and emission reduction target, and improves the computational efficiency and scheduling accuracy of large-scale power-transportation coupled networks.
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Figure CN121684448A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of power-transportation integrated network scheduling technology, specifically involving a collaborative optimization method for power-transportation coupled networks based on dynamic carbon emission factors. Background Technology
[0002] Electric vehicles (EVs) are experiencing explosive growth, gradually replacing traditional gasoline-powered vehicles (GVs). This transformation is crucial for reducing carbon emissions, mitigating climate change, and achieving carbon neutrality. Simultaneously, the new energy vehicle market is experiencing exponential growth. The widespread adoption of EVs and their increasing charging demands have significantly amplified the interaction between the transport network (TN) and the distribution network (PDN): on the one hand, EV charging may lead to localized overloads or even system failures in the distribution network; on the other hand, power supply bottlenecks may cause charging delays, while excessive concentration of EVs can cause traffic congestion. Against this backdrop, the coordinated scheduling of the power-transport coupled network (CPTN) has become a vital solution for mitigating potential risks such as excessive carbon emissions and system overload.
[0003] Current research has proposed various solutions to promote the economical and efficient scheduling of power-transport coupled networks (CPTN). The main regulatory tools include charging prices, road tolls, subsidy policies (for roads or charging services), and combinations thereof. These measures achieve network collaborative management by guiding electric vehicle charging choices and optimizing traffic flow (including EVs and GVs). One approach explores the socially optimal operating state of CPTN by introducing a nodal marginal price (LMP) mechanism; another approach guides EV behavior through charging price strategies, effectively reducing system operating costs while considering driver preferences. Such pricing strategies have been widely applied in areas such as day-ahead scheduling of EV clusters, preventing voltage exceedance risks in the distribution network, maximizing charging operator revenue, and promoting renewable energy consumption. It is noteworthy that existing research often neglects the rate boundary issue, thus introducing a subsidy pricing mechanism to minimize social costs while ensuring stable CPTN cooperation. Although the above research provides effective traffic control methods, it generally suffers from two limitations: firstly, it fails to fully consider the emission reduction benefits of large-scale EV deployment; and secondly, it ignores the continuous emissions from fuel-powered vehicles (GVs), which significantly constrains the achievement of CPTN low-carbon scheduling goals.
[0004] Recent research is beginning to focus on carbon emissions from power-transport coupled networks (CPTN). Existing research primarily aims to promote renewable energy integration and achieve low-carbon operation by incorporating distribution network carbon emissions into CPTN scheduling; however, these methods often overlook emissions from gasoline-powered vehicles. Notably, the transportation sector contributes 17% of global carbon emissions, with the 45% increase over the past two decades mainly attributed to emissions from gasoline-powered vehicles. However, current research employs diverse pricing mechanisms—including charging prices, tolls, subsidies, and combinations thereof—to regulate vehicle behavior. Nevertheless, the core issue of establishing effective user incentives to achieve optimal synergy between economic efficiency and emissions reduction remains unresolved. Summary of the Invention
[0005] The purpose of this invention is to solve the problem of how to establish effective user incentive measures to achieve optimal coordinated scheduling of economy and emission reduction. It proposes a coordinated optimization method for power-transportation coupled networks based on dynamic carbon emission factors.
[0006] The technical solution of this invention is: a collaborative optimization method for power-transportation coupled networks based on dynamic carbon emission factors, comprising the following steps: Construct a two-level optimization model to describe the coordinated operation of the power-transportation coupled network; The upper-level model of the bi-level optimization model is equivalently reconstructed and linearized, transforming it into a mixed-integer quadratic constrained programming problem, thus obtaining the reconstructed bi-level optimization model. The upper and lower models of the reconstructed bi-level optimization model are solved alternately and the coupling variables are updated until the equilibrium state of the power-transportation coupled network is reached. The equilibrium solution of the reconstructed bi-level optimization model is then output, thus completing the collaborative optimization of the power-transportation coupled network.
[0007] As a preferred option, the upper-level model of the two-layer optimization model is the transportation network operator model, and the lower-level model is the distribution network operator model. The transportation network operator model and the distribution network operator model are connected through coupling variables, which include the charging demand passed from the transportation network operator model to the distribution network operator model, and the energy-carbon integrated price (ECIP) passed from the distribution network operator model to the transportation network operator model.
[0008] As a preferred option, the objective function of the transportation network operator model is:
[0009] in, Indicates the overall economic cost. This indicates minimizing the overall economic cost; Indicates the monetary value of travel time; Indicates road segment Travel time; Indicates carbon tax; Indicates road segment Carbon emissions per unit of gasoline-powered vehicle Indicates road segment Length; Indicates road segment Traffic flow of fuel-powered vehicles; Indicates road segment Traffic to electric vehicles; Indicates connection to the distribution network bus node The section of road The combined energy-carbon price; This indicates the energy requirement of each electric vehicle; Represents a set of regular road segments; This represents a set of charging sections.
[0010] As a preferred option, the constraints of the transportation network operator model include: Constraints of the traffic network flow conservation system:
[0011]
[0012] in, Indicates road segment Total flow; This represents the origin-end point pair index of the transportation network; This represents the set of origin-end point pairs in a transportation network. Indicates the origin and destination points of the transportation network Inter-path Electric vehicle traffic on the road; Indicates the origin and destination points of the transportation network Inter-path Traffic flow of fuel-powered vehicles; This represents the road segment-path association matrix for gasoline-powered vehicles. If the path Passing through the section ,but ,otherwise, ; Represents a set of road segments in a transportation network; Indicates the origin and destination pairs connecting the transportation network. The set of routes for fuel-powered vehicles; Indicates a pair of start and end points. The set of electric vehicle routes; This represents the segment-path association matrix for electric vehicles. If the path Passing through the section ,but ,otherwise, ; Path flow constraints:
[0013] in, Indicates the origin and destination points of the transportation network The overall demand for electric vehicle transportation; Indicates the origin and destination points of the transportation network Total demand for fuel-powered vehicles; Link travel time constraints:
[0014] in, Indicates road segment Free passage time; Indicates road segment Road capacity; Indicates road segment This is the charging section, i.e. At that time, the road section On the charging time; The coefficients of the Davidson function, which represents the road travel time function; Indicates road segment This is the charging section, i.e. At that time, the road section The capacity of the charging stations on the site; This represents a set of detour routes; Link emission constraints:
[0015] Path cost constraints for electric vehicles:
[0016] in, Indicates the origin and destination points of the transportation network Inter-path Consumption of electric vehicle travel; Route cost constraints for gasoline-powered vehicles:
[0017] in, Indicates the origin and destination points of the transportation network Inter-path Consumption of fuel-powered vehicle travel expenses; Complementary conditions of Wardrop's user balance principle:
[0018]
[0019] in, Indicates the origin and destination points of the transportation network Minimum cost for an electric vehicle; Indicates the origin and destination points of the transportation network Minimum consumption for gasoline-powered vehicles; This indicates complementary relaxation.
[0020] As a preferred approach, the method of equivalent reconstruction and linearization of the upper-level model, transforming it into a mixed-integer quadratic constrained programming problem, is as follows: Based on Wardrop's user equilibrium principle, the comprehensive economic cost is linearized into a function relating... and The linear function yields the transformed comprehensive economic cost. :
[0021] The complementary conditions of the Wardrop user equilibrium principle are transformed into linear constraints involving binary variables using the Big-M method:
[0022]
[0023] in, It is a constant. and Represents the binary variables corresponding to each path; By approximating the link emission constraints and link travel time constraints through piecewise linearization, linearized link emission constraints and linearized link travel time constraints are obtained, thus completing the equivalent reconstruction and linearization of the upper-level model. The linearized link emission constraint is:
[0024] in, Represents a road segment approximated by a linear function. Carbon emissions per unit of gasoline-powered vehicle This indicates that the entire speed range is divided into Sub-intervals, Indicates the first Breakpoints of each sub-interval, express, Represents the link speed variable, with , This represents a special ordered set type 2 variable automatically assigned by the solver; The linearized link travel time constraint is:
[0025] in, and This represents an auxiliary variable.
[0026] As a preferred option, the distribution network operator model includes the energy price model of the grid operator and the carbon price model of the grid operator; the carbon price model of the grid operator is constructed based on the dynamic carbon emission factor, which is the nodal carbon intensity of each bus of the distribution network.
[0027] As a preferred option, the objective function of the energy price model for grid operators is:
[0028] in, This represents minimizing the total operating cost of the power distribution network operator system; and Indicates connection to the distribution network bus node The energy generation cost coefficient of distributed power sources; Indicates the distribution network bus node The amount of energy injected by the distributed power source; This indicates the contracted electricity price at which the distribution network operator purchases electricity from the main grid; Indicates the bus node of the power distribution network Electricity purchased from the main power grid; This represents the set of busbar nodes in the distribution network connected to the main power grid. Represents the set of busbar nodes in a distribution network; The constraints of the grid operator's energy price model include:
[0029]
[0030]
[0031]
[0032]
[0033]
[0034] in, Indicates transmission line Active power; Indicates the distribution network bus node Active power injection at the location; Indicates transmission line Active power; Indicates the distribution network bus node The functional requirements of the location; Indicates transmission line The resistance; Indicates transmission line The square value of the current on; Represents the set of power distribution network transmission lines; Indicates the distribution network bus node child nodes, Represents the set of busbar nodes in a distribution network; Indicates transmission line reactive power; Indicates the distribution network bus node Reactive power injection at the location; Indicates transmission line reactive power; Indicates the distribution network bus node Addressing traditional reactive power needs; Indicates transmission line The reactance; Indicates the distribution network bus node The square of the voltage at the point; Indicates the distribution network bus node The square of the voltage at the point; and Indicates the distribution network bus node Active power injection boundary at the location; and Indicates the distribution network bus node The reactive power injection boundary at the location; and Indicates the distribution network bus node Voltage boundary at the location; Indicates connection to the distribution network bus node The active power demand on the charging section at the location; Indicates road segment Electric vehicle traffic flow during charging sections; This represents the association matrix between charging segment and bus node. If the segment This is a charging section and is connected to the distribution network bus node. ,but ,otherwise, .
[0035] As a preferred option, the carbon pricing model for grid operators is expressed as follows:
[0036]
[0037] in, Indicates the distribution network bus node Carbon prices at the location; Represents carbon tax, Indicates the distribution network bus node Carbon density at nodes; Indicates the distribution network bus node Active power injection at the location, Indicates the distribution network bus node Injected carbon emission density at the location, Indicates transmission line Active power on Indicates transmission line Power loss on Indicates transmission line Carbon flux density of the branch lines, i.e., transmission lines Carbon strength of the upper branch, Indicates the bus node index of the distribution network. Represents the set of bus nodes in the distribution network. Indicates the distribution network bus node The parent node.
[0038] As a preferred approach, the upper and lower models of the reconstructed two-level optimization model are solved alternately, and the coupling variables are updated until the equilibrium state of the power-transportation coupled network is reached. The output is the equilibrium solution of the reconstructed two-level optimization model, which specifically includes the following steps: Initialize the number of iterations, initial traffic flow, and energy-carbon composite price; The traffic flow of the fixed upper-level model is used to solve the lower-level model, obtaining the nodal marginal electricity price and nodal carbon intensity; among which, the nodal marginal electricity price is obtained by solving the energy price model of the grid operator; The energy-carbon composite price is updated and calculated based on the marginal electricity price and carbon intensity at each node. By fixing the updated energy-carbon composite price, we solve the reconstructed upper-level model to obtain the updated traffic flow. Iteratively update the energy-carbon integrated price and traffic flow until the power-transportation coupled network reaches equilibrium, and output the equilibrium solution of the reconstructed two-level optimization model, which is the optimal scheduling result of the power-transportation coupled network.
[0039] As a preferred option, the formula for calculating the energy-carbon composite price is:
[0040] in, Indicates connection to the distribution network bus node The section of road The combined energy-carbon price; Indicates the distribution network bus node Marginal electricity price at the node; This represents the association matrix between charging segment and bus node. If the segment It is a charging section and connected to the bus node. ,but ,otherwise, ; This represents a set of charging sections.
[0041] The beneficial effects of this invention are: 1. This invention constructs a two-layer optimization model to jointly solve the user equilibrium of the transportation network and the optimal power flow of the distribution network within a unified framework. This model can automatically find and output a system state that satisfies both the individual rationality of all users in the transportation network (where costs cannot be reduced by unilaterally changing paths) and the economical and safe operation of the distribution network. This resolves the objective conflict problem that exists when optimizing the power and transportation networks independently, finds the optimal system balance point between economic costs and emission reduction targets, and achieves global collaborative optimization of "vehicle-road-network".
[0042] 2. This invention utilizes model reconstruction techniques (such as Big-M linearization of complementary conditions and piecewise linear approximation of nonlinear functions) to transform complex, non-convex problems with equilibrium constraints that are difficult to solve directly into mixed-integer programming problems that can be efficiently processed by commercial solvers. This significantly improves the computational efficiency of the model, making it possible to perform fast and accurate optimization scheduling on large-scale electric-transportation coupled networks. Attached Figure Description
[0043] Figure 1 The diagram shows a flowchart of a collaborative optimization method for power-transportation coupled networks based on dynamic carbon emission factors.
[0044] Figure 2 The diagram illustrates the energy-carbon integrated price problem from a fixed-point perspective. Detailed Implementation
[0045] Exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be understood that the embodiments shown and described in the drawings are merely exemplary and are intended to illustrate the principles and spirit of the invention, and are not intended to limit the scope of the invention.
[0046] Example 1: like Figure 1 As shown, a collaborative optimization method for power-transportation coupled networks based on dynamic carbon emission factors includes the following steps: S1. Construct a two-layer optimization model to describe the coordinated operation of the power-transportation coupled network; S2. The upper-level model of the bi-level optimization model is equivalently reconstructed and linearized to transform it into a mixed-integer quadratic constrained programming problem, thus obtaining the reconstructed bi-level optimization model; S3. Alternately solve the upper and lower models of the reconstructed bi-level optimization model and update the coupling variables until the power-transportation coupled network reaches equilibrium. Output the equilibrium solution of the reconstructed bi-level optimization model to complete the collaborative optimization of the power-transportation coupled network.
[0047] In this embodiment, the upper-level model of the two-layer optimization model is the transportation network operator model, and the lower-level model of the two-layer optimization model is the distribution network operator model. The transportation network operator model and the distribution network operator model are connected through coupling variables, which include the charging demand passed from the transportation network operator model to the distribution network operator model, and the energy-carbon integrated price (ECIP) passed from the distribution network operator model to the transportation network operator model.
[0048] In this embodiment, the objective function of the traffic network operator model is:
[0049] in, Indicates the overall economic cost. This indicates minimizing the overall economic cost; Indicates the monetary value of travel time; Indicates road segment Travel time; Indicates carbon tax; Indicates road segment Carbon emissions per unit of gasoline-powered vehicle Indicates road segment Length; Indicates road segment Traffic flow of fuel-powered vehicles; Indicates road segment Traffic to electric vehicles; Indicates connection to the distribution network bus node The section of road The combined energy-carbon price; This indicates the energy requirement of each electric vehicle; Represents a set of regular road segments; This represents a set of charging sections.
[0050] In this embodiment, the constraints of the traffic network operator model include: Constraints of the traffic network flow conservation system:
[0051]
[0052] in, Indicates road segment Total flow; This represents the origin-end point pair index of the transportation network; This represents the set of origin-end point pairs in a transportation network. Indicates the origin and destination points of the transportation network Inter-path Electric vehicle traffic on the road; Indicates the origin and destination points of the transportation network Inter-path Traffic flow of fuel-powered vehicles; This represents the road segment-path association matrix for gasoline-powered vehicles. If the path Passing through the section ,but ,otherwise, ; Represents a set of road segments in a transportation network; Indicates the origin and destination pairs connecting the transportation network. The set of routes for fuel-powered vehicles; Indicates a pair of start and end points. The set of electric vehicle routes; This represents the segment-path association matrix for electric vehicles. If the path Passing through the section ,but ,otherwise, ; Path flow constraints:
[0053] in, Indicates the origin and destination points of the transportation network The overall demand for electric vehicle transportation; Indicates the origin and destination points of the transportation network Total demand for fuel-powered vehicles; Link travel time constraints:
[0054] in, Indicates road segment Free passage time; Indicates road segment Road capacity; Indicates road segment This is the charging section, i.e. At that time, the road section On the charging time; This represents the coefficient of the Davidson function, which is the road delay function or the road travel time function, and its value is 0.05. Indicates road segment This is the charging section, i.e. At that time, the road section The capacity of the charging stations on the site; This represents a set of detour routes; Link emission constraints:
[0055] Path cost constraints for electric vehicles:
[0056] in, Indicates the origin and destination points of the transportation network Inter-path Consumption of electric vehicle travel; Route cost constraints for gasoline-powered vehicles:
[0057] in, Indicates the origin and destination points of the transportation network Inter-path Consumption of fuel-powered vehicle travel expenses; Complementary conditions of Wardrop's user balance principle:
[0058]
[0059] in, Indicates the origin and destination points of the transportation network Minimum cost for an electric vehicle; Indicates the origin and destination points of the transportation network Minimum consumption for gasoline-powered vehicles; This indicates complementary relaxation.
[0060] In this embodiment, the traffic network operator model exhibits non-convex characteristics, making direct solution difficult. Therefore, this invention proposes several methods to reconstruct the traffic network operator model into a solvable mixed-integer quadratic constrained programming (MIQCP) problem. The specific methods for transforming the upper-level model into a mixed-integer quadratic constrained programming problem through equivalent reconstruction and linearization are as follows: According to Wardrop's User Equilibrium (UE) principle, the travel cost of all selected paths between any origin and destination (OD) is equal for all selected routes between rs. Therefore, the overall economic cost is linearized as a function of... and The linear function yields the transformed comprehensive economic cost. :
[0061] By approximating the link emission constraints and link travel time constraints through piecewise linearization, linearized link emission constraints and linearized link travel time constraints are obtained, thus completing the equivalent reconstruction and linearization of the upper-level model. The complementary conditions of the Wardrop User Equilibrium (UE) principle are transformed into linear constraints containing binary auxiliary variables using the Big-M method, resulting in:
[0062]
[0063] in, and This represents a binary variable corresponding to each path; M is a sufficiently large constant (e.g., M=500), whose value can be heuristically selected based on travel cost and path flow.
[0064] Since the link emission constraint is non-convex and difficult to solve directly, this embodiment of the invention introduces an auxiliary variable to replace the link velocity variable, thereby obtaining:
[0065] in, Represents the link speed variable, for Taking the logarithm of both ends, we get:
[0066] in, Let represent the logarithmic function with a natural base. , and Since these are auxiliary variables, the link emission constraints can be approximated using a piecewise linearization method. Let... Then the function can be approximated by a set of linear functions as follows:
[0067] in, Represents a road segment approximated by a linear function. Carbon emissions per unit of gasoline-powered vehicle This indicates that the entire speed range is divided into Sub-intervals, Indicates the first Breakpoints of each sub-interval, Indicates the speed range of the first The weights of the breakpoints in each sub-interval. Represents the link speed variable, with In addition, SOS2 variables can be automatically declared by the solver.
[0068] Furthermore, we have:
[0069]
[0070] in, This represents the breakpoint of the m-th subinterval within the length interval. This represents the weight of the breakpoint in the m-th subinterval of the length interval. This represents the breakpoint of the m-th subinterval of the time interval. This represents the weight of the breakpoint in the m-th sub-interval of the time interval; By approximating the link travel time using piecewise linearization, the linearized link travel time constraint is obtained as follows:
[0071]
[0072]
[0073] in, and This represents the auxiliary variable introduced to assist the nonlinear terms in the first and second terms of the link travel time constraint; their values are equal. This represents the breakpoint of the m-th subinterval of the flow interval. This indicates that the linearization process for the first nonlinear term is performed at the breakpoint. The weight, This indicates the linearization process performed on the second nonlinear term at the breakpoint. The weight.
[0074] At this point, the Transport Network Operator (TNO) operating model has been transformed into a mixed-integer form. Given that the Distribution Network Operator (PDNO) operating model contains quadratic terms, the reconstruction of the coupled power-transport network (CPTN) ultimately becomes a mixed-integer quadratic constrained programming (MIQCP) problem.
[0075] In this embodiment, the distribution network operator model includes the energy price model of the grid operator and the carbon price model of the grid operator; the carbon price model of the grid operator is constructed based on the dynamic carbon emission factor, which is the nodal carbon intensity of each bus of the distribution network.
[0076] In this embodiment, the objective function of the energy price model for the grid operator is:
[0077] in, This represents minimizing the total operating cost of the power distribution network operator system; and Indicates connection to the distribution network bus node The energy generation cost coefficient of distributed power sources; Indicates the distribution network bus node The amount of energy injected by the distributed power source; This indicates the contracted electricity price at which the distribution network operator purchases electricity from the main grid; Indicates the bus node of the power distribution network Electricity purchased from the main power grid; This represents the set of busbar nodes in the distribution network connected to the main power grid. Represents the set of busbar nodes in a distribution network; The constraints of the grid operator's energy price model include:
[0078]
[0079]
[0080]
[0081]
[0082]
[0083] in, Indicates transmission line Active power; Indicates the distribution network bus node Active power injection at the location; Indicates transmission line Active power; Indicates the distribution network bus node The functional requirements of the location; Indicates transmission line The resistance; Indicates transmission line The square value of the current on; Represents the set of power distribution network transmission lines; Indicates the distribution network bus node child nodes, Represents the set of busbar nodes in a distribution network; Indicates transmission line reactive power; Indicates the distribution network bus node Reactive power injection at the location; Indicates transmission line reactive power; Indicates the distribution network bus node Traditional meritorious service is required; Indicates transmission line The reactance; Indicates the distribution network bus node The square of the voltage at the point; Indicates the distribution network bus node The square of the voltage at the point; and Indicates the distribution network bus node Active power injection boundary at the location; and Indicates the distribution network bus node The reactive power injection boundary at the location; and Indicates the distribution network bus node Voltage boundary at the location; Indicates connection to the distribution network bus node Charging section at the location The active demand on the platform; Indicates charging section Electric vehicle traffic on the road; This represents the association matrix between charging segment and bus node. If the charging segment This is a charging section and is connected to the distribution network bus node. ,but ,otherwise, .
[0084] In this embodiment, the carbon price model for grid operators is expressed as follows:
[0085] The formula for the carbon pricing model of power grid operators is as follows:
[0086]
[0087] in, Indicates the distribution network bus node Carbon prices at the location; Represents carbon tax, Indicates the distribution network bus node Carbon density at nodes; Indicates the distribution network bus node Active power injection at the location, Indicates the distribution network bus node Injected carbon emission density at the location, Indicates transmission line Active power on Indicates transmission line Power loss on Indicates transmission line Carbon flux density of the branch lines, i.e., transmission lines Carbon strength of the upper branch, Indicates the bus node index of the distribution network. Represents the set of bus nodes in the distribution network. Indicates the distribution network bus node The parent node.
[0088] In this embodiment, based on fixed-point theory, the existence and uniqueness of the optimal solution are rigorously proven. For example... Figure 2 The diagram illustrates the interaction mechanism between the Distribution Network Operator (PDNO) and the Transportation Network Operator (TNO), specifically the Energy-Carbon Integrated Price (ECIP) problem from a fixed-point perspective. In this process, the TNO determines the optimal route selection for electric vehicles (EVs) and gas-powered vehicles (GVs) based on a unified carbon pricing mechanism—the Energy-Carbon Integrated Price and related carbon emission costs. When User Equilibrium (UE) is reached, the charging load at each node is calculated. Subsequently, the PDNO determines the power flow and nodal marginal price (LMP) using the AC Optimal Power Flow (ACOPF) model. The carbon emission flow (CEF) model is used to obtain the carbon emission distribution and nodal carbon prices. The ECIP, composed of LMP and nodal carbon prices, then influences the traffic flow distribution. This mechanism forms a closed-loop directed system. This embodiment uses fixed-point theory to analyze the existence and uniqueness of the optimal solution, thereby ensuring the simultaneous optimality of the three optimization models.
[0089] according to Figure 2 The process shown can be defined with the following mapping relationship: After obtaining the Energy-Carbon Integrated Price (ECIP), traffic flow distribution is mapped. The characterization is achieved, and the charging load at each node can also be determined. Furthermore, the linear characteristics of the constraints and the linear dependence of the objective function on ECIP ensure that, given a sufficiently small charging load, the mapping... The continuity.
[0090] Mapping This represents the operational behavior of the Distribution Network Operator (PDNO) in formulating power flow dispatch plans and energy pricing. Generally, the continuity of this mapping cannot be guaranteed because load fluctuations typically cause abrupt changes in the nodal marginal price (LMP). However, in this embodiment, the load changes caused by electric vehicle charging are sufficiently small to allow us to consider the mapping... This continuity ensures that the set of binding constraints remains unchanged during the iteration process.
[0091] Mapping This represents the operational process of a power distribution network operator (PDNO) based on the acquisition of carbon emission flow distribution. Because the charging load is sufficiently small, this mapping is... It is continuous within the parameter space.
[0092] Mapping The design methodology for characterizing the Energy-Carbon Integrated Price (ECIP) is mathematically expressed as follows: ,in, Indicates connection to the distribution network bus node Charging section at the location The combined energy-carbon price; Indicates the distribution network bus node Marginal electricity price at the node; This represents the association matrix between charging segment and bus node. If the charging segment Connect the charging section to the bus node ,but ,otherwise, ; This represents a set of charging routes. Clearly, It is a linear constraint, and and The continuity guarantees the mapping The continuity.
[0093] Existence: After constructing the above mapping relationship, the following optimization problem can be derived: Definition 1: The Energy-Carbon Integrated Price (ECIP) (and its network equilibrium state) of a coupled power-transportation network (CPTN) can be characterized by the following fixed-point problem, and its solution is the solution to this fixed-point problem:
[0094] The mapping It is a self-mapping, and its continuity has been proven by the aforementioned analysis. Due to the ECIP space... It is convex and compact, and according to Brouwer's fixed-point theorem, the mapping... There exists a fixed point within the pricing space; this fixed point represents the optimal ECIP solution. At this fixed point, the variables related to the User Equilibrium (UE), AC Optimal Power Flow (ACOPF), and Carbon Emission Flow (CEF) models can be respectively... , and Calculated.
[0095] Uniqueness: Before proving the uniqueness of the Energy-Carbon Integrated Price (ECIP), the criteria for determining local stability must first be given: Definition 2: The local stability of the Energy-Carbon Integrated Price (ECIP) requires the following necessary conditions to be met:
[0096] It holds true at the fixed point.
[0097] The stability of the Energy-Carbon Integrated Price (ECIP) can be understood from the following mechanism: Definition 2 indicates that energy prices... carbon price With charging demand A positive correlation means that as more electric vehicles require charging services, energy prices and carbon prices will rise with the increase in charging demand. Since ECIP also has a positive correlation with energy and carbon prices, the cost of using electric vehicles will increase. To minimize travel costs, some electric vehicles will switch to other charging stations (FCS), leading to a decrease in traffic flow, charging demand, and the overall energy-carbon price. The last two conditions ensure the mapping... The local compressibility of ECIP ensures its stability.
[0098] Due to energy prices It is the dual variable of the ACOPF (Exchange Optimal Power Flow) problem, and proving its uniqueness based on existing theories is not easy. However, inspired by Definition 2, the monotonic and bounded price response characteristics can lead to a unique Energy-Carbon Integrated Price (ECIP), namely:
[0099] Under the condition that the above equation is satisfied, the mapping It has compressibility, thus ensuring the uniqueness of the fixed point.
[0100] In this embodiment, charging demand and the integrated energy-carbon price are coupled with the operational behaviors of transportation network operators and power distribution network operators, making the original problem difficult to solve. To address this, this invention proposes an iterative algorithm to decouple the interactive effects of the transportation and power systems, thereby obtaining the optimal scheduling scheme. The upper and lower models of the reconstructed two-level optimization model are solved alternately, and the coupling variables are updated until an equilibrium state of the power-transportation coupled network is reached. The output is the equilibrium solution of the reconstructed two-level optimization model, specifically including the following steps: Initialize the number of iterations, initial traffic flow, and energy-carbon composite price; The traffic flow of the fixed upper-level model is used to solve the lower-level model, obtaining the nodal marginal electricity price and nodal carbon intensity; among which, the nodal marginal electricity price is obtained by solving the energy price model of the grid operator; The energy-carbon composite price is updated and calculated based on the marginal electricity price and carbon intensity at each node. By fixing the updated energy-carbon composite price, we solve the reconstructed upper-level model to obtain the updated traffic flow. Iteratively update the energy-carbon integrated price and traffic flow until the power-transportation coupled network reaches equilibrium, and output the equilibrium solution of the reconstructed two-level optimization model, which is the optimal scheduling result of the power-transportation coupled network.
[0101] In this embodiment, the formula for calculating the energy-carbon composite price is:
[0102] in, Indicates connection to the distribution network bus node The section of road The combined energy-carbon price; Indicates the distribution network bus node Marginal electricity price at the node; This represents the association matrix between charging segment and bus node. If the segment It is a charging section and connected to the bus node. ,but ,otherwise, ; This represents a set of charging sections.
[0103] Example 2: Based on Example 1, this embodiment of the invention provides a power-transportation coupled network cooperative optimization system based on dynamic carbon emission factors, which can be used to implement the power-transportation coupled network cooperative optimization method based on dynamic carbon emission factors as described in the foregoing embodiments. The system includes: The first module is used to construct a two-layer optimization model describing the coordinated operation of the power-transportation coupled network; The second module is used to perform equivalent reconstruction and linearization on the upper-level model of the bi-level optimization model, transforming it into a mixed-integer quadratic constrained programming problem, and obtaining the reconstructed bi-level optimization model. The third module is used to alternately solve the upper and lower models of the reconstructed two-layer optimization model and update the coupling variables until the power-transportation coupled network reaches an equilibrium state. The output is the equilibrium solution of the reconstructed two-layer optimization model, thus completing the collaborative optimization of the power-transportation coupled network.
[0104] According to embodiments of the present invention, the present invention also provides an electronic device, a readable storage medium, and a computer program product.
[0105] In an exemplary embodiment, the electronic device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the power-transportation coupled network collaborative optimization method based on dynamic carbon emission factors as described in Embodiment 1 above.
[0106] In an exemplary embodiment, the readable storage medium may be a non-transitory computer-readable storage medium storing computer instructions for causing the computer to execute the power-transportation coupled network cooperative optimization method based on dynamic carbon emission factors as described in Embodiment 1 above.
[0107] In an exemplary embodiment, the computer program product includes a computer program that, when executed by a processor, implements the power-transportation coupled network collaborative optimization method based on dynamic carbon emission factors as described in Embodiment 1 above.
[0108] The program code used to implement the methods of the present invention can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code can be executed entirely on the machine, partially on the machine, as a standalone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.
[0109] In the context of this invention, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.
[0110] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).
[0111] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with embodiments of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.
[0112] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.
[0113] Those skilled in the art will recognize that the embodiments described herein are intended to help the reader understand the principles of the invention, and should be understood that the scope of protection of the invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific modifications and combinations based on the technical teachings disclosed in this invention without departing from the spirit of the invention, and these modifications and combinations are still within the scope of protection of this invention.
Claims
1. A power-transport coupled network co-optimization method based on dynamic carbon emission factors, characterized in that, The method comprises the following steps. a bi-level optimization model describing the collaborative operation of the power-traffic coupling network is constructed; the upper model of the bi-level optimization model is equivalently reconstructed and linearized to transform into a mixed integer quadratic constraint programming problem, and a reconstructed bi-level optimization model is obtained; the upper model and the lower model of the reconstructed bi-level optimization model are alternately solved and the coupling variables are updated until an equilibrium state of the power-traffic coupling network is reached, and an equilibrium solution of the reconstructed bi-level optimization model is output to complete the collaborative optimization of the power-traffic coupling network.
2. The power-transport coupled network co-optimization method based on dynamic carbon emission factors according to claim 1, characterized in that, The upper model of the bi-level optimization model is a traffic network operator model, and the lower model of the bi-level optimization model is a distribution network operator model; the traffic network operator model and the distribution network operator model are connected through coupling variables; the coupling variables comprise charging demand transmitted by the traffic network operator model to the distribution network operator model and energy-carbon comprehensive price transmitted by the distribution network operator model to the traffic network operator model.
3. The power-transport coupled network co-optimization method based on dynamic carbon emission factors according to claim 2, characterized in that, The objective function of the traffic network operator model is: in, Indicates the overall economic cost. This indicates minimizing the overall economic cost; Indicates the monetary value of travel time; Indicates road segment Travel time; Indicates carbon tax; Indicates road segment Carbon emissions per unit of gasoline-powered vehicle Indicates road segment Length; Indicates road segment Traffic flow of fuel-powered vehicles; Indicates road segment Traffic to electric vehicles; Indicates connection to the distribution network bus node The section of road The combined energy-carbon price; This indicates the energy requirement of each electric vehicle; Represents a set of regular road segments; This represents a set of charging sections.
4. The power-transport coupled network co-optimization method based on dynamic carbon emission factors according to claim 3, characterized in that, The constraint conditions of the traffic network operator model comprise: a traffic conservation system constraint of the traffic network; wherein, denotes the road segments total flow; denotes the origin-destination pair index of the traffic network; denotes the set of origin-destination pairs of the traffic network; denotes the origin-destination pair of the traffic network; path the electric vehicle flow on the path denotes the origin-destination pair of the traffic network; path the fuel vehicle flow on the path denotes the road-segment-path incidence matrix of the fuel vehicle, if the path passes through the road segment then , otherwise, ; denotes the set of road segments of the traffic network; denotes the set of fuel vehicle paths connecting the origin-destination pair ; denotes the set of electric vehicle paths connecting the origin-destination pair ; denotes the road-segment-path incidence matrix of the electric vehicle, if the path passes through the road segment then , otherwise, ; a path flow constraint; wherein denotes the total electric vehicle traffic demand between the traffic network origin-destination pair denotes the total electric vehicle traffic demand between the traffic network origin-destination pair denotes the total electric vehicle traffic demand between the traffic network origin-destination pair denotes the total electric vehicle traffic demand between the traffic network origin-destination pair a link travel time constraint; wherein denotes the free travel time of a road segment ; denotes the road capacity of a road segment ; denotes the charging time on a road segment , if the road segment is a charging road segment, i.e. ; denotes the coefficient of the Davisson function of the road travel time function; denotes the charging station capacity on a road segment , if the road segment is a charging road segment, i.e. ; denotes the set of detour road segments; a link emission constraint; a path cost constraint of the electric vehicle; wherein representing a start and end point pair of a transportation network representing a start and end point pair of a transportation network electric vehicle trip consumption; a path cost constraint of the fuel vehicle; wherein representing a traffic network origin-destination pair path upper fuel consumption for a car trip; a complementary condition of the Wardrop user equilibrium principle; in, Indicates the origin and destination points of the transportation network Minimum cost for an electric vehicle; Indicates the origin and destination points of the transportation network Minimum consumption for gasoline-powered vehicles; This indicates complementary relaxation.
5. The power-transport coupled network co-optimization method based on dynamic carbon emission factors according to claim 4, characterized in that, The method for equivalently reconstructing and linearizing the upper model to transform into a mixed integer quadratic constraint programming problem is as follows: According to Wardrop's user equilibrium principle, the integrated economic cost is linearized as a linear function of and , resulting in the transformed integrated economic cost : the complementary condition of the Wardrop user equilibrium principle is transformed into a linear constraint containing binary variables through a Big-M method; wherein is a constant, and denotes the binary variable corresponding to each path; the link emission constraint and the link travel time constraint are approximately processed through piecewise linearization to obtain a linearized link emission constraint and a linearized link travel time constraint, and the equivalent reconstruction and linearization of the upper model are completed; the linearized link emission constraint is: wherein, denotes a road segment approximated by a linear function the carbon emission of an individual vehicle, denotes that the entire speed interval is divided into sub-intervals, denotes the breakpoint of the sub-interval, denotes, denotes a link speed variable, there are , denotes a special ordered set type 2 variable given automatically by the solver; the linearized link travel time constraint is: wherein and denotes an auxiliary variable.
6. The power-transport coupled network co-optimization method based on dynamic carbon emission factors according to claim 2, characterized in that, The distribution network operator model comprises an energy price model of the power grid operator and a carbon price model of the power grid operator; the carbon price model of the power grid operator is constructed based on a dynamic carbon emission factor, and the dynamic carbon emission factor is the node carbon intensity of each bus of the distribution network.
7. The power-transport coupled network co-optimization method based on dynamic carbon emission factors according to claim 6, characterized in that, The objective function of the energy price model of the power grid operator is: wherein, denotes minimization of total operation cost of the distribution network operator system; and denotes energy generation cost coefficient of the distributed power source connected to the distribution grid bus node ; denotes energy injection amount of the distributed power source at the distribution grid bus node ; denotes contract price of electricity purchased from the main grid by the distribution grid operator; denotes amount of electricity purchased from the main grid at the distribution grid bus node ; denotes set of distribution grid bus nodes connected to the main grid; denotes set of distribution grid bus nodes; The constraint conditions of the energy price model of the power grid operator comprise: wherein denotes the active power on the transmission line ; denotes the active power injection at the distribution grid bus node ; denotes the active power on the transmission line ; denotes the active demand at the distribution grid bus node ; denotes the resistance of the transmission line ; denotes the current squared value on the transmission line ; denotes the set of distribution grid transmission lines; denotes the sub-node of the distribution grid bus node , denotes the set of distribution grid bus nodes; denotes the reactive power on the transmission line ; denotes the reactive power injection at the distribution grid bus node ; denotes the reactive power on the transmission line ; denotes the conventional reactive demand at the distribution grid bus node ; denotes the reactance of the transmission line ; denotes the voltage squared value at the distribution grid bus node ; ; denotes the voltage squared value at the distribution grid bus node ; ; and denotes the active power injection bound at the distribution grid bus node ; and denotes the reactive power injection bound at the distribution grid bus node ; and denotes the voltage bound at the distribution grid bus node ; denotes the active demand on the charging road segment connected to the distribution grid bus node ; denotes the electric vehicle flow on the road segment if the road segment is a charging road segment; denotes the charging road segment-bus node incidence matrix, if the road segment is a charging road segment and connected to the distribution grid bus node , otherwise, .
8. The power-transport coupled network co-optimization method based on dynamic carbon emission factors according to claim 7, characterized in that, The expression formula of the carbon price model of the power grid operator is: wherein denotes the carbon price at the distribution grid bus node ; denotes the carbon tax, denotes the node carbon density at the distribution grid bus node ; denotes the active power injection at the distribution grid bus node ; denotes the injected carbon emission density at the distribution grid bus node ; denotes the active power on the transmission line ; denotes the power loss on the transmission line ; denotes the branch carbon flow density on the transmission line , i.e. the branch carbon intensity on the transmission line ; denotes the distribution grid bus node index, denotes the set of distribution grid bus nodes, denotes the parent node of the distribution grid bus node .
9. The power-transport coupled network co-optimization method based on dynamic carbon emission factors according to claim 8, characterized in that, The method for alternately solving the upper model and the lower model of the reconstructed bi-level optimization model and updating the coupling variables until an equilibrium state of the power-traffic coupling network is reached, and outputting the equilibrium solution of the reconstructed bi-level optimization model, comprises the following steps: initializing the iteration number, the initial traffic flow and the energy-carbon comprehensive price; fixing the traffic flow of the reconstructed upper model, solving the lower model to obtain the node marginal electricity price and the node carbon intensity; wherein the node marginal electricity price is obtained by solving the energy price model of the power grid operator; updating the calculation of the energy-carbon comprehensive price according to the node marginal electricity price and the node carbon intensity; fixing the updated energy-carbon comprehensive price, solving the reconstructed upper model to obtain the updated traffic flow; The energy-carbon integrated price and traffic flow are iteratively updated until the power-traffic coupled network equilibrium state is reached, and the equilibrium solution of the reconstructed bi-level optimization model, i.e. the optimal scheduling result of the power-traffic coupled network, is output.
10. The power-transport coupled network co-optimization method based on dynamic carbon emission factors according to claim 9, characterized in that, The calculation formula of the energy-carbon integrated price is: in, Indicates connection to the distribution network bus node The section of road The combined energy-carbon price; Indicates the distribution network bus node Marginal electricity price at the node; This represents the association matrix between charging segment and bus node. If the segment It is a charging section and connected to the bus node. ,but ,otherwise, ; This represents a set of charging sections.