Three-micro integrated power distribution network-traffic network system operation mode design method

Abstract

The invention discloses a three-micro integrated power distribution network-traffic network system operation mode design method. The method comprises the following steps: step 1, establishing a three-micro integrated power distribution network-traffic network coupling system structure; 2, establishing a three-micro fusion power distribution network-traffic network coupling system model; and step 3, establishing a three-micro operation mode based on the Nash-Kazakh theory. According to the scheme provided by the invention, all resources can be optimally coordinated, the operation economy and the resource utilization rate can be remarkably improved, and the superiority of the three-micro fusion power distribution network-traffic network coupling system operation mode is embodied.

Description

Technical Field

[0001] The present invention relates to the field of microgrid technology, and in particular to a method for designing an operation mode of a three-microgrid integrated distribution network-transportation network system. Background Art

[0002] At present, my country is in a stage of rapid economic development. The development and utilization of renewable energy such as wind and light has become the only way for my country to cope with increasingly severe energy and environmental problems. It is particularly necessary to carry out the design of active distribution network operation mode integrating micro power station, micro energy storage and micro energy network. At the same time, my country's electric vehicle industry is developing rapidly, and the access of large-scale electric vehicles has significantly changed the operation of the power system. At present, when building electric vehicle charging stations, wind and solar energy are generally connected to form micro power stations. The traffic characteristics of electric vehicles, such as complex factors such as travel destination, time, distance and path, lead to uneven distribution of traffic in urban traffic networks, which may cause traffic congestion and reduce operation efficiency, and affect the travel cost experience of users. Therefore, it is difficult to achieve the optimal social benefit by only considering the separate dispatch of electric vehicles, transportation networks or distribution networks. It is necessary to comprehensively consider the coupling characteristics of electric vehicles to the three-micro integrated distribution network-transportation network system. In addition, as more and more three-micro entities participate in power market transactions, there are usually multiple interest entities in a distribution area, which may form a cooperative alliance. In this case, how to fairly distribute the interests of members within the alliance and ensure the fairness of market transactions is a key issue.

[0003] Against the backdrop of widespread grid connection of micro-power stations, micro-energy storage and micro-energy networks, and a significant increase in the penetration rate of electric vehicles, in response to practical problems such as low efficiency of independent operation of transportation networks and distribution networks, network congestion, and considering "road network" constraints such as transportation network structure and flow, and exploring the distribution network's active control capabilities over power flow, as well as the potential for optimizing electric vehicle charging options and travel routes, a distribution network-transportation network coupling collaborative optimization model based on the integration of the three micros is formed, which is of great significance for improving the operating efficiency of distribution networks and transportation networks. Summary of the invention

[0004] The purpose of the present invention is to provide a method for designing an operation mode of a three-micro integrated distribution network-transportation network system to solve the problems raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] The design method of the operation mode of the three-micro integrated distribution network-transportation network system includes the following steps:

[0007] Step 1: Establish the three-micro integrated distribution network-transportation network coupling system structure;

[0008] Step 2: Establish a three-micro integrated distribution network-transportation network coupling system model;

[0009] Step 3: Establish a three-micro operation mode based on Nash-Harsanyi theory.

[0010] As a further technical solution of the present invention: the traffic network model in step 1 is composed of N nodes and M roads, wherein the roads include ordinary roads and charging roads, the distribution network is modeled as an undirected connected graph, and the traffic network is defined as a directed graph, wherein vertices and edges represent geographical locations and main roads, respectively;

[0011] The distribution network and the transportation network interact through electric vehicle charging stations. At the transportation network level, electric vehicles travel from one location to another in the transportation network and use charging stations distributed in the transportation network to meet transportation needs. At the distribution network level, charging stations are connected to multiple nodes. Electric vehicles, as potential mobile energy storage systems, are charged at charging stations according to the spatiotemporal behavior of charging stations and the resulting different energy levels.

[0012] Multiple nodes of the distribution network are also connected to microgrids, micro power stations and micro energy storage facilities. Among them, charging stations and micro power stations constitute an operating entity and are connected to the same node of the distribution network.

[0013] As a further technical solution of the present invention: Step 2 specifically includes: ① establishing a transportation network model; ② establishing a distribution network model; ③ establishing a micro energy network model; ④ establishing a micro energy storage model; ⑤ establishing a micro power station model.

[0014] As a further technical solution of the present invention: the establishment of the traffic network model is specifically: using graph theory to construct a model of a large traffic network, using probability distribution functions to describe the uncertainty and randomness of roads and electric vehicles in the traffic network, given a traffic network G T (M, A), where M is the node set, A is the road set, S is the charging station set; P is the set of all car origin-destination (OD) pairs; J p is the set of paths of electric vehicles between OD pairs p∈P;

[0015] Specifically include:

[0016] 1) Road capacity: Road capacity refers to the maximum traffic volume under normal traffic conditions. In the operation of the traffic network, for road a = (m, mm) ∈ A, where m, mm ∈ M and m and mm are the starting point and end point of road a respectively, the random fluctuation of its capacity ξ C,a It is characterized by normal distribution, denoted as ξ C,a ~N(μ C,a , σ C,a ), where μ C,a and σ C,a They are respectively C,a The mean and standard deviation ofC,a The probability density function of is expressed as:

[0017]

[0018] 2) Car travel demand and initial state: Car travel demand refers to the mobility demand between OD pairs; initial state refers to the initial node position of electric vehicles and the initial battery charge, and the random fluctuation of the travel demand of electric vehicles between OD pairsξ M,p and the initial state ξ M,I Characterized by normal distribution;

[0019] 3) Mixed traffic flow model: Roads are divided into ordinary roads and charging roads. Ordinary roads refer to the roads where electric vehicles normally travel in the traffic network, and charging roads refer to the roads where electric vehicles travel when entering and exiting charging stations for charging. The travel time of ordinary roads is represented by the road resistance function. The road resistance function approximates that the actual road vehicle travel time and traffic volume are monotonically increasing. The BPR function is used to describe the travel time of ordinary roads:

[0020]

[0021] In the formula, is the travel time of ordinary roads in the road traffic network set; x a is the traffic flow of section a; is the travel time of road a when the traffic volume is 0; c a is the baseline capacity of road a;

[0022] The charging time of the charging road is the time it takes for a car to pass through a charging station. The time spent includes queuing time and charging time. For the queuing time, the Davidson formula derived based on queuing theory is used to calculate the travel time of the charging road, which is expressed as follows:

[0023]

[0024] In the formula, is the travel time of electric vehicles at charging stations; c a is the upper limit of the number of vehicles that can be served by the charging station; γ is the service capacity level of the charging station;

[0025] 4) Probabilistic traffic assignment model based on user equilibrium criterion: The probabilistic traffic assignment model is established as follows:

[0026]

[0027] Depends on (1d)-(1h)

[0028] Among them, c Tis the travel cost of all car users in the transportation network; ω is the conversion coefficient of the equivalent cost of travel time; pr n is the charging electricity price of charging station n; f j,p is the traffic flow of electric vehicles on path p between OD pairs; F a,j,p is the charging amount of the electric vehicle at the charging station on the path p between the OD pairs; a,j,p is the correlation coefficient between the electric vehicle path p and the charging station n between the OD pairs. When the electric vehicle on the path p needs to be charged at the charging station n, ζ a,j,p is 1, otherwise it is 0; a,j,p is the correlation coefficient between path p and road a of electric vehicles between OD pairs. When path p contains ordinary roads, δ a,j,p is 1, otherwise it is 0.

[0029] The objective function consists of three items: the equivalent cost of electric vehicles’ travel time on ordinary roads in the traffic network, the equivalent cost of electric vehicles’ queuing time and charging time at charging stations, and the total charging cost of electric vehicles;

[0030] 6) Optimal charging path model: Generate the possible optimal path by establishing and solving the optimization model, and find a feasible path with the lowest travel cost for electric vehicles under the current traffic flow distribution;

[0031] For electric vehicles between OD pair p, the objective function of the optimization model for generating path j is:

[0032] Δδ j,p =I p (1i)

[0033]

[0034] L i -d a η≥L min (1k)

[0035] 0≤L i ≤1 (1l)

[0036]

[0037] Depends on (1i)-(1m)

[0038] Among them, δ j,p =[δ (i1,j1),j,p ,δ (i2,j2),j,p ,…,δ (in,jn),j,p ] is the decision variable of the model; L i is the battery SOC of the electric vehicle at node i; d a is the length of road a; η is the average driving distance power consumption coefficient; Lmin Allow minimum SOC for electric vehicle batteries; c i,p is the total travel cost of the path;

[0039] Among the above constraints, formula (1i) ensures the feasibility of the path; formula (1j) is the battery SOC balance constraint of the electric vehicle after passing through the road and charging station; formulas (1k) and (1l) limit the battery SOC of the electric vehicle at each node; formula (1m) ensures that the electric vehicle can only charge at one charging station on the path.

[0040] As a further technical solution of the present invention: the establishment of the distribution network model is specifically: using a linearized power flow branch model to model the distribution network to adapt to the dynamic changes of the system topology structure, and its mathematical details are described as follows:

[0041]

[0042]

[0043] Where: P i and Q i are the sum of active and reactive power injection at node i, respectively; P ij and Q ij is the active and reactive power on branch ij; Ω l is a set of branches; r ij and x ij is the resistance and reactance of branch ij; U i is the voltage at node i; z ij is a binary variable that takes the value 1 if branch (i, j) is closed and 0 otherwise; is the active and reactive load consumption of node i; are the active and reactive load losses of node i; are the active and reactive power output by DG at node i respectively; Ω G It is a group of substations;

[0044] Specifically, equations (2a) and (2b) are the active / reactive balance limits of node i, Ohm's law on branch (i, j) is expressed as (2c), and the total active power and reactive power injection of node i are determined by equations (2d) and (2e), respectively.

[0045] As a further technical solution of the present invention: the establishment of the micro energy network model is specifically: the electricity, heat and gas load requirements of the micro energy network should be equal to the corresponding energy provided, and the following constraints need to be met:

[0046] P L,t +P P2G,t =P buy,t -Psell,t +P WT,t +P CHP,t (3a)

[0047]

[0048] V P2G,t =P CHP,t / η e,CHP / Q H2 +V HS,t (3c)

[0049] Among them, constraints (3a), (3b) and (3c) are the energy balance of electricity, heat and gas load demands respectively; P L,t represents the power load power at time t; P P2G,t P represents the power consumption of electricity-to-gas conversion at time t; buy,t represents the amount of electricity purchased at time t; P sell,t represents the amount of electricity sold at time t; P WT,t represents the wind power generation at time t; P CHP,t represents the combined heat and power power at time t; η h,CHP / η e,CHP is the electric-to-heat conversion efficiency; H L,t is the heat load; c a is the specific heat capacity of air; T Si,t and T Zi,t are the air conditioning temperature and the indoor temperature; V P2G,t represents the amount of H2 produced at time t; V HS,t is the gas storage capacity of the gas tank at time t; η e,CHP is the cogeneration conversion efficiency; Q H2 is the calorific value of H2,

[0050] 1) Power-to-gas model: The principle of power-to-gas is: CO2+4H2→CH4+2H2O, and the generated H2 is calculated as:

[0051]

[0052] Where V P2G,t represents the amount of H2 produced by power-to-gas conversion at time t; Q H2 is the calorific value of H2; η P2G is the H2 conversion rate, and constraints (3e) and (3f) limit the ramping power and start-stop of the power-to-gas equipment;

[0053] μ P2G ·P P2G,min ≤P P2G,t ≤μ P2G ·P P2G,max (3e)

[0054] |PP2G,t -P P2G,,t-1 |≤P P2G,ramp (3f)

[0055] Among them, μ P2G P is the power-to-gas efficiency; P2G,min and P P2G,max P is the power limit of the power-to-gas equipment; P2G,ramp is the maximum climbing power of the power-to-gas equipment,

[0056] 2) Cogeneration model: Cogeneration equipment generates electricity and usable heat at the same time, and its power constraint is expressed as:

[0057] P CHP,min ≤P CHP,t ≤P CHP,max (3g)

[0058] Where P CHP,min and P CHP,max Power limits for cogeneration equipment;

[0059] 3) Hydrogen storage model: The storage of hydrogen needs to consider the inlet and outlet flow, pressure and temperature in the gas tank, etc. The model is expressed as:

[0060] V H2,t =V H2,t-1 -V HS,t-1 (3h)

[0061] 0≤V H2,t ≤V H2,r (3i)

[0062] V HS,min ≤V HS,t ≤V HS,max (3j)

[0063] Where (11a) is the balance constraint of the gas tank; V H2,t represents the output flow rate of hydrogen at time t; V HS,t V is the gas storage volume in the gas tank at time t; HS,max and V HS,min The capacity limit of the gas tank;

[0064] 4) HVAC model: The physical building structure based on HVAC includes outdoor space, indoor space and walls. The heat storage and heat transfer of HVAC-based building components are usually expressed by heat capacity and thermal resistance to describe their spatially distributed thermal behavior;

[0065] The energy balance equation corresponding to area i and wall i is expressed as:

[0066]

[0067] Among them, T out ,T Z ,T W are the building exterior, interior and wall nodes; C Z ,C W is the heat capacity of the building and walls; R in ,R out ,R W is the thermal resistance between nodes; Q rad is the absorption of solar radiation by walls and interior; Q HVAC is the heat from the HVAC; Q int is the random internal heat gain.

[0068] As a further technical solution of the present invention: the establishment of the micro energy storage model is specifically: since overcharging and overdischarging will reduce the service life of the battery, the upper and lower limits of the battery capacity and the upper and lower limits of the charging and discharging power are limited:

[0069] E BES,t =E BES,t-1 +η ch P BESch,t-1 -P BESdis,t-1 / η dis (4a)

[0070] 0.1*E BES,r ≤E BES,t ≤0.9*E BES,r (4b)

[0071] 0≤P BESch,t ≤P BESch,max (4c)

[0072] 0≤P BESdis,t ≤P BESdis,max (4d)

[0073] Where P BESch,t and P BESdis,t Respectively represent the battery energy storage charging and discharging power; P BESch,max and P BESdis,max It is the charging and discharging power limit of battery energy storage; E BES,r It is the rated capacity limit of battery energy storage.

[0074] As a further technical solution of the present invention: the establishment of the micro power station model specifically includes photovoltaic power generation, wind power generation and charging station;

[0075] Since overcharging and over-discharging will reduce the battery life, the upper and lower limits of the battery power are limited, which can be expressed as the following constraints:

[0076] 0.1*E EVB,i,r ≤E EVB,i,t ≤0.9*EEVB,i,r (5a)

[0077] Among them, E EVB,i,t represents the battery capacity of the i-th car at time t; E EVB,i,r represents the rated capacity of the battery of the i-th car;

[0078] Plug-in fast charging model: The DC fast charging power of the car needs to meet the following constraints:

[0079] 0≤P EVBch,i,t ≤P EVBch,max (5b)

[0080] E EVBch,i,t =P EVBch,i,t Δt (5c)

[0081] E EVB,i,t =E EVB,i,t-1 +η ch E EVBch,i,t-1 (5d)

[0082] Among them, P EVBch,i,t P represents the DC fast charging power of the i-th car at time t; EVBch,max Indicates the maximum power of DC fast charging; E EVBch,i,t represents the charging capacity of the i-th car at time t; η ch Indicates the charging efficiency,

[0083] For each car, the energy when leaving the charging station satisfies the following constraints:

[0084]

[0085] Among them, E EVB,i,intial and E EVB,i,dep They represent the battery power of the i-th car when it enters and leaves the charging station respectively.

[0086] As a further technical solution of the present invention: Step 3 specifically includes: an incentive mechanism based on the Nash-Hasani game theory to encourage transactions between micro power stations, micro energy storage and micro energy grids, and distribute the saved income to all participants according to their contributions.

[0087] As a further technical solution of the present invention: In the Nash-Hasani game model, the micro power station, micro energy storage and micro energy grid negotiate with each other to determine their respective transaction profiles and related payments. vc,n1,n2,t represents the transaction volume purchased by micro power station n1 from micro energy grid n2 at time t, e cv,n2,n1,t represents the transaction volume sold by micro-energy grid n2 to micro-power station n1 at time t, τ vc,n1,n2 represents the payment made by micro power station n1 to micro energy grid n2, τ cv,n2,n1Indicates that micro-grid n2 receives this payment from micro-power station n1. Similarly, e vb,n1,n3,t and e bv,n3,n1,t represents the transaction volume between micro power station and micro energy storage; e cb,n2,n3,t and e bc,n3,n2,t represents the transaction volume between micro-energy grid and micro-energy storage; τ vb,n1,n3 and τ bv,n3,n1 represents the transaction payment between the micro power station and micro energy storage; τ cb,n2,n3 and τ bc,n3,n2 Represents the transaction payment between the micro-grid and micro-energy storage. Obviously, the transaction profile and payment between the micro-power station, micro-energy storage and micro-energy grid should meet the following constraints to maintain resource balance and payment balance:

[0088]

[0089]

[0090] Therefore, the objective functions of the micro-power station without and after considering transactions are:

[0091]

[0092] Similarly, the objective functions of micro-grid and micro-energy storage before and after transactions are considered are:

[0093]

[0094] Not all entities will participate in the transaction, so the following constraints need to be met before deciding to trade:

[0095]

[0096] in, is the optimal value of objective functions (6c), (6e) and (6g), indicating the individual minimum cost achieved,

[0097] Next, the Nash-Hasani bargaining game theory quantifies bargaining power from two aspects: marginal contribution rate and energy transaction contribution rate.

[0098] Marginal contribution rate: By comparing the cost changes before and after participating in the cooperation, it reflects the marginal contribution of each participant in the cooperative operation. The marginal contribution rate calculation formula is as follows:

[0099]

[0100] in, and They are defined as the cost gap of micro power station, micro energy storage and micro energy grid respectively. and represents their costs in the cooperative operation scenario,

[0101] The calculation formula for energy trading contribution rate is:

[0102]

[0103] Based on the above two parameters, the composition of each subject is obtained:

[0104]

[0105] In the formula, σ1 and σ2 are the weights of marginal contribution rate and energy transaction contribution rate in bargaining power, respectively, satisfying σ1+σ2=1,

[0106] Normalizing the above formula, we can get the bargaining power of micro power station, micro energy storage and micro energy grid, namely:

[0107]

[0108] Finally, the mathematical model of the micro power station, micro energy storage and micro energy grid transaction problem proposed based on the Nash-Hasani game theory is expressed as:

[0109]

[0110] Depends on (1a)-(6m)

[0111] The proposed (6n) is equivalently decomposed into the following two sub-problems: social allocation sub-problem (6o) and transaction benefit sharing sub-problem (6p).

[0112]

[0113] Depends on (1a)-(5e), ​​(6c)-(6i)

[0114]

[0115] Depends on (6a)-(6b), (6j)-(6m).

[0116] Compared with the prior art, the beneficial effects of the present invention are: the scheme proposed in the present invention can optimally coordinate all resources, can significantly improve operational economy and resource utilization, and reflects the superiority of the proposed three-micro-integrated distribution network-transportation network coupling system operation mode. BRIEF DESCRIPTION OF THE DRAWINGS

[0117] Figure 1 This is the structural diagram of the three-micro integrated distribution network-transportation network coupling system.

[0118] Figure 2 This is a schematic diagram of traffic network road classification.

[0119] Figure 3This is a curve diagram of electricity trading among three micro-entities in Solution 1 of the present invention.

[0120] Figure 4 This is a curve diagram of the amount of electricity purchased between the three micro-entities in Solution 1 of the present invention.

[0121] Figure 5 This is a curve diagram of the amount of electricity purchased between the three micro-entities in Solution 2 of the present invention. DETAILED DESCRIPTION

[0122] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0123] like Figure 1-Figure 5 As shown, a design method for the operation mode of a three-micro integrated distribution network-transportation network system includes the following steps:

[0124] Step 1: Establish the three-micro integrated distribution network-transportation network coupling system structure:

[0125] like Figure 1 As shown in Figure 1, this paper takes a local transportation network as an example, constructs the distribution of the highway transportation network, and couples the distribution network of the IEEE 34 node. The transportation network model consists of 20 nodes and 37 roads, including 31 ordinary roads and 6 charging roads. Among them, the distribution network is modeled as an undirected connected graph; the transportation network is defined as a directed graph, in which vertices and edges represent certain geographical locations and main roads, respectively. The above distribution network and transportation network interact through electric vehicle charging stations. At the transportation network level, electric vehicles travel from one location to another in the transportation network, and use charging stations distributed in the transportation network to meet traffic needs, while taking into account possible traffic congestion. At the distribution network level, charging stations are connected to multiple nodes, and electric vehicles, as potential mobile energy storage systems, are charged at charging stations according to the spatiotemporal behavior of charging stations and the resulting different energy levels.

[0126] In addition, multiple nodes of the distribution network are also connected to distributed energy resources such as microgrids, micropower stations and micro energy storage facilities. Among them, charging stations and micropower stations constitute an operating entity and are connected to the same node of the distribution network. These energy nodes improve the adaptability of the distribution network and provide active and reactive power support to neighboring nodes, which not only helps to achieve source-load balance, but also promotes the distributed use of renewable energy.

[0127] Step 2: Establish a three-micro integrated distribution network-transportation network coupling system model:

[0128] (1) Establishing a transportation network model:

[0129] In the field of transportation research, graph theory methods are usually used to construct models of large-scale transportation networks, and traffic flow theory is used to characterize the overall operation of transportation networks. In the description of physical transportation networks based on graph theory, nodes refer to road intersections such as crossroads, while edges represent the roads themselves. Through the interconnection of nodes and edges, a connected graph representing the transportation network is formed. In this paper, probability distribution functions are used to describe the uncertainty and randomness of roads and electric vehicles in the transportation network. For roads, this randomness is reflected in the carrying capacity of the roads; for electric vehicles, this randomness is reflected in the travel demand of the vehicle and the remaining battery charge (SOC) at different driving stages. Given a transportation network G T (M, A), where M is the node set and A is the road set. Let S be the set of charging stations; P be the set of all car origin-destination (OD) pairs; J p is the set of paths of electric vehicles between OD pairs p∈P.

[0130] 1) Road capacity:

[0131] Road capacity refers to the maximum traffic volume under normal traffic conditions. In the operation of the traffic network, for road a = (m, mm) ∈ A (m, mm ∈ M and m and mm are the starting point and end point of road a respectively), the random fluctuation of its capacity ξ C,a It can be described by normal distribution, denoted as ξ C,a ~N(μ C,a , σ C,a ). Among them, μ C,a and σ C,a They are respectively C,a The mean and standard deviation of . Therefore, ξ C,a The probability density function of can be expressed as:

[0132]

[0133] 2) Automobile travel demand and initial status:

[0134] The vehicle travel demand refers to the mobility demand between OD pairs; the initial state refers to the initial node position of the electric vehicle and the initial battery charge. The random fluctuation of the travel demand of electric vehicles between OD pairsξ M,p and the initial state ξ M,I It is characterized by normal distribution.

[0135] 3) Mixed traffic flow model:

[0136] This article divides roads into two types: ordinary roads and charging roads. Ordinary roads refer to the roads that electric vehicles normally drive on in the traffic network, while charging roads refer to the roads that electric vehicles drive on when they enter and exit charging stations for charging. Figure 2 shown.

[0137] The travel time of ordinary roads can be expressed by the road resistance function, which approximately assumes that the actual road vehicle travel time and traffic volume are in a monotonically increasing relationship. This paper uses the BPR function to describe the travel time of ordinary roads:

[0138]

[0139] In the formula, is the travel time of ordinary roads in the road traffic network set; x a is the traffic flow of section a; is the travel time of road a when the traffic volume is 0; c a is the baseline capacity of road a.

[0140] The charging time of the charging road is the time it takes for the car to pass the charging station, which includes the queuing time and the charging time. For the queuing time, this paper uses the Davidson formula derived from the queuing theory to calculate the travel time of the charging road, which is expressed as follows:

[0141]

[0142] In the formula, is the travel time of electric vehicles at charging stations; c a is the upper limit of the number of vehicles that can be served by the charging station; γ is the service capacity level of the charging station.

[0143] 4) Probabilistic traffic assignment model based on user equilibrium criterion:

[0144] Based on the user equilibrium criterion and considering the influence of road-vehicle uncertainty in the traffic network, the probabilistic traffic assignment model is established as follows:

[0145]

[0146] Depends on (1d)-(1h)

[0147] Among them, c T is the travel cost of all car users in the transportation network; ω is the conversion coefficient of the equivalent cost of travel time; pr n is the charging electricity price of charging station n; f j,p is the traffic flow of electric vehicles on path p between OD pairs; F a,j,p is the charging amount of the electric vehicle at the charging station on the path p between the OD pairs; a,j,pis the correlation coefficient between the electric vehicle path p and the charging station n between the OD pairs. When the electric vehicle on the path p needs to be charged at the charging station n, ζ a,j,p is 1, otherwise it is 0; a,j,p is the correlation coefficient between path p and road a of electric vehicles between OD pairs. When path p contains ordinary roads, δ a,j,p is 1 if the value is true, otherwise it is 0.

[0148] The objective function consists of three items: the equivalent cost of electric vehicles' travel time on ordinary roads in the transportation network, the equivalent cost of electric vehicles' queuing time and charging time at charging stations, and the total charging cost of electric vehicles.

[0149] 5) Optimal charging path model:

[0150] In the study of electric vehicle path optimization, the traffic network is modeled as a weighted directed or undirected graph. The points in the graph represent the intersections of the road sections, the line segments in the graph represent the road sections, and the weights of the line segments represent the values ​​of the objective function to be optimized when the electric vehicle is traveling on the road section, including distance, time, etc. In the process of path optimization, the actual distance of the road section between two nodes is replaced by weights of different meanings, and the optimal path under different optimization objectives between the starting point and the end point can be obtained using the path optimization algorithm. This paper studies the optimal charging path for electric vehicles to comprehensively consider multiple information of the transportation network and electric vehicle users: travel demand, charge status and range of electric vehicles, charging price information, and congestion of transportation network roads, etc., and plans the charging path for electric vehicles with the goal of minimizing travel time and charging cost.

[0151] This paper generates the possible optimal path by establishing and solving the optimization model. The principle is to find a feasible path with the lowest travel cost for electric vehicles under the current traffic flow distribution.

[0152] For electric vehicles between OD pair p, the objective function of the optimization model for generating path j is:

[0153] Δδ j,p =I p (1i)

[0154]

[0155] L i -d a η≥L min (1k)

[0156] 0≤L i ≤1 (1l)

[0157]

[0158] Depends on (1i)-(1m)

[0159] Among them, δ j,p =[δ (i1,j1),j,p ,δ (i2,j2),j,p ,…,δ (in,jn),j,p ] is the decision variable of the model; L i is the battery SOC of the electric vehicle at node i; d a is the length of road a; η is the average driving distance power consumption coefficient; L min Allow minimum SOC for electric vehicle batteries; c i,p is the total travel cost of the route.

[0160] Among the above constraints, formula (1i) ensures the feasibility of the path; formula (1j) is the battery SOC balance constraint of the electric vehicle after passing through the road and charging station; formulas (1k) and (1l) limit the battery SOC of the electric vehicle at each node; formula (1m) ensures that the electric vehicle can only charge at one charging station on the path.

[0161] (2) Establish distribution network model:

[0162] The linearized power flow branch model is used to model the distribution network to adapt to the dynamic changes of the system topology. The mathematical details are described as follows:

[0163]

[0164] Where: P i and Q i are the sum of active and reactive power injection at node i, respectively; P ij and Q ij is the active and reactive power on branch ij; Ω l is a set of branches; r ij and x ij is the resistance and reactance of branch ij; U i is the voltage at node i; z ij is a binary variable that takes the value 1 if branch (i, j) is closed and 0 otherwise; is the active and reactive load consumption of node i; are the active and reactive load losses of node i; are the active and reactive power output by DG at node i respectively; Ω G It is a group of substations.

[0165] Specifically, equations (2a) and (2b) are the active / reactive balance limits of node i, and Ohm's law on branch (i, j) is expressed as (2c). The total active power and reactive power injection at node i are determined by equations (2d) and (2e), respectively.

[0166] (3) Establishing micro-energy network model:

[0167] The electricity, heat and gas load demand of the micro energy grid should be equal to the corresponding energy provided, and the following constraints need to be met:

[0168] P L,t +P P2G,t =P buy,t -P sell,t +P WT,t +P CHP,t (3a)

[0169]

[0170] V P2G,t =P CHP,t / η e,CHP / Q H2 +V HS,t (3c)

[0171] Among them, constraints (3a), (3b) and (3c) are the energy balance of electricity, heat and gas load demands respectively; P L,t represents the power load power at time t; P P2G,t P represents the power consumption of electricity-to-gas conversion at time t; buy,t represents the amount of electricity purchased at time t; P sell,t represents the amount of electricity sold at time t; P WT,t represents the wind power generation at time t; P CHP,t represents the combined heat and power power at time t; η h,CHP / η e,CHP is the electric-to-heat conversion efficiency; H L,t is the heat load; c a is the specific heat capacity of air; T Si,t and T Zi,t are the air conditioning temperature and the indoor temperature; V P2G,t represents the amount of H2 produced at time t; V HS,t is the gas storage capacity of the gas tank at time t; η e,CHP is the cogeneration conversion efficiency; Q H2 is the calorific value of H2.

[0172] 1) Power-to-gas model: The principle of power-to-gas is: CO2+4H2→CH4+2H2O. The generated H2 is calculated as:

[0173]

[0174] Where V P2G,t represents the amount of H2 produced by power-to-gas conversion at time t; Q H2 is the calorific value of H2; η P2Gis the conversion rate of H2.

[0175] Constraints (3e) and (3f) limit the ramping power and the starting and stopping of the power-to-gas equipment.

[0176] μ P2G ·P P2G,min ≤P P2G,t ≤μ P2G ·P P2G,max (3e)

[0177] |P P2G,t -P P2G,,t-1 |≤P P2G,ramp (3f)

[0178] Among them, μ P2G P is the power-to-gas efficiency; P2G,min and P P2G,max P is the power limit of the power-to-gas equipment; P2G,ramp It is the maximum climbing power of the power-to-gas equipment.

[0179] 2) Cogeneration model: Cogeneration equipment can generate electricity and usable heat at the same time, and its power constraint is expressed as:

[0180] P CHP,min ≤P CHP,t ≤P CHP,max (3g)

[0181] Where P CHP,min and P CHP,max Power limits for CHP equipment.

[0182] 3) Hydrogen storage model: The storage of hydrogen needs to consider the inlet and outlet flow, pressure and temperature in the gas tank, etc. The model is expressed as:

[0183] V H2,t =V H2,t-1 -V HS,t-1 (3h)

[0184] 0≤V H2,t ≤V H2,r (3i)

[0185] V HS,min ≤V HS,t ≤V HS,max (3j)

[0186] Where (3h) is the balance constraint of the gas tank; V H2,t represents the output flow rate of hydrogen at time t; V HS,t V is the gas storage volume in the gas tank at time t; HS,max and V HS,min The capacity limit of the gas tank.

[0187] 4) HVAC model: The physical building structure based on HVAC includes outdoor space, indoor space and walls. The heat storage and heat transfer of HVAC-based building components are usually expressed by heat capacity and thermal resistance to describe their spatially distributed thermal behavior.

[0188] The energy balance equation corresponding to area i and wall i can be expressed as:

[0189]

[0190] Among them, T out ,T Z ,T W are the building exterior, interior and wall nodes; C Z ,C W is the heat capacity of the building and walls; R in ,R out ,R W is the thermal resistance between nodes; Q rad is the absorption of solar radiation by walls and interior; Q HVAC is the heat from the HVAC; Q int is the random internal heat gain.

[0191] (4) Establishing a micro energy storage model:

[0192] Battery energy storage model: Since overcharging and overdischarging will reduce the battery life, the upper and lower limits of battery capacity and the upper and lower limits of charge and discharge power should be enforced:

[0193] E BES,t =E BES,t-1 +η ch P BESch,t-1 -P BESdis,t-1 / η dis (4a)

[0194] 0.1*E BES,r ≤E BES,t ≤0.9*E BES,r (4b)

[0195] 0≤P BESch,t ≤P BESch,max (4c)

[0196] 0≤P BESdis,t ≤P BESdis,max (4d)

[0197] Where P BESch,t and P BESdis,t Respectively represent the battery energy storage charging and discharging power; P BESch,max and P BESdis,max It is the charging and discharging power limit of battery energy storage; EBES,r It is the rated capacity limit of battery energy storage.

[0198] (5) Establishing micro power station model:

[0199] The micro power station model established in this paper includes photovoltaic power generation, wind power generation and charging stations. Since the distribution network can absorb new energy, distributed power sources and multiple loads, there is no wind and solar power abandonment in the system.

[0200] Since overcharging and over-discharging will reduce the battery life, the upper and lower limits of the battery power are limited, which can be expressed as the following constraints:

[0201] 0.1*E EVB,i,r ≤E EVB,i,t ≤0.9*E EVB,i,r (5a)

[0202] Among them, E EVB,i,t represents the battery capacity of the i-th car at time t; E EVB,i,r Represents the rated capacity of the battery of the i-th car.

[0203] Plug-in fast charging model: The DC fast charging power of the car needs to meet the following constraints:

[0204] 0≤P EVBch,i,t ≤P EVBch,max (5b)

[0205] E EVBch,i,t =P EVBch,i,t Δt (5c)

[0206] E EVB,i,t =E EVB,i,t-1 +η ch E EVBch,i,t-1 (5d)

[0207] Among them, P EVBch,i,t P represents the DC fast charging power of the i-th car at time t; EVBch,max Indicates the maximum power of DC fast charging; E EVBch,i,t represents the charging capacity of the i-th car at time t; η ch Indicates charging efficiency.

[0208] For each car, the energy when leaving the charging station satisfies the following constraints:

[0209]

[0210] Among them, E EVB,i,intial and E EVB,i,dep They represent the battery power of the i-th car when it enters and leaves the charging station respectively.

[0211] Step 3: Establish a three-micro operation mode based on Nash-Harsanyi theory:

[0212] This section proposes an incentive mechanism based on the Nash-Hasani game theory to encourage transactions between micro-power stations, micro-energy storage and micro-energy grids, and distribute the saved income to all participants according to their contributions. Unlike the standard symmetric Nash game, the Nash-Hasani game considers the impact of various factors on the bargaining power of stakeholders.

[0213] In the proposed Nash-Hasani game model, micro-power stations, micro-energy storage and micro-energy grid negotiate with each other to determine their respective transaction profiles and related payments. vc,n1,n2,t represents the transaction volume purchased by micro power station n1 from micro energy grid n2 at time t, e cv,n2,n1,t Represents the transaction volume sold by micro-energy grid n2 to micro-power station n1 at time t. vc,n1,n2 represents the payment made by micro power station n1 to micro energy grid n2, τ cv,n2,n1 Indicates that micro-grid n2 receives this payment from micro-power station n1. vb,n1,n3,t and e bv,n3,n1,t represents the transaction volume between micro power station and micro energy storage; e cb,n2,n3,t and e bc,n3,n2,t represents the transaction volume between micro-energy grid and micro-energy storage; τ vb,n1,n3 and τ bv,n3,n1 represents the transaction payment between the micro power station and micro energy storage; τ cb,n2,n3 and τ bc,n3,n2 Represents the transaction payment between the micro-grid and micro-storage. Obviously, the transaction profile and payment between micro-power stations, micro-storage and micro-grid should meet the following constraints to maintain resource balance and payment balance:

[0214]

[0215] Therefore, the objective functions of the micro-power station without and after considering transactions are:

[0216]

[0217] Similarly, the objective functions of micro-grid and micro-energy storage before and after transactions are considered are:

[0218]

[0219]

[0220] Not all entities will participate in the transaction. Therefore, the following constraints need to be met before deciding to conduct the transaction.

[0221]

[0222] in, is the optimal value of objective functions (6c), (6e) and (6g), indicating the individual minimum cost achieved.

[0223] Next, Nash-Hasani bargaining game theory measures bargaining power by considering multiple factors, which is a powerful tool to solve the problem of profit distribution. Using Nash-Hasani bargaining game theory, bargaining power is quantified from two aspects: marginal contribution rate and energy transaction contribution rate.

[0224] Marginal contribution rate: By comparing the cost changes before and after the cooperation, the marginal contribution of each participant in the cooperative operation can be reflected. The marginal contribution rate calculation formula is as follows:

[0225]

[0226] in, and They are defined as the cost gap of micro power stations, micro energy storage and micro energy grid respectively. and Represent their costs in a cooperative operation scenario.

[0227] The calculation formula for energy trading contribution rate is:

[0228]

[0229] Based on the above two parameters, the composition of each subject can be obtained:

[0230]

[0231] In the formula, σ1 and σ2 are the weights of marginal contribution rate and energy transaction contribution rate in bargaining power, respectively, satisfying σ1+σ2=1.

[0232] Normalizing the above formula, we can get the bargaining power of micro power station, micro energy storage and micro energy grid, that is:

[0233]

[0234] Finally, the mathematical model of the micro power station, micro energy storage and micro energy grid transaction problem proposed based on the Nash-Hasani game theory can be expressed as:

[0235]

[0236] Depends on (1a)-(6m)

[0237] The proposed (6n) can be equivalently decomposed into the following two sub-problems: the social allocation sub-problem (6o) and the transaction benefit sharing sub-problem (6p).

[0238]

[0239] Depends on (1a)-(5e), ​​(6c)-(6i)

[0240]

[0241] Depends on (6a)-(6b), (6j)-(6m).

[0242] Effects after implementation of the present invention:

[0243] In the present invention, the proposed trading game scheme of the three-micro integrated distribution network-transportation network coupling system is tested. The following three schemes are compared to verify the effectiveness and superiority of the present invention: Scheme 1 is the three-micro trading scheme based on the Nash-Hasani theory proposed by the present invention; Scheme 2 is the three-micro scheduling scheme without considering transactions.

[0244] Figure 3 It is the electricity trading curve among the three micro-entities in Scheme 1. Figure 4 is the electricity purchase curve between the three micro-entities in Scheme 1-2. Figure 3 It can be found that the three micro entities have been trading within 24 hours. In the first 10 hours, because renewable energy is not enough to provide the power load of micro power stations and micro energy grids, more electricity needs to be purchased from micro energy storage. After 11 o'clock, the micro energy storage power trading curve gradually decreases. This is because the output of renewable energy increases, and micro power stations and micro energy grids do not need to purchase electricity from micro energy storage. After 12 o'clock, the micro energy storage power trading value is negative, which means that it needs to purchase electricity from the other two to supplement its energy storage. Figure 4 As shown in the figure, before 9 a.m., the electricity price is low, and a large amount of electricity is purchased for energy supply. After 9 a.m., the amount of electricity purchased is all reduced to 0. In general, the amount of electricity purchased from the market by the microgrid in Scheme 1 is generally less than that in Scheme 2, which helps it reduce its operating costs.

[0245] Table 1: Optimization results of different performance indicators of scheme 1-2;

[0246] plan 1 2 System operating cost ($) 982.64 1216.58 Battery degradation cost ($) 329.92 398.47 Power Purchased(kWh) 2213.74 2359.74

[0247] Table 1 shows the comparison of system operation cost, battery degradation cost and power purchase amount of Scheme 1-2. It can be seen that Scheme 1 can improve system operation economy and resource utilization with lower power purchase amount and resource utilization compared with Scheme 2. Compared with Scheme 2, the system operation cost of Scheme 1 is reduced by 19.23%.

[0248] Table 2: Cost and payment optimization results of Scheme 1-2;

[0249] Micro Power Station Micro Energy Network Micro Energy Storage Total Cost (no transactions) ($) 154.37 804.67 257.54 1216.58 Cost (with transaction) ($) 115.65 657.97 209.02 982.64 Payment(Transaction) ($) 103.57 -32.65 -70.92 0 Cost + Payment (with transaction) ($) 219.22 625.32 138.1 982.64

[0250] Table 2 shows the costs and payments of micro-power stations, micro-grids and micro-storage with and without transactions. It can be found that after the introduction of transactions, the costs of each of the three micro-subjects have been reduced, by 25.08%, 18.23% and 18.84% respectively. Among them, the total operating cost of the three-micro system has been reduced from 1216.58 to 982.64, a decrease of 19.23%. This result confirms the effectiveness of the proposed scheme. It can be seen that each of the three micro-subjects benefits from the transaction by weighing costs and benefits. In addition, the micro-power station paid 103.57 in the transaction, while the micro-energy grid and micro-storage received payments of 32.65 and 70.92 respectively. This shows that the micro-power station purchased resources or services from the micro-energy grid and micro-storage in the transaction. Although payments occurred, the total costs and payments remained balanced after the transaction, both of which were 982.64, indicating that the transaction system is self-balanced as a whole, without additional costs or benefits. In summary, compared with Scheme 2, the scheme proposed in the present invention can optimally coordinate all resources, significantly improve operational economy and resource utilization, and embodies the superiority of the proposed three-micro-integrated distribution network-transportation network coupling system operation mode.

Claims

1. The design method of the operation mode of the three-micro integrated distribution network-transportation network system is characterized by: The following steps are involved: Step 1: Establish the three-micro integrated distribution network-transportation network coupling system structure; Step 2: Establish a three-micro integrated distribution network-transportation network coupling system model; Step 3: Establish a three-micro operation mode based on Nash-Harsanyi theory.

2. The method for designing the operation mode of the three-micro integrated distribution network-transportation network system according to claim 1 is characterized in that: The traffic network model in step 1 consists of N nodes and M roads, where the roads include ordinary roads and charging roads. The distribution network is modeled as an undirected connected graph, and the traffic network is defined as a directed graph, where vertices and edges represent geographical locations and main roads, respectively; The distribution network and the transportation network interact through electric vehicle charging stations. At the transportation network level, electric vehicles travel from one location to another in the transportation network and use charging stations distributed in the transportation network to meet transportation needs. At the distribution network level, charging stations are connected to multiple nodes. Electric vehicles, as potential mobile energy storage systems, are charged at charging stations according to the spatiotemporal behavior of charging stations and the resulting different energy levels. Multiple nodes of the distribution network are also connected to microgrids, micro power stations and micro energy storage facilities. Among them, charging stations and micro power stations constitute an operating entity and are connected to the same node of the distribution network.

3. The method for designing the operation mode of the three-micro integrated distribution network-transportation network system according to claim 1 is characterized in that: The step 2 specifically includes: ① establishing a transportation network model; ② establishing a distribution network model; ③ establishing a micro energy network model; ④ establishing a micro energy storage model; ⑤ establishing a micro power station model.

4. The method for designing the operation mode of the three-micro-integrated distribution network-transportation network system according to claim 3 is characterized in that: The specific steps of establishing the traffic network model are as follows: using graph theory to construct a large-scale traffic network model, using probability distribution functions to describe the uncertainty and randomness of roads and electric vehicles in the traffic network, given a traffic network G T (M, A), where M is the node set, A is the road set, S is the charging station set; P is the set of all car origin-destination (OD) pairs; J p is the set of paths of electric vehicles between OD pairs p∈P; Specifically include: 1) Road capacity: Road capacity refers to the maximum traffic volume under normal traffic conditions. In the operation of the traffic network, for road a = (m, mm) ∈ A, where m, mm ∈ M and m and mm are the starting point and end point of road a respectively, the random fluctuation of its capacity ξ C,a It is characterized by normal distribution, denoted as ξ C,a ~N(μ C,a , σ C,a ), where μ C,a and σ C,a They are respectively C,a The mean and standard deviation of C,a The probability density function of is expressed as: 2) Car travel demand and initial state: Car travel demand refers to the mobility demand between OD pairs; initial state refers to the initial node position of electric vehicles and the initial battery charge, and the random fluctuation of the travel demand of electric vehicles between OD pairsξ M,p and the initial state ξ M,I Characterized by normal distribution; 3) Mixed traffic flow model: Roads are divided into ordinary roads and charging roads. Ordinary roads refer to the roads where electric vehicles normally travel in the traffic network, and charging roads refer to the roads where electric vehicles travel when entering and exiting charging stations for charging. The travel time of ordinary roads is represented by the road resistance function. The road resistance function approximates that the actual road vehicle travel time and traffic volume are monotonically increasing. The BPR function is used to describe the travel time of ordinary roads: In the formula, is the travel time of ordinary roads in the road traffic network set; x a is the traffic flow of section a; is the travel time of road a when the traffic volume is 0; c a is the baseline capacity of road a; The charging time of the charging road is the time it takes for a car to pass through a charging station. The time spent includes queuing time and charging time. For the queuing time, the Davidson formula derived based on queuing theory is used to calculate the travel time of the charging road, which is expressed as follows: In the formula, is the travel time of electric vehicles at the charging station; c a is the upper limit of the number of vehicles that can be served by the charging station; γ is the service capacity level of the charging station; 4) Probabilistic traffic assignment model based on user equilibrium criterion: The probabilistic traffic assignment model is established as follows: Depends on (1d)-(1h) Among them, c T is the travel cost of all car users in the transportation network; ω is the conversion coefficient of the equivalent cost of travel time; pr n is the charging electricity price of charging station n; f j,p is the traffic flow of electric vehicles on path p between OD pairs; F a,j,p is the charging amount of the electric vehicle at the charging station on the path p between the OD pairs; a,j,p is the correlation coefficient between the electric vehicle path p and the charging station n between the OD pairs. When the electric vehicle on the path p needs to be charged at the charging station n, ζ a,j,p is 1, otherwise it is 0; a,j,p is the correlation coefficient between path p and road a of electric vehicles between OD pairs. When path p contains ordinary roads, δ a,j,p is 1, otherwise it is 0. The objective function consists of three items: the equivalent cost of electric vehicles’ travel time on ordinary roads in the traffic network, the equivalent cost of electric vehicles’ queuing time and charging time at charging stations, and the total charging cost of electric vehicles; 5) Optimal charging path model: Generate the possible optimal path by establishing and solving the optimization model, and find a feasible path with the lowest travel cost for electric vehicles under the current traffic flow distribution; For electric vehicles between OD pair p, the objective function of the optimization model for generating path j is: Dd j,p =I p (1i) L i -d a η≥L min (1k) 0≤L i ≤1 (1l) Depends on (1i)-(1m); Among them, δ j,p =[δ (i1,j1),j,p ,δ (i2,j2),j,p ,…,δ (in,jn),j,p ] is the decision variable of the model; L i is the battery SOC of the electric vehicle at node i; d a is the length of road a; η is the average driving distance power consumption coefficient; L min Allow minimum SOC for electric vehicle batteries; c i,p is the total travel cost of the path; In the above formulas, formula (1i) ensures the feasibility of the path; formula (1j) is the battery SOC balance constraint of the electric vehicle after passing through the road and charging station; formulas (1k) and (1l) limit the battery SOC of the electric vehicle at each node; formula (1m) ensures that the electric vehicle can only charge at one charging station on the path.

5. The method for designing the operation mode of the three-micro integrated distribution network-transportation network system according to claim 3 is characterized in that: The establishment of the distribution network model is specifically: using a linearized power flow branch model to model the distribution network to adapt to the dynamic changes of the system topology structure, and its mathematical details are described as follows: Where: P i and Q i are the sum of active and reactive power injection at node i, respectively; P ij and Q ij is the active and reactive power on branch ij; Ω l is a set of branches; r ij and x ij is the resistance and reactance of branch ij; U i is the voltage at node i; z ij is a binary variable that takes the value 1 if branch (i, j) is closed and 0 otherwise; is the active and reactive load consumption of node i; are the active and reactive load losses of node i; are the active and reactive power output by DG at node i respectively; Ω G It is a group of substations; Equations (2a) and (2b) are the active / reactive balance limits of node i, Ohm's law on branch (i, j) is expressed as (2c), and the total active power and reactive power injection of node i are determined by equations (2d) and (2e), respectively.

6. The method for designing the operation mode of the three-micro integrated distribution network-transportation network system according to claim 3 is characterized in that: The establishment of the micro energy grid model is specifically: the electricity, heat and gas load requirements of the micro energy grid should be equal to the corresponding energy provided, and the following constraints need to be met: P L,t +P P2G,t =P buy,t -P sell,t +P WT,t +P CHP,t (3a) V P2G,t =P CHP,t / η e,CHP / Q H2 +V HS,t (3c) Among them, constraints (3a), (3b) and (3c) are the energy balance of electricity, heat and gas load demands respectively; P L,t represents the power load power at time t; P P2G,t P represents the power consumption of electricity-to-gas conversion at time t; buy,t represents the amount of electricity purchased at time t; P sell,t represents the amount of electricity sold at time t; P WT,t represents the wind power generation at time t; P CHP,t represents the combined heat and power power at time t; η h,CHP / η e,CHP is the electric-to-heat conversion efficiency; H L,t is the heat load; c a is the specific heat capacity of air; T Si,t and T Zi,t are the air conditioning temperature and the indoor temperature; V P2G,t represents the amount of H2 produced at time t; V HS,t is the gas storage capacity of the gas tank at time t; η e,CHP is the cogeneration conversion efficiency; Q H2 is the calorific value of H2, 1) Power-to-gas model: The principle of power-to-gas is: CO2+4H2→CH4+2H2O, and the generated H2 is calculated as: Where V P2G,t represents the amount of H2 produced by power-to-gas conversion at time t; Q H2 is the calorific value of H2; η P2G is the H2 conversion rate, and constraints (3e) and (3f) limit the ramping power and start-stop of the power-to-gas equipment; μ P2G ·P P2G,min ≤P P2G,t ≤μ P2G ·P P2G,max (3rd) |P P2G,t -P P2G,,t-1 |≤P P2G,ramp (3f) Among them, μ P2G P is the power-to-gas efficiency; P2G,min and P P2G,max P is the power limit of the power-to-gas equipment; P2G,ramp is the maximum climbing power of the power-to-gas equipment, 2) Cogeneration model: Cogeneration equipment generates electricity and usable heat at the same time, and its power constraint is expressed as: P CHP,min ≤P CHP,t ≤P CHP,max (3g) Where P CHP,min and P CHP,max Power limits for cogeneration equipment; 3) Hydrogen storage model: The storage of hydrogen needs to consider the inlet and outlet flow, pressure and temperature in the gas tank, etc. The model is expressed as: V H2,t =V H2,t-1 -V HS,t-1 (3h) 0≤V H2,t ≤V H2,r (3i) In HS,min ≤V HS,t ≤V HS,max (3j) Where (3h) is the balance constraint of the gas tank; V H2,t represents the output flow rate of hydrogen at time t; V HS,t V is the gas storage volume in the gas tank at time t; HS,max and V HS,min The capacity limit of the gas tank; 4) HVAC model: The physical building structure based on HVAC includes outdoor space, indoor space and walls. The heat storage and heat transfer of HVAC-based building components are usually expressed by heat capacity and thermal resistance to describe their spatially distributed thermal behavior; The energy balance equation corresponding to area i and wall i is expressed as: Among them, T out ,T Z ,T W are the building exterior, interior and wall nodes; C Z ,C W is the heat capacity of the building and walls; R in ,R out ,R W is the thermal resistance between nodes; Q rad is the absorption of solar radiation by walls and interior; Q HVAC is the heat from the HVAC; Q int is the random internal heat gain.

7. The method for designing the operation mode of the three-micro integrated distribution network-transportation network system according to claim 3 is characterized in that: The micro energy storage model is specifically established as follows: since overcharging and overdischarging will reduce the battery life, the upper and lower limits of battery capacity and the upper and lower limits of charge and discharge power are limited: E BES,t JE BES,t-1 +η ch P.S BESch,t-1 -P BESdis,t-1 / η dis (4a) 0.1*E BES,r ≤E BES,t ≤0.9*E BES,r (4b) 0≤P BESch,t ≤P BESch,max (4c) 0≤P BESdis,t ≤P BESdis,max (4d) Where P BESch,t and P BESdis,t Respectively represent the battery energy storage charging and discharging power; P BESch,max and P BESdis,max It is the charging and discharging power limit of battery energy storage; E BES,r It is the rated capacity limit of battery energy storage.

8. The method for designing the operation mode of the three-micro integrated distribution network-transportation network system according to claim 3 is characterized in that: The establishment of the micro power station model specifically includes photovoltaic power generation, wind power generation and charging station; Since overcharging and over-discharging will reduce the battery life, the upper and lower limits of the battery power are limited, which can be expressed as the following constraints: 0.1*E EVB,i,r ≤E EVB,i,t ≤0.9*E EVB,i,r (5a) Among them, E EVB,i,t represents the battery capacity of the i-th car at time t; E EVB,i,r represents the rated capacity of the battery of the i-th car; Plug-in fast charging model: The DC fast charging power of the car needs to meet the following constraints: 0≤P EVBch,i,t P EVBch,max (5b) AND EVBch,i,t =P EVBch,i,t Δt (5c) AND EVB,i,t =And EVB,i,t-1 +η ch AND EVBch,i,t-1 (5d) Among them, P EVBch,i,t P represents the DC fast charging power of the i-th car at time t; EVBch,max Indicates the maximum power of DC fast charging; E EVBch,i,t represents the charging capacity of the i-th car at time t; η ch Indicates the charging efficiency, For each car, the energy when leaving the charging station satisfies the following constraints: Among them, E EVB,i,intial and E EVB,i,dep They represent the battery power of the i-th car when it enters and leaves the charging station respectively.

9. The method for designing the operation mode of the three-micro integrated distribution network-transportation network system according to claim 1 is characterized in that: The step 3 is specifically: an incentive mechanism based on the Nash-Hasani game theory to encourage transactions between micro power stations, micro energy storage and micro energy grids, and distribute the saved income to all participants according to their contributions.

10. The method for designing the operation mode of the three-micro integrated distribution network-transportation network system according to claim 9 is characterized in that: In the Nash-Hasani game model, micro-power stations, micro-energy storage and micro-energy grid negotiate with each other to determine their respective transaction profiles and related payments. vc,n1,n2,t represents the transaction volume purchased by micro power station n1 from micro energy grid n2 at time t, e cv,n2,n1,t represents the transaction volume sold by micro-energy grid n2 to micro-power station n1 at time t, τ vc,n1,n2 represents the payment made by micro power station n1 to micro energy grid n2, τ cv,n2,n1 Indicates that micro-grid n2 receives this payment from micro-power station n1. Similarly, e vb,n1,n3,t and e bv,n3,n1,t represents the transaction volume between micro power station and micro energy storage; e cb,n2,n3,t and e bc,n3,n2,t represents the transaction volume between micro-energy grid and micro-energy storage; τ vb,n1,n3 and τ bv,n3,n1 represents the transaction payment between the micro power station and micro energy storage; τ cb,n2,n3 and τ bc,n3,n2 Represents the transaction payment between the micro-grid and micro-energy storage. Obviously, the transaction profile and payment between the micro-power station, micro-energy storage and micro-energy grid should meet the following constraints to maintain resource balance and payment balance: Therefore, the objective functions of the micro-power station without and after considering transactions are: Similarly, the objective functions of micro-grid and micro-energy storage before and after transactions are considered are: Not all entities will participate in the transaction, so the following constraints need to be met before deciding to trade: in, is the optimal value of objective functions (6c), (6e) and (6g), indicating the individual minimum cost achieved, Next, Josh Hasani bargaining game theory quantifies bargaining power from two aspects: marginal contribution rate and energy transaction contribution rate. Marginal contribution rate: By comparing the cost changes before and after participating in the cooperation, it reflects the marginal contribution of each participant in the cooperative operation. The marginal contribution rate calculation formula is as follows: in, and They are defined as the cost gap of micro power station, micro energy storage and micro energy grid respectively. and represents their costs in the cooperative operation scenario, The formula for calculating the energy transaction contribution rate is: Based on the above two parameters, the composition of each subject is obtained: In the formula, σ1 and σ2 are the weights of marginal contribution rate and energy transaction contribution rate in bargaining power, respectively, satisfying σ1+σ2=1, Normalizing the above formula, we can get the bargaining power of micro power station, micro energy storage and micro energy grid, namely: Finally, the mathematical model of the micro power station, micro energy storage and micro energy grid transaction problem proposed based on the Nash-Hasani game theory is expressed as: Depends on (1a)-(6m) The proposed (6n) is equivalently decomposed into the following two sub-problems: social allocation sub-problem (6o) and transaction benefit sharing sub-problem (6p). Depends on (1a)-(5e), ​​(6c)-(6i) Depends on (6a)-(6b), (6j)-(6m).

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