Power distribution network and traffic network cooperative operation method considering multi-subject benefit coupling

Abstract

The invention provides a power distribution network and traffic network cooperative operation method considering multi-subject benefit coupling, and belongs to the technical field of network system control, and the method comprises the following steps: S1, constructing a three-layer power traffic coupling network cooperative operation model, comprising a lower-layer traffic network user balance model, a middle-layer charging network operator optimal pricing model and an upper-layer power distribution network second-order cone relaxation optimal power flow model; s2, converting the lower-layer traffic network user equilibrium model into a group of constraints by applying a KKT condition, thereby converting the three-layer electric power traffic coupling network collaborative operation model into a double-layer model; and S3, solving the double-layer model by using a CCG algorithm. According to the power distribution network and traffic network cooperative operation method considering multi-agent benefit coupling, the system operation efficiency is optimized, the cooperative interaction among the operation agents is ensured, and the overall cooperative effect of the power distribution network and the traffic network is improved.

Description

Technical Field

[0001] The present invention relates to the field of network system control technology, and in particular to a method for coordinated operation of a distribution network and a transportation network taking into account the coupling of interests of multiple subjects. Background Art

[0002] With the rapid development of electric vehicles, the interdependence between urban transportation networks and distribution networks will be greatly enhanced. In order to achieve the optimal configuration and coordinated dispatch of electricity and transportation energy and make resources more efficiently utilized, coupled power transportation networks are expected to play a major role in the future energy Internet. However, when the distribution network and the transportation network are coupled, a series of problems may arise. For example, the coupling of the distribution network and the transportation network will cause the interweaving and mutual influence of interests, and it is necessary to coordinate and balance the interests of all parties to maximize the overall interests. At the same time, the coupled network involves multiple subjects, including energy suppliers, transportation management departments, electric vehicle drivers, etc., and these subjects may have different interests and goals. Therefore, how to fully consider the interests and technical needs of all parties and achieve efficient operation of the overall system through reasonable policy formulation and management coordination has become an urgent problem to be solved.

[0003] However, existing technologies still have significant deficiencies in system coupling relationships and pricing strategies: in some systems, the coupling relationship between distribution network operators, charging network operators and transportation network operators is not clearly described; the joint incentive effect of the three is not brought into play; and there is no clear reference standard for charging network operators when formulating charging service fees.

[0004] Therefore, the prior art has the following disadvantages:

[0005] (1) Unclear coupling relationship: The existing technology fails to clearly describe the coupling relationship between distribution network operators, charging network operators and transportation network operators, and lacks an effective collaborative operation mechanism. This leads to insufficient coordination among the three in resource scheduling and load management, and it is impossible to achieve the optimal operation of the overall system.

[0006] (2) Lack of joint incentives: Existing technologies fail to give full play to the joint incentives among the three operators. The three parties operate independently and fail to optimize charging time or behavior through effective incentive mechanisms, which affects the overall network benefits.

[0007] (3) Unclear pricing standards: Charging network operators lack unified and clear standards when setting charging service fees. The pricing mechanism cannot effectively motivate users' behavioral choices, resulting in the failure to fully utilize the operating efficiency of the charging network.

[0008] To sum up, in order to achieve efficient and coordinated operation of the system, give full play to the joint incentive effect of the three, and formulate reasonable charging pricing standards, we urgently need to design a new technical solution. Summary of the invention

[0009] The purpose of the present invention is to provide a method for the coordinated operation of a distribution network and a transportation network taking into account the coupling of interests of multiple entities, thereby optimizing the system operation efficiency, ensuring the coordinated interaction between various operating entities, and improving the overall synergy effect of the distribution network and the transportation network.

[0010] To achieve the above object, the present invention provides a method for coordinated operation of a distribution network and a transportation network considering the coupling of interests of multiple subjects, comprising the following steps:

[0011] Step S1, constructing a three-layer electric-traffic coupling network collaborative operation model, including a user equilibrium model of the lower-layer transportation network, an optimal pricing model of the middle-layer charging network operator, and an optimal power flow model of the second-order cone relaxation of the upper-layer distribution network;

[0012] Step S2: using KKT conditions to convert the lower-layer transportation network user equilibrium model into a set of constraints, thereby converting the three-layer power-transportation coupling network collaborative operation model into a two-layer model;

[0013] Step S3: Use CCG algorithm to solve the double-layer model.

[0014] Preferably, the objective function of the lower layer traffic network user equilibrium model is:

[0015]

[0016] Where A is the set of road sections in the traffic network; x a is the traffic flow on road section a; ω is the monetary value of travel time; t a (θ) is the travel time function of the road section a with the traffic flow θ as the independent variable; θ is the integrand; The charging service fee on road section a; is the unit charging price of electric vehicles on road section a; is the charging demand on section a;

[0017] The calculation formula is as follows:

[0018]

[0019] Among them, η a is the charging rate per unit traffic flow on road section a; m and n are the proportional coefficient and bias value describing the charging rate and price respectively;

[0020] The road section travel time function adopts the public road bureau function:

[0021]

[0022] Among them, t a (x a ) is the traffic flow x on road section a a is the road segment travel time function of the independent variable; is the free travel time of section a; c a is the road capacity of section a;

[0023] x a The feasible interval of is divided into several subintervals. In each subinterval, the SOS2 variable is used to approximate t a (x a ), the specific constraints are as follows:

[0024]

[0025] Among them, h is the number of feasible interval segments; is the feasible interval x a The average value of Assign variables from 0 to 1.

[0026] Preferably, the constraints of the user equilibrium model of the lower layer transportation network are as follows:

[0027]

[0028] Among them, (r,s) is a start-stop pair; is the flow rate of the pth path of (r,s); is the link-path association matrix element; q rs is the traffic demand of (r,s).

[0029] Preferably, the objective function of the optimal pricing model for the mid-level charging network operator is as follows:

[0030]

[0031] in, The electricity purchase price for the electrified road serving node j; The charging demand of the electrified road served by node j; The replacement energy production cost coefficient provided by the charging service operator at node j; The amount of energy replacement for node j provided to the charging service operator; β aj Link-node association matrix element; E N is the node set of the distribution network; C(j) is the set of electrified roads served by node j; U CNO Revenue for charging network operators.

[0032] Preferably, the constraints of the optimal pricing model for the mid-level charging network operator are as follows:

[0033]

[0034] in, are the upper and lower limits of the charging service fee for section a respectively; are the upper and lower limits of the amount of replacement energy for node j respectively.

[0035] Preferably, the objective function of the optimal power flow model of the second-order cone relaxation of the upper distribution network is as follows:

[0036]

[0037] Among them, a j 、b j 、c j The production cost coefficient for providing energy to the distribution network operator at node j; is the active power generated by the generator at node j.

[0038] Preferably, the constraints of the optimal power flow model of the second-order cone relaxation of the upper distribution network are as follows:

[0039]

[0040] Among them, V i 、V j are the voltage vectors at node i and node j respectively; is the impedance on branch l; is the current vector on branch l; E L is a collection of branches in the power distribution network; is the power injected into node j; π(j) is the set of nodes connected to node j; is the power flowing from node j to downstream node k; is the power flowing from node i to downstream node j; V i f are the upper and lower limits of the voltage amplitude at node i; are the upper and lower limits of the complex power amplitude of the generator at node i; is the upper limit of the current amplitude on branch l; for conjugation of; is the complex power of the generator at node i;

[0041] For formula (15), we can get the following through relaxation:

[0042]

[0043] Among them, v i 、v j are the squares of the voltage amplitudes at nodes i and j respectively; is the resistance on branch l; is the active power on branch l; is the reactance on branch l; is the reactive power on branch l; is the square of the current amplitude on branch l;

[0044] Formula (16) is obtained by relaxation:

[0045]

[0046] For formula (17), the complex power is split into active power and reactive power, and considering the generator and load, it is restated as:

[0047]

[0048] in, is the total active power demand and total reactive power demand of node j; is the active power and reactive power flowing from node j to downstream node k; is the reactive power generated by the generator at node j;

[0049] in as follows:

[0050]

[0051] in, is the active power of the base load on node j.

[0052] Preferably, in step S2, the KKT condition is used to convert the underlying traffic network user equilibrium model into a set of constraints, as follows:

[0053]

[0054] Among them, μ rs , is the Lagrange multiplier corresponding to formula (7) and formula (8); is the Lagrangian function; δ is the symbol of partial derivative;

[0055] Use the Big M method to convert formula (26) into linear constraints on integer variables:

[0056]

[0057] Among them, M is not less than 100000; γ is a 0~1 variable.

[0058] Preferably, in step S3, the CCG algorithm is used to solve the double-layer model, and the specific operations are as follows:

[0059] First, initialize the upper and lower bounds of the objective function of the optimal pricing model for the mid-level charging network operator and the iteration counter f;

[0060] The initial solution of the main problem without CCG constraints is obtained by calculation and the lower limit is updated, and the calculated power purchase price is transmitted to the sub-problem;

[0061] Calculate the subproblem, save the corresponding solution and update the upper limit;

[0062] Determine whether the calculation has converged to the expected tolerance level, that is, determine whether the approximate error of the main problem to the sub-problem target is within the expected tolerance level. If it has converged, stop the iteration; if it has not converged, update the iteration counter;

[0063] Then, continue to calculate the solution of the main problem and update the lower bound, where the CCG constraints are composed of the f-1 cut sets generated by the solutions of all iterated subproblems;

[0064] Finally, use the solution of the main problem of the current k-th iteration to solve the subproblem and update the upper limit;

[0065] Among them, the main problem is formula (12)-(14) and formula (18)-(29); the sub-problems are formula (9)-(13) and formula (26)-(27).

[0066] Preferably, the CCG constraints are as follows:

[0067]

[0068] Among them, (x a ) q , (μ rs ) q , are the x of the qth iteration respectively. a , μ rs The value of In the q-1th iteration, The optimal value of .

[0069] Therefore, the present invention adopts the above-mentioned method for coordinated operation of distribution network and transportation network considering the coupling of interests of multiple subjects, and the beneficial technical effects are as follows:

[0070] (1) Application of three-tier pricing mechanism: The present invention establishes a three-tier optimization model, in which the distribution network operator is the upper layer, the charging network operator is the middle layer, and the electric vehicle driver is the lower layer, respectively controlling the power supply price, charging service fee, replacement energy quantity, and travel path selection. The focus is on solving the problem of mutual influence and dependence between the distribution network and the transportation network, and through multi-level interest coordination, the overall system efficiency is effectively improved.

[0071] (2) Utilization of optimization algorithms: In the present invention, the optimization problem of the power-transportation coupling network involves multiple stakeholders and their complex interrelationships. In order to effectively solve this multi-agent, multi-level optimization problem, the present invention introduces an intelligent scheduling and optimization algorithm, specifically using the KKT condition and CCG algorithm to solve it. The algorithm ensures real-time optimization and dynamic adjustment of the system during operation, maximizes the interests of all parties and improves the overall resource utilization efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 A framework diagram of a method for coordinated operation of a distribution network and a transportation network taking into account the coupling of interests of multiple entities according to the present invention;

[0073] Figure 2 This is the CCG algorithm flow chart. DETAILED DESCRIPTION

[0074] The technical solution of the present invention is further described below through the accompanying drawings and embodiments.

[0075] Unless otherwise defined, technical or scientific terms used in the present invention shall have the common meanings understood by one having ordinary skills in the field to which the present invention belongs.

[0076] Embodiment 1

[0077] The present invention provides a method for coordinated operation of a distribution network and a transportation network taking into account the coupling of interests of multiple entities. The coordinated operation framework is as follows: Figure 1 As shown, the following steps are included:

[0078] Step S1, construct a three-layer electric-traffic coupling network collaborative operation model, including a lower-layer transportation network user equilibrium model, a middle-layer charging network operator optimal pricing model, and an upper-layer distribution network second-order cone relaxation optimal power flow model.

[0079] The objective function of the user equilibrium model of the lower-level transportation network is:

[0080]

[0081] Where A is the set of road sections in the traffic network; x a is the traffic flow on road section a; ω is the monetary value of travel time; ta (θ) is the travel time function of the road section a with the traffic flow θ as the independent variable; θ is the integrand; The charging service fee on road section a; is the unit charging price of electric vehicles on road section a; is the charging demand on section a;

[0082] The calculation formula is as follows:

[0083]

[0084] Among them, η a is the charging rate per unit traffic flow on road section a; m and n are the proportional coefficient and bias value describing the charging rate and price respectively;

[0085] The road section travel time function adopts the public road bureau function:

[0086]

[0087] Among them, t a (x a ) is the traffic flow x on road section a a is the road segment travel time function of the independent variable; is the free travel time of section a; c a is the road capacity of section a;

[0088] x a The feasible interval of is divided into several subintervals. In each subinterval, the SOS2 variable is used to approximate t a (x a ), the specific constraints are as follows:

[0089]

[0090] Among them, h is the number of feasible interval segments; is the feasible interval x a The average value of Assign variables 0-1.

[0091] The constraints of the user equilibrium model of the lower-level transportation network are as follows:

[0092]

[0093] Among them, (r,s) is a start-stop pair; is the flow rate of the pth path of (r,s); is the link-path association matrix element; q rs is the traffic demand of (r,s).

[0094] The objective function of the optimal pricing model for mid-tier charging network operators is as follows:

[0095]

[0096] in, The electricity purchase price for the electrified road serving node j; The charging demand of the electrified road served by node j; The replacement energy production cost coefficient provided by the charging service operator at node j; The amount of energy replacement for node j provided to the charging service operator; β aj Link-node association matrix element; E N is the node set of the distribution network; C(j) is the set of electrified roads served by node j; U CNO Revenue for charging network operators.

[0097] The constraints of the optimal pricing model for mid-tier charging network operators are as follows:

[0098]

[0099] in, They are the upper and lower limits of the charging service fee for section a respectively; are the upper and lower limits of the amount of replacement energy for node j respectively.

[0100] The objective function of the optimal power flow model of the second-order cone relaxation of the upper distribution network is as follows:

[0101]

[0102] Among them, a j 、b j 、c j The production cost coefficient for providing energy to the distribution network operator at node j; is the active power generated by the generator at node j.

[0103] The constraints of the optimal power flow model of the second-order cone relaxation of the upper distribution network are as follows:

[0104]

[0105] Among them, V i 、V j are the voltage vectors at node i and node j respectively; is the impedance on branch l; is the current vector on branch l; E L is a collection of branches in the power distribution network; is the power injected into node j; π(j) is the set of nodes connected to node j; is the power flowing from node j to downstream node k; is the power flowing from node i to downstream node j; V i r 、V i f are the upper and lower limits of the voltage amplitude at node i; are the upper and lower limits of the complex power amplitude of the generator at node i; is the upper limit of the current amplitude on branch l; for conjugation of; is the complex power of the generator at node i;

[0106] For formula (15), we can get the following through relaxation:

[0107]

[0108] Among them, v i 、v j are the squares of the voltage amplitudes at nodes i and j respectively; is the resistance on branch l; is the active power on branch l; is the reactance on branch l; is the reactive power on branch l; is the square of the current amplitude on branch l;

[0109] Formula (16) is obtained by relaxation:

[0110]

[0111] For formula (17), the complex power is split into active power and reactive power, and considering the generator and load, it is restated as:

[0112]

[0113] in, is the total active power demand and total reactive power demand of node j; is the active power and reactive power flowing from node j to downstream node k; is the reactive power generated by the generator at node j;

[0114] in There are the following definitions:

[0115]

[0116] in, is the active power of the base load on node j.

[0117] Step S2: Use KKT conditions to convert the lower-layer transportation network user equilibrium model into a set of constraints, thereby converting the three-layer power-transportation coupling network collaborative operation model into a two-layer model.

[0118] In step S2, the KKT condition is used to convert the underlying traffic network user equilibrium model into a set of constraints, as follows:

[0119]

[0120]

[0121] Among them, μ rs , is the Lagrange multiplier corresponding to formula (7) and formula (8); is the Lagrangian function; δ is the symbol of partial derivative;

[0122] Use the Big M method to convert formula (26) into linear constraints on integer variables:

[0123]

[0124] Among them, M is not less than 100000; γ is a 0-1 variable.

[0125] The three-layer problem is as follows: Equation (14), Equation (18)-Equation (25) constitute the upper layer problem; Equation (9)-Equation (13) constitute the middle layer problem; and Equation (26) constitutes the lower layer problem. Among them, the auxiliary cuts and columns are generated by solving the sub-problems of the relaxation problem. The core is to use the dual information of the current solution to find the direction of improvement. This method constructs a solution set that is closer to the original problem by gradually adding constraints and variables, thereby efficiently approaching the global optimal solution.

[0126] Step S3: Use CCG algorithm to solve the double-layer model.

[0127] First, initialize the upper and lower bounds of the objective function of the optimal pricing model for the mid-level charging network operator and the iteration counter f; Figure 2 The upper limit is UB and the lower limit is LB;

[0128] The initial solution of the main problem without CCG constraints is obtained by calculation and the lower limit is updated, and the calculated power purchase price is transmitted to the sub-problem;

[0129] Calculate the subproblem, save the corresponding solution and update the upper limit;

[0130] Determine whether the calculation converges to the expected tolerance level ε, that is, determine whether the approximate error of the main problem to the sub-problem target is within the expected tolerance level. If it has converged, stop the iteration; if it has not converged, update the iteration counter;

[0131] Then, continue to calculate the solution of the main problem and update the lower bound, where the CCG constraints are composed of the f-1 cut sets generated by the solutions of all iterated subproblems;

[0132] Finally, use the solution of the main problem of the current k-th iteration to solve the subproblem and update the upper limit;

[0133] Among them, the main problem is formula (12)-(14) and formula (18)-(29); the sub-problems are formula (9)-(13) and formula (26)-(27).

[0134] The CCG constraints are as follows:

[0135]

[0136] Among them, (x a ) q , (μ rs ) q , are the x of the qth iteration respectively. a , μ rs , The value of In the q-1th iteration, The optimal value of .

[0137] It is worth noting that the contents not elaborated in detail in the present invention are all prior art and are well known to those skilled in the art.

[0138] Therefore, the present invention adopts the above-mentioned method for coordinated operation of distribution network and transportation network taking into account the coupling of interests of multiple subjects, optimizes the system operation efficiency, ensures the coordinated interaction among various operating subjects, and improves the overall synergistic effect of the distribution network and transportation network.

[0139] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solution of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solution to deviate from the spirit and scope of the technical solution of the present invention.

Claims

1. A method for coordinated operation of distribution network and transportation network considering the coupling of interests of multiple subjects, characterized in that: The following steps are involved: Step S1, constructing a three-layer electric-traffic coupling network collaborative operation model, including a user equilibrium model of the lower-layer transportation network, an optimal pricing model of the middle-layer charging network operator, and an optimal power flow model of the second-order cone relaxation of the upper-layer distribution network; Step S2: using KKT conditions to convert the lower-layer transportation network user equilibrium model into a set of constraints, thereby converting the three-layer power-transportation coupling network collaborative operation model into a two-layer model; Step S3: Use CCG algorithm to solve the double-layer model.

2. According to claim 1, a method for coordinated operation of a distribution network and a transportation network taking into account the coupling of interests of multiple subjects is characterized in that: The objective function of the user equilibrium model of the lower-level transportation network is: Where A is the set of road sections in the traffic network; x a is the traffic flow on road section a; ω is the monetary value of travel time; t a (θ) is the travel time function of the road section a with the traffic flow θ as the independent variable; θ is the integrand; The charging service fee on road section a; is the unit charging price of electric vehicles on road section a; is the charging demand on section a; The calculation formula is as follows: Among them, η a is the charging rate per unit traffic flow on road section a; m and n are the proportional coefficient and bias value describing the charging rate and price respectively; The road section travel time function adopts the public road bureau function: Among them, t a (x a ) is the traffic flow x on road section a a is the road segment travel time function of the independent variable; is the free travel time of section a; c a is the road capacity of section a; x a The feasible interval of is divided into several subintervals. In each subinterval, the SOS2 variable is used to approximate t a (x a ), the specific constraints are as follows: Among them, h is the number of feasible interval segments; is the feasible interval x a The average value of Assign variables from 0 to 1.

3. According to claim 2, a method for coordinated operation of a distribution network and a transportation network taking into account the coupling of interests of multiple subjects is characterized in that: The constraints of the user equilibrium model of the lower-level transportation network are as follows: Among them, (r,s) is a start-stop pair; is the flow rate of the pth path of (r,s); is the link-path association matrix element; q rs is the traffic demand of (r,s).

4. A method for coordinated operation of a distribution network and a transportation network considering the coupling of interests of multiple subjects according to claim 3, characterized in that: The objective function of the optimal pricing model for mid-tier charging network operators is as follows: in, The electricity purchase price for the electrified road serving node j; The charging demand of the electrified road served by node j; The replacement energy production cost coefficient provided by the charging service operator at node j; The amount of energy replacement for node j provided to the charging service operator; β aj Link-node association matrix element; E N is the node set of the distribution network; C(j) is the set of electrified roads served by node j; U CNO Revenue for charging network operators.

5. A method for coordinated operation of a distribution network and a transportation network considering the coupling of interests of multiple subjects according to claim 4, characterized in that: The constraints of the optimal pricing model for mid-tier charging network operators are as follows: in, They are the upper and lower limits of the charging service fee for section a respectively; are the upper and lower limits of the amount of replacement energy for node j respectively.

6. A method for coordinated operation of a distribution network and a transportation network considering the coupling of interests of multiple entities according to claim 5, characterized in that: The objective function of the optimal power flow model of the second-order cone relaxation of the upper distribution network is as follows: Among them, a j 、b j 、c j The production cost coefficient for providing energy to the distribution network operator at node j; is the active power generated by the generator at node j.

7. A method for coordinated operation of a distribution network and a transportation network considering the coupling of interests of multiple subjects according to claim 6, characterized in that: The constraints of the optimal power flow model of the second-order cone relaxation of the upper distribution network are as follows: Among them, V i 、V j are the voltage vectors at node i and node j respectively; is the impedance on branch l; is the current vector on branch l; E L is a collection of branches in the power distribution network; is the power injected into node j; π(j) is the set of nodes connected to node j; is the power flowing from node j to downstream node k; is the power flowing from node i to downstream node j; are the upper and lower limits of the voltage amplitude at node i; are the upper and lower limits of the complex power amplitude of the generator at node i; is the upper limit of the current amplitude on branch l; for conjugation of; is the complex power of the generator at node i; For formula (15), we can get the following through relaxation: Among them, v i 、v j are the squares of the voltage amplitudes at nodes i and j respectively; is the resistance on branch l; is the active power on branch l; is the reactance on branch l; is the reactive power on branch l; is the square of the current amplitude on branch l; Formula (16) is obtained by relaxation: For formula (17), the complex power is split into active power and reactive power, and considering the generator and load, it is restated as: in, is the total active power demand and total reactive power demand of node j; is the active power and reactive power flowing from node j to downstream node k; is the reactive power generated by the generator at node j; in as follows: in, is the active power of the base load on node j.

8. A method for coordinated operation of a distribution network and a transportation network considering the coupling of interests of multiple entities according to claim 7, characterized in that: In step S2, the KKT condition is used to convert the underlying traffic network user equilibrium model into a set of constraints, as follows: Among them, μ rs , is the Lagrange multiplier corresponding to formula (7) and formula (8); is the Lagrangian function; δ is the symbol of partial derivative; Use the Big M method to convert formula (26) into linear constraints on integer variables: Among them, M is not less than 100000; γ is a 0~1 variable.

9. A method for coordinated operation of a distribution network and a transportation network considering the coupling of interests of multiple entities according to claim 8, characterized in that: In step S3, the CCG algorithm is used to solve the two-layer model. The specific operations are as follows: First, initialize the upper and lower bounds of the objective function of the optimal pricing model for the mid-level charging network operator and the iteration counter f; The initial solution of the main problem without CCG constraints is obtained by calculation and the lower limit is updated, and the calculated power purchase price is transmitted to the sub-problem; Calculate the subproblem, save the corresponding solution and update the upper limit; Determine whether the calculation has converged to the expected tolerance level, that is, determine whether the approximate error of the main problem to the sub-problem target is within the expected tolerance level. If it has converged, stop the iteration; if it has not converged, update the iteration counter; Then, continue to calculate the solution of the main problem and update the lower bound, where the CCG constraints are composed of the f-1 cut sets generated by the solutions of all iterated subproblems; Finally, use the solution of the main problem of the current k-th iteration to solve the subproblem and update the upper limit; Among them, the main problem is formula (12)-(14) and formula (18)-(29); the sub-problems are formula (9)-(13) and formula (26)-(27).

10. A method for coordinated operation of a distribution network and a transportation network considering the coupling of interests of multiple entities according to claim 9, characterized in that: The CCG constraints are as follows: Among them, (xa) q , (μ rs ) q , are the x of the qth iteration respectively. a , μ rs , The value of In the q-1th iteration, The optimal value of .

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