A coordinated operation method of distribution network and transportation network considering the coupling of multi-agent interests

Abstract

The present invention provides a method for the coordinated operation of a distribution network and a transportation network that takes into account the coupling of interests of multiple subjects, which belongs to the field of network system control technology and includes the following steps: Step S1, constructing a three-layer electric-transportation coupling network coordinated 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; Step S2, using the KKT condition to convert the lower-layer transportation network user equilibrium model into a set of constraints, thereby converting the three-layer electric-transportation coupling network coordinated operation model into a two-layer model; Step S3, using the CCG algorithm to solve the two-layer model. The present invention adopts the above-mentioned method for the coordinated operation of a distribution network and a transportation network that takes into account the coupling of interests of multiple subjects, optimizes the system operation efficiency, ensures the coordinated interaction between the various operating subjects, and improves the overall synergistic effect of the distribution network and the transportation network.

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 significantly increase. To optimize the allocation and coordinated dispatch of electricity and transportation energy, ensuring more efficient resource utilization, coupled power and transportation networks are expected to play a key role in the future Energy Internet. However, the coupling of distribution and transportation networks can present a series of challenges. For example, the coupling of distribution and transportation networks can lead to intertwined interests and mutual impact, necessitating coordination and balancing of these interests to maximize overall benefits. Furthermore, coupled networks involve multiple stakeholders, including energy suppliers, transportation authorities, and electric vehicle drivers, who may have varying interests and objectives. Therefore, how to fully consider the interests and technical requirements of these stakeholders and achieve efficient operation of the overall system through appropriate policy formulation and management coordination has become a pressing issue.

[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 shortcomings:

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

[0006] (2) Lack of joint incentives: Existing technologies fail to fully leverage 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 charging network operating efficiency not being fully utilized.

[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 distribution networks and transportation networks that takes into account the coupling of interests of multiple entities, optimizes the system operation efficiency, ensures the coordinated interaction between various operating entities, and improves the overall synergy effect of the distribution network and transportation network.

[0010] To achieve the above objectives, 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, comprising the following steps:

[0011] Step S1: Construct a three-layer electric-transportation coupled network collaborative operation model, including a user equilibrium model for the lower-layer transportation network, an optimal pricing model for charging network operators in the middle layer, and an optimal power flow model with second-order cone relaxation for the upper-layer distribution network;

[0012] Step S2: Using KKT conditions, the user equilibrium model of the lower-layer transportation network is converted 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 user equilibrium model of the lower-level transportation network is:

[0015]

[0016] Where A is the set of road segments 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 on road segment a with 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 relationship between 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 t 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-level transportation network are as follows:

[0027]

[0028] Where (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 of the electrified road serving node j; Charging demand for 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 correlation 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 Profits 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 charging service fees for road section a; are the upper and lower limits of the amount of energy replacement for node j, respectively.

[0035] Preferably, the objective function of the optimal power flow model of the upper distribution network second-order cone relaxation 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 upper distribution network second-order cone relaxation 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 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 obtain 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 can be reformulated 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, specifically 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 Equation (26) into a linear constraint on integer variables:

[0056]

[0057] Where M is not less than 100,000; γ is a variable ranging from 0 to 1.

[0058] Preferably, in step S3, the CCG algorithm is used to solve the two-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 of the mid-level charging network operator and the iteration counter f;

[0060] Obtain the initial solution of the main problem without CCG constraints by calculation and update the lower bound, and transfer the calculated electricity purchase price 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 desired tolerance level, that is, whether the approximate error of the main problem to the sub-problem target is within the desired tolerance level. If it has converged, stop the iteration; if not, 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 all iterated subproblem solutions;

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

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

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

[0067]

[0068] Among them, (x a ) q 、 (μ rs ) q 、 are 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 a three-tier pricing mechanism: This paper establishes a three-tier optimization model, with the distribution network operator as the upper layer, the charging network operator as the middle layer, and the electric vehicle driver as the lower layer, respectively controlling the power supply price, charging service fee, the amount of alternative energy, and travel route selection. This model focuses on resolving the mutual influence and dependency between the distribution network and the transportation network, and effectively improves the overall system efficiency through multi-level interest coordination.

[0071] (2) Utilization of Optimization Algorithms: In this invention, the optimization problem of the power-transportation coupled network involves multiple stakeholders and their complex interrelationships. To effectively solve this multi-agent, multi-level optimization problem, this invention introduces an intelligent scheduling and optimization algorithm, specifically using the KKT condition and CCG algorithm. This algorithm ensures real-time optimization and dynamic adjustment of the system during operation, maximizing the interests of all parties and improving overall resource utilization efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 This is a framework diagram of a method for collaborative operation of a distribution network and a transportation network that considers the coupling of interests of multiple entities in 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 with reference to the accompanying drawings and embodiments.

[0075] Unless otherwise defined, technical or scientific terms used in the present invention shall have the same meaning as commonly understood by one of ordinary skill in the art to which the present invention belongs.

[0076] Example 1

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

[0078] Step S1: Construct a three-layer electric-transportation coupled 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 upper-layer distribution network second-order cone relaxation.

[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 segments 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 on road segment a with 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 relationship between 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 t 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 a variable to 0-1.

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

[0092]

[0093] Where (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-level charging network operators is as follows:

[0095]

[0096] in, The electricity purchase price of the electrified road serving node j; Charging demand for 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 correlation 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 Profits for charging network operators.

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

[0098]

[0099] 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 energy replacement for node j, respectively.

[0100] The objective function of the optimal power flow model with second-order cone relaxation in 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 with second-order cone relaxation in 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 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 obtain 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 can be reformulated 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 The following definitions are given:

[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 Equation (26) into a linear constraint on integer variables:

[0123]

[0124] Where M is not less than 100,000; γ is a 0-1 variable.

[0125] The three-layer problem is as follows: Equations (14), (18)-(25) constitute the upper-layer problem; Equations (9)-(13) constitute the middle-layer problem; and Equation (26) constitutes the lower-layer problem. Auxiliary cuts and columns are generated by solving subproblems of the relaxation problem. The core is to use the dual information of the current solution to discover improvement directions. 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 of 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] Obtain the initial solution of the main problem without CCG constraints by calculation and update the lower bound, and transfer the calculated electricity purchase price 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 desired tolerance level ε, that is, whether the approximate error of the main problem to the sub-problem target is within the desired tolerance level. If it has converged, stop the iteration; if not, 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 all iterated subproblem solutions;

[0132] Finally, use the solution of the main problem at 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 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 between various operating subjects, and improves the overall synergistic effect of distribution network and transportation network.

[0139] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit the same. 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 solutions of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions 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: Construct a three-layer electric-transportation coupled network collaborative operation model, including a user equilibrium model for the lower-layer transportation network, an optimal pricing model for charging network operators in the middle layer, and an optimal power flow model with second-order cone relaxation for the upper-layer distribution network; The objective function of the user equilibrium model of the lower-level transportation network is: （1）； in, is the collection of road segments in the transportation network; For road sections Traffic flow on is the monetary value of travel time; For road sections Traffic flow is the road segment travel time function of the independent variable; is the integrand of the integral; For road sections Charging service fee on For road sections The unit charging price of electric vehicles; For road sections Charging needs on The calculation formula is as follows: （2）； （3）； in, For road sections Charging rate per unit of traffic flow; 、 They are the proportional coefficient and bias value describing the relationship between charging rate and price respectively; The road section travel time function adopts the public road bureau function: （4）； in, For road sections Traffic flow is the road segment travel time function of the independent variable; For road sections Free travel time; For road sections road capacity; Will The feasible interval is divided into several subintervals, and in each subinterval, the SOS2 variable is used to approximate , the specific constraints are as follows: （5）； in, is the number of feasible interval segments; In the feasible range The average value of Assign variables from 0 to 1; The objective function of the optimal pricing model for mid-level charging network operators is as follows: （9）； （10）； （11）； in, For nodes The purchase price of electricity for the electrified roads served; For nodes Charging needs of the electrified roads served; 、 、 For nodes The replacement energy production cost coefficient provided by the charging service operator; Nodes for charging service operators Amount of alternative energy; Link-node association matrix elements; is a set of nodes in the power distribution network; For nodes the collection of electrified roads served; Revenue for charging network operators; The objective function of the optimal power flow model with second-order cone relaxation in the upper distribution network is as follows: （14）； in, 、 、 For nodes The production cost coefficient of energy provided by the distribution network operator; For nodes The active power generated by the generator; Step S2: Using KKT conditions, the user equilibrium model of the lower-layer transportation network is converted 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. A method for coordinated operation of a distribution network and a transportation network considering the coupling of interests of multiple entities according to claim 1, characterized in that: The constraints of the user equilibrium model of the lower-level transportation network are as follows: （6）； （7）； （8）； in, is a start-stop pair; for No. Path flow; is the link-path association matrix element; for traffic demand.

3. The method for coordinated operation of a distribution network and a transportation network considering the coupling of interests of multiple entities according to claim 2 is characterized in that: The constraints of the optimal pricing model for mid-tier charging network operators are as follows: （12）； （13）； in, 、 Road sections The upper and lower limits of charging service fees; 、 Node Upper and lower limits on the amount of alternative energy.

4. The method for coordinated operation of a distribution network and a transportation network considering the coupling of interests of multiple entities according to claim 3 is characterized in that: The constraints of the optimal power flow model with second-order cone relaxation in the upper distribution network are as follows: （15）； （16）； （17）； （18）； （19）； （20）； in, 、 Node and nodes The voltage vector at ; For branch Impedance on For branch The current vector on ; is a collection of branches in the distribution network; Injection node Power; For and node A set of connected nodes; For slave nodes Flow out to downstream nodes Power; For slave nodes Flow out to downstream nodes Power; 、 For nodes The upper and lower limits of the voltage amplitude; 、 For nodes The upper and lower limits of the generator complex power amplitude; For branch Upper limit of current amplitude on ; for conjugation of; For nodes The generator complex power; For formula (15), we can get the following through relaxation: （21）； in, 、 Node and nodes The square of the voltage amplitude at For branch The resistance on For branch Active power on For branch reactance on ; For branch Reactive power on For branch The square of the upper current amplitude; Formula (16) is obtained by relaxation: （22）； For formula (17), the complex power is split into active power and reactive power, and considering the generator and load, it can be reformulated as: （23）； （24）； in, 、 For nodes Total active power demand and total reactive power demand; 、 For slave nodes Flow out to downstream nodes Active power and reactive power; For nodes The reactive power generated by the generator; in as follows: （25）； in, For nodes Active power of the base load on .

5. The method for coordinated operation of a distribution network and a transportation network considering the coupling of interests of multiple entities according to claim 4 is 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: （26）； （27）； in, 、 is the Lagrange multiplier corresponding to formula (7) and formula (8); is the Lagrangian function; is the symbol of partial derivative; Use the big M method to convert Equation (26) into linear constraints on integer variables: （28）； Wherein, M is not less than 100,000; It is a 0~1 variable.

6. The 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: 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 and the iteration counter of the optimal pricing model of the mid-level charging network operator f ; Obtain the initial solution of the main problem without CCG constraints by calculation and update the lower bound, and transfer the calculated electricity purchase price to the sub-problem; Calculate the subproblem, save the corresponding solution and update the upper limit; Determine whether the calculation has converged to the desired tolerance level, that is, whether the approximate error of the main problem to the sub-problem target is within the desired tolerance level. If it has converged, stop the iteration; if not, update the iteration counter; Then, continue computing the solution to the master problem and updating the lower bound, where the CCG constraints are generated by the solutions to all the iterated subproblems f −1 cut set; Finally, use the current k The solution of the main problem of the next iteration solves the subproblem and updates the upper bound; Among them, the main problem is formula (12)-(14), formula (18)-(29); the sub-problems are formula (9)-(13), formula (26)-(27).

7. The method for coordinated operation of a distribution network and a transportation network considering the coupling of interests of multiple entities according to claim 6, characterized in that: The CCG constraints are as follows: （29）； in, 、 、 、 Respectively Iteration 、 、 、 The value of 、 Respectively In the iteration 、 The optimal value of .

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