Master-Slave Distribution Network Flexibility Collaborative Optimization Method Based on Column and Constraint Generation Algorithm

Abstract

The present invention discloses a main and distribution network flexibility collaborative optimization method based on a column sum constraint generation algorithm, which relates to the technical field of power systems and includes the following steps: Step S1, constructing a three-stage flexibility resource collaborative optimization model, including an upper layer, a middle layer, and a lower layer; Step S2, solving the three-stage flexibility resource collaborative optimization model by using a column sum constraint generation algorithm, decomposing the three-layer optimization problem into a master problem and a sub-problem, and obtaining the optimal solution through iteration. The present invention adopts the above-mentioned main and distribution network flexibility collaborative optimization method based on a column sum constraint generation algorithm, constructs a three-stage hierarchical collaborative optimization method with the transmission network - distribution network - user as the main body, fully excavates the flexibility resources of the active distribution network, and effectively solves the problems of high randomness and strong volatility generated by the large-scale grid connection of renewable energy through market means.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a main and distribution network flexibility collaborative optimization method based on a column and constraint generation algorithm. Background Art

[0002] The power industry is accelerating its transformation and actively building a new power system with renewable energy generation as the main body. Renewable energy generation, such as photovoltaic and wind power generation, is greatly affected by natural environmental conditions, resulting in difficult-to-accurately predict power generation and frequent fluctuations. A large amount of flexibility resources are urgently needed to ensure the stable operation of the power system. However, with the continuous expansion of the grid-connected scale of renewable energy generation, the problem of insufficient flexibility resources in the power system is becoming increasingly prominent. How to develop diverse flexibility resources to solve the large-scale new energy accommodation has become an urgent problem to be solved.

[0003] However, there are still significant deficiencies in the existing technologies for exploring flexibility resources: Currently, the flexibility of the power system is mainly provided by the power generation side. In the scenario of high proportion of renewable energy access, it is difficult for the generator sets with adjustable output alone to fully respond to the fluctuations of new energy generation; although a few systems consider the flexibility resources on the load side, most of them focus on local small networks and ignore the collaborative interaction between the upper and lower level networks; in addition, in the existing research on the collaborative acquisition of flexibility resources in the transmission and distribution networks, the strategic behaviors of different market participants, that is, individual rationality, are not considered, resulting in unstable factors in the market.

[0004] Therefore, the existing technologies have the following disadvantages:

[0005] (1) Single way to obtain flexibility: The flexibility resources of the power system are mainly provided by large centralized generator sets, and the flexibility potential in the load side of the distribution network is not fully explored.

[0006] (2) Insufficient coordination among different levels of power grids: Although a few studies consider the flexibility resources in the load side of the distribution network, most of them only focus on the distribution network level and ignore the coordinated scheduling with the transmission network.

[0007] (3) Ignoring the individual rationality of participants: The flexibility resource scheduler may ignore the strategic behaviors of market participants, which may lead to differences between the coordinated scheduling plan and the self-scheduling plan, resulting in market instability.

[0008] In summary, in order to fully explore the flexibility resources on the distribution network side, realize the coordinated scheduling of the flexibility resources of the transmission and distribution networks, and at the same time consider the strategic behaviors of different flexibility resource suppliers, we urgently need to design a new collaborative model for the flexibility resources of the transmission and distribution networks. This model is based on a hierarchical collaborative optimization method, considers the strategic behaviors of each subject, and realizes the coordination of the flexibility resources of the transmission and distribution networks with the highest efficiency. Summary of the Invention

[0009] The objective of the present invention is to provide a main and distribution network flexibility collaborative optimization method based on a column and constraint generation algorithm, to solve the problems raised in the above background technology. By considering the strategic behaviors of various market participants, the principle of individual rationality is ensured, enabling the distribution network operator and users to obtain benefits, fully exploiting the flexibility resources in the distribution network, and improving the enthusiasm of flexibility service providers in the distribution network to participate in flexibility services.

[0010] To achieve the above objective, the present invention provides a main and distribution network flexibility collaborative optimization method based on a column and constraint generation algorithm, including the following steps:

[0011] Step S1, construct a three-stage flexibility resource collaborative optimization model, including an upper layer, a middle layer, and a lower layer:

[0012] The upper layer is the flexibility resource scheduling model of the transmission network operator: minimizing the cost of required flexibility resources by deciding the interface flow price of the transmission and distribution network changes and the output changes of centralized generating units;

[0013] The middle layer is the flexibility resource scheduling and demand-side response pricing model of the distribution network operator: maximizing the revenue of providing flexibility services to the transmission network by deciding to change the interface flow of the transmission and distribution network, pricing the demand response of load-side users, and scheduling distributed generation devices;

[0014] The lower layer is the user demand response decision model: changing the electricity consumption demand to maximize the revenue according to the demand response incentives set by the distribution network operator;

[0015] Step S2, the three-stage flexibility resource collaborative optimization model is solved using a column and constraint generation algorithm, decomposing the three-layer optimization problem into a master problem and a sub-problem, and obtaining the optimal solution through iteration.

[0016] Preferably, the objective function of the upper layer in step S1 is:

[0017]

[0018] Among them, Ξ U is a decision variable, are respectively the price and quantity of upward and downward flexibility obtained from the distribution network operator; and are respectively the costs of upward and downward flexibility obtained from the distribution network, and + are the costs of obtaining flexibility resources from the generating units;

[0019] and are the regulation cost functions of the adjustable output unit g of the transmission network:

[0020]

[0021] Among them, is a coefficient, is the upward and downward regulation amounts of the adjustable output unit.

[0022] Preferably, the constraints of the upper-layer problem in the step S1 are:

[0023]

[0024] Among them, Equation (3) is the power grid flexibility resource supply-demand balance equation, ΔP u , ΔP d are the demands for upward and downward flexibility resources of the TSO; Equation (4) is the net injection power equation of node n of the power grid, is the net injection power before and after the flexibility resource scheduling of node n, and the subscripts n(m), n(g) respectively represent that the nodes of the power grid where the distribution network m and the generator set g are located are node n; Equation (5) is the active power flow equation on the power grid line, where is the power flow between nodes c and d of the line, G (c,d) is the power transfer distribution factor matrix; Equation (6) is the line transmission constraint, is the maximum power flow between nodes c and d of the line; Equations (7) and (8) are the upper limit constraints of the equipment providing flexibility resources, is the maximum upward and downward adjustable output value of the generator set g.

[0025] Preferably, the objective function of the middle layer in the step S1 is:

[0026]

[0027] Among them, Ξ M is a decision variable, are respectively the price and quantity of the user providing upward and downward flexibility through demand response; and are respectively the revenues obtained by the distribution network operator from the transmission network operator for upward and downward scheduling, and are respectively the costs of upward and downward scheduling of the user demand-side response in the distribution network, and are the scheduling costs of the flexibility resources owned by the distribution network itself;

[0028] and are the price functions of the flexibility resources of the distributed generator j in the distribution network:

[0029]

[0030] Among them, is a coefficient, is the amount of upward and downward flexibility resources provided by the distributed generator j.

[0031] Preferably, the constraint of the middle-level problem in the step S1 is:

[0032]

[0033] Among them, Equation (11) is the supply-demand balance equation of the flexibility resources of the distribution network; Equation (12) is the net injection power equation of the distribution network node a, is the net injection power before and after the flexibility resource scheduling of the distribution network node a, and the subscripts a(i), a(j) represent the user i and the distributed generation device j located at the node a; Equation (13) is the active power flow equation on the distribution network line, where is the power flow between the nodes c and d of the line, g (c,d) is the power transfer distribution factor matrix; Equation (14) is the line transmission constraint, is the maximum power flow between the nodes c and d of the line; Equations (15)-(18) are the upper limit constraints for providing flexibility resources, is the upper limit of the distribution network m to provide upward and downward flexibility, is the upper limit of the distributed generation j to provide upward and downward flexibility.

[0034] Preferably, the objective function of the lower layer in the step S1 is:

[0035]

[0036] Among them, where Ξ L is a decision variable, d i represents the electricity consumption of the user i before participating in the demand response, and Δd i is the changed electricity consumption of the user i, and there is

[0037] U(x) is the utility function of the user's electricity consumption:

[0038]

[0039] Among them, α i and ω i are non-negative constants, and ω i is the electricity consumption elasticity coefficient of the user i.

[0040] Preferably, the constraint of the lower-level problem in the step S1 is:

[0041]

[0042] Among them, Equations (21) and (22) are the constraints for the upper limits of the upward and downward flexibility resources that user i can provide. They are respectively the upper limits of the upward and downward flexibility resources that user i can provide. ζ i , ν i , They are the corresponding Lagrange multipliers respectively.

[0043] Preferably, the specific steps of step S2 include:

[0044] Step S21: Write out the KKT conditions of the lower-layer problem;

[0045] Step S22: Write out the master problem and the sub-problem.

[0046] Preferably, step S21 specifically includes:

[0047] Convert the objective function of the lower layer into the standard form, and its Lagrange multiplier equation is:

[0048]

[0049] Then the KKT conditions are:

[0050]

[0051] Preferably, step S21 specifically includes:

[0052] The objective function of the sub-problem is:

[0053]

[0054] Among them, Ξ SP ={Ξ M , Ξ L}, is the optimal solution of the master problem in the k-th iteration process;

[0055] The objective function of the master problem is:

[0056]

[0057] The column sum and cut generation constraint of the column sum and constraint generation algorithm in the k-th iteration is:

[0058]

[0059] Among them, is the optimal solution to the (q - 1)-th sub-problem.

[0060] Therefore, the present invention adopts the above-mentioned main and distribution network flexibility collaborative optimization method based on the column sum constraint generation algorithm, and has the following beneficial effects:

[0061] (1) It provides a new perspective on the collaborative mechanism of transmission and distribution network flexibility resources: This method constructs a three-stage hierarchical collaborative optimization method with the transmission network - distribution network - users as the main body, fully explores the flexibility resources of the active distribution network, and effectively solves the problems of high randomness and strong volatility generated by the large-scale grid connection of renewable energy through market means.

[0062] (2) It ensures the individual rationality of market participants: This method considers the benefits of each market participant, protects the stability of the market, and makes the collaborative scheduling plan consistent with the self-scheduling plan.

[0063] The technical solutions of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Description of the Drawings

[0064] Figure 1 is a schematic structural diagram of an embodiment of the main and distribution network flexibility collaborative optimization method based on the column sum constraint generation algorithm of the present invention;

[0065] Figure 2 is a front view of an embodiment of the main and distribution network flexibility collaborative optimization method based on the column sum constraint generation algorithm of the present invention;

[0066] Figure 3 is a side view of an embodiment of the main and distribution network flexibility collaborative optimization method based on the column sum constraint generation algorithm of the present invention. Detailed Embodiments

[0067] The technical solutions of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0068] Unless otherwise defined, the technical terms or scientific terms used in the present invention should have the ordinary meanings understood by those of ordinary skill in the field to which the present invention belongs. The "first", "second" and similar terms used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. The terms such as "including" or "comprising" mean that the elements or objects appearing before this word cover the elements or objects listed after this word and their equivalents, without excluding other elements or objects. The terms such as "connected" or "coupled" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The terms such as "upper", "lower", "left", "right" are only used to represent relative positional relationships, and when the absolute position of the object being described changes, the relative positional relationship may also change accordingly.

[0069] Embodiment

[0070] Please refer to Figures 1-3 , the present invention provides a main and distribution network flexibility collaborative optimization method based on a column and constraint generation algorithm, including the following steps:

[0071] Step S1, construct a three-stage flexibility resource collaborative optimization model, including an upper layer, a middle layer, and a lower layer. The upper layer is a flexibility resource scheduling model for the Transmission System Operator (TSO): minimize the required flexibility resource cost by determining the interface flow price of the transmission and distribution network changes and the output changes of the centralized generating units. The middle layer is a flexibility resource scheduling and demand-side response pricing model for the Distribution System Operator (DSO): maximize the revenue of providing flexibility services to the transmission network by determining the change in the interface flow of the transmission and distribution network, pricing the demand response of the load-side users, and scheduling the distributed generation devices. The lower layer is a user demand response decision model: change the electricity demand to maximize the revenue according to the demand response incentive set by the distribution network operator.

[0072] Consider a transmission network operated by the TSO, to which multiple distribution networks are connected, and each distribution network is managed and operated by an independent DSO. The transmission network is represented by G T =(N T ,L T ), where N T is the node set and L T is the line set. The set G represents the centralized generating units with adjustable output in the transmission network. The subset represents the TSO nodes connected to each distribution system. Each radial distribution network that can provide flexibility resources is described by G m =(N m ,L m ), and this distribution network is called DSO-m, where N m ,L m are the node set and line set of the distribution network respectively. There are user set m U and distributed energy set m G in each distribution network.

[0073] The structure of the three-stage flexibility resource collaborative optimization model is as Figure 1 shown.

[0074] The objective function of the upper layer is:

[0075]

[0076] Among them, ΞU is a decision variable, are respectively the price and quantity of upward and downward flexibility obtained from the distribution network operator; and are respectively the costs of upward and downward flexibility obtained from the distribution network, and + is the cost of obtaining flexibility resources from the generating units;

[0077] and is the regulation cost function of the adjustable output unit g of the transmission network:

[0078]

[0079] where, is a coefficient, are the upward and downward regulation amounts of the adjustable output unit.

[0080] The constraints of the upper-level problem are:

[0081]

[0082] where, Equation (3) is the supply-demand balance equation of the transmission network flexibility resources, ΔP u , ΔP d are the demands for upward and downward flexibility resources of the TSO; Equation (4) is the net injection power equation of node n of the transmission network, is the net injection power before and after the flexibility resource scheduling of node n, and the subscripts n(m), n(g) respectively indicate that the nodes of the transmission network where the distribution network m and the generating unit g are located are node n; Equation (5) is the active power flow equation on the transmission network line, where is the power flow of the line between nodes c and d, G (c,d) is the power transfer distribution factor matrix; Equation (6) is the line transmission constraint, is the maximum power flow of the line between nodes c and d; Equations (7) and (8) are the upper limit constraints of the equipment providing flexibility resources, is the maximum upward and downward adjustable output value of the generating unit g.

[0083] The objective function of the middle layer is:

[0084]

[0085] where, Ξ M is a decision variable, are respectively the price and quantity of upward and downward flexibility provided by users through demand response; and are the upward and downward dispatch revenues obtained by the distribution network operator from the transmission network operator, respectively, and are the upward and downward dispatch costs of the user demand-side response in the distribution network, respectively, and is the dispatch cost of the flexibility resources owned by the distribution network itself.

[0086] and are the price functions of the flexibility resources of distributed generator j in the distribution network:

[0087]

[0088] where is the coefficient, is the amount of upward and downward flexibility resources provided by distributed generator j.

[0089] The constraints of the middle-level problem are:

[0090]

[0091] where Equation (11) is the supply-demand balance equation of the distribution network flexibility resources; Equation (12) is the net injection power equation of distribution network node a, is the net injection power before and after the flexibility resource dispatch of distribution network node a, and the subscripts a(i), a(j) represent user i and distributed generation equipment j located at node a; Equation (13) is the active power flow equation on the distribution network line, where is the power flow between nodes c and d of the line, g (c,d) is the power transfer distribution factor matrix; Equation (14) is the line transmission constraint, is the maximum power flow between nodes c and d of the line; Equations (15)-(18) are the upper limit constraints for providing flexibility resources, is the upper limit of the upward and downward flexibility provided by distribution network m, is the upper limit of the upward and downward flexibility provided by distributed generation j.

[0092] The objective function of the lower layer is:

[0093]

[0094] where Ξ L is the decision variable, d i represents the electricity consumption of user i before participating in the demand-side response, and Δd i is the changed electricity consumption of user i, and there is

[0095] U(x) is the utility function of the user's electricity consumption:

[0096]

[0097] where α i and ω i are non - negative constants, and ω i is the electricity consumption elasticity coefficient of user i.

[0098] The constraints of the lower - level problem are:

[0099]

[0100] Among them, equations (21) and (22) are the constraints on the upper limits of the upward and downward flexibility resources that user i can provide. They are respectively the upper limits of the upward and downward flexibility resources that user i can provide. ζ i , ν i , They are the corresponding Lagrange multipliers respectively.

[0101] Step S2: The three - stage flexibility resource collaborative optimization model is solved by the column - and - constraint generation algorithm. The three - layer optimization problem is decomposed into a master problem and a sub - problem, and the optimal solution is obtained through iteration. The above three - stage flexibility resource collaborative optimization model cannot be directly solved by a commercial solver, and the optimization problem needs to be processed. Since the lower - level problem is a convex optimization problem, the KKT (Karush - Kuhn - Tucker) conditions are used to transform it into a set of constraint conditions and substitute them into the middle - level problem, turning the three - stage problem into a two - stage problem. This method intends to use the improved C&CG algorithm, which decomposes the optimization problem into a master problem and a sub - problem and iteratively solves until the convergence condition is reached.

[0102] The process of the C&CG algorithm is as Figure 2 shown. The specific steps include:

[0103] Step S21: Write out the KKT conditions of the lower - level problem. Specifically, it includes:

[0104] Convert the objective function of the lower - level into the standard form, and its Lagrange multiplier equation is:

[0105]

[0106] Then the KKT conditions are:

[0107]

[0108] Step S22: Write out the master problem and the sub - problem. Specifically, it includes:

[0109] The objective function of the sub-problem is:

[0110]

[0111] where, Ξ SP ={Ξ M , Ξ L}}, is the optimal solution of the master problem in the k-th iteration process;

[0112] The objective function of the master problem is:

[0113]

[0114] The column sum cutting generation constraint of the column sum and constraint generation algorithm at the k-th iteration is:

[0115]

[0116] where, is the optimal solution of the (q - 1)-th sub-problem.

[0117] C&CG algorithm:

[0118] Input:

[0119] (Common information), {ΔP u , ΔP d} (TSO flexibility requirements), k (number of iterations), ε (convergence gap)

[0120] Output:

[0121] 1. Initialization, let O M upper bound UB = +∞, lower bound LB = -∞, k = 1 2.

[0123] 2.1: Solve the master problem (31) → obtain Ξ U(1)* , Ξ M , Ξ L → update LB = max{LB, O M (Ξ U(1)* , Ξ M , Ξ L )}

[0124] 2.2: Solve the sub-problem (30) → obtain Ξ M(1)* , Ξ L(1)* → update UB = min{UB, O M (Ξ U(1)* , Ξ M(1)* , ΞL(1)* )} 3.

[0126] 3.1: If Update k = k + 1

[0127] 3.2: Solve the master problems (31) & (32) → Obtain Ξ U(k)* , Ξ M , Ξ L → Update LB = max{LB, O M (Ξ U(k)* , Ξ M , Ξ L )}

[0128] 3.3: Solve the sub - problem (30) → Obtain Ξ M(k)* , Ξ L(k)* → Update UB = min{UB, O M (Ξ U(k)* , Ξ M(k)* , Ξ L(k)* )}

[0129] Repeat step 3 until the convergence condition is met

[0130] 4. Ξ U = Ξ U(k)* , Ξ M = Ξ M(k)* , Ξ L = Ξ L(k)* ,

[0131] Output

[0132] The three - layer model in this method ensures the principle of individual rationality by considering the strategic behaviors of market participants, enabling the distribution network operator and users to obtain benefits, fully exploiting the flexibility resources in the distribution network, and enhancing the enthusiasm of flexibility service providers in the distribution network to participate in flexibility services. The column - and - constraint generation algorithm (Column - and - Constraint Generation, C&CG) is used to solve the three - layer optimization problem by decomposing it into master problems and sub - problems, obtaining the optimal solution through iteration, reducing the difficulty of solving, and improving the solving efficiency.

[0133] Therefore, the present invention adopts the above - mentioned master - distribution network flexibility collaborative optimization method based on the column - and - constraint generation algorithm, constructs a three - stage hierarchical collaborative optimization method with the transmission network - distribution network - users as the main body, fully exploits the flexibility resources of the active distribution network, and effectively solves the problems of high randomness and strong volatility caused by the large - scale grid connection of renewable energy through market means.

[0134] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions of the present invention or make equivalent replacements, and these modifications or equivalent replacements do not enable the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for collaborative optimization of main distribution network flexibility based on column and constraint generation algorithm, characterized in that: The following steps are involved: Step S1: Construct a three-stage flexibility resource collaborative optimization model, including upper, middle and lower layers: The upper layer is the flexibility resource scheduling model of the transmission network operator: it minimizes the required flexibility resource cost by deciding the interface flow price of the transmission and distribution network changes and the output changes of the centralized generator sets; The middle layer is the distribution network operator's flexibility resource scheduling and demand-side response pricing model: by making decisions to change the transmission and distribution network interface flow, pricing the load-side user demand response, and scheduling distributed generation devices to maximize the benefits of providing flexibility services to the transmission network; The lower layer is the user demand response decision model: according to the demand response incentives set by the distribution network operator, the electricity demand is changed to maximize the revenue; Step S2: The three-stage flexible resource collaborative optimization model is solved by using a column and constraint generation algorithm to decompose the three-layer optimization problem into a main problem and sub-problems, and the optimal solution is obtained through iteration; The specific steps of step S2 include: Step S21, write the KKT conditions of the lower level problem; Step S22: Write out the main problem and sub-problems.

2. The main distribution network flexibility collaborative optimization method based on column and constraint generation algorithm according to claim 1 is characterized in that: The objective function of the upper layer in step S1 is: （1） in, is the decision variable, , are the price and amount of upward and downward flexibility obtained from the DNO, respectively; and are the costs of upward and downward flexibility obtained from the distribution network, respectively, and Cost of obtaining flexibility resources from generators; and For the transmission network adjustable output unit The adjustment cost function is: （2） in, is the coefficient, It is the upward and downward adjustment amount of the adjustable output unit.

3. The main distribution network flexibility collaborative optimization method based on column and constraint generation algorithm according to claim 2 is characterized in that: The constraints of the upper-level problem in step S1 are: （3） （4） （5） （6） （7） （8） Wherein, formula (3) is the supply and demand balance equation of the transmission network flexibility resources, is the demand for upward and downward flexibility resources of TSO; Equation (4) is the transmission network node The net injected power equation, It is a transmission network node n The net injected power after flexible resource scheduling, It is a transmission network node n The net injected power before the flexible resource scheduling, Respectively represent the distribution network and generator sets The node of the transmission network is the node ; Formula (5) is the active power flow equation on the transmission network line, where Is a node The power flow between the lines, is the power transmission transfer distribution factor matrix; Equation (6) is the line transmission constraint, Is a node The maximum power flow of the line between them; Equations (7) and (8) are the upper limit constraints of the equipment that provides flexibility resources, It is a generator set The maximum upward and downward adjustable force values.

4. The main distribution network flexibility collaborative optimization method based on column and constraint generation algorithm according to claim 3 is characterized by: The objective function of the middle layer in step S1 is: （9） in, is the decision variable, , the price and volume, respectively, of upward and downward flexibility provided to users through demand response; and are the benefits of upward and downward dispatch obtained by the distribution network operator from the transmission network operator, and are the costs of upward and downward dispatching of user demand-side response in the distribution network, and The cost of dispatching the flexibility resources provided by the distribution network itself; and Distributed generators in distribution networks Price function of flexibility resources: （10） in, is the coefficient, For distributed generators Provides upward and downward flexibility in the amount of resources.

5. The main distribution network flexibility collaborative optimization method based on column and constraint generation algorithm according to claim 4 is characterized in that: The constraints of the middle-level problem in step S1 are: （11） （12） （13） （14） （15） （16） （17） （18） Among them, formula (11) is the distribution network flexibility resource supply and demand balance equation; formula (12) is the distribution network node The net injected power equation, It is a distribution network node The net injected power after flexible resource scheduling, It is a distribution network node The net injected power before the flexible resource scheduling, Indicates that it is located at the node Users and distributed generation equipment ; Formula (13) is the active power flow equation on the distribution network line, where Node The power flow between the lines, is the power transmission transfer distribution factor matrix; Equation (14) is the line transmission constraint, node The maximum power flow of the line between them; Equations (15)-(18) are the upper limit constraints for providing flexibility resources, Distribution Network Provides an upper limit of upward and downward flexibility, It is distributed generation Provides caps for upward and downward flexibility.

6. The main distribution network flexibility collaborative optimization method based on column and constraint generation algorithm according to claim 5 is characterized in that: The objective function of the lower layer in step S1 is: （19） Among them, is the decision variable, , Representative User The electricity consumption before participating in demand-side response, Is a user Changes in electricity consumption, ; is the utility function of the user's electricity consumption: （20） in, and is a non-negative constant, For users The electricity elasticity coefficient.

7. The main distribution network flexibility collaborative optimization method based on column and constraint generation algorithm according to claim 6 is characterized in that: The constraints of the lower layer problem in step S1 are: （21） （22） Among them, formula (21) and formula (22) are user Constraints on resource caps that provide upward and downward flexibility, The users are Provides flexibility both upward and downward in resource caps, are the corresponding Lagrange multipliers respectively.

8. The main distribution network flexibility collaborative optimization method based on column and constraint generation algorithm according to claim 7 is characterized in that: The step S21 specifically includes: Convert the objective function of the lower layer into standard form, and its Lagrange multiplier equation is: （23） Then the KKT condition is: （24） （25） （26） （27） （28） （29）。 9. The main distribution network flexibility collaborative optimization method based on column and constraint generation algorithm according to claim 8 is characterized in that: The step S21 specifically includes: The objective function of the subproblem is: （30） in, , It is The optimal solution of the main problem in the iteration process; The objective function of the main problem is: （31） The column and constraint generation algorithm is The column and cut generation constraints at the iteration are: （32） in, , , It is The optimal solution to the subproblem.

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