Cooperative optimization method for flexibility of main and distribution networks based on column sum constraint generation algorithm

Abstract

The invention discloses a main and distribution network flexibility collaborative optimization method based on a column sum constraint generation algorithm, and relates to the technical field of electric power systems, and the method comprises the following steps: S1, constructing a three-stage flexibility resource collaborative optimization model which comprises an upper layer, a middle layer and a lower layer; and S2, solving the three-stage flexible resource collaborative optimization model by adopting a column sum constraint generation algorithm, decomposing a three-layer optimization problem into a main problem and a sub-problem, and obtaining an optimal solution through iteration. According to the main and distribution network flexibility collaborative optimization method based on the column sum constraint generation algorithm, a three-stage hierarchical collaborative optimization method with a power transmission network-power distribution network-user as a main body is constructed, and flexibility resources of the active power distribution network are fully mined; the problems of high randomness and relatively strong volatility generated by large-scale grid connection of renewable energy sources are effectively solved through market means.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a main 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 as the main body. Renewable energy generation, such as photovoltaic and wind power generation, is greatly affected by natural environmental conditions, which makes it difficult to accurately predict power generation and fluctuates frequently. A large amount of flexibility resources are urgently needed to ensure the stable operation of the power system. However, as the scale of renewable energy power generation and grid connection continues to expand, the problem of insufficient flexibility resources in the power system is becoming increasingly prominent. How to develop diversified flexibility resources to solve the problem of large-scale new energy consumption has become an urgent problem that needs to be solved.

[0003] However, existing technologies still have significant shortcomings in tapping into flexibility resources: currently, the flexibility of the power system is mainly provided by the power generation side. In scenarios where a high proportion of renewable energy is connected, it is difficult to fully respond to fluctuations in new energy power generation by relying solely on generators with adjustable output. Although a few systems consider load-side flexibility resources, most of them focus on small local networks and ignore the coordinated interaction between upper and lower networks. In addition, in existing research on the coordinated acquisition of flexibility resources by transmission and distribution networks, the strategic behavior of different market participants, namely individual rationality, is not taken into account, resulting in instability in the market.

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

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

[0006] (2) Insufficient coordination among power grids at all levels: Although a few studies have considered the flexibility resources on the load side of the distribution network, most of them only focus on the distribution network level and ignore the coordinated scheduling between the power grid and the transmission network.

[0007] (3) Ignoring the individual rationality of participants: Flexible resource schedulers may ignore the strategic behavior of market participants, which may lead to differences between coordinated scheduling plans and self-scheduling plans, resulting in market instability.

[0008] In summary, in order to fully tap the flexibility resources on the distribution network side, realize the coordinated dispatch of the flexibility resources of the transmission and distribution network, and consider the strategic behavior of different flexibility resource suppliers, we urgently need to design a new transmission and distribution network flexibility resource coordination model. This model is based on the hierarchical collaborative optimization method, considers the strategic behavior of each subject, and realizes the coordination of the flexibility resources of the transmission and distribution network with the maximum efficiency. Summary of the invention

[0009] The purpose of the present invention is to provide a main distribution network flexibility collaborative optimization method based on column and constraint generation algorithm to solve the problems raised in the above background technology. By considering the strategic behavior of each market participant, the principle of individual rationality is guaranteed, so that distribution network operators and users can obtain benefits, the flexibility resources in the distribution network are fully tapped, and the enthusiasm of flexibility service providers in the distribution network to participate in flexibility services is improved.

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

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

[0012] 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;

[0013] 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;

[0014] 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;

[0015] Step S2: The three-stage flexible resource collaborative optimization model is solved by using a column and constraint generation algorithm, which decomposes the three-layer optimization problem into a main problem and sub-problems, and obtains the optimal solution through iteration.

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

[0017]

[0018] Among them, U 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;

[0019] and is the regulation cost function of the adjustable output unit g in the transmission network:

[0020]

[0021] in, is the coefficient, It is the upward and downward adjustment amount of the adjustable output unit.

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

[0023]

[0024] Wherein, formula (3) is the supply and demand balance equation of the transmission network flexibility resources, ΔP u ,ΔP d is the demand for upward and downward flexibility resources of TSO; Equation (4) is the net injected power equation of transmission grid node n, is the net injected power before and after the flexible resource scheduling of node n, the subscripts n(m) and n(g) respectively indicate that the node of the transmission network where the distribution network m and the generator g are located is node n; Formula (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 transmission transfer distribution factor matrix; Formula (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 that provides flexibility resources, It is the maximum upward and downward adjustable output value of the generator set g.

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

[0026]

[0027] Among them, M 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 dispatch of user demand-side response in the distribution network, and The cost of dispatching the flexibility resources provided by the distribution network itself;

[0028] and is the price function of the flexibility resource of distributed generator j in the distribution network:

[0029]

[0030] in, is the coefficient, The amount of upward and downward flexibility resources provided to distributed generator j.

[0031] Preferably, the constraints of the middle-level problem in step S1 are:

[0032]

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

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

[0035]

[0036] Among them, L is the decision variable, d i represents the electricity consumption of user i before participating in demand-side response, Δd i is the change in electricity consumption of user i,

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

[0038]

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

[0040] Preferably, the constraints of the lower layer problem in step S1 are:

[0041]

[0042] Among them, equations (21) and (22) are the constraints on the upper and lower flexibility resource limits that user i can provide. are the upper and lower limits of the flexibility resources that user i can provide, ζ i , ν i , are the corresponding Lagrange multipliers respectively.

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

[0044] Step S21, write the KKT conditions of the lower level problem;

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

[0046] Preferably, the step S21 specifically includes:

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

[0048]

[0049] Then the KKT condition is:

[0050]

[0051] Preferably, the step S21 specifically includes:

[0052] The objective function of the subproblem is:

[0053]

[0054] Among them, SP ={Ξ M ,Ξ L}, is the optimal solution to the main problem during the kth iteration;

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

[0056]

[0057] The column and cut generation constraints of the column and constraint generation algorithm at the kth iteration are:

[0058]

[0059] in, It is the optimal solution to the q-1th subproblem.

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

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

[0062] (2) Guaranteeing the individual rationality of market participants: This method takes into account the benefits of each market participant, protects the stability of the market, and makes the coordinated scheduling scheme and the self-scheduling scheme consistent.

[0063] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

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

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

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

[0067] The technical solution of the present invention is further described below through the accompanying drawings and embodiments.

[0068] Unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0069] Example

[0070] See also Figure 1-3 The present invention provides a main distribution network flexibility collaborative optimization method based on a column and constraint generation algorithm, comprising the following steps:

[0071] Step S1, construct a three-stage flexibility resource collaborative optimization model, including the upper layer, the middle layer and the lower layer. The upper layer is the Transmission System Operator (TSO) flexibility resource scheduling model: by deciding the interface flow price of the transmission and distribution network and the output change of the centralized generator set to minimize the required flexibility resource cost. The middle layer is the Distribution System Operator (DSO) flexibility resource scheduling and demand-side response pricing model: by deciding to change the interface flow of the transmission and distribution network, pricing the load-side user demand response, and scheduling distributed power 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 benefits.

[0072] Consider a transmission network operated by a TSO, with multiple distribution networks connected to it, each of which is managed and operated by an independent DSO. T =(N T ,L T ) indicates that N T is a node set, L T is a set of lines. Set G represents the centralized generators with adjustable output in the transmission network. Subset Represents the TSO nodes connected to each distribution system. Each radial distribution network that can provide flexibility resources By G m =(N m ,L m ) description, 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 sets m in each distribution network. U and distributed energy clusters G .

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

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

[0075]

[0076] Among them,U 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;

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

[0078]

[0079] in, is the coefficient, It is the upward and downward adjustment amount of the adjustable output unit.

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

[0081]

[0082] Wherein, formula (3) is the supply and demand balance equation of the transmission network flexibility resources, ΔP u ,ΔP d is the demand for upward and downward flexibility resources of TSO; Equation (4) is the net injected power equation of transmission grid node n, is the net injected power before and after the flexible resource scheduling of node n, the subscripts n(m) and n(g) respectively indicate that the node of the transmission network where the distribution network m and the generator g are located is node n; Formula (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 transmission transfer distribution factor matrix; Formula (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 that provides flexibility resources, It is the maximum upward and downward adjustable output value of the generator set g.

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

[0084]

[0085] Among them, M 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 dispatch of user demand-side response in the distribution network, and The cost of dispatching the flexibility resources provided by the distribution network itself.

[0086] and is the price function of the flexibility resource of distributed generator j in the distribution network:

[0087]

[0088] in, is the coefficient, The amount of upward and downward flexibility resources provided to distributed generator j.

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

[0090]

[0091] Among them, formula (11) is the supply and demand balance equation of distribution network flexibility resources; formula (12) is the net injection power equation of distribution network node a, is the net injected power before and after the flexible resource scheduling of distribution network node a, subscripts a(i) and a(j) represent user i and distributed generation equipment j located at node a; Formula (13) is the active power flow equation on the distribution network line, where The power flow of the line between nodes c and d, g (c,d) is the power transmission transfer distribution factor matrix; Formula (14) is the line transmission constraint, The maximum power flow of the line between nodes c and d; Equations (15)-(18) are the upper limit constraints for providing flexibility resources, The distribution network m provides an upper limit for upward and downward flexibility, 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] Among them, L is the decision variable, d i represents the electricity consumption of user i before participating in demand-side response, Δd i is the change in electricity consumption of user i,

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

[0096]

[0097] Among them, α i and ω i is a non-negative constant, ω i is the electricity elasticity coefficient of user i.

[0098] The constraints of the underlying problem are:

[0099]

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

[0101] Step S2, the three-stage flexible resource collaborative optimization model is solved by using a column and constraint generation algorithm, which decomposes the three-layer optimization problem into a main problem and sub-problems, and obtains the optimal solution through iteration. The above three-stage flexible resource collaborative optimization model cannot be solved directly by commercial solvers, and the optimization problem needs to be processed. Since the lower-level problem is a convex optimization problem, the KKT (Karush-Kuhn-Tucker) condition is used to convert it into a set of constraints and substitute them into the middle-level problem, reducing the three-stage problem to two stages. This method intends to use an improved C&CG algorithm, which decomposes the optimization problem into a main problem and sub-problems, and solves it iteratively until the convergence condition is reached.

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

[0103] Step S21, write out the KKT conditions of the lower level problem. Specifically include:

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

[0105]

[0106] Then the KKT condition is:

[0107]

[0108] Step S22: Write out the main problem and sub-problems. Specifically include:

[0109] The objective function of the subproblem is:

[0110]

[0111] Among them, SP ={Ξ M ,Ξ L}, is the optimal solution to the main problem during the kth iteration;

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

[0113]

[0114] The column and cut generation constraints of the column and constraint generation algorithm at the kth iteration are:

[0115]

[0116] in, It is the optimal solution to the q-1th subproblem.

[0117] C&CG algorithm:

[0118] enter:

[0119] (Public Information),{ΔP u ,ΔP d}(TSO flexibility requirement), k(number of iterations), ε(convergence gap)

[0120] Output:

[0121] 1. Initialize, let O M Upper limit UB = +∞, lower limit LB = -∞, k = 1 2.

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

[0124] 2.2: Solve subproblem (30) → get 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 main problem (31) & (32) → get Ξ U(k)* ,Ξ M ,Ξ L → Update LB=max{LB,O M (Ξ U(k)* ,Ξ M ,Ξ L )}

[0128] 3.3: Solve subproblem (30) → get Ξ M(k)* ,Ξ L(k)* → Update UB = min{UB,O M (Ξ U(k)* ,Ξ M(k)* ,Ξ L(k)* )}

[0129] Repeat 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 behavior of each market participant, so that distribution network operators and users can obtain benefits, fully tap the flexibility resources in the distribution network, and improve the enthusiasm of flexibility service providers in the distribution network to participate in flexibility services. The column and constraint generation algorithm (Column-andConstraint Generation, C&CG) is used to solve the three-layer optimization problem, which is decomposed into a main problem and sub-problems. The optimal solution is obtained through iteration, which reduces the difficulty of solving and improves the efficiency of solving.

[0133] Therefore, the present invention adopts the above-mentioned main distribution network flexibility collaborative optimization method based on column and constraint generation algorithm, constructs a three-stage hierarchical collaborative optimization method with transmission network-distribution network-user as the main body, fully taps the flexibility resources of the active distribution network, and effectively solves the high randomness and strong volatility problems caused by 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 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 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, which decomposes the three-layer optimization problem into a main problem and sub-problems, and obtains the optimal solution through iteration.

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: Among them, U 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 is the regulation cost function of the adjustable output unit g in the transmission network: 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: Wherein, formula (3) is the supply and demand balance equation of the transmission network flexibility resources, ΔP u ,ΔP d is the demand for upward and downward flexibility resources of TSO; Equation (4) is the net injected power equation of transmission grid node n, is the net injected power before and after the flexible resource scheduling of node n, the subscripts n(m) and n(g) respectively indicate that the node of the transmission network where the distribution network m and the generator g are located is node n; Formula (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 transmission transfer distribution factor matrix; Formula (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 that provides flexibility resources, It is the maximum upward and downward adjustable output value of the generator set g.

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: Among them, M 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 is the price function of the flexibility resource of distributed generator j in the distribution network: in, is the coefficient, The amount of upward and downward flexibility resources provided to distributed generator j.

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

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: Among them, L is the decision variable, d i represents the electricity consumption of user i before participating in demand-side response, Δd i is the change in electricity consumption of user i, U(x) is the utility function of the user's electricity consumption: Among them, α i and ω i is a non-negative constant, ω i is the electricity elasticity coefficient of user i.

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: Among them, equations (21) and (22) are the constraints on the upper and lower flexibility resource limits that user i can provide. are the upper and lower limits of the flexibility resources that user i can provide, ζ i ν i , 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 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.

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: Convert the objective function of the lower layer into standard form, and its Lagrange multiplier equation is: Then the KKT condition is:

10. The main distribution network flexibility collaborative optimization method based on column and constraint generation algorithm according to claim 9 is characterized in that: The step S21 specifically includes: The objective function of the subproblem is: Among them, SP ={Ξ M ,Ξ L },, is the optimal solution to the main problem during the kth iteration; The objective function of the main problem is: The column and cut generation constraints of the column and constraint generation algorithm at the kth iteration are: in, It is the optimal solution to the q-1th subproblem.

Images