Alternating current interconnection system net rack planning method and system based on two-stage robust optimization

Abstract

The invention relates to the technical field of power system planning, and discloses an alternating current interconnection system grid planning method and system based on two-stage robust optimization. The method comprises the following steps: constructing a target network frame planning model based on the comprehensive cost of minimizing the network frame planning of the AC interconnection system; constructing an uncertainty set based on the new energy output uncertainty of the AC interconnection system, and performing robust optimization on the target grid planning model according to the uncertainty set to obtain a two-stage grid planning model; the improved Camp is adopted; and solving the two-stage net rack planning model by a CG algorithm to obtain the optimal net rack of the alternating current interconnection system. According to the method, while wind power uncertainty is considered, a line extension decision and interconnection device addressing are accommodated into a unified optimization framework for the first time, and the limitation that line extension is independently considered in a traditional net rack planning method is broken through.

Description

Technical Field

[0001] The present invention relates to the technical field of power system planning, and in particular to a method and system for planning an AC interconnected system grid based on two-stage robust optimization. Background Art

[0002] The large-scale integration of high-proportion distributed renewable energy into the power grid and the surge in regional loads have posed significant challenges to grid planning. Existing grid planning methods focus on transmission line expansion planning, determining optimal line corridors and capacities by minimizing line construction and operating costs. However, they neglect the coordinated planning of regional flexible interconnection devices, resulting in poor operational flexibility within grid zones and an inability to dynamically adjust inter-regional power exchange based on operational needs. To address uncertainty, existing AC interconnected system grid planning methods primarily employ deterministic optimization, stochastic optimization, and robust optimization. However, deterministic optimization ignores the volatility of sources and loads, resulting in poor robustness of the resulting planning schemes. Stochastic optimization relies on probability distribution assumptions, resulting in high computational complexity and difficulty handling extreme scenarios. Robust optimization can mitigate the risks of extreme scenarios, but its traditional uncertainty set has fixed boundaries and cannot dynamically adjust the conservatism of the planning scheme based on the decision maker's risk preferences. Regarding solution methods, existing methods primarily include heuristic algorithms, Benders decomposition algorithms, and column and constraint generation (C&CG) algorithms. However, although the heuristic algorithm avoids complex mathematical modeling, its random search characteristics make it difficult to ensure the quality of the solution, and the calculation time increases too quickly with the problem size, making it difficult to apply to actual large-scale power grid planning problems; the Benders decomposition algorithm decomposes the problem into a main problem and sub-problems for iterative solution. Although it can theoretically guarantee convergence, it faces the problem of low computational efficiency in practical applications; although the C&CG algorithm can handle two-stage robust optimization problems well, its defect is that the sub-problems require the construction of complex dual models.

[0003] Therefore, considering the uncertainty of high-penetration wind power and combining it with the expansion of the transmission network and the siting and sizing of flexible interconnection devices is of great significance for the grid planning of AC interconnection systems. Summary of the Invention

[0004] In order to overcome the deficiencies of the prior art, the present invention provides a method and system for AC interconnection system grid planning based on two-stage robust optimization.

[0005] In a first aspect, an embodiment of the present invention provides a method for planning an AC interconnection system network based on two-stage robust optimization, comprising: Constructing a target grid planning model based on minimizing the comprehensive cost of AC interconnection system grid planning, wherein the comprehensive cost includes construction cost and operation cost; An uncertainty set is constructed based on the uncertainty of the renewable energy output of the AC interconnected system, and a two-stage grid planning model is obtained by robustly optimizing the target grid planning model according to the uncertainty set; The improved C&CG algorithm is used to solve the two-stage grid planning model to obtain the optimal grid of the AC interconnection system.

[0006] Preferably, the target grid planning model is constructed based on minimizing the comprehensive cost of AC interconnection system grid planning, including: An initial grid planning model is constructed based on minimizing the construction and operating costs of AC interconnection system grid planning, wherein the construction costs include the transmission line construction costs and the flexible interconnection device construction costs, and the operating costs include the power generation costs of the generator sets and the AC interconnection system risk costs; Based on the physical operating characteristics of the AC interconnection system, constraint conditions are constructed, and the initial grid planning model is constrained according to the constraint conditions to obtain a target grid planning model, wherein the constraint conditions include grid construction constraint conditions and grid operation constraint conditions.

[0007] Preferably, the following formula is used to characterize the initial grid planning model: in, Represents the comprehensive cost, represents the construction cost, Indicates the equivalence factor between the equivalent annual value of construction cost and the equivalent annual value of operating cost, represents the power generation cost of the generator set, Represents the risk cost of the AC interconnection system.

[0008] Preferably, the construction cost is represented by the following formula: in, represents the construction cost, represents the set of transmission line corridors, represents the set of candidate routes for the (i, j) corridor channel, represents the construction cost of the k-th line between nodes i and j, A Boolean variable representing the k-th line construction decision between nodes i and j, Represents a collection of flexible interconnection device installation lines, represents the unit capacity construction cost of the flexible interconnection device, Indicates the construction capacity of flexible interconnection devices; The following formula is used to characterize the power generation cost of the generator set: in, represents the power generation cost of the generator set, Represents a collection of nodes, represents the set of generators connected to node i, 、 、 represents the cost consumption characteristic coefficient of the generator set connected to node i, represents the power generation of the generator set connected to node i; The AC interconnection system risk cost includes the wind curtailment risk cost, solar curtailment risk cost, load shedding risk cost, and flexible interconnection device loss cost. The following formula is used to represent the AC interconnection system risk cost: in, represents the risk cost of the AC interconnection system, represents the penalty cost coefficient for wind curtailment at node i, represents the penalty cost coefficient of node i for abandoning light, represents the load shedding penalty cost coefficient, represents the power loss coefficient of the flexible interconnection device, represents the amount of wind curtailment at node i, represents the amount of abandoned light at node i, represents the load shedding amount of node i, Indicates the power loss of the flexible interconnection device.

[0009] Preferably, the grid construction constraints include transmission line sequence construction constraints, corridor channel line quantity constraints, and flexible interconnection device investment and construction quantity constraints; The grid operation constraints include the flow constraints of existing lines, the flow constraints of lines to be selected, the capacity constraints of existing lines, the capacity constraints of lines to be selected, the capacity constraints of flexible interconnection devices, the node voltage phase angle constraints, the node power balance constraints, the generator output constraints, the load shedding constraints and the node wind and solar power abandonment constraints.

[0010] Preferably, the uncertainty set is constructed based on the uncertainty of the new energy output of the AC interconnected system, and the target grid planning model is robustly optimized according to the uncertainty set to obtain a two-stage grid planning model, including: Based on the uncertainty of wind and solar power output in AC interconnected systems, an uncertainty set is constructed by introducing uncertainty adjustment parameters; Based on the uncertainty set, the target grid planning model is subjected to two-stage robust optimization to obtain a two-stage grid planning model.

[0011] Preferably, the following formula is used to characterize the uncertainty set: in, represents an uncertain set, u represents an uncertain quantity, represents the wind power output in the tth period within the scheduling cycle, represents the photovoltaic output of the tth period in the scheduling cycle, T represents the total number of periods in the scheduling cycle, Indicates the minimum wind power output, Indicates the maximum wind power output, represents a binary variable, represents the uncertainty adjustment parameter of wind power output, Indicates the total number of periods during which wind power output reaches the minimum or maximum value of the fluctuation range within the dispatch period. Indicates the minimum photovoltaic output, represents a binary variable, Indicates the maximum photovoltaic output, represents the uncertainty adjustment parameter of photovoltaic output, Indicates the total number of time periods during the scheduling period when the PV output reaches the minimum or maximum value of the fluctuation range.

[0012] Preferably, the two-stage grid planning model is characterized by the following formula: in, represents the objective function, x represents the decision variables of the first stage, c represents the coefficient vector related to the decision variables of the first stage, represents the uncertainty set, u represents the uncertainty quantity, y represents the decision variable of the second stage, b represents the coefficient vector related to the decision variable of the second stage, F(x,u) represents the feasible domain of the decision variable of the second stage, A represents the coefficient matrix, d represents the constant vector, represents the feasible region of the first-stage decision variables, Represents the set of second-stage decision variables, G represents the coefficient matrix, h represents the constant vector, E represents the coefficient matrix, and M represents the coefficient matrix.

[0013] Preferably, the improved C&CG algorithm is used to solve the two-stage grid planning model to obtain the optimal grid of the AC interconnection system, including: The C&CG algorithm combining KKT conditions and the big M method is used to solve the two-stage grid planning model and obtain the optimal grid of the AC interconnection system.

[0014] In a second aspect, an embodiment of the present invention provides an AC interconnection system grid planning system based on two-stage robust optimization, comprising: A first model building module is used to build a target grid planning model based on minimizing the comprehensive cost of AC interconnection system grid planning, wherein the comprehensive cost includes construction cost and operation cost; A second model building module is used to build an uncertainty set based on the uncertainty of the new energy output of the AC interconnected system, and to perform robust optimization on the target grid planning model according to the uncertainty set to obtain a two-stage grid planning model; The optimal grid determination module is used to solve the two-stage grid planning model using an improved C&CG algorithm to obtain the optimal grid of the AC interconnection system.

[0015] Compared with the existing technology, the embodiment of the present invention provides an AC interconnection system grid planning method and system based on two-stage robust optimization, and its beneficial effects are as follows: constructing a mixed integer optimization model for the coordinated planning of transmission lines and flexible interconnection devices, and for the first time incorporating line expansion decisions and interconnection device site selection decisions into a unified optimization framework, breaking through the limitation of traditional grid planning methods that consider line expansion in isolation; by introducing uncertainty adjustment parameters to construct uncertainty sets, dynamic control of the wind and solar output fluctuation range is achieved, overcoming the limitation of fixed boundary uncertainty sets in traditional robust optimization; the improved C&CG algorithm based on KKT conditional linearization significantly improves the efficiency of solving high-dimensional uncertainty problems by converting two-layer optimization subproblems into mixed integer linear programming, which is conducive to quickly obtaining the optimal grid of the AC interconnection system. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a flow chart of a method for AC interconnection system grid planning based on two-stage robust optimization according to an embodiment of the present invention; Figure 2 1 is a schematic structural diagram of an AC interconnection system grid planning system based on two-stage robust optimization according to an embodiment of the present invention; Reference numerals: 1. First model construction module; 2. Second model construction module; 3. Optimal grid determination module. DETAILED DESCRIPTION

[0017] The following embodiments of the present invention are described in further detail with reference to the accompanying drawings and examples. The following examples are used to illustrate the present invention but are not intended to limit the scope of the present invention.

[0018] In the description of the present invention, it should be understood that the terms "first" and "second" etc. are used in the present invention to distinguish different objects rather than to describe a specific order.

[0019] In describing the present invention, it should be noted that, unless otherwise defined, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. Those skilled in the art will understand the specific meanings of the above terms in the present invention in specific circumstances.

[0020] like Figure 1 As shown, an embodiment of the present invention provides an AC interconnection system grid planning method based on two-stage robust optimization, comprising the steps of: S1. Construct a target grid planning model based on minimizing the comprehensive cost of AC interconnection system grid planning; It should be noted that the target grid planning model of the present invention is a mixed integer optimization model for the coordinated planning of transmission lines and flexible interconnection devices. It is the first time that line expansion decisions and interconnection device site selection decisions are incorporated into a unified optimization framework, breaking through the limitations of traditional grid planning methods that only consider line expansion in isolation.

[0021] The flexible interconnection device of the AC interconnection system of this embodiment is described in detail below: 1) The following formula is used to characterize the active power constraint of the flexible interconnection device: 2) The following formula is used to characterize the capacity constraint of the flexible interconnection device: in, and Indicates the port number where the flexible interconnection device is connected to the AC power grid. Flexible interconnection device Active power of the port, Flexible interconnection device Active power of the port, Flexible interconnection device Active power loss of the port, Flexible interconnection device Active power loss of the port, Flexible interconnection device The loss coefficient of the port, Flexible interconnection device The loss coefficient of the port, Flexible interconnection device Reactive power of the port, Flexible interconnection device Reactive power of the port, Indicates the construction capacity of flexible interconnection devices.

[0022] It can be understood that the first formula in the active power constraint of the flexible interconnection device is the balance expression of the transmission power and loss power at both ends of the interconnection device, and the second formula is the balance expression of the flexible interconnection device. Definition of active power loss of the port, the third formula is for the flexible interconnection device The definition of active power loss at a port and the formula for the flexible interconnection device capacity constraint indicate that the operating power of the interconnection device must be less than the construction capacity. These two constraints fully characterize the flexible interconnection device.

[0023] Specifically, the comprehensive cost includes construction cost and operation cost. Further, step S1 includes: 1) Construct an initial grid planning model based on minimizing the construction and operating costs of AC interconnection system grid planning; Specifically, the following formula is used to characterize the initial grid planning model: in, Represents the comprehensive cost, represents the construction cost, Indicates the equivalence factor between the equivalent annual value of construction cost and the equivalent annual value of operating cost, represents the power generation cost of the generator set, It is understood that the above formula is also the objective function of the initial grid planning model.

[0024] The construction cost includes the construction cost of the transmission line and the construction cost of the flexible interconnection device. Specifically, the construction cost is represented by the following formula: in, represents the construction cost, represents the set of transmission line corridors, represents the set of candidate routes for the (i, j) corridor channel, represents the construction cost of the k-th line between nodes i and j, A Boolean variable representing the k-th line construction decision between nodes i and j. , then the transmission line will be put into construction, otherwise the transmission line will not be put into construction. Represents a collection of flexible interconnection device installation lines, represents the unit capacity construction cost of the flexible interconnection device, Indicates the construction capacity of flexible interconnection devices.

[0025] The operating cost includes the power generation cost of the generator set and the risk cost of the AC interconnection system. Specifically, the power generation cost of the generator set is represented by the following formula: in, represents the power generation cost of the generator set, Represents a collection of nodes, represents the set of generators connected to node i, 、 、 represents the cost consumption characteristic coefficient of the generator set connected to node i, Represents the power generation of the generator set connected to node i.

[0026] The risk cost of the AC interconnection system includes the risk cost of wind curtailment, the risk cost of solar curtailment, the risk cost of load shedding, and the loss cost of flexible interconnection devices. Specifically, the following formula is used to represent the risk cost of the AC interconnection system: in, represents the risk cost of the AC interconnection system, represents the penalty cost coefficient for wind curtailment at node i, represents the penalty cost coefficient of node i for abandoning light, represents the load shedding penalty cost coefficient, represents the power loss coefficient of the flexible interconnection device, represents the amount of wind curtailment at node i, represents the amount of abandoned light at node i, represents the load shedding amount of node i, Indicates the power loss of the flexible interconnection device.

[0027] 2) Based on the physical operating characteristics of the AC interconnection system, constraints are established and the initial grid planning model is constrained according to the constraints to obtain the target grid planning model.

[0028] Specifically, the constraints include grid construction constraints and grid operation constraints. Furthermore, grid construction constraints include transmission line sequence constraints, corridor line quantity constraints, and flexible interconnection device quantity constraints. Grid operation constraints include power flow constraints on existing lines, power flow constraints on candidate lines, capacity constraints on existing lines, capacity constraints on candidate lines, capacity constraints on flexible interconnection devices, node voltage phase angle constraints, node power balance constraints, generator output constraints, load shedding constraints, and node wind and solar curtailment constraints.

[0029] The constraints on grid construction are described in detail below: 1) Transmission line sequence construction constraints For transmission lines with the same endpoints, the sequential construction loop constraint is satisfied. Specifically, the following formula is used to represent the transmission line sequential construction constraint: in, express Not included A collection of represents the set of all loops between nodes i and j. It is understandable that if the kth loop is not built, then the k+1th to No circuit will be built. Only when the kth circuit is built can the k+1th circuit be built.

[0030] 2) Corridor channel line quantity constraints Since the number of lines allowed to be built in each corridor is limited, it is necessary to meet the corridor line quantity constraint. Specifically, the following formula is used to express the corridor line quantity constraint: in, Indicates the minimum number of lines allowed to be constructed in the (i, j) corridor. Indicates the maximum number of lines allowed to be constructed in the (i, j) corridor.

[0031] 3) Constraints on the number of flexible interconnection devices to be built Since the number of interconnection lines to be selected in the AC interconnection system is limited, it is necessary to meet the constraints on the number of flexible interconnection devices to be built. Specifically, the following formula is used to express the constraints on the number of flexible interconnection devices to be built: in, A Boolean variable representing the construction decision of the flexible interconnection device between nodes i and j. , then the flexible interconnection device is put into construction, otherwise the flexible interconnection device is not put into construction, Indicates the total number of lines to be selected for the flexible interconnection device.

[0032] The following is a detailed description of the grid operation constraints: 1) Power flow constraints of existing lines For existing transmission lines in the grid, the power flow equation constraints of the existing lines are met. Specifically, the following formula is used to represent the power flow constraints of the existing lines: in, represents the transmission power of the kth established line between nodes i and j, represents the susceptance of the k-th established line between nodes i and j, represents the voltage phase angle of node i, represents the voltage phase angle at node j.

[0033] 2) Power flow constraints of selected routes For the candidate lines and interconnected lines in the grid, if the line is put into construction, the power flow constraint is satisfied; if the line is not put into construction, the power flow transmission of the line is zero, satisfying the power flow equation constraint of the candidate line. Specifically, the following formula is used to represent the power flow constraint of the candidate line: 3) Capacity constraints of existing lines Specifically, the following formula is used to represent the capacity constraint of the built line: in, Indicates the maximum active transmission power of line (i, j).

[0034] 4) Capacity constraints of the selected lines Specifically, the following formula is used to represent the capacity constraint of the candidate line: If the candidate line is put into construction, its transmission power cannot exceed the upper limit of the line power flow capacity; if the candidate line is not put into construction, the transmission power is limited to zero.

[0035] 5) Capacity constraints of flexible interconnection devices Specifically, the following formula is used to characterize the capacity constraint of the flexible interconnection device: in, Indicates the maximum capacity of the flexible interconnection device. If the flexible interconnection device is put into construction, its capacity cannot exceed the maximum capacity limit; if the flexible interconnection device is not put into construction, the capacity is limited to zero.

[0036] 6) Node voltage phase angle constraints Specifically, the following formula is used to represent the node voltage phase angle constraint: in, Indicates the minimum value of the node voltage phase angle, Indicates the maximum value of the node voltage phase angle.

[0037] 7) Node power balance constraints Specifically, the following formula is used to represent the node power balance constraint: in, Indicates AC power grid A collection of Indicates that the flexible interconnection device is connected to the AC grid The active power transmitted by the i-th node, Indicates AC power grid A collection of Indicates that the flexible interconnection device is connected to the AC grid The active power transmitted by the i-th node, Represents the wind turbine connected to node i The output power, Represents the photovoltaic power station connected to node i The output power, represents the load connected to node i The required power, represents the set of wind turbines connected to node i, represents the set of photovoltaic power stations connected to node i, represents the load set connected to node i, represents the set of lines with node i as the power receiving end, represents the set of lines with node i as the power sending end.

[0038] 8) Generator output constraints Specifically, the following formula is used to represent the generator set output constraint: in, Indicates a generator set The minimum output power, Indicates a generator set Maximum output power.

[0039] 9) Load shedding constraints In some cases, appropriate load shedding is beneficial to the safe and economical operation of the system. Specifically, the following formula is used to represent the load shedding constraint condition: in, It represents the maximum load shedding ratio allowed for node i.

[0040] 10) Node curtailment constraints In order to promote the consumption of wind power and photovoltaic renewable energy, it is necessary to meet the node wind and solar power curtailment constraints. Specifically, the following formula is used to represent the node wind and solar power curtailment constraints: in, represents the maximum wind curtailment ratio allowed for node i, Indicates the maximum allowed abandoned light ratio of node i.

[0041] S2. Based on the uncertainty of the renewable energy output of the AC interconnected system, an uncertainty set is constructed, and the target grid planning model is robustly optimized according to the uncertainty set to obtain a two-stage grid planning model; Specifically, step S2 includes: 1) Based on the uncertainty of wind and solar power output in AC interconnected systems, an uncertainty set is constructed by introducing uncertainty adjustment parameters; The actual operation of AC interconnected systems faces numerous random factors, such as the impact of wind and photovoltaic power output, making output forecast accuracy difficult to guarantee. Because grid planning schemes based on traditional deterministic optimization models often lack robustness, it is necessary to account for the impact of uncertainty in the model. To account for the uncertainty of wind and photovoltaic output, an uncertainty set containing uncertain adjustment parameters is introduced.

[0042] Specifically, the following formula is used to characterize the uncertainty set: in, represents an uncertain set, u represents an uncertain quantity, represents the wind power output in the tth period within the scheduling cycle, represents the photovoltaic output of the tth period in the scheduling cycle, T represents the total number of periods in the scheduling cycle, Indicates the minimum wind power output, Indicates the maximum wind power output, represents a binary variable, when When , the wind power uncertainty variable in the corresponding period takes the maximum value of the fluctuation range. When , the wind power uncertainty variable in the corresponding period takes the minimum value of the fluctuation range, Indicates the uncertainty adjustment parameter of wind power output, which is an integer between 0 and T. It indicates the total number of periods during which wind power output reaches the minimum or maximum value of the fluctuation range within the scheduling period. It is used to adjust the conservatism of the optimal solution. The larger the value, the more conservative the grid planning scheme, and vice versa. Indicates the minimum photovoltaic output, represents a binary variable, when When , the photovoltaic uncertainty variable of the corresponding period takes the maximum value of the fluctuation range. When , the photovoltaic uncertainty variable of the corresponding period takes the minimum value of the fluctuation range, Indicates the maximum photovoltaic output, Indicates the uncertainty adjustment parameter of photovoltaic output, which is an integer between 0 and T. It indicates the total number of time periods during which the PV output reaches the minimum or maximum value of the fluctuation range within the scheduling period. It is used to adjust the conservatism of the optimal solution. The larger the value, the more conservative the grid planning scheme, and vice versa.

[0043] 2) Based on the uncertainty set, a two-stage robust optimization is performed on the target grid planning model to obtain a two-stage grid planning model.

[0044] The first stage determines the construction decision, and the second stage adjusts the operation decision under the influence of uncertainty to ensure that the system still meets the operation requirements in the worst scenario. Specifically, the following formula is used to represent the two-stage grid planning model: in, represents the objective function, x represents the decision variables of the first stage, c represents the coefficient vector related to the decision variables of the first stage, represents the uncertainty set, u represents the uncertainty quantity, y represents the decision variable of the second stage, b represents the coefficient vector related to the decision variable of the second stage, F(x,u) represents the feasible domain of the decision variable of the second stage, A represents the coefficient matrix, d represents the constant vector, represents the feasible region of the first-stage decision variables, Represents the set of second-stage decision variables, G represents the coefficient matrix, h represents the constant vector, E represents the coefficient matrix, and M represents the coefficient matrix.

[0045] It is understandable that the core idea of ​​two-stage robust optimization is that the decision made in the first stage can ensure that the decision made in the second stage minimizes the objective function value in the worst-case scenario. The decision variable x is determined before the uncertainty is determined, and the decision variable y is determined after the decision variable x is determined. The variables in the above two-stage grid planning model are expressed as: x = , y= .

[0046] S3. Use the improved C&CG algorithm to solve the two-stage grid planning model and obtain the optimal grid of the AC interconnection system.

[0047] Specifically, the C&CG algorithm combining KKT conditions and the big M method is used to solve the two-stage grid planning model and obtain the optimal grid of the AC interconnection system.

[0048] The C&CG algorithm was used to divide the two-stage robust optimization problem into a main problem (MP) and subproblems (SP) for iterative solution. Given the complexity of the "max-min" structure within the subproblem, the KKT optimality condition and the large-M method were used to transform it into a single-level mixed-integer linear programming (MILP) problem. This was then efficiently solved using a commercial solver, and the entire two-stage grid planning model was ultimately converged through iteration.

[0049] MP is a lower bound value determined by the objective function. If the uncertain scene is in discrete form, , then the corresponding decision variable x is Since the uncertainty scenarios are not all scenarios, the optimization result is a lower bound of the objective function. The listed uncertain scenarios are irrelevant to the optimization result. This is because the scenarios solved by SP are constantly added to MP, which continuously increases the lower bound of the objective function in MP. Generally speaking, if the proportion of uncertain scenarios in the total scenarios is relatively large, the convergence speed will be faster. The MP and SP models are as follows: in, It is the abbreviation of objective. Indicates the main problem goal, represents the sub-problem goal, An auxiliary variable introduced by the C&CG algorithm, representing the objective function value of the second stage in the optimal solution. The decision variable y obtained by MP is brought into SP to obtain the uncertain scenario and the decision variable x. Since the resulting scenario corresponds to the decision variable in the MP, the value obtained in the SP serves as an upper bound for the objective function. By then adding the scenario in the SP to the MP, the MP's decision variable provides a lower bound for the objective function. This process is repeated until the difference between the upper bound and the determined value meets the convergence criterion, at which point the iteration ends.

[0050] Since SP involves the "max-min" problem, it cannot be solved directly using commercial solvers. In this paper, the KKT condition and the big M method are used to linearize the nonlinear terms and convert SP into a single-level mixed integer linear programming model as shown below: in, represents the Lagrange multiplier, and Represents the 0-1 variable introduced in the Big M method, represents a very large number defined in the Big M method. SP is thus transformed into a mixed integer linear programming problem, which can be solved efficiently using commercial solvers.

[0051] For ease of understanding, the following is a detailed description of the solution process of the C&CG algorithm based on the KKT condition and the big M method: 1) Setup , , k=1; randomly initialize the scene and set the convergence criterion ; 2) Solve the master problem (MP) and get and , update the lower bound ; 3) Bring in the subproblem (SP), solve SP, and get the worst scenario and target value , update the upper bound ; 4) If , then stop, otherwise continue to step 5); 5) If there is a solution in SP in 3), create a new variable , and add the following constraints to MP, and return to step 2): If there is no solution for SP in 3), create a new variable , and add the following constraints to MP, return to step 2) until convergence.

[0052] The embodiment of the present invention provides a grid planning method for an AC interconnected system based on two-stage robust optimization, constructs a mixed integer optimization model for the coordinated planning of transmission lines and flexible interconnected devices, and for the first time incorporates line expansion decisions and interconnected device site selection decisions into a unified optimization framework, breaking through the limitation of traditional grid planning methods that consider line expansion in isolation; by introducing uncertainty adjustment parameters to construct an uncertainty set, dynamic control of the fluctuation range of wind and solar power output is achieved, overcoming the limitation of fixed-boundary uncertainty sets in traditional robust optimization; the improved C&CG algorithm based on KKT conditional linearization significantly improves the efficiency of solving high-dimensional uncertainty problems by converting two-layer optimization subproblems into mixed integer linear programming, which is conducive to quickly obtaining the optimal grid of the AC interconnected system.

[0053] Based on the above two-stage robust optimization-based AC interconnection system grid planning method, Figure 2As shown, an embodiment of the present invention provides an AC interconnection system grid planning system based on two-stage robust optimization, including: A first model building module 1 is used to build a target grid planning model based on minimizing the comprehensive cost of AC interconnection system grid planning, wherein the comprehensive cost includes construction cost and operation cost; The second model building module 2 is used to build an uncertainty set based on the uncertainty of the renewable energy output of the AC interconnected system, and to perform robust optimization on the target grid planning model according to the uncertainty set to obtain a two-stage grid planning model; The optimal grid determination module 3 is used to solve the two-stage grid planning model using the improved C&CG algorithm to obtain the optimal grid of the AC interconnection system.

[0054] It should be noted that each module in the above-mentioned AC interconnection system grid planning system based on two-stage robust optimization can be fully or partially implemented through software, hardware and their combination. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules. For the specific definition of an AC interconnection system grid planning system based on two-stage robust optimization, please refer to the above definition of an AC interconnection system grid planning method based on two-stage robust optimization. The two have the same functions and effects and will not be repeated here.

[0055] In summary, the embodiment of the present invention provides a grid planning method and system for an AC interconnected system based on two-stage robust optimization, constructs a mixed integer optimization model for the coordinated planning of transmission lines and flexible interconnected devices, and for the first time incorporates line expansion decisions and interconnected device site selection decisions into a unified optimization framework, breaking through the limitation of traditional grid planning methods that consider line expansion in isolation; by introducing uncertainty adjustment parameters to construct an uncertainty set, dynamic control of the fluctuation range of wind and solar power output is achieved, overcoming the limitation of fixed-boundary uncertainty sets in traditional robust optimization; the improved C&CG algorithm based on KKT conditional linearization significantly improves the efficiency of solving high-dimensional uncertainty problems by converting two-layer optimization subproblems into mixed integer linear programming, which is conducive to quickly obtaining the optimal grid of the AC interconnected system.

[0056] Each embodiment in this specification is described in a progressive manner, and the same or similar parts of each embodiment can be directly referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. It should be noted that the various technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the various technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0057] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and substitutions can be made without departing from the technical principles of the present invention. These improvements and substitutions should also be regarded as the scope of protection of the present invention.

Claims

1. A two-stage robust optimization-based AC interconnection system grid planning method, characterized in that: include: Constructing a target grid planning model based on minimizing the comprehensive cost of AC interconnection system grid planning, wherein the comprehensive cost includes construction cost and operation cost; An uncertainty set is constructed based on the uncertainty of the renewable energy output of the AC interconnected system, and a two-stage grid planning model is obtained by robustly optimizing the target grid planning model according to the uncertainty set; The improved C&CG algorithm is used to solve the two-stage grid planning model to obtain the optimal grid of the AC interconnection system.

2. The AC interconnection system grid planning method based on two-stage robust optimization according to claim 1 is characterized in that: The target grid planning model is constructed based on minimizing the comprehensive cost of AC interconnection system grid planning, including: An initial grid planning model is constructed based on minimizing the construction and operating costs of AC interconnection system grid planning, wherein the construction costs include the transmission line construction costs and the flexible interconnection device construction costs, and the operating costs include the power generation costs of the generator sets and the AC interconnection system risk costs; Based on the physical operating characteristics of the AC interconnection system, constraint conditions are constructed, and the initial grid planning model is constrained according to the constraint conditions to obtain a target grid planning model, wherein the constraint conditions include grid construction constraint conditions and grid operation constraint conditions.

3. The AC interconnection system grid planning method based on two-stage robust optimization according to claim 2 is characterized in that: The following formula is used to characterize the initial grid planning model: in, Represents the comprehensive cost, represents the construction cost, Indicates the equivalence factor between the equivalent annual value of construction cost and the equivalent annual value of operating cost, represents the power generation cost of the generator set, Represents the risk cost of the AC interconnection system.

4. The AC interconnection system grid planning method based on two-stage robust optimization according to claim 3 is characterized in that: The construction cost is represented by the following formula: in, represents the construction cost, represents the set of transmission line corridors, represents the set of candidate routes for the (i, j) corridor channel, represents the construction cost of the k-th line between nodes i and j, A Boolean variable representing the k-th line construction decision between nodes i and j, Represents a collection of flexible interconnection device installation lines, represents the unit capacity construction cost of the flexible interconnection device, Indicates the construction capacity of flexible interconnection devices; The following formula is used to represent the power generation cost of the generator set: in, represents the power generation cost of the generator set, Represents a collection of nodes, represents the set of generators connected to node i, 、 、 represents the cost consumption characteristic coefficient of the generator set connected to node i, represents the power generation of the generator set connected to node i; The AC interconnection system risk cost includes the wind curtailment risk cost, solar curtailment risk cost, load shedding risk cost, and flexible interconnection device loss cost. The following formula is used to represent the AC interconnection system risk cost: in, represents the risk cost of the AC interconnection system, represents the penalty cost coefficient for wind curtailment at node i, represents the penalty cost coefficient of node i for abandoning light, represents the load shedding penalty cost coefficient, represents the power loss coefficient of the flexible interconnection device, represents the amount of wind curtailment at node i, represents the amount of abandoned light at node i, represents the load shedding amount of node i, Indicates the power loss of the flexible interconnection device.

5. The AC interconnection system grid planning method based on two-stage robust optimization according to claim 2 is characterized in that: The grid construction constraints include transmission line sequence construction constraints, corridor channel line quantity constraints, and flexible interconnection device investment and construction quantity constraints; The grid operation constraints include the flow constraints of existing lines, the flow constraints of lines to be selected, the capacity constraints of existing lines, the capacity constraints of lines to be selected, the capacity constraints of flexible interconnection devices, the node voltage phase angle constraints, the node power balance constraints, the generator output constraints, the load shedding constraints and the node wind and solar power abandonment constraints.

6. The AC interconnection system grid planning method based on two-stage robust optimization according to claim 1 is characterized in that: The uncertainty set is constructed based on the uncertainty of the new energy output of the AC interconnected system, and the target grid planning model is robustly optimized according to the uncertainty set to obtain a two-stage grid planning model, including: Based on the uncertainty of wind and solar power output in AC interconnected systems, an uncertainty set is constructed by introducing uncertainty adjustment parameters; Based on the uncertainty set, the target grid planning model is subjected to two-stage robust optimization to obtain a two-stage grid planning model.

7. The AC interconnection system grid planning method based on two-stage robust optimization according to claim 6 is characterized in that: The uncertainty set is characterized by the following formula: in, represents an uncertain set, u represents an uncertain quantity, represents the wind power output in the tth period within the scheduling cycle, represents the photovoltaic output of the tth period in the scheduling cycle, T represents the total number of periods in the scheduling cycle, Indicates the minimum wind power output, Indicates the maximum wind power output, represents a binary variable, represents the uncertainty adjustment parameter of wind power output, Indicates the total number of periods during which wind power output reaches the minimum or maximum value of the fluctuation range within the dispatch period. Indicates the minimum photovoltaic output, represents a binary variable, Indicates the maximum photovoltaic output, represents the uncertainty adjustment parameter of photovoltaic output, Indicates the total number of time periods during the scheduling period when the PV output reaches the minimum or maximum value of the fluctuation range.

8. The AC interconnection system grid planning method based on two-stage robust optimization according to claim 6 is characterized in that: The two-stage grid planning model is characterized by the following formula: in, represents the objective function, x represents the decision variables of the first stage, c represents the coefficient vector related to the decision variables of the first stage, represents the uncertainty set, u represents the uncertainty quantity, y represents the decision variable of the second stage, b represents the coefficient vector related to the decision variable of the second stage, F(x,u) represents the feasible domain of the decision variable of the second stage, A represents the coefficient matrix, d represents the constant vector, represents the feasible region of the first-stage decision variables, Represents the set of second-stage decision variables, G represents the coefficient matrix, h represents the constant vector, E represents the coefficient matrix, and M represents the coefficient matrix.

9. The AC interconnection system grid planning method based on two-stage robust optimization according to claim 1, characterized in that: The improved C&CG algorithm is used to solve the two-stage grid planning model to obtain the optimal grid of the AC interconnection system, including: The C&CG algorithm combining KKT conditions and the big M method is used to solve the two-stage grid planning model and obtain the optimal grid of the AC interconnection system.

10. A two-stage robust optimization-based AC interconnection system grid planning system, characterized in that: include: A first model building module is used to build a target grid planning model based on minimizing the comprehensive cost of AC interconnection system grid planning, wherein the comprehensive cost includes construction cost and operation cost; A second model building module is used to build an uncertainty set based on the uncertainty of the new energy output of the AC interconnected system, and to perform robust optimization on the target grid planning model according to the uncertainty set to obtain a two-stage grid planning model; The optimal grid determination module is used to solve the two-stage grid planning model using an improved C&CG algorithm to obtain the optimal grid of the AC interconnection system.

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