Interconnection system network planning method and system based on two-stage robust optimization

Abstract

The application relates to the technical field of power system planning, and discloses an alternating current interconnection system network frame 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 minimization of the comprehensive cost of alternating current interconnection system network frame planning; constructing an uncertainty set based on the uncertainty of new energy output of the alternating current interconnection system, and performing robust optimization on the target network frame planning model according to the uncertainty set to obtain a two-stage network frame planning model; and solving the two-stage network frame planning model by using an improved C&CG algorithm to obtain the optimal network frame of the alternating current interconnection system. While considering the uncertainty of wind power, the application firstly integrates line expansion decision and interconnection device site selection and capacity determination into a unified optimization framework, and breaks through the limitation of traditional network frame planning methods which consider line expansion in isolation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power system planning, in particular to an AC interconnected system network framework planning method and system based on two-stage robust optimization. BACKGROUND

[0002] Due to the large-scale access of high-proportion distributed new energy to the power grid and the surge of regional load, the power grid planning is facing severe challenges. The existing network framework planning method focuses on the expansion planning of transmission lines, determines the optimal line corridor and capacity by minimizing the line construction cost and operation cost, but ignores the coordinated planning of regional flexible interconnection devices, resulting in poor flexibility of power grid partition operation and inability to dynamically adjust the power exchange between regions according to the operation demand. For uncertainty processing, the existing AC interconnected system network framework planning method mainly adopts deterministic optimization, stochastic optimization and robust optimization. However, deterministic optimization ignores the volatility of source and load, and the obtained planning scheme has poor robustness; stochastic optimization relies on probability distribution assumption, has high computational complexity and is difficult to cope with extreme scenarios; robust optimization can avoid extreme scenario risks, but its traditional uncertain set boundary is fixed and cannot dynamically adjust the conservatism of the planning scheme according to the risk preference of decision makers. In terms of solution, the existing solution methods mainly include heuristic algorithm, Benders decomposition algorithm and column and constraint generation (C&CG) algorithm. However, although the heuristic algorithm avoids complex mathematical modeling, its random search characteristics make it difficult to guarantee the solution quality, and the calculation time grows too fast 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 master problem and a sub-problem for iterative solution, which can theoretically guarantee convergence, but in practical application, it faces the problem of low computational efficiency; the C&CG algorithm can better handle two-stage robust optimization problems, but its defect is that the sub-problem needs to construct a complex dual model.

[0003] Therefore, it is of great significance for AC interconnected system network framework planning to consider the uncertainty of high-penetration wind power while combining transmission network expansion and flexible interconnection device siting and sizing. SUMMARY

[0004] In order to overcome the deficiencies of the prior art, the present application provides an AC interconnected system network framework planning method and system based on two-stage robust optimization.

[0005] In a first aspect, an AC interconnected system network framework planning method based on two-stage robust optimization is provided, comprising:

[0006] Based on minimizing the comprehensive cost of AC interconnected system network framework planning, a target network framework planning model is constructed, wherein the comprehensive cost includes construction cost and operation cost;

[0007] An uncertainty set is constructed based on the uncertainty of new energy output of the AC interconnected system, and a two-stage network planning model is obtained by robust optimization of the target network planning model according to the uncertainty set;

[0008] An improved C&CG algorithm is used to solve the two-stage network planning model to obtain the optimal network of the AC interconnected system.

[0009] Preferably, the target network planning model is constructed based on minimizing the comprehensive cost of the AC interconnected system network planning, and the target network planning model comprises:

[0010] An initial network planning model is constructed based on minimizing the construction cost and operation cost of the AC interconnected system network planning, wherein the construction cost comprises the transmission line construction cost and the flexible interconnection device construction cost, and the operation cost comprises the generator set power generation cost and the AC interconnected system risk cost.

[0011] Constraint conditions are constructed based on the physical operation characteristics of the AC interconnected system, and the target network planning model is obtained by constraining the initial network planning model according to the constraint conditions, wherein the constraint conditions comprise network construction constraint conditions and network operation constraint conditions.

[0012] Preferably, the initial network planning model is characterized by the following formula:

[0013]

[0014] wherein, C represents the comprehensive cost, C represents the construction cost, C represents the equivalent factor between the equal annual construction cost and the equal annual operation cost, C represents the generator set power generation cost, C represents the AC interconnected system risk cost.

[0015] Preferably, the construction cost is characterized by the following formula:

[0016]

[0017] wherein, C represents the construction cost, C represents the set of transmission line corridors, C represents the set of (i,j) corridor channels to be selected, C represents the construction cost of the kth line between nodes i and j, C represents the Boolean variable of the kth line construction decision between nodes i and j, C represents the set of flexible interconnection device installation lines, C represents the unit capacity construction cost of the flexible interconnection device, represents the construction capacity of the flexible interconnection device;

[0018] The power generation cost of the generator set is characterized by the following formula:

[0019]

[0020] wherein, represents the power generation cost of the generator set, represents the node set, represents the generator set connected to node i, represents the cost consumption characteristic coefficient of the generator set connected to node i, represents the power generation power of the generator set connected to node i;

[0021] The AC interconnection system risk cost includes wind curtailment risk cost, light curtailment risk cost, load shedding risk cost and flexible interconnection device loss cost, and the AC interconnection system risk cost is characterized by the following formula:

[0022]

[0023] wherein, represents the AC interconnection system risk cost, represents the wind curtailment penalty cost coefficient of node i, represents the light curtailment penalty cost coefficient of node i, represents the load shedding penalty cost coefficient, represents the flexible interconnection device power loss coefficient, represents the wind curtailment amount of node i, represents the light curtailment amount of node i, represents the load shedding amount of node i, represents the flexible interconnection device loss power.

[0024] Preferably, the network framework construction constraint condition includes a transmission line sequence construction constraint condition, a corridor line quantity constraint condition and a flexible interconnection device construction quantity constraint condition;

[0025] The network framework operation constraint condition includes a built line power flow constraint condition, a to-be-selected line power flow constraint condition, a built line capacity constraint condition, a to-be-selected line capacity constraint condition, a flexible interconnection device capacity constraint condition, a node voltage phase angle constraint condition, a node power balance constraint condition, a generator set output constraint condition, a load shedding constraint condition and a node wind and light curtailment constraint condition.

[0026] ​​Preferably, the new energy output uncertainty based on the alternating current mutual system constructs an uncertainty set, and performs robust optimization on the target network planning model according to the uncertainty set to obtain a two-stage network planning model, comprising:

[0027] Based on the wind and light output uncertainty of the alternating current mutual system, an uncertainty set is constructed by introducing an uncertainty adjustment parameter;

[0028] Based on the uncertainty set, two-stage robust optimization is performed on the target network planning model to obtain a two-stage network planning model.

[0029] Preferably, the uncertainty set is characterized by the following formula:

[0030]

[0031] Among them, The uncertainty set is represented by u, which represents the uncertainty amount, represents the wind power output in the t th period of the scheduling period, represents the photovoltaic output in the t th period of the scheduling period, and T represents the total number of periods in the scheduling period, represents the minimum value of the wind power output, represents the maximum value of the wind power output, represents a binary variable, represents the uncertainty adjustment parameter of the wind power output, represents the total number of periods in the scheduling period in which the wind power output takes the minimum value or the maximum value of the fluctuation interval, represents the minimum value of the photovoltaic output, represents a binary variable, represents the maximum value of the photovoltaic output, represents the uncertainty adjustment parameter of the photovoltaic output, represents the total number of periods in the scheduling period in which the photovoltaic output takes the minimum value or the maximum value of the fluctuation interval.

[0032] Preferably, the two-stage network planning model is characterized by the following formula:

[0033]

[0034]

[0035] Among them, represents the objective function, x represents the decision variable of the first stage, and c represents the coefficient vector related to the first stage decision variable, represents an uncertain set, u represents an uncertain quantity, y represents a decision variable of the second stage, b represents a coefficient vector related to the decision variable of the second stage, F(x, u) represents a feasible region of the decision variable of the second stage, A represents a coefficient matrix, d represents a constant vector, represents a feasible region of the decision variable of the first stage, represents a set of the decision variable of the second stage, G represents a coefficient matrix, h represents a constant vector, E represents a coefficient matrix, and M represents a coefficient matrix.

[0036] Preferably, the improved C&CG algorithm is used to solve the two-stage network planning model to obtain the optimal network of the alternating current interconnected system.

[0037] The C&CG algorithm combined with the KKT condition and the large M method is used to solve the two-stage network planning model to obtain the optimal network of the alternating current interconnected system.

[0038] In a second aspect, an embodiment of the present application provides an alternating current interconnected system network planning system based on two-stage robust optimization, comprising:

[0039] A first model construction module is configured to construct a target network planning model based on minimizing the comprehensive cost of the alternating current interconnected system network planning, wherein the comprehensive cost comprises a construction cost and an operation cost.

[0040] A second model construction module is configured to construct an uncertain set based on the uncertainty of new energy output of the alternating current interconnected system, and to obtain a two-stage network planning model by robust optimization of the target network planning model according to the uncertain set.

[0041] An optimal network determination module is configured to solve the two-stage network planning model by using an improved C&CG algorithm to obtain the optimal network of the alternating current interconnected system.

[0042] Compared with the prior art, the alternating current interconnected system network planning method and system based on two-stage robust optimization has the following beneficial effects: a mixed integer optimization model for coordinated planning of power transmission lines and flexible interconnected devices is constructed, line expansion decision and interconnected device site selection and capacity are first incorporated into a unified optimization framework, and the limitation of traditional network planning methods that only consider line expansion is broken through; an uncertain set is constructed by introducing an uncertainty adjustment parameter, dynamic control of wind and light output fluctuation ranges is realized, and the limitation problem of fixed boundary uncertain sets in traditional robust optimization is overcome; based on the improved C&CG algorithm of KKT condition linearization, a bi-level optimization subproblem is converted into a mixed integer linear programming, the solving efficiency of high-dimensional uncertainty problems is significantly improved, and the optimal network of the alternating current interconnected system is quickly obtained. BRIEF DESCRIPTION OF DRAWINGS

[0043] Figure 1 is a flowchart of a two-stage robust optimization-based AC interconnected system network framework planning method according to an embodiment of the present application;

[0044] Figure 2 is a structural diagram of a two-stage robust optimization-based AC interconnected system network framework planning system according to an embodiment of the present application;

[0045] Reference signs:

[0046] 1, first model construction module; 2, second model construction module; 3, optimal network framework determination module. DETAILED DESCRIPTION

[0047] The specific embodiments of the present application will be further described in detail below with reference to the accompanying drawings and embodiments. The following embodiments are used to illustrate the present application, but are not used to limit the scope of the present application.

[0048] In the description of the present application, it should be understood that the terms "first" and "second" and the like are used to distinguish different objects, and are not used to describe a specific order.

[0049] In the description of the present application, it should be noted that, unless otherwise defined, all technical and scientific terms used in the present application have the same meaning as understood by those skilled in the art. The terms used in the specification of the present application are only for the purpose of describing the specific embodiments, and are not intended to limit the present application. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0050] As shown in Figure 1 , the embodiment of the present application provides a two-stage robust optimization-based AC interconnected system network framework planning method, which comprises the following steps:

[0051] S1, based on minimizing the comprehensive cost of AC interconnected system network framework planning, a target network framework planning model is constructed;

[0052] It should be noted that the target network framework planning model of the present application is a mixed integer optimization model for planning of power transmission lines and flexible interconnected devices. For the first time, line expansion decision and interconnected device site selection and capacity are included in a unified optimization framework, breaking through the limitations of traditional network framework planning methods which consider line expansion in isolation.

[0053] The flexible interconnected device of the AC interconnected system in the present embodiment will be described in detail as follows:

[0054] 1) The active power constraint of the flexible interconnected device is characterized by the following formula:

[0055]

[0056] 2) The capacity constraint of the flexible interconnection device is represented by the following formula:

[0057]

[0058] wherein, and denote the port number of the flexible interconnection device connected to the alternating current power grid, denote the active power of the flexible interconnection device port, denote the active power of the flexible interconnection device port, denote the active power loss of the flexible interconnection device port, denote the active power loss of the flexible interconnection device port, denote the loss coefficient of the flexible interconnection device port, denote the loss coefficient of the flexible interconnection device port, denote the reactive power of the flexible interconnection device port, denote the reactive power of the flexible interconnection device port, denote the construction capacity of the flexible interconnection device.

[0059] It can be understood that the first formula in the active power constraint of the flexible interconnection device is a balance expression of the transmission power and the loss power at both ends of the interconnection device, the second formula is a definition of the active power loss of the flexible interconnection device port, and the third formula is a definition of the active power loss of the flexible interconnection device port. The formulas in the capacity constraint of the flexible interconnection device represent that the operating power of the interconnection device is less than the construction capacity. The two constraints can fully characterize the flexible interconnection device.

[0060] Specifically, the comprehensive cost includes construction cost and operating cost. Further, step S1 includes:

[0061] 1) Based on minimizing the construction cost and operating cost of the alternating current interconnection system grid planning, an initial grid planning model is constructed;

[0062] Specifically, the initial grid planning model is represented by the following formula:

[0063]

[0064] wherein, denotes the comprehensive cost, denotes the construction cost, denotes the equivalent factor between the equivalent annual construction cost and the equivalent annual operation cost, denotes the generation cost of the generator unit, denotes the risk cost of the AC interconnected system. It can be understood that the above formula is also the objective function of the initial network planning model.

[0065] The construction cost includes the transmission line construction cost and the flexible interconnection device construction cost. Specifically, the construction cost is represented by the following formula:

[0066]

[0067] wherein, denotes the construction cost, denotes the set of transmission line corridors, denotes the set of candidate lines of the (i, j) corridor channel, denotes the construction cost of the kth line between nodes i and j, denotes the Boolean variable of the construction decision of the kth line between nodes i and j, if the transmission line is built, otherwise the transmission line is not built, denotes the set of lines on which the flexible interconnection device is installed, denotes the unit capacity construction cost of the flexible interconnection device, denotes the construction capacity of the flexible interconnection device.

[0068] The operation cost includes the generation cost of the generator unit and the risk cost of the AC interconnected system. Specifically, the generation cost of the generator unit is represented by the following formula:

[0069]

[0070] wherein, denotes the generation cost of the generator unit, denotes the set of nodes, denotes the set of generator units connected to node i, , , denotes the cost consumption characteristic coefficient of the generator unit connected to node i, denotes the generation power of the generator unit connected to node i.

[0071] The risk cost of the AC interconnected system includes the wind curtailment risk cost, the light curtailment risk cost, the load shedding risk cost and the flexible interconnection device loss cost. Specifically, the risk cost of the AC interconnected system is represented by the following formula:

[0072]

[0073] wherein, a risk cost of the AC interconnected system, a wind curtailment penalty cost coefficient of node i, a light curtailment penalty cost coefficient of node i, a load shedding penalty cost coefficient, a flexible interconnection device power loss coefficient, a wind curtailment amount of node i, a light curtailment amount of node i, a load shedding amount of node i, a flexible interconnection device loss power.

[0074] 2) constructing constraint conditions based on physical operation characteristics of the AC interconnected system, and constraining the initial grid framework planning model according to the constraint conditions to obtain a target grid framework planning model.

[0075] Specifically, the constraint conditions include grid construction constraint conditions and grid operation constraint conditions. Further, the grid construction constraint conditions include transmission line sequence construction constraint conditions, corridor passage line quantity constraint conditions and flexible interconnection device construction quantity constraint conditions, and the grid operation constraint conditions include built line power flow constraint conditions, to-be-selected line power flow constraint conditions, built line capacity constraint conditions, to-be-selected line capacity constraint conditions, flexible interconnection device capacity constraint conditions, node voltage phase angle constraint conditions, node power balance constraint conditions, generator unit output constraint conditions, load shedding constraint conditions and node wind and light curtailment constraint conditions.

[0076] The grid construction constraint conditions are specifically described as follows:

[0077] 1) Transmission line sequence construction constraint conditions

[0078] For transmission lines with the same end points, the sequence construction loop constraint is satisfied. Specifically, the transmission line sequence construction constraint conditions are characterized by the following formula:

[0079]

[0080]

[0081] wherein, represents a set not containing , and represents a set of all loop lines between nodes i and j. It can be understood that if the kth loop line is not constructed, then the k+1th to loop lines are not constructed, and only when the kth loop line is constructed, the k+1th loop line can be constructed.

[0082] 2) Corridor passage line quantity constraint conditions

[0083] Since the number of lines allowed to be built in each corridor is limited, the corridor line number constraint needs to be satisfied. Specifically, the corridor line number constraint is characterized by the following equation:

[0084]

[0085] wherein, denotes the minimum value of the number of lines allowed to be built in the (i, j) corridor, denotes the maximum value of the number of lines allowed to be built in the (i, j) corridor.

[0086] 3) Flexible interconnection device construction number constraint

[0087] Since the number of interconnection lines to be selected for the AC interconnection system is limited, the flexible interconnection device construction number constraint needs to be satisfied. Specifically, the flexible interconnection device construction number constraint is characterized by the following equation:

[0088]

[0089] wherein, denotes the Boolean variable of the flexible interconnection device construction decision between nodes i and j, if the flexible interconnection device is built, otherwise the flexible interconnection device is not built, denotes the total number of lines to be selected for the flexible interconnection device.

[0090] The following describes the grid operation constraint conditions:

[0091] 1) Built line power flow constraint condition

[0092] For the existing transmission lines of the grid, the built line power flow equation constraint needs to be satisfied. Specifically, the built line power flow constraint condition is characterized by the following equation:

[0093]

[0094] wherein, denotes the transmission power of the kth built line between nodes i and j, denotes the susceptance of the kth built line between nodes i and j, denotes the voltage phase angle of node i, denotes the voltage phase angle of node j.

[0095] 2) Selected line power flow constraint condition

[0096] For the selected line in the grid and the interconnection line, if the line is built, it meets the power flow constraint; if the line is not built, the power flow transmission of the line is zero, which meets the power flow equation constraint of the selected line. Specifically, the following formula is used to represent the power flow constraint condition of the selected line:

[0097]

[0098] 3) Capacity constraint condition of built line

[0099] Specifically, the following formula is used to represent the capacity constraint condition of the built line:

[0100]

[0101] wherein, represents the maximum active power transmission power of the line (i, j).

[0102] 4) Capacity constraint condition of selected line

[0103] Specifically, the following formula is used to represent the capacity constraint condition of the selected line:

[0104]

[0105] If the selected line is built, its transmission power cannot exceed the upper limit value of the line power flow capacity; if the selected line is not built, the transmission power is limited to zero.

[0106] 5) Capacity constraint condition of flexible interconnection device

[0107] Specifically, the following formula is used to represent the capacity constraint condition of the flexible interconnection device:

[0108]

[0109] wherein, represents the maximum capacity of the flexible interconnection device, if the flexible interconnection device is built, its capacity cannot exceed the maximum capacity limit; if the flexible interconnection device is not built, the capacity is limited to zero.

[0110] 6) Node voltage phase angle constraint condition

[0111] Specifically, the following formula is used to represent the node voltage phase angle constraint condition:

[0112]

[0113] wherein, represents the minimum value of the node voltage phase angle, represents the maximum value of the node voltage phase angle.

[0114] 7) Node power balance constraint condition

[0115] Specifically, the nodal power balance constraint is characterized by the following formula:

[0116]

[0117] in, Indicates AC power grid The set, This indicates that the flexible interconnection device is connected to the AC power grid. The active power transmitted by the i-th node. Indicates AC power grid The set, This indicates that the flexible interconnection device is connected to the AC power grid. The active power transmitted by the i-th node. This represents the wind turbine unit connected to node i. 'output power' Represents the photovoltaic power station connected to node i. 'output power' Indicates the load connected to node i The required power, This represents the set of wind turbine units connected to node i. This represents the set of photovoltaic power stations connected to node i. This represents the set of loads connected to node i. Let i represent the set of lines with node i as the power receiver. This represents the set of lines with node i as the power transmitter.

[0118] 8) Generator output constraints

[0119] Specifically, the following formula is used to characterize the output constraint conditions of the generator set:

[0120]

[0121] in, Indicates generator set Minimum output power Indicates generator set The maximum output power.

[0122] 9) Load shedding constraints

[0123] In some cases, appropriate load shedding is beneficial for the safe and economical operation of the system. Specifically, the load shedding constraint is characterized by the following formula:

[0124]

[0125] in, This represents the maximum allowable load shedding ratio for node i.

[0126] 10) Node wind and light abandonment constraint condition

[0127] In order to promote the consumption of wind and light renewable energy, the node wind and light abandonment constraint needs to be met. Specifically, the node wind and light abandonment constraint condition is characterized by the following formula:

[0128]

[0129] Wherein, represents the maximum value of the wind abandonment ratio allowed by the node i, represents the maximum value of the light abandonment ratio allowed by the node i.

[0130] S2, based on the uncertainty set of the output of new energy of alternating current interconnection system, and according to the uncertainty set, the two-stage network framework planning model is obtained by robust optimization of the target network framework planning model;

[0131] Specifically, step S2 includes:

[0132] 1) Based on the wind and light output uncertainty of alternating current interconnection system, the uncertainty set is constructed by introducing uncertainty adjustment parameter;

[0133] In the actual operation of alternating current interconnection system, many random factors are faced, such as the influence of wind power and photovoltaic output, which leads to the difficulty in ensuring the prediction accuracy of its output. Since the network framework planning scheme obtained based on the traditional deterministic optimization model often lacks robustness, it is necessary to consider the influence of uncertainty in the model. Considering the wind and light output uncertainty, the uncertainty set containing uncertainty adjustment parameter is introduced.

[0134] Specifically, the uncertainty set is characterized by the following formula:

[0135]

[0136] Wherein, represents the uncertainty set, and u represents the uncertainty amount, represents the wind power output in the t th period in the dispatching cycle, represents the photovoltaic output in the t th period in the dispatching cycle, and T represents the total number of periods in the dispatching cycle, represents the minimum value of wind power output, represents the maximum value of wind power output, represents a binary variable, when , the wind power uncertainty variable of the corresponding period takes the maximum value of the fluctuation interval, when , the wind power uncertainty variable of the corresponding period takes the minimum value of the fluctuation interval, represents the uncertainty adjustment parameter of wind power output, which takes an integer value in 0~T, represents the total number of time periods in which the wind power output takes the minimum or maximum value of the fluctuation interval within the scheduling period, used to adjust the conservatism of the optimal solution, the larger the value, the more conservative the network planning scheme obtained, and vice versa, represents the minimum value of the photovoltaic output, represents a binary variable, when the photovoltaic uncertainty variable of the corresponding time period takes the maximum value of the fluctuation interval, and when the photovoltaic uncertainty variable of the corresponding time period takes the minimum value of the fluctuation interval, represents the maximum value of the photovoltaic output, represents an uncertainty adjustment parameter of the photovoltaic output, taking an integer value within 0-T, represents the total number of time periods in which the photovoltaic output takes the minimum or maximum value of the fluctuation interval within the scheduling period, used to adjust the conservatism of the optimal solution, the larger the value, the more conservative the network planning scheme obtained, and vice versa.

[0137] 2) Based on the uncertainty set, a two-stage robust optimization is performed on the target network planning model to obtain a two-stage network planning model.

[0138] 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 two-stage network planning model is represented by the following formula:

[0139]

[0140]

[0141] wherein, represents the objective function, x represents the decision variable of the first stage, c represents the coefficient vector related to the decision variable 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 region of the decision variable of the second stage, A represents the coefficient matrix, and d represents the constant vector, represents the feasible region of the decision variable of the first stage, represents the set of decision variables of the second stage, G represents the coefficient matrix, h represents the constant vector, E represents the coefficient matrix, and M represents the coefficient matrix.

[0142] It can be understood that the core idea of the two-stage robust optimization is that the decision made in the first stage can ensure that the decision made in the second stage has the minimum objective function value in the worst scenario. The decision variable x is determined before the uncertainty quantity is determined, and the decision variable y is determined after the decision variable x is determined. The variable in the above two-stage network planning model is represented as: x= , y = 0 .

[0143] S3, the improved C&CG algorithm is used to solve the two-stage network planning model to obtain the optimal network of the AC interconnected system.

[0144] Specifically, the C&CG algorithm combining the KKT condition and the large M method is used to solve the two-stage network planning model to obtain the optimal network of the AC interconnected system.

[0145] The C&CG algorithm is used to divide the two-stage robust optimization problem into a master problem (MP) and a sub-problem (SP) for iterative solution. In view of the complexity of the "max-min" structure in the sub-problem, the KKT optimality condition and the large M method are used to transform it into a single-layer mixed integer linear programming (MILP) problem, so as to be solved efficiently by means of a commercial solver, and the final convergence of the entire two-stage network planning model is realized through iteration.

[0146] MP is a lower bound value of the objective function. If the uncertain scenario is discrete, that is , then the corresponding decision variable x is . Since the uncertain scenario is not all scenarios, the optimization result is a lower bound value of the objective function, and the uncertain scenario listed has nothing to do with the optimization result, because the scenario solved by SP is constantly added to MP, making the lower bound value of the objective function in MP constantly increase. Generally speaking, if the proportion of uncertain scenarios in all scenarios is relatively large, the convergence speed will be accelerated, and the MP and SP models are as follows:

[0147]

[0148]

[0149]

[0150]

[0151] wherein, objective is the abbreviation of objective, represents the main problem objective, represents the sub-problem objective, is 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 decision variable x. Since the resulting scenario is the scenario corresponding to the decision variable in MP, the value obtained by SP is an upper bound of the objective function, and then adding the scenario in SP to MP, the decision variable in MP can obtain a lower bound of the objective function. Repeat this process until the difference between the upper bound and the determined value meets the convergence criterion, at which time the iteration ends.

[0152] Since the "max-min" problem is involved in SP, it cannot be directly solved by using commercial solvers. The present application linearizes the nonlinear term by using KKT condition and big M method, and converts SP into a single-layer mixed integer linear programming model as follows:

[0153]

[0154]

[0155] wherein, represents Lagrange multiplier, and represents 0-1 variable introduced in big M method, represents a very large number defined in big M method. Thus, SP is converted into a mixed integer linear programming problem, which can be efficiently solved by using commercial solvers.

[0156] In order to facilitate understanding, the solving process of C&CG algorithm based on KKT condition and big M method is specifically explained as follows:

[0157] 1) setting , , k = 1; randomly initializing scenario, setting convergence criterion ;

[0158] 2) solving main problem (MP) to obtain and , updating lower bound ;

[0159] 3) bringing into sub-problem (SP) to solve SP to obtain worst scenario and objective value , updating upper bound ;

[0160] 4) if , stop, otherwise continue to execute step 5);

[0161] 5) if SP in 3) has a solution, creating new variable and adding the following constraints to MP, and returning to step 2):

[0162]

[0163] If the SP in 3) is not solved, create a new variable and add the following constraints to the MP, return to step 2) until convergence.

[0164]

[0165] The embodiment of the application is an AC interconnected system network planning method based on two-stage robust optimization, a mixed integer optimization model for coordinated planning of power transmission lines and flexible interconnected devices is constructed, for the first time, line expansion decision and interconnected device site selection and capacity are included in a unified optimization framework, breaking the limitations of traditional network planning methods which consider line expansion in isolation; by introducing uncertainty adjustment parameters to construct an uncertainty set, dynamic control of wind and light output fluctuation range is realized, overcoming the limitations of fixed boundary uncertainty set in traditional robust optimization; based on the improved C&CG algorithm of KKT condition linearization, by converting the double-layer optimization subproblem into a mixed integer linear programming, the solving efficiency of high-dimensional uncertainty problem is significantly improved, which is conducive to quickly obtaining the optimal network of AC interconnected system.

[0166] Based on the above-mentioned AC interconnected system network planning method based on two-stage robust optimization, as shown in Figure 2 The embodiment of the application provides an AC interconnected system network planning system based on two-stage robust optimization, which comprises:

[0167] A first model construction module 1 is used for constructing a target network planning model based on minimizing the comprehensive cost of AC interconnected system network planning, wherein the comprehensive cost comprises construction cost and operation cost;

[0168] A second model construction module 2 is used for constructing an uncertainty set based on the uncertainty of new energy output of the AC interconnected system, and performing robust optimization on the target network planning model according to the uncertainty set to obtain a two-stage network planning model;

[0169] An optimal network determination module 3 is used for solving the two-stage network planning model by using an improved C&CG algorithm to obtain the optimal network of the AC interconnected system.

[0170] It should be noted that the above various modules of the AC interconnected system network planning system based on two-stage robust optimization can be realized by software, hardware and combinations thereof, wholly or partially. The above modules can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory in the computer device in software form, so as to be called and executed by the processor to perform the operations corresponding to the above various modules. For specific limitations of the AC interconnected system network planning system based on two-stage robust optimization, refer to the limitations of the AC interconnected system network planning method based on two-stage robust optimization, which have the same functions and effects, and will not be described here.

[0171] In summary, the AC interconnected system network planning method and system based on two-stage robust optimization of the embodiment of the application constructs a mixed integer optimization model for coordinated planning of power transmission lines and flexible interconnected devices, for the first time, line expansion decision and interconnected device site selection and capacity determination are included in a unified optimization framework, breaking through the limitations of traditional network planning methods which consider line expansion in isolation; by introducing an uncertainty adjustment parameter to construct an uncertainty set, dynamic control of wind and light output fluctuation range is realized, overcoming the limitations of fixed boundary uncertainty set in traditional robust optimization; based on the improved C&CG algorithm of KKT condition linearization, by converting the double-layer optimization sub-problem into a mixed integer linear programming, the solving efficiency of high-dimensional uncertainty problems is significantly improved, which is conducive to quickly obtaining the optimal network of the AC interconnected system.

[0172] Each embodiment in the specification is described in a progressive manner, and the same or similar parts of each embodiment can be referred to each other. Each embodiment focuses on the difference from other embodiments. Especially, the system embodiment is basically similar to the method embodiment, so the description is relatively simple, and the related parts can be referred to the part of the method embodiment. It should be noted that the technical features of the above embodiments can be combined arbitrarily, and in order to make the description simple, all possible combinations of the technical features in the above embodiments are not described, however, as long as the combination of the technical features does not exist contradictory, it should be considered as the scope of the specification.

[0173] The above is only the preferred embodiment of the application, and it should be noted that for ordinary skilled in the art, without departing from the technical principles of the application, a number of improvements and replacements can be made, which should be considered as the protection scope of the application.

Claims

1. A network planning method for an AC interconnection system based on two-stage robust optimization, characterized in that, include: Based on minimizing the overall cost of the AC interconnection system network planning, a target network planning model is constructed, wherein the overall cost includes construction cost and operating cost; An uncertainty set is constructed based on the uncertainty of new energy output in the AC interconnection system, and a two-stage grid planning model is obtained by robustly optimizing the target grid planning model based on the uncertainty set. An improved C&CG algorithm is used to solve the two-stage grid planning model to obtain the optimal grid for the AC interconnection system. The construction of the target grid planning model based on minimizing the overall cost of AC interconnection system grid planning includes: Based on minimizing the construction and operating costs of the AC interconnection system network planning, an initial network planning model is constructed. The construction costs include the construction costs of transmission lines and flexible interconnection devices, and the operating costs include the power generation costs of generator sets and the risk costs of the AC interconnection system. Constraints are constructed based on the physical operating characteristics of the interconnected system, and the initial grid planning model is constrained according to the constraints to obtain the target grid planning model. The constraints include grid construction constraints and grid operation constraints. The initial space frame planning model is characterized by the following formula: in, Indicates total cost. Indicates construction cost, This represents the equivalence factor between equivalent annual construction costs and equivalent annual operating costs. This indicates the cost of generating electricity from the generator set. This indicates the risk and cost of the interconnected system; The construction cost is represented by the following formula: in, Indicates construction cost, This represents the set of transmission line corridors. Let (i,j) be the set of candidate routes for the corridor. This represents the construction cost of the k-th loop between nodes i and j. A Boolean variable representing the decision on the construction of the k-th loop between nodes i and j. This represents a collection of installation lines for flexible interconnect devices. This indicates the unit capacity construction cost of flexible interconnect devices. Indicates the construction capacity of flexible interconnection devices; The power generation cost of the generator set is characterized by the following formula: in, This indicates the cost of generating electricity from the generator set. Represents a set of nodes. This represents the set of generator sets connected to node i. , , This represents the cost consumption characteristic coefficient of the generator set connected to node i. This represents the power output of the generator set connected to node i; The risk cost of the AC interconnection system includes wind curtailment risk cost, solar curtailment risk cost, load shedding risk cost, and flexible interconnection device loss cost, which is characterized by the following formula: in, Indicates the risk cost of the communication and interconnection system. This represents the wind curtailment penalty cost coefficient for node i. This represents the cost coefficient of light wastage penalty at node i. This represents the load shedding penalty cost coefficient. This represents the power loss coefficient of a flexible interconnect device. This represents the amount of wind curtailed at node i. This represents the amount of light discarded at node i. This represents the load shedding amount at node i. This indicates the power loss of flexible interconnect devices.

2. The AC interconnection system network planning method based on two-stage robust optimization according to claim 1, characterized in that, The constraints on the grid construction include constraints on the construction of transmission line sequences, constraints on the number of corridor lines, and constraints on the number of flexible interconnection devices to be deployed. The constraints on the grid operation include power flow constraints of existing lines, power flow constraints of candidate lines, capacity constraints of existing lines, capacity constraints of candidate lines, capacity constraints of 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.

3. The AC interconnection system network planning method based on two-stage robust optimization according to claim 1, characterized in that, The uncertainty set of the new energy output uncertainty based on the AC interconnection system is constructed, 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 the AC interconnection system, an uncertainty set is constructed by introducing uncertainty adjustment parameters; Based on the uncertainty set, a two-stage robust optimization is performed on the target space frame planning model to obtain a two-stage space frame planning model.

4. The AC interconnection system network planning method based on two-stage robust optimization according to claim 3, characterized in that, The uncertainty set is characterized by the following formula: in, Let u represent an uncertain set, and let u represent an uncertain quantity. This represents the wind power output during the t-th time period within the scheduling cycle. This represents the photovoltaic output during the t-th time period within the scheduling cycle, where T represents the total number of time periods within the scheduling cycle. This represents the minimum wind power output. This indicates the maximum output power of the wind power plant. Represents binary variables. This indicates the adjustment parameter for the uncertainty of wind power output. This represents the total number of time periods within the dispatch cycle where wind power output reaches either the minimum or maximum value of the fluctuation range. This represents the minimum photovoltaic output. Represents binary variables. This indicates the maximum output of photovoltaic power. The parameter representing the uncertainty adjustment of photovoltaic output This represents the total number of periods during which photovoltaic power output reaches its minimum or maximum value within the fluctuation range within the scheduling cycle.

5. The AC interconnection system network planning method based on two-stage robust optimization according to claim 3, characterized in that, The two-stage space frame planning model is characterized by the following formula: in, Let x represent the objective function, x represent the decision variables in the first stage, and c represent the coefficient vector related to the decision variables in the first stage. Let represent the uncertainty set, u represent the uncertainty quantity, y represent the decision variables for the second stage, b represent the coefficient vector related to the decision variables for the second stage, F(x,u) represent the feasible region of the decision variables for the second stage, A represent the coefficient matrix, and d represent the constant vector. This represents the feasible region of the decision variables in the first stage. Let G represent the set of decision variables for the second stage, h represent the constant vector, E represent the coefficient matrix, and M represent the coefficient matrix.

6. The AC interconnection system network 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 network planning model to obtain the optimal network for the AC interconnection system, including: The C&CG algorithm, which combines the KKT conditions and the Big M method, is used to solve the two-stage grid planning model to obtain the optimal grid for the AC interconnection system.

7. A network planning system for AC interconnection systems based on two-stage robust optimization, characterized in that, The method for planning the network structure of an AC interconnection system based on two-stage robust optimization as described in any one of claims 1 to 6 includes: The first model construction module is used to construct a target network planning model based on minimizing the comprehensive cost of the AC interconnection system network planning, wherein the comprehensive cost includes construction cost and operating cost; The second model construction module is used to construct an uncertainty set based on the uncertainty of new energy output in the AC interconnection system, and to perform robust optimization on the target grid planning model based on the uncertainty set to obtain a two-stage grid planning model. The optimal network structure determination module is used to solve the two-stage network structure planning model using an improved C&CG algorithm to obtain the optimal network structure for the AC interconnection system.

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