A new energy sending type provincial power grid source-network-load-storage integrated hierarchical planning method

Abstract

The application discloses a new energy external sending type provincial power grid source-network-load-storage integrated hierarchical planning method, belongs to the technical field of power system operation control, and comprises an upper layer which is an external sending type provincial power grid planning problem and a lower layer which is a provincial and local collaborative optimization operation problem; then, based on a generalized master-slave splitting theory, the lower layer provincial and local collaborative optimization operation problem is decoupled into a provincial optimization submodel and a city-level optimization submodel which meet power grid constraint conditions and are solved respectively; after the optimal solution of the lower layer provincial power grid optimization subproblem is obtained by adopting an Anderson acceleration thought, the optimal solution of the dual problem of the lower layer provincial power grid optimization subproblem can be obtained by strong duality, and a Benders optimal cut is formed. The application can fully utilize the integrated information of the provincial source-network-load-storage, establish an efficient provincial and local double-layer optimization model, and provide important support for the external sending type provincial power grid planning of new energy rich areas.

Description

Technical Field

[0001] This invention belongs to the field of power system operation and control technology, specifically relating to a hierarchical planning method for integrated source-grid-load-storage of a provincial power grid for new energy transmission. Background Technology

[0002] Due to the high proportion of new energy sources connected to the power grid, wind and solar power generation, characterized by strong randomness, centralization, and large capacity, are directly connected to the transmission system. Because wind and solar power are difficult to absorb locally, these new energy sources cannot be fully utilized, resulting in severe wind and solar curtailment problems. To fully absorb new energy sources while ensuring the safe operation of the power grid and minimizing wind and solar curtailment, it is necessary to study a hierarchical planning method for integrated generation, grid, load, and storage of provincial power grids for new energy transmission, sending surplus new energy resources out through planned transmission channels.

[0003] Meanwhile, as renewable energy continues to be integrated into municipal and industrial parks, municipal power grids may sometimes exhibit the characteristics of a "source," meaning they transmit power back to the provincial power grid. Therefore, the approach of treating the distribution network as an equivalent load is no longer applicable. Especially after a high proportion of renewable energy is integrated into the grid, the increasingly frequent interactions between provincial and municipal power grids challenge the independent control and planning models of these grids. Summary of the Invention

[0004] In view of this, the purpose of this invention is to provide a hierarchical planning method for the integrated source-grid-load-storage of a provincial power grid for new energy transmission, which is used to solve the problem of convergence deterioration at the provincial boundary caused by multiple uncertainties in the source and load, and improves convergence performance while connecting energy storage into the power grid.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a hierarchical planning method for integrated source-grid-load-storage of a provincial power grid for new energy transmission, comprising the following steps:

[0006] Step 1: Based on the integer and continuous variables in the planning problem, the planning problem is divided into the objective function of the upper-level provincial power grid planning problem and the objective function of the lower-level provincial-local collaborative optimization operation problem;

[0007] Step 2: Based on the generalized master-slave splitting method, the lower-level provincial-local collaborative optimization operation problem is decoupled into provincial-level optimization sub-models and municipal-level optimization sub-models that satisfy power grid constraints, and then solved separately.

[0008] Step 3: Based on the Anderson acceleration method, we improve the method by expanding the boundary sensitivity information and introducing historical iteration corrections to update the current boundary state, thus forming an acceleration method suitable for master-slave optimization problems.

[0009] Step 4: After obtaining the optimal solution of the lower-level provincial power grid optimization subproblem in Steps 2 and 3, the optimal solution of the dual problem of the lower-level provincial power grid optimization subproblem can be obtained by strong duality, forming the Benders optimal cut, and finally forming the solution framework of Benders decomposition and combination to accelerate the generalized master-slave splitting method.

[0010] Furthermore, in step one, the objective function of the provincial power grid planning problem for power transmission mentioned above includes the following:

[0011] The investment cost of the transmission line can be expressed as:

[0012]

[0013] In the formula, c I,l For line investment costs; x l Ω represents the investment decision variable for provincial-level power transmission corridors, indicating whether the l-th line will be constructed; LC This is a set of candidate routes;

[0014] The generation cost of provincial power grid units is expressed as follows:

[0015]

[0016] In the formula, Ω s For a set of scenes; ρ s For scene weights; Ω P For the set of provincial network nodes; c c,j The cost per unit of electricity generated; P c,j,t,s For power output from conventional units, the subscript j represents the node number, the subscript t represents the time, and the subscript s represents the scenario number;

[0017] The expression for the load shedding cost of a provincial power grid is as follows:

[0018]

[0019] In the formula, c r,i To incur the cost of load shedding; r i,t,s The load shedding value is the size of the load shedding, where the subscript i represents the node number;

[0020] The cost of energy storage in a provincial power grid is expressed as follows:

[0021]

[0022] In the formula, c e,i For charging and discharging operating costs; P echar,t,s P edis,t,s The charging power and discharging power are indicated separately, in Ω. PE This refers to the set of nodes in the provincial power grid that include energy storage power stations.

[0023] The cost of wind and solar power curtailment in a provincial power grid is expressed as follows:

[0024]

[0025] In the formula, c q The unit cost of curtailment of wind and solar power for renewable energy; P w,j,t,s Power output for wind turbines; P pv,j,t,s Powering the photovoltaic units; This represents the maximum output of the wind turbine during a certain period. This refers to the maximum output of the photovoltaic unit at a certain time period; wind and solar curtailment are calculated by subtracting the actual output from the maximum output of renewable energy.

[0026] The objective function of the provincial-local collaborative optimization operation problem described in the lower layer is:

[0027]

[0028] In the formula, k = 1, 2, ..., N represents the number of prefecture-level cities; y P , Let y represent the state variable vectors within a provincial network excluding boundary nodes and within a municipal network k excluding boundary nodes, respectively; B,k It is the state variable vector of the boundary node between the provincial network and the municipal network k; u P , These represent the control variables used for the provincial network and the control variables used for the municipal network (k), respectively; f, h, and g represent the objective function, equality constraint, and inequality constraint, respectively; subscripts P, B, and C... k These represent the provincial network, the border network, and the municipal network, respectively.

[0029] Furthermore, in step two, the generalized master-slave splitting method is used to decouple the provincial-local collaborative optimization model, and boundary variable S is introduced. B,k Let represent the power value transferred from the provincial power grid to the power grid of the i-th prefecture-level city. The provincial boundary equation constraint is decomposed into:

[0030]

[0031] In the formula, k = 1, 2, ..., N; h P,B,k , These are the boundary equality constraints of the decomposed provincial network and the boundary equality constraints of the kth municipal network, respectively.

[0032] Through decoupling, the objective function of the provincial network optimization sub-model is:

[0033]

[0034] In the formula, The operating cost of the provincial network is expressed as:

[0035]

[0036] ξ auxC The expression representing the impact of the ground network optimization results on the provincial network, to ensure the optimality condition of the global model, is as follows:

[0037] ξ auxC =(γ BC ) T x B (10)

[0038]

[0039]

[0040] In the formula, Represents the objective function of the city network k; vector γ BC This represents the objective function and constraints of the provincial power grid with respect to the boundary variable x. B The sensitivity matrix; Let represent the Lagrange multiplier vectors of the equality constraints at the city network k and the equality constraints at the boundary, respectively. The Lagrange multiplier vector represents the inequality constraint at the boundary of the city network k.

[0041] The provincial power grid plan adopts DC power flow, while the local power grid plan adopts AC power flow. Considering the variable interaction during the provincial-local collaborative optimization calculation, an extended DC power flow model that takes into account voltage amplitude is adopted.

[0042] In a provincial power grid, let θ ij <15°, let sinθ ij ≈θ ij , Assuming the voltage amplitude is close to 1, we have The power flow of the branches between nodes i and j can be represented as follows:

[0043]

[0044] Let V ij =V i -V j ,have right Performing a second Taylor expansion, we have Will Treating V as a linear variable i,0 and V j,0 The initial voltage amplitude is a known quantity, which transforms the original nonlinear model into a linear model.

[0045] The provincial network constraints include:

[0046] The power balance of provincial grid nodes is expressed as follows:

[0047]

[0048] In the formula, Let i be the set of regular units on node i; Let i be the set of wind turbine units at node i; Let i be the set of photovoltaic units on node i; Let node i be the set of charging and discharging batteries for the energy storage battery, respectively; P i,load Let i be the active load on node i;

[0049] The generator output constraint of the provincial grid's conventional generating units is expressed as follows:

[0050]

[0051] In the formula, P c,j These represent the upper and lower limits of the generator output at node j, respectively.

[0052] The power constraint of the provincial network branch is expressed as follows:

[0053]

[0054]

[0055] In the formula, Ω represents the transmission power and upper limit of line (i,j), respectively; PL For collection of intra-provincial network lines; Ω EL This is a collection of inter-provincial transmission routes;

[0056] The provincial grid load shedding constraint is expressed as follows:

[0057]

[0058] In the formula, The maximum allowable load shedding;

[0059] Provincial grid energy storage constraints include:

[0060] The state of charge / discharge limit is expressed as follows:

[0061]

[0062] In the formula, These represent the discharge and charging states of energy storage, respectively.

[0063] The power limit is expressed as:

[0064]

[0065] In the formula, These are the upper and lower limits for energy storage charging, respectively. and These are the upper and lower limits for energy storage discharge, respectively.

[0066] The route investment decision is a {0,1} variable:

[0067] x l ∈{0,1} (20)

[0068] Similarly, the optimization function for the prefecture-level city network sub-model is listed, and its expression is:

[0069]

[0070] Part 1: City grid operation costs include:

[0071]

[0072] The power generation cost of municipal power grid units is expressed as follows:

[0073]

[0074] In the formula, Ω C For the city-level network node set; Ω s For a set of scenes; ρ s For scene weights; Ω P For the set of provincial network nodes; c c,j The cost per unit of electricity generated; P c,j,t,s For the output of conventional generating units, the subscript t represents time, and the subscript s represents the scenario number. The cost of load shedding for the municipal power grid is expressed as:

[0075]

[0076] In the formula, c r,i To incur the cost of load shedding; r i,t,s The magnitude of the load shearing;

[0077] The cost of wind and solar power curtailment for municipal power grids is expressed as follows:

[0078]

[0079] In the formula, c q The unit cost of curtailment of wind and solar power for renewable energy; P w,j,t,s Power output for wind turbines; P pv,j,t,s Contribute to distributed photovoltaic power; This represents the maximum output of the wind turbine during a certain period. This represents the maximum output of the photovoltaic unit at a certain time period; wind and solar curtailment are calculated by subtracting the actual output from the maximum output of renewable energy.

[0080] Part Two: The expression for the auxiliary function is:

[0081]

[0082] In the formula, Indicates the impact of distribution network optimization results on the municipal grid k; λ PB,k S represents the Lagrange multiplier vector representing the boundary equality constraints of the distribution network; B,k This represents the equivalent power of the city grid k at the boundary;

[0083] The city network model employs a second-order cone optimization method to transform the original nonlinear problem into a linear problem for solution. I ij Let V be the current amplitude on branch (i,j). i Let be the voltage magnitude at node i; the model constraints include the following:

[0084]

[0085]

[0086]

[0087]

[0088]

[0089]

[0090] In the formula, Ω C,L P represents the set of city network branch roads; j and Q j These represent the active and reactive power injected into node j, respectively; P jk and Q jk These represent the active power and reactive power flowing from node j to node k, respectively; r ij and x ij Let g be the resistance and reactance of branch (i,j), respectively. j and b j These are the ground conductance and susceptance at node j, respectively.

[0091] Furthermore, in step three, the Anderson acceleration method involves storing the boundary iteration information from the first m steps, then using this information along with the current state to correct the current value. Since the value of m is variable, historical data is used to optimize the value of m using a neural network, thereby determining the optimal initial value of m to prepare for optimization. Because the case where the number of iterations is less than m needs to be considered, the dimension of the correction coefficient is defined as m. n :

[0092] m n =min{m,n},n>0 (33)

[0093] In the formula, n represents the number of iterations;

[0094] The boundary variables are extended on the Anderson acceleration method, that is, the interaction variables are... New boundary voltage From the previous m n The sum of the current boundary voltages in each iteration Weighted average yields:

[0095]

[0096] Let g(x) = xf(x) be its iterative residual, then This can be obtained by solving an optimization problem that minimizes the residual:

[0097]

[0098] To facilitate the solution, the optimization problem is transformed into an unconstrained least squares model for direct solution, defining a set of coefficients. satisfy:

[0099]

[0100] The original problem then becomes:

[0101] min||g n -H n b|| (37)

[0102] In the formula,

[0103]

[0104] If matrix H k If it is reversible, then the solution to formula (37) is:

[0105]

[0106] make in but The iterative expression is:

[0107]

[0108] Matrix E n , H n The latest m is stored respectively n The difference between the residual and the reactive power optimization calculation is calculated for each operation of the power transmission and distribution network. n Hn An update is performed, and then the new iteration value is calculated according to formula (40).

[0109] The convergence of the Benders method used in step four is explained as follows: Benders decomposition achieves hierarchical solutions for complex integer and continuous variables in the upper-level planning problem and the lower-level running problem. By enumerating the extreme points of the dual feasible region of the lower-level problem, an equivalent primal problem is constructed. The cutting plane generated by the extreme points approximates the primal problem, generating the optimal cut when the lower-level problem has a solution, and generating a feasible cut when there is no solution. Since the extreme points of the dual feasible region of the subproblem are finite, this method always converges within a finite number of iterations.

[0110] On the other hand, the purpose of this invention is achieved through a hierarchical planning system for integrated generation, grid, load and storage of a provincial power grid for new energy export. The system includes: a memory, a processor and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, it implements the hierarchical planning method for integrated generation, grid, load and storage of a provincial power grid for new energy export as described above.

[0111] On the other hand, the object of the present invention is achieved by a computer-readable storage medium storing an information transmission implementation program, which, when executed by a processor, implements the steps of the above-described hierarchical planning method for a new energy export-oriented provincial power grid integrating source, grid, load and storage.

[0112] This invention has the following advantages and beneficial effects: It can fully utilize the integrated information of power grid, load, and energy storage within the province, organically combining multiple elements such as power grid, power source, load, and energy storage to form an integrated planning scheme. It establishes an efficient provincial-level dual-layer optimization model, enabling the power grid system to meet electricity demand while achieving efficient energy utilization and intelligent dispatching of energy storage. Furthermore, the adoption of hierarchical management and control strategies helps improve the stability and security of the power grid. Simultaneously, by constructing transmission lines, power resources are transported from areas with sufficient supply to areas with insufficient supply, thereby solving the problem of energy imbalance and achieving an economical and reliable energy supply. This provides important support for the planning of provincial power grids with abundant new energy sources for external transmission. Attached Figure Description

[0113] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0114] The present invention will be further described below with reference to the accompanying drawings. The present invention is described through some embodiments. As will be known to those skilled in the art, various changes or equivalent substitutions can be made to these features and embodiments without departing from the spirit and scope of the present invention.

[0115] Example 1

[0116] like Figure 1 As shown, this embodiment first divides the original problem into two layers based on the integer and continuous variables in the planning problem. The upper layer is a provincial power grid planning problem for power transmission, and the lower layer is a provincial-local collaborative optimization operation problem. Then, based on the generalized master-slave splitting theory, the lower-level provincial-local collaborative optimization operation problem is decoupled into provincial-level optimization sub-models and municipal-level optimization sub-models that satisfy power grid constraints, and solved separately. Next, the Anderson acceleration idea in mathematics is improved by expanding boundary sensitivity information and introducing historical iteration corrections to update the current boundary state, forming an acceleration method suitable for master-slave optimization problems. Finally, after obtaining the optimal solution of the lower-level provincial power grid optimization sub-problem in steps two and three, the optimal solution of the dual problem of the lower-level provincial power grid optimization sub-problem can be obtained through strong duality, forming a Benders optimal cut. Ultimately, a solution framework combining Benders decomposition and the generalized master-slave splitting method is formed. The integrated hierarchical planning method of this embodiment has the following specific steps:

[0117] Step 1: Based on the integer and continuous variables in the planning problem, the planning problem is divided into the objective function of the upper-level provincial power grid planning problem and the objective function of the lower-level provincial-local collaborative optimization operation problem;

[0118] The objective function of the provincial power grid planning problem for power transmission mentioned above includes the following:

[0119] The investment cost of the transmission line can be expressed as:

[0120]

[0121] In the formula, c I,l For line investment costs; x l Ω represents the investment decision variable for provincial-level power transmission corridors, indicating whether the l-th line will be constructed; LC This is a set of candidate routes;

[0122] The generation cost of provincial power grid units is expressed as follows:

[0123]

[0124] In the formula, Ω s For a set of scenes; ρ s For scene weights; Ω P For the set of provincial network nodes; c c,j The cost per unit of electricity generated; P c,j,t,s For power output from conventional units, the subscript j represents the node number, the subscript t represents the time, and the subscript s represents the scenario number;

[0125] The expression for the load shedding cost of a provincial power grid is as follows:

[0126]

[0127] In the formula, c r,i To incur the cost of load shedding; r i,t,s The load shedding value is the size of the load shedding, where the subscript i represents the node number;

[0128] The cost of energy storage in a provincial power grid is expressed as follows:

[0129]

[0130] In the formula, c e,i For charging and discharging operating costs; P echar,t,s P edis,t,s The charging power and discharging power are indicated separately, in Ω. PE This refers to the set of nodes in the provincial power grid that include energy storage power stations.

[0131] The cost of wind and solar power curtailment in a provincial power grid is expressed as follows:

[0132]

[0133] In the formula, c q The unit cost of curtailment of wind and solar power for renewable energy; P w,j,t,s Power output for wind turbines; P pv,j,t,s Powering the photovoltaic units; This represents the maximum output of the wind turbine during a certain period. This refers to the maximum output of the photovoltaic unit at a certain time period; wind and solar curtailment are calculated by subtracting the actual output from the maximum output of renewable energy.

[0134] The objective function of the provincial-local collaborative optimization operation problem described in the lower layer is:

[0135]

[0136] In the formula, k = 1, 2, ..., N represents the number of prefecture-level cities; y P , Let y represent the state variable vectors within a provincial network excluding boundary nodes and within a municipal network k excluding boundary nodes, respectively; B,k It is the state variable vector of the boundary node between the provincial network and the municipal network k; u P , These represent the control variables used for the provincial network and the control variables used for the municipal network (k), respectively; f, h, and g represent the objective function, equality constraint, and inequality constraint, respectively; subscripts P, B, and C... k These represent the provincial network, the border network, and the municipal network, respectively.

[0137] Step 2: Based on the generalized master-slave splitting method, the lower-level provincial-local collaborative optimization operation problem is decoupled into provincial-level optimization sub-models and municipal-level optimization sub-models that satisfy power grid constraints, and then solved separately.

[0138] The generalized master-slave splitting method is used to decouple the provincial-local collaborative optimization model, and boundary variable S is introduced. B,k Let represent the power value transferred from the provincial power grid to the power grid of the i-th prefecture-level city. The provincial boundary equation constraint is decomposed into:

[0139]

[0140] In the formula, k = 1, 2, ..., N; h P,B,k , These are the boundary equality constraints of the decomposed provincial network and the boundary equality constraints of the kth municipal network, respectively.

[0141] Through decoupling, the objective function of the provincial network optimization sub-model is:

[0142]

[0143] In the formula, The operating cost of the provincial network is expressed as:

[0144]

[0145] ξ auxC The expression representing the impact of the ground network optimization results on the provincial network, to ensure the optimality condition of the global model, is as follows:

[0146] ξ auxC =(γ BC ) T x B (10)

[0147]

[0148]

[0149] In the formula, Represents the objective function of the city network k; vector γ BC This represents the objective function and constraints of the provincial power grid with respect to the boundary variable x. B The sensitivity matrix; Let represent the Lagrange multiplier vectors of the equality constraints at the city network k and the equality constraints at the boundary, respectively. The Lagrange multiplier vector represents the inequality constraint at the boundary of the city network k.

[0150] The provincial power grid plan adopts DC power flow, while the local power grid plan adopts AC power flow. Considering the variable interaction during the provincial-local collaborative optimization calculation, an extended DC power flow model that takes into account voltage amplitude is adopted.

[0151] In a provincial power grid, let θ ij <15°, let sinθ ij ≈θ ij , Assuming the voltage amplitude is close to 1, then V i V j θ ij ≈θ ij , The power flow of the branches between nodes i and j can be represented as follows:

[0152]

[0153] Let V ij =V i -V j ,have right Performing a second Taylor expansion, we have Will Treating V as a linear variable i,0 and V j,0 The initial voltage amplitude is a known quantity, which transforms the original nonlinear model into a linear model.

[0154] The provincial network constraints include:

[0155] The power balance of provincial grid nodes is expressed as follows:

[0156]

[0157] In the formula, Let i be the set of regular units on node i; Let i be the set of wind turbine units at node i; Let i be the set of photovoltaic units on node i; Let node i be the set of charging and discharging batteries for the energy storage battery, respectively; P i,load Let i be the active load on node i;

[0158] The generator output constraint of the provincial grid's conventional generating units is expressed as follows:

[0159]

[0160] In the formula, P c,j These represent the upper and lower limits of the generator output at node j, respectively.

[0161] The power constraint of the provincial network branch is expressed as follows:

[0162]

[0163]

[0164] In the formula, Ω represents the transmission power and upper limit of line (i,j), respectively; PL For collection of intra-provincial network lines; Ω EL This is a collection of inter-provincial transmission routes;

[0165] The provincial grid load shedding constraint is expressed as follows:

[0166]

[0167] In the formula, The maximum allowable load shedding;

[0168] Provincial grid energy storage constraints include:

[0169] The state of charge / discharge limit is expressed as follows:

[0170]

[0171] In the formula, These represent the discharge and charging states of energy storage, respectively.

[0172] The power limit is expressed as:

[0173]

[0174] In the formula, These are the upper and lower limits for energy storage charging, respectively. and These are the upper and lower limits for energy storage discharge, respectively.

[0175] The route investment decision is a {0,1} variable:

[0176] x l ∈{0,1} (20)

[0177] Similarly, the optimization function for the prefecture-level city network sub-model is listed, and its expression is:

[0178]

[0179] Part 1: City grid operation costs include:

[0180]

[0181] The power generation cost of municipal power grid units is expressed as follows:

[0182]

[0183] In the formula, Ω C For the city-level network node set; Ω s For a set of scenes; ρ s For scene weights; Ω P For the set of provincial network nodes; c c,j The cost per unit of electricity generated; P c,j,t,s For the output of conventional generating units, the subscript t represents time, and the subscript s represents the scenario number. The cost of load shedding for the municipal power grid is expressed as:

[0184]

[0185] In the formula, c r,i To incur the cost of load shedding; r i,t,s The magnitude of the load shearing;

[0186] The cost of wind and solar power curtailment for municipal power grids is expressed as follows:

[0187]

[0188] In the formula, c q The unit cost of curtailment of wind and solar power for renewable energy; P w,j,t,s Power output for wind turbines; P pv,j,t,s Contribute to distributed photovoltaic power; This represents the maximum output of the wind turbine during a certain period. This represents the maximum output of the photovoltaic unit at a certain time period; wind and solar curtailment are calculated by subtracting the actual output from the maximum output of renewable energy.

[0189] Part Two: The expression for the auxiliary function is:

[0190]

[0191] In the formula, Indicates the impact of distribution network optimization results on the municipal grid k; λ PB,k S represents the Lagrange multiplier vector representing the boundary equality constraints of the distribution network; B,k This represents the equivalent power of the city grid k at the boundary;

[0192] The city network model employs a second-order cone optimization method to transform the original nonlinear problem into a linear problem for solution. I ij Let V be the current amplitude on branch (i,j). i Let be the voltage magnitude at node i; the model constraints include the following:

[0193]

[0194]

[0195]

[0196]

[0197]

[0198]

[0199] In the formula, Ω C,L P represents the set of city network branch roads; j and Q j These represent the active and reactive power injected into node j, respectively; P jk and Q jk These represent the active power and reactive power flowing from node j to node k, respectively; r ij and x ij Let g be the resistance and reactance of branch (i,j), respectively. j and b j These are the ground conductance and susceptance at node j, respectively;

[0200] Step 3: Based on the Anderson acceleration method, we improve the method by expanding the boundary sensitivity information and introducing historical iteration corrections to update the current boundary state, thus forming an acceleration method suitable for master-slave optimization problems.

[0201] Anderson's accelerated method involves storing the boundary iteration information from the first m steps, then using this information along with the current state to correct the current value. Since the value of m is variable, historical data is used to optimize the value of m using a neural network, thereby determining the optimal initial value of m to prepare for optimization. Because the number of iterations needs to be considered (less than m), the dimension of the correction coefficient is defined as m. n :

[0202] m n =min{m,n},n>0 (33)

[0203] In the formula, n represents the number of iterations;

[0204] The boundary variables are extended on the Anderson acceleration method, that is, the interaction variables are... New boundary voltage From the previous m n The sum of the current boundary voltages in each iteration Weighted average yields:

[0205]

[0206] Let g(x) = xf(x) be its iterative residual, then This can be obtained by solving an optimization problem that minimizes the residual:

[0207]

[0208] To facilitate the solution, the optimization problem is transformed into an unconstrained least squares model for direct solution, defining a set of coefficients. satisfy:

[0209]

[0210] The original problem then becomes:

[0211] min||g n -H n b|| (37)

[0212] In the formula,

[0213]

[0214] If matrix H k If it is reversible, then the solution to formula (37) is:

[0215]

[0216] make in but The iterative expression is:

[0217]

[0218] Matrix E n , H n The latest m is stored respectively n The difference between the residual and the reactive power optimization calculation is calculated for each operation of the power transmission and distribution network. n H n An update is performed, and then the new iteration value is calculated according to formula (40).

[0219] Step 4: After obtaining the optimal solution to the lower-level provincial power grid optimization subproblem from Steps 2 and 3, the optimal solution to the dual problem of the lower-level provincial power grid optimization subproblem can be obtained through strong duality, forming the Benders optimal cut. This ultimately forms the solution framework combining Benders decomposition with the accelerated generalized master-slave splitting method. Benders method convergence explanation: Benders decomposition achieves hierarchical solutions for complex integer and continuous variables in the upper-level planning problem and the lower-level operation problem. By enumerating the extremes of the dual feasible region of the lower-level problem, an equivalent primal problem is constructed. The cut plane generated by the extremes approximates the primal problem, generating the optimal cut when the lower-level problem has a solution, and generating a feasible cut when there is no solution. Since the extremes of the dual feasible region of the subproblem are finite, this method always converges within a finite number of iterations.

[0220] Example 2

[0221] This embodiment provides a hierarchical planning system for a provincial power grid integrating source, grid, load and storage for new energy export, including: a memory, a processor and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, it implements a hierarchical planning method for a provincial power grid integrating source, grid, load and storage for new energy export as provided in Embodiment 1.

[0222] Example 3

[0223] This embodiment provides a computer-readable storage medium storing an information transmission implementation program. When the program is executed by a processor, it implements the steps of a hierarchical planning method for a new energy-exporting provincial power grid integrating source, grid, load, and storage, as provided in Embodiment 1.

[0224] It is understood that the present invention has been described through some embodiments, and those skilled in the art will recognize that various changes or equivalent substitutions can be made to these features and embodiments without departing from the spirit and scope of the invention. Furthermore, under the teachings of the present invention, these features and embodiments can be modified to adapt to specific situations and materials without departing from the spirit and scope of the invention. Therefore, the present invention is not limited to the specific embodiments disclosed herein, and all embodiments falling within the scope of the claims of this application are within the protection scope of the present invention.

Claims

1. A hierarchical planning method for integrated source-grid-load-storage of a provincial power grid for new energy transmission, characterized in that, The steps are as follows: Step 1: Based on the integer and continuous variables in the planning problem, the planning problem is divided into the objective function of the upper-level provincial power grid planning problem and the objective function of the lower-level provincial-local collaborative optimization operation problem; Step 2: Based on the generalized master-slave splitting method, the lower-level provincial-local collaborative optimization operation problem is decoupled into provincial-level optimization sub-models and municipal-level optimization sub-models that satisfy the power grid constraints, and then solved separately. Step 3: Based on the Anderson acceleration method, we improve the method by expanding the boundary sensitivity information and introducing historical iteration corrections to update the current boundary state, thus forming an acceleration method suitable for master-slave optimization problems. Step 4: After obtaining the optimal solution of the lower-level provincial power grid optimization subproblem in Steps 2 and 3, the optimal solution of the dual problem of the lower-level provincial power grid optimization subproblem can be obtained by strong duality, forming the Benders optimal cut, and finally forming the solution framework of Benders decomposition and combination to accelerate the generalized master-slave splitting method.

2. The hierarchical planning method for integrated source-grid-load-storage of a provincial power grid for new energy transmission as described in claim 1, characterized in that, In step one, the objective function of the provincial power grid planning problem for power transmission mentioned above includes the following: The investment cost of the transmission line can be expressed as: In the formula, c I,l For line investment costs; x l Ω represents the investment decision variable for provincial-level power transmission corridors, indicating whether the l-th line will be constructed; LC This is a set of candidate routes; The generation cost of provincial power grid units is expressed as follows: In the formula, Ω s For a set of scenes; ρ s For scene weights; Ω P For the set of provincial network nodes; c c,j Cost per unit of electricity generation; P c,j,t,s For power output from conventional units, the subscript j represents the node number, the subscript t represents the time, and the subscript s represents the scenario number; The expression for the load shedding cost of a provincial power grid is as follows: In the formula, c r,i To incur the cost of load shedding; r i,t,s The load shedding value is the size of the load shedding, where the subscript i represents the node number; The cost of energy storage in a provincial power grid is expressed as follows: In the formula, c e,i For charging and discharging operating costs; P echar,t,s P edis,t,s The charging power and discharging power are indicated separately, in Ω. PE This refers to the set of nodes in the provincial power grid that include energy storage power stations. The cost of wind and solar power curtailment in a provincial power grid is expressed as follows: In the formula, c q The unit cost of curtailment of wind and solar power for renewable energy; P w,j,t,s Power output for wind turbines; P pv,j,t,s Powering the photovoltaic units; This represents the maximum output of the wind turbine during a certain period. This refers to the maximum output of the photovoltaic unit at a certain time period; wind and solar curtailment are calculated by subtracting the actual output from the maximum output of renewable energy. The objective function of the provincial-local collaborative optimization operation problem described in the lower layer is: In the formula, k = 1, 2, ..., N represents the number of prefecture-level cities; y P y Ck Let y represent the state variable vectors within a provincial network excluding boundary nodes and within a municipal network k excluding boundary nodes, respectively; B,k It is the state variable vector of the boundary node between the provincial network and the municipal network k; u P u Ck These represent the control variables used for the provincial network and the control variables used for the municipal network (k), respectively; f, h, and g represent the objective function, equality constraint, and inequality constraint, respectively; subscripts P, B, and C... k These represent the provincial network, the border network, and the municipal network, respectively.

3. The integrated hierarchical planning method for source-grid-load-storage in a provincial power grid for new energy transmission as described in claim 2, characterized in that, In step two, the generalized master-slave splitting method is used to decouple the provincial-local collaborative optimization model, and boundary variable S is introduced. B,k Let represent the power value transferred from the provincial power grid to the power grid of the i-th prefecture-level city. The provincial boundary equation constraint is decomposed into: In the formula, k = 1, 2, ..., N; h P,B,k h Ck,B These are the boundary equality constraints of the decomposed provincial network and the boundary equality constraints of the kth municipal network, respectively. Through decoupling, the objective function of the provincial network optimization sub-model is: In the formula, The operating cost of the provincial network is expressed as: ξ auxC The expression representing the impact of the ground network optimization results on the provincial network, to ensure the optimality condition of the global model, is as follows: x auxC =(γ BC ) T x B (10) In the formula, Represents the objective function of the city network k; vector γ BC This represents the objective function and constraints of the provincial power grid with respect to the boundary variable x. B The sensitivity matrix; Let represent the Lagrange multiplier vectors of the equality constraints at the city network k and the equality constraints at the boundary, respectively. The Lagrange multiplier vector represents the inequality constraint at the boundary of the city network k. The provincial power grid plan adopts DC power flow, while the local power grid plan adopts AC power flow. Considering the variable interaction during the provincial-local collaborative optimization calculation, an extended DC power flow model that takes into account voltage amplitude is adopted. In a provincial power grid, let θ ij <15°, let sinθ ij ≈θ ij , Assuming the voltage amplitude is close to 1, then V i V j θ ij ≈θ ij , The power flow of the branch between nodes i and j is represented as follows: Let V ij =V i -V j ,have right Performing a second Taylor expansion, we have Will Treating V as a linear variable i,0 and V j,0 The initial voltage amplitude is a known quantity, which transforms the original nonlinear model into a linear model. The provincial network constraints include: The power balance of provincial grid nodes is expressed as follows: In the formula, Let i be the set of regular units on node i; Let i be the set of wind turbine units at node i; Let i be the set of photovoltaic units on node i; Let node i be the set of charging and discharging batteries for the energy storage battery, respectively; P i,load Let i be the active load on node i; The generator output constraint of the provincial grid's conventional generating units is expressed as follows: In the formula, P c,j These represent the upper and lower limits of the generator output at node j, respectively. The power constraint of the provincial network branch is expressed as follows: In the formula, Ω represents the transmission power and upper limit of line (i,j), respectively; PL For collection of intra-provincial network lines; Ω EL This is a collection of inter-provincial transmission routes; The provincial grid load shedding constraint is expressed as follows: In the formula, The maximum allowable load shedding; Provincial grid energy storage constraints include: The state of charge / discharge limit is expressed as follows: In the formula, These represent the discharge and charging states of energy storage, respectively. The power limit is expressed as: In the formula, These are the upper and lower limits for energy storage charging, respectively. and These are the upper and lower limits for energy storage discharge, respectively. The route investment decision is a {0,1} variable: x l ∈{0,1} (20) Similarly, the optimization function for the prefecture-level city network sub-model is given by the following expression: Part 1: City grid operation costs include: The power generation cost of municipal power grid units is expressed as follows: In the formula, Ω C For the city-level network node set; Ω s For a set of scenes; ρ s For scene weights; Ω P For the set of provincial network nodes; c c,j Cost per unit of electricity generation; P c,j,t,s For power output from conventional units, the subscript t represents time, and the subscript s represents the scenario number. The expression for the load shedding cost of a municipal power grid is as follows: In the formula, c r,i To incur the cost of load shedding; r i,t,s The magnitude of the load shearing; The cost of wind and solar power curtailment for municipal power grids is expressed as follows: In the formula, c q The unit cost of curtailment of wind and solar power for renewable energy; P w,j,t,s Power output for wind turbines; P pv,j,t,s Contribute to distributed photovoltaic power; This represents the maximum output of the wind turbine during a certain period. This represents the maximum output of the photovoltaic unit at a certain time period; wind and solar curtailment are calculated by subtracting the actual output from the maximum output of renewable energy. Part Two: The expression for the auxiliary function is: In the formula, Indicates the impact of distribution network optimization results on the municipal grid k; λ PB,k S represents the Lagrange multiplier vector representing the boundary equality constraints of the distribution network; B,k This represents the equivalent power of the city grid k at the boundary; The city network model employs a second-order cone optimization method to transform the original nonlinear problem into a linear problem for solution. I ij Let V be the current amplitude on branch (i,j). i Let be the voltage magnitude at node i; the model constraints include the following: In the formula, Ω C,L P represents the set of city network branch roads; j and Q j These represent the active and reactive power injected into node j, respectively; P jk and Q jk These represent the active power and reactive power flowing from node j to node k, respectively; r ij and x ij Let g be the resistance and reactance of branch (i,j), respectively. j and b j These are the ground conductance and susceptance at node j, respectively.

4. The hierarchical planning method for integrated source-grid-load-storage of a provincial power grid for new energy transmission as described in claim 3, characterized in that, In step three, the Anderson acceleration method involves storing the boundary iteration information from the first m steps, then using this information along with the current state to correct the current value. Since the value of m is variable, historical data is used to optimize the value of m using a neural network, thereby determining the optimal initial value of m to prepare for optimization. Because the number of iterations needs to be considered (less than m), the dimension of the correction coefficient is defined as m. n : m n =min{m,n},n>0 (33) In the formula, n represents the number of iterations; The boundary variables are extended on the Anderson acceleration method, that is, the interaction variables are... New boundary voltage From the previous m n The sum of the current boundary voltages in each iteration Weighted average yields: Let g(x) = xf(x) be its iterative residual, then This can be obtained by solving an optimization problem that minimizes the residual: To facilitate the solution, the optimization problem is transformed into an unconstrained least squares model for direct solution, defining a set of coefficients. satisfy: The original problem then becomes: min||g n -H n b|| (37) In the formula, If matrix H k If it is reversible, then the solution to formula (37) is: make in but The iterative expression is: Matrix E n , H n The latest m is stored respectively n The difference between the residual and the reactive power optimization calculation is calculated for each operation of the power transmission and distribution network. n H n Then perform an update and calculate the new iteration value according to formula (40).

5. A hierarchical planning system for integrated source-grid-load-storage of a provincial power grid for new energy transmission, characterized in that, include: The system includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, it implements a hierarchical planning method for integrated source-grid-load-storage of a provincial power grid for new energy transmission as described in any one of claims 1-4.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores an information transmission implementation program, which, when executed by a processor, implements the steps of a hierarchical planning method for integrated source-grid-load-storage of a provincial power grid for new energy transmission as described in any one of claims 1-4.

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