Power transmission line and energy storage combined planning method and device

Abstract

The invention discloses a power transmission line and energy storage combined planning method and device, and belongs to the technical field of power system planning construction, and the method comprises the following steps: constructing a deterministic objective function about investment and operation cost; variables in the deterministic objective function are extracted to reconstruct a three-level optimization model used for processing the uncertainty problem, and an optimal power transmission line and energy storage combined planning scheme is obtained through solving under constraints such as demand response constraints, power transmission line circuit constraints, virtual power plant constraints and flexibility constraints. Meanwhile, the invention discloses a device based on the method, the method and the device are suitable for the condition that a large number of renewable energy sources are accessed in the prior art, distributed energy injection and virtual power plant modeling are considered, distributed energy resources are integrated into a virtual power plant, and energy and capacity reserve is provided for a power transmission system. Cooperative operation of the distributed energy and the electric power system is achieved, and the permeability and the utilization efficiency of the distributed energy in the electric power system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power system planning and construction, and particularly to a method and device for jointly planning a transmission line and energy storage. Background Art

[0002] With the energy structural transformation of the new urban power grid, new energy has become an important part of the energy supply of the power system. The large-scale access of new energy significantly reduces the demand of the new urban power grid for traditional fossil energy, reduces the power supply cost, and effectively improves the environmental indicators. The proportion of new energy power generation in the power structure is increasing continuously. However, the new energy power generation centers are usually located in areas far from the load centers, and the capacity of cross-regional power grid transmission is affected by various factors, including transmission distance, transmission efficiency, and load matching, etc. These factors lead to a relatively high phenomenon of wind and light abandonment in some areas. The existing methods for jointly planning transmission lines and energy storage only consider deterministic factors such as investment and operation costs, and do not consider non-deterministic factors, making the existing planning methods inapplicable to the current situation of a large amount of renewable energy access. Summary of the Invention

[0003] The purpose of the present invention is to provide a method and device for jointly planning a transmission line and energy storage to solve the above technical problems.

[0004] To achieve the above purpose, the present invention provides a method for jointly planning a transmission line and energy storage, and the specific steps are as follows:

[0005] Step S1: Obtain power grid structure data, variable renewable energy data, and energy storage system data;

[0006] Step S2: Construct a deterministic objective function for investment and operation costs;

[0007] Step S3: Extract the variables in Step S2 and reconstruct a three-level optimization model for dealing with uncertainty problems. The expression of the three-level optimization model is as follows:

[0008]

[0009] Wherein, y is an investment decision vector, C T is the transpose matrix of the investment cost vector C, u is an uncertainty variable, U is a set of uncertainty variables, x is an operation variable vector, B T is the transpose matrix of the operation cost vector B, Ω( y,u ) is a set of investment decisions and uncertainty variables, and min and max are the minimum value function and the maximum value function respectively.

[0010] Step S4: Under the constraints of power balance constraint, power constraint, demand response constraint, transmission line circuit constraint, energy storage constraint, virtual power plant constraint, and flexibility constraint, the optimal joint planning scheme of transmission lines and energy storage is obtained through iterative optimization using the continuous conditional Gaussian algorithm.

[0011] Preferably, the deterministic objective function expression is as follows:

[0012]

[0013] Among them, min is the function to find the minimum value, d(t) is the discount function with respect to time t, C inv is the investment cost, C opr is the operating cost, C lc is the load shedding cost, C vrec is the renewable energy curtailment cost;

[0014] The calculation formula of the discount function is as follows:

[0015]

[0016] Among them, dr is the discount rate.

[0017] Preferably, the calculation formula of the investment cost is as follows:

[0018]

[0019] Among them, C1, C2, C3, C4, and C5 are the existence coefficients of the new circuit in line l, the existence coefficient of the candidate dispatchable power generation unit at node b, the existence coefficient of the candidate non-dispatchable power generation unit at node b, the existence coefficient of the battery energy storage, and the existence coefficient of the virtual power line, respectively. Existence is 1 and non-existence is 0;

[0020] is the investment cost of the new line circuit on line l at time t, IC cd,b,t is the investment cost of the new candidate dispatchable power generation unit at node b at time t, IC cnd,b,t is the investment cost of the new candidate non-dispatchable power generation unit at node b at time t, IC h,t is the investment cost of the battery energy storage at time t, is the investment cost of the virtual power line vl at time t.

[0021] Preferably, the operating cost includes:

[0022] The operating cost of the virtual power plant vp at node b at time t under scenario w;

[0023] The operating cost of all demand dimensions s of the virtual power plant vp at node b at time t under scenario w;

[0024] The operating cost of candidate dispatchable power generation units for all demand dimensions s of node b at time t under scenario w;

[0025] The upward and downward flexibility costs for demand dimension s of node b at time t;

[0026] The upward and downward flexibility costs for responding to demand for demand dimension s of node b at time t;

[0027] And the operating cost of battery energy storage h for demand dimension s at time t;

[0028] The calculation formula for the operating cost is as follows:

[0029]

[0030] Among them, is the cost coefficient of the virtual power plant, and are the active power and reactive power of the virtual power plant vp in the trading market at node b, time t, and scenario w respectively; and are the active power and reactive power of the virtual power plant vp at node b, time t, demand stage s, and scenario w respectively; and are the operating cost coefficients of existing and candidate dispatchable power generation units, and are the operating active power of existing and candidate dispatchable power generation units respectively; and are the upward and downward flexibility cost coefficients respectively, and are the active power of upward and downward flexibility respectively; and are the upward and downward flexibility cost coefficients for responding to demand respectively; and are the active power of upward and downward flexibility for responding to demand respectively; is the energy storage cost coefficient of battery energy storage h for demand dimension s at time t, and are the active power of battery energy storage h during charging and discharging for demand dimension s at time t respectively.

[0031] Preferably, the calculation formula for the load shedding cost is as follows:

[0032]

[0033] Among them, is the load shedding cost coefficient of node b for demand dimension s at time t, is the active power of load curtailment at node b when the demand dimension is s at time t under scenario w;

[0034] The calculation formula for the cost of renewable energy curtailment is as follows:

[0035]

[0036] where is the active power of curtailment of candidate non-dispatchable power generation units at node b when the demand dimension is s at time t under scenario w.

[0037] Preferably, the power balance constraint formula is as follows:

[0038] Active power balance constraint:

[0039]

[0040] where is the active power of the virtual power plant vp in the trading market at node b, at time t, in demand stage s, and under scenario w; and are respectively the active power of dispatchable power generation and the active power of non-dispatchable power generation of the virtual power plant vp at node b, at time t, in demand stage s, and under scenario w; is the active power of the existing dispatchable power generation units of the virtual power plant vp at node b at time t, in demand stage s, and under scenario w; is the active power of the candidate dispatchable power generation units at node b at time t, in demand stage s, and under scenario w; is the active power of the candidate non-dispatchable power generation units at node b at time t, in demand stage s, and under scenario w; is the active power of the load curtailed at node b at time t, in demand stage s, and under scenario w, is the sum of the active powers of the lines connected to node b at time t, in demand stage s, and under scenario w, f l,t,s,w is the active power of line l at time t, in demand stage s, and under scenario w; is the curtailed active power of the candidate non-dispatchable power generation units at node b at time t, in demand stage s, and under scenario w; is the net demand active power at node b at time t, in demand stage s, and under scenario w; is the active power of demand response at node b at time t and in demand stage s;

[0041] Reactive power balance constraint:

[0042]

[0043] where Reactive power demanded by the virtual power plant vp from the reserve market at node b, at time t, during demand stage s, and in scenario w; Reactive power of non-dispatchable generation by the virtual power plant vp at node b, at time t, during demand stage s, and in scenario w; Reactive power of the candidate dispatchable generation unit at node b at time t, during demand stage s, and in scenario w; Reactive power of the candidate non-dispatchable generation unit at node b at time t, during demand stage s, and in scenario w; Sum of the reactive power of the lines connected to node b at time t, during demand stage s, and in scenario w on line l, fQ l,t,s,w Reactive power of line l at time t, during demand stage s, and in scenario w; ndQ b,t,s,w Net demand reactive power of node b at time t, during demand stage s, and in scenario w;

[0044] Demand response reactive power of node b at time t and during demand stage s;

[0045] The power constraints are as follows:

[0046]

[0047]

[0048] Among them, and Are the maximum active power and maximum reactive power of the existing dispatchable generation units at node b, respectively; Is the operating reactive power of the existing dispatchable generation units, and Are the maximum active power and maximum reactive power of the candidate dispatchable generation units at node b, respectively, Is the reactive power of the candidate dispatchable generation units at node b at time t, during demand stage s, and in scenario w.

[0049] Preferably, the demand response constraint formula is as follows:

[0050]

[0051] Among them, and Are the upward flexibility active power and downward flexibility active power of the demand response at node b at time t, during demand stage s, and in scenario w, respectively;

[0052] Is the demand response active power of node b at time t and during demand stage s;

[0053] is the flexibility range coefficient available for node b at time t and demand stage s;

[0054] The transmission line circuit constraints are as follows:

[0055]

[0056] where Line_maxC l is the maximum number of circuits of line l, is the maximum active power of circuit c on line l;

[0057] The energy storage constraints are formulated as follows:

[0058]

[0059] where ESS h,t,w and ESS h,t-1,w are the electrical energies of battery energy storage h at time t and at time t - 1 under scenario w respectively, Dur s is the duration of demand dimension s, is the maximum electrical energy value of battery energy storage h, and are the maximum charging power and the maximum discharging power of battery energy storage h respectively;

[0060] The virtual power plant constraints include virtual power plant line constraints and virtual power plant power constraints;

[0061] The virtual power plant line constraints are as follows:

[0062] -VP l,t,p,s,w *M ≤ f l,t,s,w ≤ VP l,t,p,s,w *M

[0063] where VP l,t,p,s,w is the charge-discharge state variable with a value of 0 or 1, and M is a positive number used to limit the value range of the power flow;

[0064] The virtual power plant power constraints are as follows:

[0065]

[0066] is the maximum active power of dispatchable power generation of virtual power plant vp at node b, time t, demand stage s, and scenario w, is the maximum active power of virtual power plant vp at node b, time t, demand stage s, and scenario w; is the maximum reactive power of dispatchable power generation of virtual power plant vp at node b, time t, demand stage s, and scenario w, It is the maximum reactive power of the virtual power plant vp at node b, at time t, in demand stage s, and in scenario w;

[0067] The flexibility constraints are as follows:

[0068]

[0069] Among them, D b,t is the downward flexibility coefficient at node b and time t, with a value of 0 or 1, is the maximum active power of downward flexibility, u b,t is the upward flexibility coefficient at node b and time t, with a value of 0 or 1, is the maximum active power of upward flexibility.

[0070] Preferably, the investment decision vector y = [C1, C2, C3, C4, C5];

[0071]

[0072] u is or

[0073] The operating variable vector is as follows:

[0074]

[0075] The operating cost vector is as follows:

[0076]

[0077] Preferably, the solution process of the optimal transmission line and energy storage joint planning scheme is as follows:

[0078] Set the initial parameters, including the convergence threshold and the maximum number of iterations;

[0079] Solve the lower bound of the operating cost and the optimized investment decision vector through the first-layer function of the three-level optimization model, and solve the corresponding operating variables in the worst-case scenario through the second-layer function of the three-level optimization model using the optimized investment decision vector, and obtain the upper bound of the operating cost;

[0080] When the difference between the upper bound and the lower bound is less than or equal to the convergence threshold or the maximum number of iterations is reached, stop the iteration, and the corresponding investment decision vector is the optimal transmission line and energy storage joint planning scheme.

[0081] A device based on the above method for joint planning of transmission lines and energy storage includes,

[0082] A data acquisition module for acquiring power grid structure data, variable renewable energy data, and energy storage system data;

[0083] A deterministic objective function module for constructing a deterministic objective function regarding investment and operating costs based on the data of the data acquisition module;

[0084] A reconstruction module for reconstructing a three - level optimization model for handling uncertainty problems through the variables in the deterministic objective function;

[0085] A solution module for solving the optimal transmission line and energy storage joint planning scheme through the set constraints and the reconstructed three - level optimization model.

[0086] Therefore, by adopting the above - mentioned method and device for joint planning of transmission lines and energy storage, the present invention has the following beneficial effects:

[0087] (1) It can effectively handle the uncertainties of demand and variable renewable energy generation. In different scenarios, through the three - level optimization model, this method reasonably adjusts investment and operation strategies, enabling the system to still maintain good operating performance in an uncertain environment and ensuring the reliability and stability of power supply.

[0088] (2) The deterministic objective function covers investment costs, operating costs, load shedding costs, and renewable energy curtailment costs. At the same time, it considers various constraint conditions, takes into account distributed energy injection and virtual power plant modeling, integrates distributed energy resources into a virtual power plant to provide energy and capacity reserves for the transmission system. Meanwhile, through demand response flexibility, it realizes the coordinated operation of distributed energy and the power system, improves the penetration rate and utilization efficiency of distributed energy in the power system, and promotes the transformation of the energy structure towards renewable energy.

[0089] Next, through the accompanying drawings and embodiments, the technical solutions of the present invention will be further described in detail. Description of the Drawings

[0090] Figure 1 It is a flowchart of a method for joint planning of transmission lines and energy storage according to the present invention;

[0091] Figure 2 It is a principle block diagram of the device according to the present invention. Detailed Embodiments

[0092] In the description of the present invention, it should be noted that the orientation or positional relationship indicated by terms such as "upper", "lower", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings, or the orientation or positional relationship in which the inventive product is usually placed during use. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation to the present invention. In the description of the present invention, it should also be noted that unless otherwise clearly specified and defined, the terms "set", "installed", "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection, or an electrical connection; it can be directly connected, or indirectly connected through an intermediate medium, and it can be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0093] The following will describe the embodiments of the present invention in detail with reference to the drawings.

[0094] As Figure 1 shown, a combined planning method for a transmission line and energy storage is as follows:

[0095] Step S1: Obtain power grid structure data, variable renewable energy data, and energy storage system data.

[0096] Step S2: Construct a deterministic objective function for investment and operating costs.

[0097] The expression of the deterministic objective function is as follows:

[0098]

[0099] Among them, min is the function for finding the minimum value, d(t) is the discount function with respect to time t, C inv is the investment cost, C opr is the operating cost, C lc is the load shedding cost, C vrec is the renewable energy curtailment cost;

[0100] The calculation formula of the discount function is as follows:

[0101]

[0102] Among them, dr is the discount rate.

[0103] The calculation formula of the investment cost is as follows:

[0104]

[0105] Among them, C1, C2, C3, C4, and C5 are the existence coefficients of the newly added circuit in line l, the existence coefficient of the candidate dispatchable power generation unit at node b, the existence coefficient of the candidate non-dispatchable power generation unit at node b, the existence coefficient of the battery energy storage, and the existence coefficient of the virtual power line, respectively. If it exists, it is 1; if it does not exist, it is 0.

[0106] is the investment cost of adding a new line circuit on line l at time t, IC cd,b,t is the investment cost of adding a candidate dispatchable power generation unit at node b at time t, IC cnd,b,t is the investment cost of adding a candidate non-dispatchable power generation unit at node b at time t, IC h,t is the investment cost of the battery energy storage at time t, is the investment cost on the virtual power line vl at time t.

[0107] The operating costs include:

[0108] The operating cost of the virtual power plant vp at node b in scenario w at time t;

[0109] The operating costs of all demand dimensions s of the virtual power plant vp at node b in scenario w at time t;

[0110] The operating costs of the candidate dispatchable power generation units of all demand dimensions s at node b in scenario w at time t;

[0111] The upward and downward flexibility costs when the demand dimension at node b is s at time t;

[0112] The upward and downward flexibility costs for responding to demand when the demand dimension at node b is s at time t;

[0113] And the operating cost of the battery energy storage h under demand dimension s at time t;

[0114] The calculation formula for the operating cost is as follows:

[0115]

[0116] Among them, is the cost coefficient of the virtual power plant, and are the active power and reactive power of the virtual power plant vp in the trading market at node b, at time t, and in scenario w, respectively; and are the active power and reactive power of the virtual power plant vp at node b, at time t, in demand stage s, and in scenario w, respectively; and are the operating cost coefficients of the existing and candidate dispatchable power generation units, and are the active power of the existing and candidate dispatchable power generation units respectively; and are the upward and downward flexibility cost coefficients respectively, and are the active power of the upward and downward flexibility respectively; and are the upward and downward flexibility cost coefficients in response to demand respectively; and are the active power of the upward and downward flexibility in response to demand respectively; is the energy storage cost coefficient of battery energy storage h under demand dimension s at time t, and are the active power during charging and discharging of battery energy storage h under demand dimension s at time t respectively.

[0117] The calculation formula for the load shedding cost is as follows:

[0118]

[0119] where, is the load shedding cost coefficient of node b at time t under demand dimension s, is the active power of load shedding of node b at time t under demand dimension s in scenario w;

[0120] The calculation formula for the renewable energy curtailment cost is as follows:

[0121]

[0122] where, is the active power of curtailment of the candidate non-dispatchable power generation unit of node b at time t under demand dimension s in scenario w.

[0123] Step S3: Extract the variables in Step S2 and reconstruct a three-level optimization model for dealing with uncertainty problems. The expression of the three-level optimization model is as follows:

[0124]

[0125] where y is the investment decision vector, C is the investment cost vector, u is the uncertainty variable, U is the set of uncertainty variables, x is the operation variable vector, and B is the operation cost vector;

[0126] Step S4: Under the constraints of power balance constraint, power constraint, demand response constraint, transmission line circuit constraint, energy storage constraint, virtual power plant constraint, and flexibility constraint, the optimal joint planning scheme of transmission lines and energy storage is obtained through iterative optimization using the continuous conditional Gaussian algorithm. The power balance constraint formula is as follows:

[0127] Active power balance constraint:

[0128]

[0129] where is the active power of the virtual power plant vp in the trading market at node b, at time t, in demand stage s, and in scenario w; and are the active power of dispatchable generation and the active power of non-dispatchable generation of the virtual power plant vp at node b, at time t, in demand stage s, and in scenario w, respectively; is the active power of the existing dispatchable generation units of the virtual power plant vp at node b at time t, in demand stage s, and in scenario w; is the active power of the candidate dispatchable generation units at node b at time t, in demand stage s, and in scenario w; is the active power of the candidate non-dispatchable generation units at node b at time t, in demand stage s, and in scenario w; is the active power of the load curtailed at node b at time t, in demand stage s, and in scenario w, is the sum of the active powers of the lines connected to node b at time t, in demand stage s, and in scenario w, f l,t,s,w is the active power of line l at time t, in demand stage s, and in scenario w; is the curtailed active power of the candidate non-dispatchable generation units at node b at time t, in demand stage s, and in scenario w; is the net demand active power at node b at time t, in demand stage s, and in scenario w; is the demand response active power at node b at time t and in demand stage s;

[0130] Reactive power balance constraint:

[0131]

[0132] where is the reactive power demanded by the virtual power plant vp from the reserve market at node b, at time t, in demand stage s, and in scenario w; is the reactive power of non-dispatchable generation of the virtual power plant vp at node b, at time t, in demand stage s, and in scenario w; is the reactive power of the candidate dispatchable generation units at node b at time t, in demand stage s, and in scenario w; is the reactive power of the candidate non-dispatchable generation units at node b at time t, in demand stage s, and in scenario w; is the sum of the reactive powers of the lines connected to node b at time t, in demand stage s, and in scenario w, fQ l,t,s,wReactive power of line l at time t, demand stage s, and scenario w; ndQ b,t,s,w Net demand reactive power of node b at time t, demand stage s, and scenario w;

[0133] Reactive power of demand response of node b at time t and demand stage s;

[0134] The power constraints are as follows:

[0135]

[0136]

[0137] Among them, and are the maximum active power and maximum reactive power of the existing dispatchable power generation units at node b, respectively; is the operating reactive power of the existing dispatchable power generation units, and are the maximum active power and maximum reactive power of the candidate dispatchable power generation units at node b, respectively, is the reactive power of the candidate dispatchable power generation units at node b at time t, demand stage s, and scenario w.

[0138] The demand response constraint formula is as follows:

[0139]

[0140] Among them, and are the upward flexibility active power and downward flexibility active power of demand response at node b at time t, demand stage s, and scenario w, respectively;

[0141] is the demand response active power of node b at time t and demand stage s;

[0142] is the available flexibility range coefficient of node b at time t and demand stage s;

[0143] The transmission line circuit constraints are as follows:

[0144]

[0145] Among them, Line_maxC l is the maximum number of circuits of line l, is the maximum active power of circuit c on line l;

[0146] The energy storage constraint formula is as follows:

[0147]

[0148] Among them, ESS h,t,w and ESS h,t-1,w are the electrical energy of battery energy storage h at time t and at time t - 1 under scenario w respectively, and Dur s is the duration of demand dimension s, is the maximum electrical energy value of battery energy storage h, and are the maximum charging power and the maximum discharging power of battery energy storage h respectively;

[0149] The virtual power plant constraints include virtual power plant line constraints and virtual power plant power constraints;

[0150] The virtual power plant line constraints are as follows:

[0151] -VP l,t,p,s,w *M ≤ f l,t,s,w ≤ VP l,t,p,s,w *M

[0152] Among them, VP l,t,p,s,w is the charge and discharge state variable with a value of 0 or 1, and M is a positive number used to limit the value range of the power flow;

[0153] The virtual power plant power constraints are as follows:

[0154]

[0155] is the maximum active power of dispatchable power generation of virtual power plant vp at node b, time t, demand stage s, and scenario w, is the maximum active power of virtual power plant vp at node b, time t, demand stage s, and scenario w; is the maximum reactive power of dispatchable power generation of virtual power plant vp at node b, time t, demand stage s, and scenario w, is the maximum reactive power of virtual power plant vp at node b, time t, demand stage s, and scenario w;

[0156] The flexibility constraints are as follows:

[0157]

[0158] Among them, D b,t is the downward flexibility coefficient at node b and time t, with a value of 0 or 1, is the maximum active power of downward flexibility, and u b,t is the upward flexibility coefficient at node b and time t, with a value of 0 or 1, is the maximum active power of upward flexibility.

[0159] The investment decision vector y = [C1, C2, C3, C4, C5];

[0160]

[0161] u is or

[0162] The operating variable vector is as follows:

[0163]

[0164] The operating cost vector is as follows:

[0165]

[0166] The solution process of the optimal transmission line and energy storage joint planning scheme is as follows:

[0167] Set the initial parameters, including the convergence threshold and the maximum number of iterations;

[0168] Solve the lower bound of the operating cost and the optimized investment decision vector through the first-layer function of the three-level optimization model, and solve the corresponding operating variables in the worst-case scenario through the optimized investment decision vector by the second-layer function of the three-level optimization model, and obtain the upper bound of the operating cost;

[0169] When the difference between the upper bound and the lower bound is less than or equal to the convergence threshold or the maximum number of iterations is reached, stop the iteration, and the corresponding investment decision vector is the optimal transmission line and energy storage joint planning scheme.

[0170] As Figure 2 shown, the device based on the above-mentioned transmission line and energy storage joint planning method includes

[0171] A data acquisition module for acquiring power grid structure data, variable renewable energy data, and energy storage system data;

[0172] A deterministic objective function module for constructing a deterministic objective function regarding investment and operating costs based on the data of the data acquisition module;

[0173] A reconstruction module for reconstructing a three-level optimization model for dealing with uncertainty problems through the variables in the deterministic objective function;

[0174] A solution module for solving the optimal transmission line and energy storage joint planning scheme through the set constraints and the reconstructed three-level optimization model.

[0175] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions of the present invention or make equivalent replacements, and these modifications or equivalent replacements cannot make the modified technical solutions deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A combined planning method for a power transmission line and energy storage, characterized in that The specific steps are as follows: Step S1: Obtain power grid structure data, variable renewable energy data, and energy storage system data; Step S2: Construct a deterministic objective function for investment and operating costs; Step S3: Extract the variables in Step S2 and reconstruct a three-level optimization model for handling uncertainty problems. The expression of the three-level optimization model is as follows: where y is the investment decision vector, C T is the transpose matrix of the investment cost vector C, u is the uncertainty variable, U is the set of uncertainty variables, x is the operating variable vector, B T is the transpose matrix of the operating cost vector B, Ω( y,u ) is the set of investment decisions and uncertainty variables, min and max are the minimum function and the maximum function respectively; Step S4: Under the constraints of power balance constraints, power constraints, demand response constraints, transmission line circuit constraints, energy storage constraints, virtual power plant constraints, and flexibility constraints, iteratively optimize and solve through the continuous conditional Gaussian algorithm to obtain the optimal joint planning scheme for transmission lines and energy storage.

2. The method for jointly planning a transmission line and energy storage according to claim 1, wherein The expression of the deterministic objective function is as follows: where min is the minimum value function, d(t) is the discount function with respect to time t, C inv is the investment cost, C opr is the operating cost, C lc is the load shedding cost, C vrec is the renewable energy curtailment cost; The calculation formula of the discount function is as follows: Among them, dr is the discount rate.

3. The method for jointly planning a power transmission line and energy storage according to claim 2, wherein The calculation formula of the investment cost is as follows: Among them, C1, C2, C3, C4, and C5 are the existence coefficients of the new circuit in line l, the existence coefficient of the candidate dispatchable power generation unit at node b, the existence coefficient of the candidate non-dispatchable power generation unit at node b, the existence coefficient of the battery energy storage, and the existence coefficient of the virtual power line, respectively. If it exists, it is 1; if it does not exist, it is 0; The investment cost for adding a line circuit on line l at time t, IC cd,b,t The investment cost for adding a candidate dispatchable power generation unit at node b at time t, IC cnd,b,t The investment cost for adding a candidate non-dispatchable power generation unit at node b at time t, IC h,t The investment cost for battery energy storage at time t, The investment cost on virtual power line vl at time t.

4. A method for jointly planning a transmission line and energy storage according to claim 3, characterized in that, The operating costs include: The operating cost of the virtual power plant vp at time t, node b, and scenario w; The operating costs of all demand dimensions s of the virtual power plant vp at time t, node b, and scenario w; The operating costs of the candidate dispatchable power generation units of all demand dimensions s at time t, node b, and scenario w; The upward and downward flexibility costs at time t, node b, and demand dimension s; The upward and downward flexibility costs for responding to demand at time t, node b, and demand dimension s; And the operating cost of the battery energy storage h at time t and demand dimension s; The calculation formula of the operating cost is as follows: Among them, is the cost coefficient of the virtual power plant, and are the active power and reactive power of the virtual power plant vp in the trading market at node b, at time t, and in scenario w, respectively; and are the active power and reactive power of the virtual power plant vp at node b, at time t, in demand stage s, and in scenario w, respectively; and are the operating cost coefficients of existing and candidate dispatchable generation units, and are the operating active powers of existing and candidate dispatchable generation units, respectively; and are the upward and downward flexibility cost coefficients, and are the active powers of upward and downward flexibility, respectively; and are the upward and downward flexibility cost coefficients for responding to demand, respectively; and are the active powers of upward and downward flexibility for responding to demand, respectively; is the energy storage cost coefficient of the battery energy storage h in demand dimension s at time t, and are the active powers of the battery energy storage h during charging and discharging in demand dimension s at time t, respectively.

5. A method for jointly planning a transmission line and energy storage according to claim 4, characterized in that, The calculation formula of the load shedding cost is as follows: Among them, is the load curtailment cost coefficient of node b at time t for demand dimension s, is the active power of load curtailment of node b at time t for demand dimension s under scenario w; The calculation formula of the renewable energy curtailment cost is as follows: Among them, is the active power reduction of the candidate non-schedulable generating unit of node b when the demand dimension s is required at time t under scenario w.

6. A method for jointly planning a power transmission line and energy storage according to claim 5, characterized in that, The power balance constraint formula is as follows: Active power balance constraint: Among them, is the active power of the virtual power plant vp in the trading market at node b, at time t, in demand stage s, and in scenario w; and are respectively the active power of dispatchable generation and the active power of non-dispatchable generation of the virtual power plant vp at node b, at time t, in demand stage s, and in scenario w; is the active power of the existing dispatchable generation unit of the virtual power plant vp at node b at time t, in demand stage s, and in scenario w; is the active power of the candidate dispatchable generation unit at node b at time t, in demand stage s, and in scenario w; is the active power of the candidate non-dispatchable generation unit at node b at time t, in demand stage s, and in scenario w; is the active power of the load curtailed at node b at time t, in demand stage s, and in scenario w, is the sum of the active powers of the lines connected to node b at time t, in demand stage s, and in scenario w on line l, f l,t,s,w is the active power of line l at time t, in demand stage s, and in scenario w; is the curtailed active power of the candidate non-dispatchable generation unit at node b at time t, in demand stage s, and in scenario w; is the net demand active power at node b at time t, in demand stage s, and in scenario w; is the demand response active power at node b at time t and in demand stage s; Reactive power balance constraint: Among them, is the reactive power demanded by the virtual power plant vp from the reserve market at node b, at time t, during demand stage s, and in scenario w; is the reactive power of non-dispatchable generation of the virtual power plant vp at node b, at time t, during demand stage s, and in scenario w; is the reactive power of the candidate dispatchable generation unit at node b at time t, during demand stage s, and in scenario w; is the reactive power of the candidate non-dispatchable generation unit at node b at time t, during demand stage s, and in scenario w; is the sum of the reactive powers of the lines connected to node b at time t, during demand stage s, and in scenario w on line l, fQ l,t,s,w is the reactive power of line l at time t, during demand stage s, and in scenario w; ndQ b,t,s,w is the net demand reactive power at node b at time t, during demand stage s, and in scenario w; is the reactive power of demand response for node b at time t and in demand stage s; The power constraint is as follows: Among them, and are the maximum active power and maximum reactive power of the existing dispatchable power generation units at node b, respectively; is the operating reactive power of the existing dispatchable power generation units, and are the maximum active power and maximum reactive power of the candidate dispatchable power generation units at node b, respectively, is the reactive power of the candidate dispatchable power generation units at node b at time t, demand stage s, and scenario w.

7. A method for jointly planning a power transmission line and energy storage according to claim 6, characterized in that, The demand response constraint formula is as follows: Among them, and are the active power of upward flexibility of demand response and the active power of downward flexibility of demand response of node b at time t, in demand stage s, and in scenario w, respectively; is the active power of demand response for node b at time t and in demand stage s; is the flexibility range coefficient available for node b at time t and in demand stage s; The transmission line circuit constraint is as follows: Among them, Line_maxC l is the maximum number of circuits of line l, and is the maximum active power of circuit c on line l; The energy storage constraint formula is as follows: Among them, ESS h,t,w and ESS h,t-1,w are the electrical energy of battery energy storage h at time t and at time t-1 under scenario w respectively, Dur s is the duration of demand dimension s, is the maximum electrical energy value of battery energy storage h, and are the maximum charging power and the maximum discharging power of battery energy storage h respectively; The virtual power plant constraints include virtual power plant line constraints and virtual power plant power constraints; The virtual power plant line constraint is as follows: -VP l,t,p,s,w *M ≤ f l,t,s,w ≤ VP l,t,p,s,w *M Among them, VP l,t,p,s,w is the charge-discharge state variable with a value of 0 or 1, and M is a positive number used to limit the value range of the power flow; The virtual power plant power constraint is as follows: is the maximum active power of dispatchable power generation of virtual power plant vp at node b, at time t, in demand stage s, and in scenario w, is the maximum active power of virtual power plant vp at node b, at time t, in demand stage s, and in scenario w; is the maximum reactive power of dispatchable power generation of virtual power plant vp at node b, at time t, in demand stage s, and in scenario w, is the maximum reactive power of virtual power plant vp at node b, at time t, in demand stage s, and in scenario w; The flexibility constraint is as follows: Among them, D b,t is the downward flexibility coefficient at nodes b and t, taking values of 0 or 1, is the maximum active power of downward flexibility, u b,t is the upward flexibility coefficient at nodes b and t, taking values of 0 or 1, is the maximum active power of upward flexibility.

8. A method for jointly planning a power transmission line and energy storage according to claim 7, characterized in that, The investment decision vector y = [C1, C2, C3, C4, C5]; u is or The operating variable vector is as follows: The operating cost vector is as follows:

9. The method for jointly planning a power transmission line and energy storage according to claim 8, wherein The solution process of the optimal joint planning scheme for transmission lines and energy storage is as follows: Set the initial parameters, including the convergence threshold and the maximum number of iterations; Solve the lower bound of the operating cost and the optimized investment decision vector through the first-layer function of the three-level optimization model. Solve the corresponding operating variables in the worst-case scenario through the optimized investment decision vector by the second-layer function of the three-level optimization model, and obtain the upper bound of the operating cost; When the difference between the upper bound and the lower bound is less than or equal to the convergence threshold or the maximum number of iterations is reached, stop the iteration. The corresponding investment decision vector is the optimal joint planning scheme for transmission lines and energy storage.

10. An apparatus for a transmission line and energy storage joint planning method according to claim 9, characterized in that: Including, A data acquisition module for obtaining power grid structure data, variable renewable energy data, and energy storage system data; A deterministic objective function module for constructing a deterministic objective function regarding investment and operating costs based on the data of the data acquisition module; A reconstruction module for reconstructing a three-level optimization model for handling uncertainty problems through the variables in the deterministic objective function; A solution module for solving the optimal joint planning scheme of transmission lines and energy storage through the set constraints and the reconstructed three-level optimization model.

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