Double-layer optimization method for improving total direct current feed-in scale and total new energy grid-connected capacity of receiving-end power grid

Abstract

The invention provides a double-layer optimization method for improving the total direct current feed-in scale and the total new energy grid-connected capacity of a receiving-end power grid, and relates to the technical field of power systems, and the method comprises the steps: S1, obtaining a parameter set of an initial receiving-end power grid model, building a double-layer optimization model based on the parameter set of the initial receiving-end power grid model, the double-layer optimization model comprises an upper-layer optimization model and a lower-layer optimization model; s2, optimizing the total direct current feed-in scale and the total new energy grid-connected capacity of the initial receiving-end power grid model through an upper-layer optimization model, and optimizing the total cost of the initial receiving-end power grid model through a lower-layer optimization model to obtain an optimized receiving-end power grid model; and S3, partitioning the optimized receiving-end power grid model based on the regional geographic position to obtain a final receiving-end power grid model. The double-layer optimization model constructed by the method comprises an upper-layer optimization model and a lower-layer optimization model; the upper-layer optimization model takes the maximum operation power as an objective function, and constructs a constraint through a quantitative index set; the lower-layer optimization model takes the minimum total cost as an objective function, and constructs constraints according to the operation state; the initial receiving-end power grid is optimized through the double-layer optimization model, so that the total cost is as low as possible while the operation power of the obtained optimized receiving-end power grid is as high as possible, and the direct-current feed-in and new energy access scale of the receiving-end power grid is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a double-layer optimization method for improving the total DC feed-in scale and total renewable energy grid-connected capacity of a receiving-end power grid. Background Art

[0002] With the continuous development of renewable energy, the proportion of new energy has gradually increased. In order to ensure the effective absorption and efficient utilization of large-scale new energy electricity, high-voltage direct current transmission technology, as a key link connecting energy-rich areas and load centers, has developed rapidly.

[0003] The future will see the emergence of a new operating scenario for receiving-end power grids, characterized by the coexistence of multiple DC feed-ins and a high proportion of renewable energy integration. Under these new scenarios, the stability mechanisms of receiving-end power grids exhibit many new characteristics, and the mechanisms and evolution of cascading failures differ significantly from those of traditional AC power grids. On the one hand, the intermittent and uncertain nature of renewable energy generation presents unprecedented challenges to power balance and voltage control in the power grid; on the other hand, the addition of DC transmission systems further complicates the fault propagation and recovery characteristics of the power grid. Consequently, existing power grid optimization methods struggle to quantitatively assess the safe and stable operation of receiving-end power grids, and lack measures to increase the scale of DC feed-in and renewable energy integration. Summary of the Invention

[0004] In order to solve the above problems, the present invention provides a two-layer optimization method for improving the total DC feed-in scale and total renewable energy grid-connected capacity of the receiving-end power grid, comprising the following steps:

[0005] S1: Obtaining a parameter set of an initial receiving-end power grid model, and constructing a two-layer optimization model based on the parameter set of the initial receiving-end power grid model. The two-layer optimization model includes an upper-layer optimization model and a lower-layer optimization model.

[0006] S2: The upper-level optimization model is used to optimize the total DC feed-in scale and total renewable energy grid-connected capacity of the initial receiving-end grid model. The lower-level optimization model is used to optimize the total cost of the initial receiving-end grid model to obtain the optimized receiving-end grid model.

[0007] S3: Partition the optimized receiving-end power grid model based on regional geographical locations to obtain the final receiving-end power grid model.

[0008] Optionally, the construction process of the upper-level optimization model includes:

[0009] The total DC feed-in scale and total renewable energy grid-connected capacity are obtained from the parameter set of the initial receiving-end grid model, and the objective function of the upper-layer optimization model is constructed based on the total DC feed-in scale and total renewable energy grid-connected capacity.

[0010] The DC multi-infeed short-circuit ratio, the renewable energy grid-connected short-circuit ratio, and the renewable energy grid-connected inertia are obtained from the parameter set of the initial receiving-end grid model. Constraints for the upper-level optimization model are constructed using these factors.

[0011] The upper-level optimization model is constructed through the objective function of the upper-level optimization model and the constraints of the upper-level optimization model.

[0012] Optional:

[0013] The objective function F1 of the upper optimization model is expressed as:

[0014] F1=maxW

[0015] Where W represents the sum of the total DC feed-in scale and the total renewable energy grid-connected capacity, and maxW represents the maximization of the sum of the total DC feed-in scale and the total renewable energy grid-connected capacity.

[0016] The constraint expression of the upper optimization model is:

[0017]

[0018] Among them, i represents the node number, MESCR i The DC multi-infeed short-circuit ratio of node i, MESCR min and MESCR max Indicates the minimum and maximum values ​​of the DC multi-infeed short-circuit ratio (SCR-U) i Represents the short-circuit ratio of the renewable energy grid at node i, (SCR-U) min and (SCR-U) max Indicates the minimum and maximum values ​​of the short-circuit ratio of the new energy grid, H sys Represents the inertia of the new energy grid connected to the initial receiving-end grid model, H min and H max Indicates the minimum and maximum values ​​of the grid inertia of renewable energy grid-connected.

[0019] Optionally, the construction process of the lower-level optimization model includes:

[0020] Obtain the total cost from the parameter set of the initial receiving-end power grid model, and construct the objective function of the lower-level optimization model based on the total cost;

[0021] Obtaining the thermal power unit operating data, wind turbine unit operating data, energy storage operating data, DC interconnection line safety and stability operating data, power flow operating data, and power balance operating data from the parameter set of the initial receiving-end power grid model; constructing the constraints of the lower-level optimization model using the thermal power unit operating data, wind turbine unit operating data, energy storage operating data, DC interconnection line safety and stability operating data, power flow operating data, and power balance operating data;

[0022] The lower-level optimization model is constructed through the objective function of the lower-level optimization model and the constraints of the lower-level optimization model.

[0023] Optional:

[0024] The expression of the objective function F2 of the lower optimization model is:

[0025] F2=minC

[0026] Where C represents the total cost, minC represents the minimization of the total cost, and the total cost C is the sum of the total thermal power unit operating cost, the total DC electricity cost, the total DC upward additional adjustment cost, the total construction cost, and the total energy storage unit operating cost;

[0027] The constraints of the lower-level optimization model include: the values ​​of the thermal power unit operating data are within the thermal power unit constraint range, the values ​​of the wind turbine unit operating data are within the wind turbine unit constraint range, the values ​​of the energy storage operating data are within the energy storage constraint range, the values ​​of the DC interconnection line safety and stability operating data are within the DC interconnection line safety and stability constraint range, the values ​​of the flow operating data are within the flow constraint range, and the values ​​of the power balance operating data are within the power balance constraint range.

[0028] Optionally, step S2 specifically includes:

[0029] S21: taking the initial receiving-end power grid model as the current receiving-end power grid model;

[0030] S22: Obtain a parameter set of the current receiving-end power grid model, input the parameter set of the current receiving-end power grid model into an upper-level optimization model, solve the upper-level optimization model using a snow melting algorithm, and calculate a first optimal solution; optimize the parameter set of the current receiving-end power grid model using the first optimal solution to obtain a first-stage receiving-end power grid model, and calculate the sum of the total DC feed-in scale and the total renewable energy grid-connected capacity of the first-stage receiving-end power grid model;

[0031] S23: If the sum of the total DC feed-in scale and the total renewable energy grid-connected capacity of the first-stage receiving-end power grid model satisfies power flow convergence, proceed to step S24; otherwise, the first-stage receiving-end power grid model is used as the current receiving-end power grid model, and the process returns to step S22;

[0032] S24: Inputting the sum of the total DC feed-in scale and the total renewable energy grid-connected capacity of the first-stage receiving-end power grid model into the lower-level optimization model, solving the lower-level optimization model using a linear programming algorithm, and calculating a second optimal solution; optimizing the parameter set of the first-stage receiving-end power grid model using the second optimal solution to obtain a second-stage receiving-end power grid model, and calculating the total cost of the second-stage receiving-end power grid model;

[0033] S25: If the sum of the total DC feed-in scale and the total renewable energy grid-connected capacity of the first-stage receiving-end power grid model is greater than the first preset value, and the total cost of the second-stage receiving-end power grid model is less than the second preset value, the second-stage receiving-end power grid model is used as the optimized receiving-end power grid model; otherwise, the second-stage receiving-end power grid model is used as the current receiving-end power grid model, and the process returns to step S22.

[0034] Optionally, step S3 specifically includes:

[0035] S31: setting a regional geographical location, partitioning the optimized receiving-end power grid model according to the regional geographical location, obtaining a partitioned receiving-end power grid model, and calculating a transient voltage stability margin vector of each node in the partitioned receiving-end power grid model;

[0036] S32: Calculate the WARD distance within each node and the WARD distance between each node using the transient voltage stability margin vector of each node, obtain the maximum WARD distance among the WARD distances, and obtain the incremental index using the maximum WARD distance.

[0037] S33: If the maximum WARD distance is less than the third preset value and the incremental index is less than the fourth preset value, the partitioned receiving-end power grid model is used as the final receiving-end power grid model; otherwise, the regional geographic location is updated and the process returns to step S31.

[0038] The present invention further provides a dual-layer optimization device for increasing the total DC feed-in scale and total renewable energy grid-connected capacity of a receiving-end power grid, which is used to implement the dual-layer optimization method for increasing the total DC feed-in scale and total renewable energy grid-connected capacity of the receiving-end power grid. The device comprises:

[0039] A two-layer optimization model construction module is used to obtain a parameter set of an initial receiving-end power grid model and construct a two-layer optimization model based on the parameter set of the initial receiving-end power grid model. The two-layer optimization model includes an upper-layer optimization model and a lower-layer optimization model.

[0040] The receiving-end grid model optimization module is used to optimize the total DC feed-in scale and total renewable energy grid-connected capacity of the initial receiving-end grid model through the upper-layer optimization model, and to optimize the total cost of the initial receiving-end grid model through the lower-layer optimization model to obtain the optimized receiving-end grid model;

[0041] The receiving-end power grid model partitioning module is used to partition the optimized receiving-end power grid model based on regional geographical locations to obtain the final receiving-end power grid model.

[0042] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the two-layer optimization method for increasing the total DC feed-in scale and total renewable energy grid-connected capacity of the receiving-end power grid is implemented.

[0043] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the two-layer optimization method for increasing the total DC feed-in scale and the total renewable energy grid-connected capacity of the receiving-end power grid is implemented.

[0044] The present invention has the following beneficial effects:

[0045] 1. The constructed two-layer optimization model consists of an upper-layer optimization model and a lower-layer optimization model. The upper-layer optimization model takes maximizing operating power as its objective function and establishes constraints based on a set of quantitative indicators. The lower-layer optimization model takes minimizing total cost as its objective function and establishes constraints based on operating status. Using this two-layer optimization model, the initial receiving-end power grid is optimized to maximize the operating power of the optimized receiving-end power grid while minimizing the total cost, thereby increasing the scale of DC feed-in and renewable energy access in the receiving-end power grid.

[0046] 2. Based on the transient voltage stability margin vector, the receiving-end power grid is partitioned to improve the similarity of transient voltage characteristics of each node in the partition, further improving the performance of the receiving-end power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 A flowchart of a two-layer optimization method for increasing the total DC feed-in scale and total renewable energy grid-connected capacity of the receiving-end power grid;

[0048] Figure 2 This is a structural diagram of the double-layer optimization model;

[0049] Figure 3 This is a schematic diagram of a multi-binary table and weight coefficients for busbar transient voltage drops;

[0050] Figure 4 This is the wiring diagram of the final IEEE 30-node system;

[0051] Figure 5 This is a simulation diagram of the double-layer optimization model without energy storage;

[0052] Figure 6 This is a simulation diagram of a two-layer optimization model with energy storage;

[0053] Figure 7This is the BUS4 transient voltage drop curve;

[0054] Figure 8 This is the BUS10 transient voltage drop curve;

[0055] Figure 9 This is the BUS15 transient voltage drop curve;

[0056] Figure 10 This is the BUS28 transient voltage drop curve;

[0057] Figure 11 A structural diagram of a double-layer optimization device for increasing the total DC feed-in scale and total renewable energy grid-connected capacity of the receiving-end power grid;

[0058] Figure 12 A structural diagram of an electronic device provided by the present invention;

[0059] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0060] The following will be combined with the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0061] Reference Figure 1 The present invention provides a two-layer optimization method for increasing the total DC feed-in scale and total renewable energy grid-connected capacity of a receiving-end power grid, comprising the following steps:

[0062] S1: Obtaining a parameter set of an initial receiving-end power grid model, and constructing a two-layer optimization model based on the parameter set of the initial receiving-end power grid model. The two-layer optimization model includes an upper-layer optimization model and a lower-layer optimization model.

[0063] In some embodiments, the structure of the two-level optimization model is as follows Figure 2 As shown, the basic form of two-level optimization can be described as:

[0064]

[0065] Where W represents the operating power, C represents the total cost, x and y represent the decision variables of the upper and lower layers; H, h:R n *R m =R p Indicates the equality constraints of the upper and lower layers; G, g:R n *R m =Rq Represents the inequality constraints of the upper and lower levels.

[0066] In some embodiments:

[0067] The construction process of the upper-level optimization model includes:

[0068] The total DC feed-in scale and total renewable energy grid-connected capacity are obtained from the parameter set of the initial receiving-end grid model, and the objective function of the upper-layer optimization model is constructed based on the total DC feed-in scale and total renewable energy grid-connected capacity.

[0069] The DC multi-infeed short-circuit ratio, the renewable energy grid-connected short-circuit ratio, and the renewable energy grid-connected inertia are obtained from the parameter set of the initial receiving-end grid model. Constraints for the upper-level optimization model are constructed using these factors.

[0070] The upper-level optimization model is constructed through the objective function of the upper-level optimization model and the constraints of the upper-level optimization model.

[0071] In some embodiments:

[0072] The objective function F1 of the upper optimization model is expressed as:

[0073] F1=maxW

[0074] Where W represents the sum of the total DC feed-in scale and the total renewable energy grid-connected capacity, and maxW represents the maximization of the sum of the total DC feed-in scale and the total renewable energy grid-connected capacity.

[0075] The constraint expression of the upper optimization model is:

[0076]

[0077] Among them, i represents the node number, MESCR i The DC multi-infeed short-circuit ratio of node i, MESCR min and MESCR max Indicates the minimum and maximum values ​​of the DC multi-infeed short-circuit ratio (SCR-U) i Represents the short-circuit ratio of the renewable energy grid at node i, (SCR-U) min and (SCR-U) max Indicates the minimum and maximum values ​​of the short-circuit ratio of the new energy grid, H sys Represents the inertia of the new energy grid connected to the initial receiving-end grid model, H min and H max Indicates the minimum and maximum values ​​of the grid inertia of renewable energy grid-connected.

[0078] In some embodiments, (1) the DC multi-infeed short-circuit ratio is defined as follows:

[0079] The multi-port Thevenin equivalent method is used to simplify the multi-infeed AC / DC system model. The DC multi-infeed short-circuit ratio is defined as:

[0080]

[0081] Among them, S aci is the short-circuit capacity of the DC feed-in commutation bus; P deqi is the equivalent DC power after considering the influence of other DC circuits.

[0082] Assuming that the multi-infeed AC / DC system consists of n DC loops and all variables are calculated in per-unit values, the short-circuit ratio of the multi-infeed DC system can be further expressed as:

[0083]

[0084] Among them, Z eqij is the equivalent node impedance matrix Z viewed from each DC busbar eq The i-th row and j-th column element, S aci U represents the short-circuit capacity of the ith DC-fed commutation busbar in the receiving grid; i is the converter bus voltage. If per-unit value is used for calculation, its rated voltage is 1. The equivalent power of the i-th DC feed in the receiving grid is P deqi .P dNi represents the rated power of the i-th DC line.

[0085] If the influence of parallel reactive power compensation at the DC infeed node is further considered, and the voltage of the infeed commutation busbar i is taken as the rated per-unit value, the short-circuit ratio of the DC multi-infeed can be optimized as follows:

[0086]

[0087] Where Q ci Represents the parallel reactive compensation power of the DC feed-in node, P di is the feed-in power of the i-th DC line.

[0088] (2) The definition of the new energy grid-connected short-circuit ratio is as follows:

[0089] According to the definition of short-circuit ratio, a short-circuit ratio index SCR-S based on capacity calculation is proposed for systems with multiple DC feed-ins and a high proportion of renewable energy access. It is expressed as:

[0090]

[0091] Among them, (SCR-S) iIt represents the voltage support strength at the grid connection point i, that is, the ratio of the AC system short-circuit capacity to the equivalent grid connection capacity of renewable energy, that is, the renewable energy grid connection short-circuit ratio. When evaluating the voltage support strength of renewable energy multi-feed systems, it can measure the voltage support capacity of each grid connection point. ac,i Indicates the short-circuit capacity at the grid connection point i, S eq,i U represents the equivalent grid-connected capacity of new energy at grid-connected point i; N Indicates the nominal voltage of the grid connection point; represents the elements in the node impedance matrix; is the AC system potential; represents the new energy capacity directly connected to nodes i and j; Indicates the node operating voltage; * indicates the conjugate operation.

[0092] The ratio of the rated voltage at the renewable energy grid connection point to the voltage change caused by renewable energy access to the grid is:

[0093]

[0094] in, Indicates the voltage change caused by the grid connection of new energy; Indicates the line current.

[0095] Further deduction, we can get:

[0096]

[0097] The right side of the equation is the short-circuit ratio (SCR-S) indicator, calculated based on capacity. In systems with multiple DC feeds and a high proportion of renewable energy, the short-circuit ratio at grid connection point i can be constructed using voltage:

[0098]

[0099] Among them, SCR-U i Represents the voltage support strength at the grid connection point i.

[0100] The extreme value of the critical short-circuit ratio represents the weakest system. Based on the voltage support strength assessment of systems with multiple DC feed-ins and a high proportion of renewable energy, a preliminary classification of system strength can be made. When the system short-circuit ratio is greater than the critical short-circuit ratio, the system is considered to be in a steady state. Generally, the extreme value of the critical short-circuit ratio, 2, is used as the criterion for judging system strength. Systems with SCR-S > 2 or SCR-U > 2 are considered strong, while systems with SCR-S < 2 or SCR-U < 2 are considered weak.

[0101] (3) The definition of grid inertia of new energy grid-connected is as follows:

[0102] The generalized inertia constant of the system is:

[0103]

[0104] Among them, S sys The total rated power generation capacity of the system, including conventional power sources and new energy units; E sys is the generalized kinetic energy of the system.

[0105] When considering the high proportion of renewable energy connected to the grid, the external characteristics of renewable energy are equivalent to generators connected to the grid. The inertia of the grid connected with renewable energy is expressed as:

[0106]

[0107] Among them, H sys Indicates the grid inertia when the wind power and photovoltaic units are connected to the grid, H SGi 、S SGi are the inertia and capacity of synchronous generator i in the power grid respectively; H Vj 、S Vj are the inertia and capacity of the new energy unit j in the power grid respectively.

[0108] New energy inertia response based on virtual inertia control. Conventional virtual inertia control takes the converter output current as the target control variable and the output power as the control variable. The expression is:

[0109]

[0110] Where K is the virtual inertia coefficient; f is the AC current frequency of the rotating magnetic field on the stator side; ΔP represents the active power control amount.

[0111] In some embodiments:

[0112] The construction process of the lower-level optimization model includes:

[0113] Obtain the total cost from the parameter set of the initial receiving-end power grid model, and construct the objective function of the lower-level optimization model based on the total cost;

[0114] Obtaining the thermal power unit operating data, wind turbine unit operating data, energy storage operating data, DC interconnection line safety and stability operating data, power flow operating data, and power balance operating data from the parameter set of the initial receiving-end power grid model; constructing the constraints of the lower-level optimization model using the thermal power unit operating data, wind turbine unit operating data, energy storage operating data, DC interconnection line safety and stability operating data, power flow operating data, and power balance operating data;

[0115] The lower-level optimization model is constructed through the objective function of the lower-level optimization model and the constraints of the lower-level optimization model.

[0116] In some embodiments:

[0117] The expression of the objective function F2 of the lower optimization model is:

[0118] F2=minC

[0119] Where C represents the total cost, minC represents the minimization of the total cost, and the total cost C is the sum of the total thermal power unit operating cost, the total DC electricity cost, the total DC upward additional adjustment cost, the total construction cost, and the total energy storage unit operating cost;

[0120] The constraints of the lower-level optimization model include: the values ​​of the thermal power unit operating data are within the thermal power unit constraint range, the values ​​of the wind turbine unit operating data are within the wind turbine unit constraint range, the values ​​of the energy storage operating data are within the energy storage constraint range, the values ​​of the DC interconnection line safety and stability operating data are within the DC interconnection line safety and stability constraint range, the values ​​of the flow operating data are within the flow constraint range, and the values ​​of the power balance operating data are within the power balance constraint range.

[0121] In some embodiments, the total cost is expressed as:

[0122]

[0123] Where T is the scheduling period; t is the time period number; N m is the number of thermal power units; i is the number of thermal power units; C m,i,t is the operating cost of thermal power unit i in period t; f DC is the unit cost of DC electricity; p DC,t is the DC transmission power during period t; C DC,t,e is the additional cost of DC upward adjustment in period t; C S,d,i,t C is the construction cost of the i-th energy storage unit allocated to period t; S,o,i,t is the operating cost of the i-th energy storage unit in period t.

[0124] Among them, the average construction cost of energy storage per day is:

[0125]

[0126] Where C s,d P is the average construction cost of energy storage per day, s and E s are the rated power and capacity of energy storage, c ps and c ws are the power unit price and energy unit price of energy storage, T s is the operating life of the energy storage, and r represents the number of days the energy storage is put into use.

[0127] The operating cost of energy storage is:

[0128] C s,o =∑Pch,i c ch,i -P dis,i c dis,i

[0129] Among them, P ch,i and P dis,i are the charging power and discharging power of energy storage at different time periods, c ch,i and c dis,i They are respectively the charging and discharging charges for energy storage at different time periods.

[0130] The operating cost of a thermal power unit mainly includes the fuel cost generated by the unit output and the startup and shutdown costs, which can be expressed as:

[0131] C m,i,t =C p,i,t +C u,i,t +C d,i,t

[0132] C p,i,t 、C u,i,t 、C d,i,t are respectively the fuel cost, startup cost, and shutdown cost of thermal power unit i in period t.

[0133] In some embodiments:

[0134] (1) Constraints on thermal power units

[0135] 1) Power generation constraints

[0136] P m,i,t +R i,t,w,up +R i,t,o ≤P i,max v i,t

[0137] P i,min v i,t ≤p m,i,t -R i,t,w,down

[0138] R i,t,w,up , R i,t,w,down ,R i,t,o ≥0

[0139] Among them, P m,i,t is the output of thermal power unit i in period t; P i,max 、P i,min is the upper and lower limits of the output of thermal power unit i; R i,t,w,up 、R i,t,w,down is the load and load spinning reserve capacity of thermal power unit i during period t; R i,t,o is the rotational accident reserve capacity of thermal power unit i in period t; v i,tis the on / off status of thermal power unit i in period t: the value is 1 when it is on and 0 when it is off.

[0140] 2) Climbing constraints

[0141] p m,i,t -R i,t,w,down ≥p m,i,t-1 -S RDi v i,t-1 -P i,max (1-v i,t )

[0142] p m,i,t +R i,t,w,up +R i,t,o ≤p m,i,t-1 +S RUi v i,t-1 +S SUi (v i,t -v i,t-1 )+P i,max (1-v i,t )

[0143] p m,i,t +R i,t,w,up +R i,t,o ≤S SDi (v i,t -v i,t-1 )+P i,max v i,t+1

[0144] Among them, S RUi 、S RDi 、S SUi 、S SDi They are respectively the ramp-up rate, ramp-down rate, startup rate and shutdown rate of thermal power unit i.

[0145] (2) Wind turbine constraints

[0146] 1) Wind power output constraints

[0147] p w,k,t ≤p w,k,t,predicted

[0148] Among them, the number of the wind farm is represented by k; p w,k,t represents the dispatch value of wind farm k in period t; p w,k,t,predicted Represents the predicted value of wind farm k in period t.

[0149] 2) Considering the reserve constraints of wind power uncertainty

[0150]

[0151] (3) Energy storage constraints

[0152] The energy storage charging and discharging power and SOC need to be within a certain range. The specific constraints are as follows:

[0153] 0≤P ch,i ≤P S ·u i / η ch

[0154] 0≤P dis,i ≤P S ·u i ·η dis

[0155] E i+1 =E i +Δt·P ch,i ·η ch -Δt·P dis,i / η dis

[0156] SOC min ·E S ≤E i ≤SOC max ·E S

[0157] Where u i is a variable indicating the energy storage state, u i =1 when energy storage charging, u i =0 when the energy storage discharges; P ch,i and P dis,i are the charging power and discharging power of energy storage at different time periods; E i is the capacity of energy storage in the i-th scheduling period, SOC max and SOC min are the upper and lower limits of energy storage SOC, η ch and η dis They are respectively the charging and discharging efficiency of energy storage. As follows:

[0158] E1=E n

[0159] Where n represents the last scheduling cycle.

[0160] (4) DC tie line safety and stability constraints

[0161] Coupling relationship expression:

[0162]

[0163] Where y is the DC tie line transmission power interval number; n is the number of DC transmission power intervals; N open,y 、R o,y、P DC,y They are the minimum number of thermal power units in operation, the minimum rotating emergency reserve capacity of thermal power units, and the boundary value of the transmission power interval corresponding to the DC tie line in the yth transmission power interval.

[0164] Use the Big-M method to linearize it and decouple the logical constraint. For any logical constraint, it can be converted into a linear constraint expression:

[0165]

[0166] Where M y,1 、M y,2 、M y,3 is a number with a larger absolute value; y,t is an integer variable, 0-1 indicates whether the DC is within the power range during time period t; ε is the accuracy parameter of the strict inequality.

[0167] (5) Flow constraints

[0168] The linear power flow constraints are:

[0169]

[0170] Where, l is the main transmission line number; N h is the number of energy storage stations; b is the load node bus number; N bus is the number of load node buses; S l,m,i 、S l,h,j 、S l,w,k 、S l,load,b are the DC power flow transmission coefficients of thermal power unit i, energy storage j, wind farm k, and load node bus b corresponding to transmission line l; S l,DC is the DC tie line transmission coefficient; p load,b,t is the load forecast value of bus b in period t; P l,max 、P l,min is the forward and reverse transmission power limit of the lth transmission line.

[0171] The node power angle constraint is:

[0172] B node θ=P

[0173]

[0174] Among them, B node represents the node susceptance matrix; θ represents the node power angle matrix; P is the node injection power matrix; is the power angle of nodes α and β at both ends of the lth transmission line; θ min ,θ max Indicates the extreme value of the power angle difference.

[0175] (6) Power balance constraints

[0176]

[0177] Among them, p else,t Indicates the output of the power source not participating in the optimization during period t.

[0178] S2: The upper-level optimization model is used to optimize the total DC feed-in scale and total renewable energy grid-connected capacity of the initial receiving-end grid model. The lower-level optimization model is used to optimize the total cost of the initial receiving-end grid model to obtain the optimized receiving-end grid model.

[0179] In some embodiments:

[0180] Step S2 specifically includes:

[0181] S21: taking the initial receiving-end power grid model as the current receiving-end power grid model;

[0182] S22: Obtain a parameter set of the current receiving-end power grid model, input the parameter set of the current receiving-end power grid model into an upper-level optimization model, solve the upper-level optimization model using a snow melting algorithm, and calculate a first optimal solution; optimize the parameter set of the current receiving-end power grid model using the first optimal solution to obtain a first-stage receiving-end power grid model, and calculate the sum of the total DC feed-in scale and the total renewable energy grid-connected capacity of the first-stage receiving-end power grid model;

[0183] In some embodiments, the Snow Ablation Algorithm (SAO) simulates the sublimation and melting behavior of snow. When snow or liquid water converted from snow turns into steam, the search individuals exhibit highly scattered characteristics due to the irregularity of the movement. Therefore, this stage uses Brownian motion to simulate this situation:

[0184]

[0185] Elite(t)∈[G(t),Z second (t),Z third (t),Z c (t)]

[0186]

[0187] Among them, Z i (t+1) represents the i-th solution in the population at time t+1, Z i (t) is the i-th solution in the population; BM i (t) is the Gaussian distribution based on Brownian motion, is a product operation; G(t) represents the current optimal solution, Elite(t) represents an individual randomly selected from several elite groups in the group, represents the group center of mass, Z second (t) represents the suboptimal individual in the current population; Z third (t) represents the third best individual in the current population; Z c (t) represents the centroid position of the individuals whose fitness values ​​rank in the top 50%, N is the number of populations, and N1 represents the number of leaders, that is, N1 is equal to half the size of the entire group.

[0188] The process of snow melting to convert into liquid water is simulated as shown in the following equation:

[0189] M=DDF×(T-T1)

[0190] Where DDF is the snow ablation coefficient, which ranges from [0.35, 0.6]; T is the daily average temperature; and T1 is the base temperature.

[0191] The expression of DDF changing with time is:

[0192]

[0193] After simulating the snow melting process, the position is updated:

[0194]

[0195] Where M represents the snow melting rate and θ2 is a random number in the range [-1,1].

[0196] S23: If the sum of the total DC feed-in scale and the total renewable energy grid-connected capacity of the first-stage receiving-end power grid model satisfies power flow convergence, proceed to step S24; otherwise, the first-stage receiving-end power grid model is used as the current receiving-end power grid model, and the process returns to step S22;

[0197] S24: Inputting the sum of the total DC feed-in scale and the total renewable energy grid-connected capacity of the first-stage receiving-end power grid model into the lower-level optimization model, solving the lower-level optimization model using a linear programming algorithm, and calculating a second optimal solution; optimizing the parameter set of the first-stage receiving-end power grid model using the second optimal solution to obtain a second-stage receiving-end power grid model, and calculating the total cost of the second-stage receiving-end power grid model;

[0198] S25: If the sum of the total DC feed-in scale and the total renewable energy grid-connected capacity of the first-stage receiving-end power grid model is greater than the first preset value, and the total cost of the second-stage receiving-end power grid model is less than the second preset value, the second-stage receiving-end power grid model is used as the optimized receiving-end power grid model; otherwise, the second-stage receiving-end power grid model is used as the current receiving-end power grid model, and the process returns to step S22.

[0199] S3: Partition the optimized receiving-end power grid model based on regional geographical locations to obtain the final receiving-end power grid model.

[0200] In some embodiments:

[0201] Step S3 specifically includes:

[0202] S31: setting a regional geographical location, partitioning the optimized receiving-end power grid model according to the regional geographical location, obtaining a partitioned receiving-end power grid model, and calculating a transient voltage stability margin vector of each node in the partitioned receiving-end power grid model;

[0203] In some embodiments, voltage and frequency transient change curves are used to identify weak points, using a multi-binary table and weight coefficients as criteria:

[0204] The transient voltage stability margin of the busbar is calculated using the integral method, and the expression is:

[0205]

[0206] Among them, η i is the voltage stability margin of node i; t s , t e The node voltage is lower than the set value V N The start and end time of V i (t) is the real-time voltage of node i; D i Is a penalty term. When the voltage drop meets the above criteria, D i =0, otherwise D i =p.

[0207] According to the idea of ​​assigning different weights to different drop degrees, the margin index considering the drop degree can be defined as follows:

[0208]

[0209] Among them, X represents the state quantity, t1 and t1′ are the time when the state quantity falls below X. cr.1 and recovery process is higher than X cr.1 The moment of K k is the weight coefficient, when X is lower than X cr.n , assign different weight coefficients K n ,…,K1.

[0210] The voltage drops below a certain threshold V cr , lasting for a specified time T cr The binary table (V cr ,T cr ) to measure whether the voltage deviation is within the acceptable range, and to establish a bus voltage drop below a certain voltage value V crk Duration T crk Multi-binary table (V crk ,Tcrk ), determine the weight coefficient K of the kth voltage drop based on the multi-binary table k (1≤k≤n):

[0211]

[0212] Regarding the busbar transient voltage stability after a fault, the transient voltage stability margin index of node i can be expressed as:

[0213]

[0214] Among them, η Vi is the transient voltage stability margin of node i, t′ is the instant when the system voltage recovers after the fault is cleared, which is the starting point of the integral calculation; V iN is the rated voltage reference value.

[0215] Based on the receiving grid's topology, the receiving grid is initially partitioned by geographic location. For all m nodes in the initial partition, the transient voltage stability margins under n different scenarios are calculated and organized into the following format.

[0216]

[0217] Take the stability margin value η of the i-th (i=1, 2, ..., m) node in the k-th (k=1, 2, ..., n) scenario in turn ik , the sum of the transient voltage stability margin of the node under n scenarios can be obtained and recorded as M i , choose M i The node corresponding to the median is taken as the partition representative node.

[0218] Based on the similarity of node transient voltages, the initial partitioning results are further clustered to complete the reasonable partitioning of the receiving power grid. Taking each row, the transient voltage stability margin vector of the corresponding node can be obtained. Without loss of generality, for nodes i and j, the corresponding transient voltage stability margin vector [η i1 ,η i2 ,···,η in ] and [η j1 ,η j2 ,···,η jn ].

[0219] S32: Calculate the WARD distance within each node and the WARD distance between each node using the transient voltage stability margin vector of each node, obtain the maximum WARD distance among the WARD distances, and obtain the incremental index using the maximum WARD distance.

[0220] In some embodiments, the transient voltage similarity between nodes i and j can be determined by the Euclidean distance D corresponding to the node transient voltage stability margin vector. ij To measure, that is

[0221]

[0222] Euclidean distance D ij The larger the WARD distance, the greater the distance between nodes i and j. The lower the transient voltage similarity between nodes i and j, and vice versa. Therefore, the WARD distance within a sub-partition and between different sub-partitions can be calculated to describe the degree of transient voltage similarity between the nodes.

[0223] Let subpartition S a The internal WARD distance is d a :

[0224]

[0225] Where a=1,2,···,A, where A is the number of existing sub-partitions; p and q are sub-partitions S a Nodes in .

[0226] Let subpartition S i and S j The WARD distance between them is l ij ,have

[0227]

[0228] If partition S i and S j Merge and produce new partition S k , then the new partition S k Internal WARD distance d k become:

[0229] d k =d i +d j +l lj

[0230] At the same time, you also need to update this new partition S k With other partitions (partition S l For example), the WARD distance between

[0231] l kl =l il +l jl

[0232] After obtaining the internal WARD distances of all sub-partitions, the maximum value d max =max(d a) Construct the standardized incremental index Δd:

[0233]

[0234] S33: If the maximum WARD distance is less than the third preset value and the incremental index is less than the fourth preset value, the partitioned receiving-end power grid model is used as the final receiving-end power grid model; otherwise, the regional geographic location is updated and the process returns to step S31.

[0235] In some embodiments, d max and Δd are used as indicators to comprehensively measure the partitioning effect. It is expected that both of them take the minimum value at the same time to indicate that the transient voltage characteristics of each node in the partition are highly similar and the corresponding number of partitions is small, so as to determine the optimal partitioning scheme.

[0236] Implementation Cases:

[0237] Step 1: Based on the PSD-BPA power system analysis program, analyze and calculate the initial IEEE 30-node system, perform grid transient stability simulation calculations under three different scenarios, and obtain transient voltage data of the studied nodes.

[0238] Step 2: Based on the simulation results of step 1, establish the bus voltage drop below a certain voltage value V crk Duration T crk Multi-binary table (V crk ,T crk ), and determine the weight coefficient K of the kth voltage drop based on the multi-binary table k (1≤k≤n):

[0239]

[0240] Among them, V N is the rated voltage of the bus. The bus transient voltage drop multi-binary table and weight coefficient are shown in the attached figure of the specification. Figure 3 shown.

[0241] Calculate the transient voltage stability margin of the bus based on the multi-binary table according to the weight coefficient:

[0242]

[0243] Step 3: Organize the transient voltage stability margins under the three different scenarios obtained in step 2 into the following form:

[0244]

[0245] Calculate the comprehensive transient voltage stability margin M of the i-th (i=1, 2, ..., 30) node under three scenarios in sequence i , choose M iThe node corresponding to the median is taken as the partition representative node.

[0246] Calculate the Euclidean distance D between node i and node j ij To measure transient voltage similarity:

[0247]

[0248] Calculate the WARD distance between all sub-partitions to describe the similarity of transient voltages of representative nodes:

[0249]

[0250] According to the maximum value d of all sub-partitions max =max(d a ) Construct the standardized incremental index Δd:

[0251]

[0252] Where N represents the current number of sub-partitions, Δd is the standardized increment index, and d max Indicates the maximum WARD distance in the sub-partition.

[0253] According to the obtained d max and Δd are used as indicators to comprehensively measure the partitioning effect, thereby determining the optimal partitioning scheme and obtaining the final IEEE 30-node system.

[0254] In order to verify the double-layer optimization method of the present invention for improving the DC and renewable energy scale of the receiving-end power grid, the application of Figure 4 The final IEEE 30-node system was simulated. The synchronous generators at nodes 11 and 13 were replaced with wind turbines, and a photovoltaic power station was added at node 9. Two DC lines were fed into nodes 3 and 7. As can be seen, the final IEEE 30-node system is a typical receiving-end grid with multiple DC feeds and renewable energy integration.

[0255] The partitioning effect is comprehensively evaluated and the optimal partitioning scheme is determined. The AC part of the final IEEE 30-bus system is divided into four partitions, as shown in Table 1.

[0256] Table 1 Final partition table of IEEE 30-node system

[0257]

[0258]

[0259] A two-layer optimization model is constructed, taking into account the constraints of thermal power units, wind power units, energy storage, DC interconnection line safety and stability, power flow, and power balance. The optimization goal is to minimize the operating cost of thermal power units in the receiving grid, DC electricity costs, and DC additional adjustment costs. Run the simulation algorithm to obtain the simulation graph. Figure 5 This is the simulation diagram of the optimization model (excluding energy storage), Figure 6 This is a simulation diagram of the optimization model (including energy storage). After adding 100MW of energy storage and optimizing the operation mode based on this model, the system's DC load capacity increased from 150MW to 250MW, and its renewable energy load capacity increased from 350MW to 550MW.

[0260] It can be seen that the two-layer optimization model proposed in the present invention can effectively improve the receiving-end power grid's ability to accept DC feed-in and new energy access.

[0261] According to the above specific implementation method, based on the PSD-BPA power system analysis program, the IEEE30 node model is analyzed and calculated, and by applying a metallic three-phase short-circuit fault between node 2 and node 6, transient voltage drop data under representative node faults in four partitions are obtained.

[0262] Instructions attached Figure 7-10 The transient voltage drop curves for nodes 4, 10, 15, and 28 under different DC and renewable energy scales are shown in Table 2.

[0263] Table 2 Scale of three types of DC and new energy

[0264] Serial number DC (MW) New energy (MW) Energy storage (MW) 1 150 350 / 2 250 550 / 3 250 550 100

[0265] according to Figure 7 、 Figure 8 、 Figure 9 、 Figure 10 The transient voltage drop curves show that after increasing the DC capacity from 150MW to 250MW and the renewable energy capacity from 350MW to 550MW, the transient voltage drops at each node increased significantly, and the fault recovery time increased significantly. This is because the increase in the DC and renewable energy scale leads to an increase in power electronic devices in the system, reducing inertia support and reactive power support. After the deployment of 100MW of energy storage equipment, the transient voltage stability of each node has been significantly improved compared to the pre-storage period, and the transient voltage stability of nodes 10 and 28 is even better than before the DC and renewable energy scale increases.

[0266] like Figure 11 As shown, the present invention further provides a two-layer optimization device 110 for improving the total DC feed-in scale and total renewable energy grid-connected capacity of a receiving-end power grid, which is used to implement the two-layer optimization method for improving the total DC feed-in scale and total renewable energy grid-connected capacity of the receiving-end power grid. The device includes:

[0267] A two-layer optimization model construction module 111 is used to obtain a parameter set of an initial receiving-end power grid model and construct a two-layer optimization model based on the parameter set of the initial receiving-end power grid model. The two-layer optimization model includes an upper-layer optimization model and a lower-layer optimization model.

[0268] The receiving-end power grid model optimization module 112 is configured to optimize the total DC feed-in scale and total renewable energy grid-connected capacity of the initial receiving-end power grid model using the upper-layer optimization model, and optimize the total cost of the initial receiving-end power grid model using the lower-layer optimization model to obtain an optimized receiving-end power grid model.

[0269] The receiving-end power grid model partitioning module 113 is configured to partition the optimized receiving-end power grid model based on regional geographical locations to obtain a final receiving-end power grid model.

[0270] In some embodiments, see Figure 12 , Figure 12 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device 120 provided in an embodiment of the present application includes a memory 121 and a processor 122. The memory 121 stores a computer program, wherein the computer program, when executed by the processor, implements the dual-layer optimization method for increasing the total DC feed-in scale and total renewable energy grid-connected capacity of the receiving-end power grid.

[0271] Specifically, the processor 122 may include, for example, a general-purpose microprocessor, an instruction set processor and / or a related chipset and / or a dedicated microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 122 may also include onboard memory for caching purposes. The processor 122 may be a single processing unit or multiple processing units for executing different actions of the method flow according to the embodiments of the present application.

[0272] Memory 121 can be, for example, any medium capable of containing, storing, conveying, disseminating, or transmitting instructions. For example, memory 121 can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, components, or propagation media. Specific examples of memory 121 include: magnetic storage devices, such as magnetic tape or hard disk drives (HDDs); optical storage devices, such as compact discs (CD-ROMs); random access memory (RAM) or flash memory; and / or wired or wireless communication links.

[0273] The present application also provides a computer-readable medium having a computer program stored thereon. When executed by a processor, the program implements the two-tier optimization method for increasing the total DC feed-in scale and total renewable energy grid-connected capacity of the receiving-end power grid. The computer-readable medium may be included in the device / apparatus / system described in the above embodiments; or it may exist independently and not be incorporated into the device / apparatus / system. The computer-readable medium carries one or more programs. When the one or more programs are executed, the method of the embodiment of the present application is implemented.

[0274] According to an embodiment of the present application, a computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium or any combination thereof. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or component, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device, or component. In the present application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical cable, radio frequency signals, or any suitable combination thereof.

[0275] Those skilled in the art will understand that the features described in the various embodiments and / or claims of the present application may be combined and / or combined in a variety of ways, even if such combinations or combinations are not explicitly described in the present application. In particular, without departing from the spirit and teachings of the present application, the features described in the various embodiments and / or claims of the present application may be combined and / or combined in a variety of ways. All of these combinations and / or combinations fall within the scope of the present application. Therefore, the scope of the present application should not be limited to the above-mentioned embodiments, but should be determined not only by the attached claims, but also by the equivalents of the attached claims. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the scope of protection of the present application.

Claims

1. A two-layer optimization method for increasing the total DC feed-in scale and total renewable energy grid-connected capacity of the receiving-end power grid, characterized in that: Including steps: S1: Obtaining a parameter set of an initial receiving-end power grid model, and constructing a two-layer optimization model based on the parameter set of the initial receiving-end power grid model. The two-layer optimization model includes an upper-layer optimization model and a lower-layer optimization model. S2: The upper-level optimization model is used to optimize the total DC feed-in scale and total renewable energy grid-connected capacity of the initial receiving-end grid model. The lower-level optimization model is used to optimize the total cost of the initial receiving-end grid model to obtain the optimized receiving-end grid model. S3: Partition the optimized receiving-end power grid model based on regional geographical locations to obtain the final receiving-end power grid model.

2. The double-layer optimization method for increasing the total DC feed-in scale and total renewable energy grid-connected capacity of the receiving-end power grid according to claim 1 is characterized in that: The construction process of the upper-level optimization model includes: The total DC feed-in scale and total renewable energy grid-connected capacity are obtained from the parameter set of the initial receiving-end grid model, and the objective function of the upper-layer optimization model is constructed based on the total DC feed-in scale and total renewable energy grid-connected capacity. The DC multi-infeed short-circuit ratio, the renewable energy grid-connected short-circuit ratio, and the renewable energy grid-connected inertia are obtained from the parameter set of the initial receiving-end grid model. Constraints for the upper-level optimization model are constructed using these factors. The upper-level optimization model is constructed through the objective function of the upper-level optimization model and the constraints of the upper-level optimization model.

3. The two-layer optimization method for increasing the total DC feed-in scale and total renewable energy grid-connected capacity of the receiving-end power grid according to claim 2 is characterized by: Objective function of the upper optimization model The expression is: in, represents the sum of the total DC feed-in scale and the total renewable energy grid-connected capacity, It represents the maximization of the sum of the total DC feed-in scale and the total renewable energy grid-connected capacity; The constraint expression of the upper optimization model is: Where i represents the node number, represents the DC multi-infeed short-circuit ratio of node i, and Indicates the minimum and maximum values ​​of the DC multi-infeed short-circuit ratio, represents the short-circuit ratio of the new energy grid at node i, and Indicates the minimum and maximum values ​​of the short-circuit ratio of new energy grid connection, Represents the inertia of the new energy grid connected to the initial receiving-end grid model, and Indicates the minimum and maximum values ​​of the grid inertia of renewable energy grid-connected power.

4. The double-layer optimization method for increasing the total DC feed-in scale and total renewable energy grid-connected capacity of the receiving-end power grid according to claim 1 is characterized in that: The construction process of the lower-level optimization model includes: Obtain the total cost from the parameter set of the initial receiving-end power grid model, and construct the objective function of the lower-level optimization model based on the total cost; Obtaining the thermal power unit operating data, wind turbine unit operating data, energy storage operating data, DC interconnection line safety and stability operating data, power flow operating data, and power balance operating data from the parameter set of the initial receiving-end power grid model; constructing the constraints of the lower-level optimization model using the thermal power unit operating data, wind turbine unit operating data, energy storage operating data, DC interconnection line safety and stability operating data, power flow operating data, and power balance operating data; The lower-level optimization model is constructed through the objective function of the lower-level optimization model and the constraints of the lower-level optimization model.

5. The double-layer optimization method for increasing the total DC feed-in scale and total renewable energy grid-connected capacity of the receiving-end power grid according to claim 4 is characterized in that: The objective function of the lower-level optimization model The expression is: Where C represents the total cost, It represents the minimization of the total cost, and the total cost C is the sum of the total thermal power unit operating cost, the total DC electricity cost, the total DC upward additional adjustment cost, the total construction cost and the total energy storage unit operating cost; The constraints of the lower-level optimization model include: the values ​​of the thermal power unit operating data are within the thermal power unit constraint range, the values ​​of the wind turbine unit operating data are within the wind turbine unit constraint range, the values ​​of the energy storage operating data are within the energy storage constraint range, the values ​​of the DC interconnection line safety and stability operating data are within the DC interconnection line safety and stability constraint range, the values ​​of the flow operating data are within the flow constraint range, and the values ​​of the power balance operating data are within the power balance constraint range.

6. The double-layer optimization method for increasing the total DC feed-in scale and total renewable energy grid-connected capacity of the receiving-end power grid according to claim 1, characterized in that: Step S2 specifically includes: S21: taking the initial receiving-end power grid model as the current receiving-end power grid model; S22: Obtain a parameter set of the current receiving-end power grid model, input the parameter set of the current receiving-end power grid model into an upper-level optimization model, solve the upper-level optimization model using a snow melting algorithm, and calculate a first optimal solution; optimize the parameter set of the current receiving-end power grid model using the first optimal solution to obtain a first-stage receiving-end power grid model, and calculate the sum of the total DC feed-in scale and the total renewable energy grid-connected capacity of the first-stage receiving-end power grid model; S23: If the sum of the total DC feed-in scale and the total renewable energy grid-connected capacity of the first-stage receiving-end power grid model satisfies power flow convergence, proceed to step S24; otherwise, the first-stage receiving-end power grid model is used as the current receiving-end power grid model, and the process returns to step S22; S24: Inputting the sum of the total DC feed-in scale and the total renewable energy grid-connected capacity of the first-stage receiving-end power grid model into the lower-level optimization model, solving the lower-level optimization model using a linear programming algorithm, and calculating a second optimal solution; optimizing the parameter set of the first-stage receiving-end power grid model using the second optimal solution to obtain a second-stage receiving-end power grid model, and calculating the total cost of the second-stage receiving-end power grid model; S25: If the sum of the total DC feed-in scale and the total renewable energy grid-connected capacity of the first-stage receiving-end power grid model is greater than the first preset value, and the total cost of the second-stage receiving-end power grid model is less than the second preset value, the second-stage receiving-end power grid model is used as the optimized receiving-end power grid model; otherwise, the second-stage receiving-end power grid model is used as the current receiving-end power grid model, and the process returns to step S22.

7. The double-layer optimization method for increasing the total DC feed-in scale and total renewable energy grid-connected capacity of the receiving-end power grid according to claim 1 is characterized in that: Step S3 specifically includes: S31: setting a regional geographical location, partitioning the optimized receiving-end power grid model according to the regional geographical location, obtaining a partitioned receiving-end power grid model, and calculating a transient voltage stability margin vector of each node in the partitioned receiving-end power grid model; S32: Calculate the WARD distance within each node and the WARD distance between each node using the transient voltage stability margin vector of each node, obtain the maximum WARD distance among the WARD distances, and obtain the incremental index using the maximum WARD distance. S33: If the maximum WARD distance is less than the third preset value and the incremental index is less than the fourth preset value, the partitioned receiving-end power grid model is used as the final receiving-end power grid model; otherwise, the regional geographic location is updated and the process returns to step S31.

8. A two-layer optimization device for increasing the total DC feed-in scale and total renewable energy grid-connected capacity of a receiving-end power grid, for implementing the two-layer optimization method for increasing the total DC feed-in scale and total renewable energy grid-connected capacity of a receiving-end power grid as claimed in any one of claims 1 to 7, characterized in that: The device comprises: A two-layer optimization model construction module is used to obtain a parameter set of an initial receiving-end power grid model and construct a two-layer optimization model based on the parameter set of the initial receiving-end power grid model. The two-layer optimization model includes an upper-layer optimization model and a lower-layer optimization model. The receiving-end grid model optimization module is used to optimize the total DC feed-in scale and total renewable energy grid-connected capacity of the initial receiving-end grid model through the upper-layer optimization model, and to optimize the total cost of the initial receiving-end grid model through the lower-layer optimization model to obtain the optimized receiving-end grid model; The receiving-end power grid model partitioning module is used to partition the optimized receiving-end power grid model based on regional geographical locations to obtain the final receiving-end power grid model.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the two-layer optimization method for improving the total DC feed-in scale and the total renewable energy grid-connected capacity of the receiving-end power grid is implemented as described in any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the double-layer optimization method for increasing the total DC feed-in scale and total renewable energy grid-connected capacity of the receiving-end power grid as described in any one of claims 1 to 7 is implemented.

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