Wind-solar-thermal storage integrated delivery system capacity planning method considering transient overvoltage constraint

Abstract

The invention belongs to the technical field of power system new energy direct current delivery, and provides a wind-solar-thermal storage integrated delivery system capacity planning method considering transient overvoltage constraint. Comprising the steps of nonparametric probability prediction model construction, uniformly distributed vector group acquisition, scene set acquisition, transient overvoltage calculation formula construction, overall short-circuit capacity calculation formula construction, transient overvoltage constraint construction, objective function construction, upper-layer model constraint condition and lower-layer model constraint condition construction and optimal capacity planning solving. According to the method, the scene set is acquired by utilizing quantile regression fitting and Gaussian distribution, and the system uncertainty is described; the minimum short-circuit capacity is determined through an overall short-circuit capacity calculation formula; optimization of system cost and daily scheduling cost and optimal capacity planning are realized through upper-layer model constraint conditions and lower-layer model constraint conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy direct current transmission of power systems, and in particular to a capacity planning method for a wind, solar, thermal and storage integrated transmission system taking transient overvoltage constraints into account. Background Art

[0002] In recent years, environmental protection issues caused by the large-scale use of fossil fuels have restricted the further development of enterprises. Renewable energy represented by wind power and photovoltaics has developed rapidly due to its advantages of easy access, sustainability and pollution-free.

[0003] However, the randomness, volatility and uncontrollability of renewable energy output have brought great challenges to the safe and stable operation of the power system. In addition, large-scale renewable energy is prone to transient overvoltage when transmitted via UHV DC, but how to avoid the impact of transient overvoltage caused by direct current transmission of wind, solar, thermal and storage bundles and improve the stability of the transmission system is an urgent problem to be solved. Summary of the invention

[0004] In order to overcome the shortcomings of the prior art, the purpose of the present invention is to provide a capacity planning method for an integrated wind, solar, thermal and storage transmission system taking into account transient overvoltage constraints, and to achieve a description of system uncertainty by obtaining a set of scenarios using quantile regression fitting and Gaussian distribution; to achieve determination of the minimum short-circuit capacity through an overall short-circuit capacity calculation formula; and to achieve optimization of system cost and daily dispatch cost and planning of optimal capacity through upper-level model constraints and lower-level model constraints.

[0005] To achieve the above object, the present invention provides the following solutions:

[0006] A capacity planning method for a wind-solar-thermal-storage integrated transmission system taking into account transient overvoltage constraints comprises:

[0007] A linear mapping formula is constructed using a quantile regression fitting method, and the parameters of the linear mapping formula are estimated using quantile regression theory to obtain a non-parametric probability prediction model;

[0008] The historical actual output sequence is converted into a multi-dimensional Gaussian distribution, and the multi-dimensional Gaussian distribution is clustered using a GMM algorithm to obtain a uniformly distributed vector group;

[0009] Performing point-by-point linear interpolation using the non-parametric probability prediction model according to the vector group to obtain a scene set;

[0010] Constructing a power balance relationship formula during normal operation, constructing a reactive power surplus calculation formula during DC blocking based on the power balance relationship formula, and constructing a transient overvoltage calculation formula using the reactive power surplus calculation formula;

[0011] Constructing short-circuit capacity calculation formulas for the wind farm and the thermal power farm at the commutation busbar respectively, integrating the short-circuit capacity calculation formulas for the wind farm and the thermal power farm to obtain an overall short-circuit capacity calculation formula;

[0012] Construct transient overvoltage constraints based on short-circuit ratio;

[0013] Constructing objective functions, upper model constraints and lower model constraints; the upper model constraints include: planning capacity constraints and power generation capacity constraints; the lower model constraints include: power balance constraints, wind, solar and thermal output constraints, ramp and standby constraints, energy storage operation constraints, system standby constraints, grid support power constraints and transmission channel new energy proportion constraints;

[0014] Based on the upper model constraints and the lower model constraints, the transient overvoltage calculation formula, the overall short-circuit capacity calculation formula and the objective function are solved according to the scenario set to obtain the optimal capacity planning.

[0015] Preferably, the transient overvoltage calculation formula is:

[0016]

[0017] Among them, U Lr,pu is the transient overvoltage calculation result; S C is the calculation result of the overall short-circuit capacity; Q Cr_rated is the rated value of the reactive power compensation device in the target converter station; Q ac_N The reactive power fed into the rectifier station for the target AC.

[0018] Preferably, the overall short-circuit capacity calculation formula is:

[0019]

[0020] in, S T =k ST P T_total ;P T_total =N g P T ; S w_L,i is the short-circuit capacity of the wind farm at the commutation bus; S p_L,j Short-circuit capacity provided for a single photovoltaic power station; N w is the number of wind farms; N p is the number of the photovoltaic power stations; S T is the short-circuit capacity of the thermal power plant at the commutation bus; S w,i The short-circuit capacity provided by the wind farm at the grid connection point; Z P_L,iis the equivalent impedance of the wind farm at the commutation bus; U PCC,i is the rated voltage; k ST The total amount of short-circuit capacity provided for the thermal power plant; P T_total is the total installed capacity of the thermal power plant; N g is the number of thermal power plants; T is the capacity of a single thermal power plant; i represents the i-th wind farm; j represents the j-th photovoltaic power plant.

[0021] Preferably, the transient overvoltage constraint is:

[0022]

[0023] in, S Cmin is the minimum short-circuit capacity; k Sw,i The proportion of short-circuit capacity provided for the wind farm; P aw_total,i is the equivalent operating capacity of the wind farm; P w_total,i is the installed capacity of a single wind farm; c w is the wind power average capacity correction factor; P w_cf,i is the wind power output at a given confidence level.

[0024] Preferably, the planned capacity is approximately:

[0025]

[0026] Among them, P w is the capacity of a single wind farm; P p is the capacity of a single photovoltaic power station; N w1 、N p1 are the number of wind turbines and photovoltaic units respectively; c w,min 、c p,min 、c g,min are the lower limits of installed capacity of wind power, photovoltaic power and thermal power in the system; c w,max 、c p,max 、c g,max They are the upper limits of installed capacity of wind power, photovoltaic power and thermal power in the system respectively.

[0027] Preferably, the power generation capacity constraint is:

[0028]

[0029] in, k w_e , k p_e are the average output levels of wind power and photovoltaic power, respectively; C g P is the investment and construction cost of a single thermal power plant;dN P is the rated DC transmission power; w_e,i is the equivalent power of the wind farm; P p_e,j is the equivalent power of the photovoltaic power station.

[0030] Preferably, the objective function is:

[0031]

[0032] in,

[0033] F = min() is the minimum value function; N T is the number of scheduling periods; C g,t is the operating cost of the thermal power plant; is the startup and shutdown cost of the thermal power plant; is the cost of wind and solar power curtailment; t represents the tth period; g represents the gth thermal power plant; a g , b g 、c g are all power generation cost coefficients; P g,t is the output of the thermal power plant in period t; is the startup cost of the thermal power plant; is the shutdown cost of the thermal power plant; is the startup state variable; is the shutdown state variable; λ is the wind and solar power abandonment penalty cost coefficient; is the predicted power of the wind farm; t dw is the dispatching power of the wind farm; is the predicted power of the photovoltaic power station; is the dispatching power of the photovoltaic power station.

[0034] Preferably, the new energy proportion constraint of the transmission channel is:

[0035]

[0036] Among them, δ is the required proportion of renewable energy electricity in the transmission channel; P t HVDC The external load power.

[0037] The present invention discloses the following technical effects:

[0038] The present invention provides a capacity planning method for an integrated wind, solar, thermal and storage transmission system taking into account transient overvoltage constraints. By using quantile regression fitting and Gaussian distribution to obtain a scenario set, the problem of difficulty in quantifying the uncertainty of new energy output in a power system with high new energy penetration is solved, and the description of system uncertainty is achieved. Through the overall short-circuit capacity calculation formula, the problem of system transient overvoltage and influencing factor analysis is solved, and the determination of the minimum short-circuit capacity is achieved. Through the upper-level model constraints and the lower-level model constraints, the system coordination and optimization problem is solved, and the optimization of system cost and daily dispatch cost and the planning of optimal capacity are achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0040] Figure 1 A schematic diagram of a system capacity planning process provided by an embodiment of the present invention;

[0041] Figure 2 A schematic diagram of a wind-solar-thermal-storage integrated DC transmission system provided in an embodiment of the present invention;

[0042] Figure 3 A typical curve of the external transmission load provided by the embodiment of the present invention;

[0043] Figure 4 A typical scenario of wind power output provided by an embodiment of the present invention;

[0044] Figure 5 A typical photovoltaic output scenario provided by an embodiment of the present invention;

[0045] Figure 6 A schematic diagram of transient overvoltage conditions under a DC system fault provided by an embodiment of the present invention;

[0046] Figure 7 A schematic diagram of the operation results of the planning scheme 1 provided in the embodiment of the present invention;

[0047] Figure 8 A schematic diagram of the operation results of the Scheme 2 planning scheme provided in the embodiment of the present invention;

[0048] Fig. 9 A schematic diagram of a transient overvoltage condition under a DC fault provided by an embodiment of the present invention;

[0049] Fig.10 A comparison chart of short-circuit ratio results of the transmission system of different schemes provided in the embodiments of the present invention. DETAILED DESCRIPTION

[0050] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0051] The purpose of the present invention is to provide a capacity planning method for an integrated wind, solar, thermal and storage transmission system taking into account transient overvoltage constraints. The method achieves a description of system uncertainty by obtaining a set of scenarios using quantile regression fitting and Gaussian distribution. The method also achieves determination of the minimum short-circuit capacity through an overall short-circuit capacity calculation formula. The method also achieves optimization of system cost and daily dispatch cost and planning of optimal capacity through upper-level model constraints and lower-level model constraints.

[0052] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0053] Figure 1 A schematic diagram of a system capacity planning process provided by an embodiment of the present invention is shown in FIG. Figure 1 As shown, the present invention provides a capacity planning method for a wind-solar-thermal-storage integrated transmission system taking into account transient overvoltage constraints, comprising:

[0054] A linear mapping formula is constructed using a quantile regression fitting method, and the parameters of the linear mapping formula are estimated using quantile regression theory to obtain a non-parametric probability prediction model;

[0055] The historical actual output sequence is converted into a multi-dimensional Gaussian distribution, and the multi-dimensional Gaussian distribution is clustered using a GMM algorithm to obtain a uniformly distributed vector group;

[0056] Performing point-by-point linear interpolation using the non-parametric probability prediction model according to the vector group to obtain a scene set;

[0057] Constructing a power balance relationship formula during normal operation, constructing a reactive power surplus calculation formula during DC blocking based on the power balance relationship formula, and constructing a transient overvoltage calculation formula using the reactive power surplus calculation formula;

[0058] Constructing short-circuit capacity calculation formulas for the wind farm and the thermal power farm at the commutation busbar respectively, integrating the short-circuit capacity calculation formulas for the wind farm and the thermal power farm to obtain an overall short-circuit capacity calculation formula;

[0059] Construct transient overvoltage constraints based on short-circuit ratio;

[0060] Constructing objective functions, upper model constraints and lower model constraints; the upper model constraints include: planning capacity constraints and power generation capacity constraints; the lower model constraints include: power balance constraints, wind, solar and thermal output constraints, ramp and standby constraints, energy storage operation constraints, system standby constraints, grid support power constraints and transmission channel new energy proportion constraints;

[0061] Based on the upper model constraints and the lower model constraints, the transient overvoltage calculation formula, the overall short-circuit capacity calculation formula and the objective function are solved according to the scenario set to obtain the optimal capacity planning.

[0062] Preferably, the transient overvoltage calculation formula is:

[0063]

[0064] Among them, U Lr,pu is the transient overvoltage calculation result; S C is the calculation result of the overall short-circuit capacity; Q Cr_rated is the rated value of the reactive power compensation device in the target converter station; Q ac_N The reactive power fed into the rectifier station for the target AC.

[0065] Specifically, the overall short-circuit capacity calculation formula is:

[0066]

[0067] in, S T =k ST P T_total ;P T_total =N g P T ; S w_L,i is the short-circuit capacity of the wind farm at the commutation bus; S p_L,j Short-circuit capacity provided for a single photovoltaic power station; N w is the number of wind farms; N p is the number of the photovoltaic power stations; S T is the short-circuit capacity of the thermal power plant at the commutation bus; S w,i The short-circuit capacity provided by the wind farm at the grid connection point; Z P_L,i is the equivalent impedance of the wind farm at the commutation bus; U PCC,i is the rated voltage; k ST The total amount of short-circuit capacity provided for the thermal power plant; P T_total is the total installed capacity of the thermal power plant; N gis the number of thermal power plants; T is the capacity of a single thermal power plant; i represents the i-th wind farm; j represents the j-th photovoltaic power plant.

[0068] Preferably, the transient overvoltage constraint is:

[0069]

[0070] in, S Cmin is the minimum short-circuit capacity; k Sw,i The proportion of short-circuit capacity provided for wind farms; P aw_total,i is the equivalent operating capacity of the wind farm; P w_total,i is the installed capacity of a single wind farm; c w is the wind power average capacity correction factor; P w_cf,i is the wind power output at a given confidence level.

[0071] Specifically, the planned capacity is approximately:

[0072]

[0073] Among them, P w is the capacity of a single wind farm; P p is the capacity of a single photovoltaic power station; N w1 、N p1 are the number of wind turbines and photovoltaic units respectively; c w,min 、c p,min 、c g,min are the lower limits of installed capacity of wind power, photovoltaic power and thermal power in the system; c w,max 、c p,max 、c g,max They are the upper limits of installed capacity of wind power, photovoltaic power and thermal power in the system respectively.

[0074] Preferably, the power generation capacity constraint is:

[0075]

[0076] in, k w_e , k p_e are the average output levels of wind power and photovoltaic power, respectively; C g P is the investment and construction cost of a single thermal power plant; dN P is the rated DC transmission power; w_e,i is the equivalent power of the wind farm; P p_e,j is the equivalent power of the photovoltaic power station.

[0077] Specifically, the objective function is:

[0078]

[0079] in,

[0080] F = min() is the minimum value function; N T is the number of scheduling periods; C g,t is the operating cost of the thermal power plant; is the startup and shutdown cost of the thermal power plant; is the cost of wind and solar power curtailment; t represents the tth period; g represents the gth thermal power plant; a g , b g 、c g are power generation cost coefficients; P g,t is the output of the thermal power plant in period t; is the startup cost of the thermal power plant; is the shutdown cost of the thermal power plant; is the startup state variable; is the shutdown state variable; λ is the wind and solar power abandonment penalty cost coefficient; is the predicted power of the wind farm; t dw is the dispatching power of the wind farm; is the predicted power of the photovoltaic power station; t dp is the dispatching power of the photovoltaic power station.

[0081] Preferably, the new energy proportion constraint of the transmission channel is:

[0082]

[0083] Among them, δ is the required proportion of renewable energy electricity in the transmission channel; P t HVDC is the system's external load power during period t.

[0084] Specifically, the method for generating typical wind and solar scenarios. Wind power and photovoltaics are characterized by high data complexity and large data volume. In order to accurately characterize the uncertainty of wind power and photovoltaic output and ensure the safe and stable operation of the system, quantile regression and Gaussian mixture model clustering are used to generate typical net load scenarios. A non-parametric probability prediction model is established to process wind power and photovoltaic samples, and variables are characterized by features and regression conditions. Taking wind power as an example, a linear fitting relationship between the actual wind power and the wind power forecast data is first constructed, and the quantile regression fitting method is used to describe the linear mapping relationship.

[0085]

[0086] in, is the predicted data of the wind power sampling point, is the actual wind power data corresponding to the quantile, a τ and b τ are the quantile linear mapping parameters respectively.

[0087] Based on the quantile regression theory, the parameter a τ and b τ Make an estimate, which is expressed as the following optimization problem:

[0088]

[0089] Among them, J τ is the objective function corresponding to the τ quantile, φ τ is the detection function, d∈D, t∈T. D is the number of sampling days, T is the number of daily sampling times, φ() is the detection function, is the actual sampling value and x is the sample data.

[0090] Construct a non-parametric probability prediction model and input the predicted wind power output Get the corresponding quantile

[0091]

[0092] in, are parameter estimates, and the actual quantile set is used to characterize the uncertainty of wind power output.

[0093] Obtaining actual wind power output through linear interpolation calculation The corresponding quantile The uniformly distributed τ w Converted to a standard normal distribution w . Convert the actual historical wind power output sequence into a multidimensional Gaussian distribution N K (μ,Σ) for clustering. The GMM algorithm can estimate the sample as a linear superposition of S multi-dimensional Gaussian distribution probabilities, corresponding to the probability density function f GMM (x):

[0094]

[0095] Among them, ω S is the weight of the s-th Gaussian distribution, N(x|μ S ,Σ S ) is the probability density function of the s-th Gaussian distribution, x is the sample data, and the maximum expectation method is used for estimation.

[0096] The S multi-dimensional Gaussian distributions obtained after clustering are converted into S groups of uniformly distributed vectors. Based on the non-parametric prediction model, the quantile matrix is ​​obtained, and the predicted output sequence corresponding to the wind power scene set is calculated point by point by linear interpolation. The photovoltaic output sample generates the photovoltaic power station scene set according to the above process.

[0097] Furthermore, transient overvoltage and influencing factors of the integrated wind, solar, thermal and storage DC transmission system are analyzed. Analysis of influencing factors of transient overvoltage under DC blocking:

[0098] Typical wind, solar, thermal and storage DC transmission systems are as follows: Figure 2 The UHV DC converter consumes a large amount of reactive power during normal operation, which is generally provided by the capacitor bank and AC filter bank in the converter station. The power balance relationship between the AC and DC systems during normal operation can be expressed as:

[0099]

[0100] Among them, P ac_N , Q ac_N are respectively the active power and reactive power fed into the rectifier station by AC; P d_N , Q d_N are the active power and reactive power of DC transmission respectively; Q Cr_N Reactive power provided by the reactive power compensation device in the converter station; the subscript N indicates the normal operating state.

[0101] When a DC blocking fault occurs, a large amount of reactive power surplus ΔQ will appear in the integrated wind, solar, thermal and storage system at the sending end, which can be expressed as:

[0102] ΔQ=Q ac_N +Q Cr

[0103] Q Cr The relationship with the commutation bus voltage on the rectifier side is as follows:

[0104]

[0105] Among them, U Lr,pu is the per unit value of the commutation bus voltage on the rectifier side; Q Cr_rated It is the rated value of the reactive power compensation device in the converter station.

[0106] The transient overvoltage at the commutation bus on the rectifier side after DC blocking can be calculated as:

[0107]

[0108] Integration gives:

[0109]

[0110] Voltage U at the wind farm grid connection point after DC blocking Lr,pu satisfy:

[0111] U Lw,pu ≤U Lr,pu

[0112] The factors affecting the transient overvoltage amplitude after DC blocking are mainly: the rated value of the reactive power compensation device in the converter station is related to the short-circuit capacity at the converter bus. The reactive power compensation in the converter station, the DC transmission power in the wind-solar-thermal-storage integrated DC transmission system remains basically unchanged, and the reactive power consumed by the rectifier during normal operation will not change significantly from the rated value of the reactive power compensation device in the converter station. The different wind-thermal grid-connected capacity of the wind-solar-thermal-storage integrated system will affect S C The short-circuit capacity S at the commutation busbar C It is the dominant factor affecting transient overvoltage.

[0113] Specifically, the steps for analyzing and modeling the impact of new energy access on system strength are as follows:

[0114] The capacity of a single wind farm is P w , the number of wind turbines in a single wind farm is n w , installed capacity of a single wind farm P w_total,i It can be expressed as:

[0115] P w_total,i =n w P w

[0116] The number of supporting thermal stations in the wind-solar-thermal-storage integrated DC transmission system is N g , the capacity of a single unit is P T , then the total installed capacity of thermal power P T_total It can be expressed as:

[0117] P T_total =N g P T

[0118] Assume that the short-circuit capacity provided by the thermal power plant is k ST The DC supporting thermal power is generally configured near the converter station. It can be approximately considered that the equivalent electrical distance between the thermal power plant and the sending end converter bus is 0. The short-circuit capacity S provided by the thermal power plant at the converter bus is T It can be expressed as:

[0119] S T =k ST P T_total

[0120] Considering the confidence level of wind power output, the equivalent operating capacity of the wind farm P T_total Expressed as

[0121]

[0122] The short-circuit capacity provided by the wind farm at the grid connection point is k times its own capacity. Sw,i , U PCC,i is the rated voltage at PCC point, Z w,i is the equivalent impedance of the wind farm, Z P_L,i is the equivalent impedance from the wind farm to the commutation busbar, then the short-circuit capacity S provided by the wind farm at the grid connection point is w,i It can be expressed as:

[0123]

[0124] The short-circuit capacity provided by the wind farm at the commutation bus can be calculated as:

[0125]

[0126] Integration gives:

[0127]

[0128] The short-circuit capacity analysis process of a photovoltaic power station is similar to that of a wind farm. The short-circuit current provided by a small energy storage capacity is limited. When the short-circuit calculation point is far away from the energy storage, the short-circuit current provided can even be ignored, so the impact of energy storage is ignored. The short-circuit capacity provided by the wind, solar, thermal and storage bundled system at the commutation busbar can be expressed as:

[0129]

[0130] Furthermore, the capacity optimization model of the wind-solar-thermal-storage integrated transmission system is as follows:

[0131] 1) Transient overvoltage constraint based on short-circuit ratio:

[0132] The high voltage ride-through capability of the wind farm is U Lr,pu To ensure that the voltage at the wind power grid connection point does not exceed U HVRT , then the commutation bus voltage caused by the DC fault should satisfy:

[0133] U Lr,pu ≤U HVRT

[0134] Minimum short circuit capacity S Cmin It can be calculated as:

[0135]

[0136] The integration can obtain the transient overvoltage constraint based on the short-circuit ratio:

[0137]

[0138] 2) Two-layer planning model of wind, solar, thermal and storage capacity:

[0139] The upper-level planning model is based on the total system cost C total Minimum target, including power supply investment cost C inv , Maintenance cost C maint and operating cost C op .

[0140] minC total =C inv +C maint +C op

[0141]

[0142] Among them, C w , C p and C g N is the investment and construction cost of a single wind power, photovoltaic and thermal power plant; w1 、N p1 、N g is the number of wind power, photovoltaic and thermal power units; Y is the planning period; is the maintenance factor of thermal power plant g; r d is the discount rate; r maint is the growth rate of maintenance cost; is the typical daily operating cost of the system; k is the effective service life of the farm.

[0143] Specifically, the upper model constraints are as follows:

[0144] 1) Wind, solar and thermal power planning capacity constraints:

[0145]

[0146] 2) Wind, solar and thermal power generation capacity constraints:

[0147] The equivalent power of wind farm and photovoltaic power station is P w_e,i and P p_e,j , and the installed capacity P w_total,i and P p_total,j The relationship can be expressed as:

[0148]

[0149] Assume that the rated DC transmission power is P dN , to meet the DC power demand,

[0150]

[0151] Preferably, the lower optimization model takes into account the power balance, thermal power plant output and ramp rate, rotating reserve and other operating constraints, and meets the external load demand through the coordinated dispatch of wind, solar and thermal storage to achieve the minimum system operating cost. The goal is to minimize the comprehensive cost consisting of the thermal power plant's power generation cost, start-up and shutdown costs, and the penalty cost for wind and solar abandonment, and achieve the goal of minimizing operating costs and maximizing wind and solar power consumption. The objective function is as follows:

[0152]

[0153]

[0154] Specifically, the operating constraints of the lower-level scheduling model are as follows:

[0155] 1) Power balance constraints:

[0156]

[0157] Among them, P t HVDC is the external load power of the system during period t; P t s is the active output of the system energy storage during period t; is the grid support power during period t.

[0158] 2) Wind, solar and thermal power output constraints:

[0159] u g,t P g,min ≤P g,t ≤u g,t P g,max

[0160]

[0161] Among them, P g,max , P g,min are the upper and lower limits of active output of thermal power plant g respectively; u g,t It is the operating status of the conventional field g in period t, 0 is the shutdown status and 1 is the operating status.

[0162] 3) Thermal power plant ramping and standby constraints:

[0163]

[0164] in, are the upward and downward climbing rates of field g respectively; ΔT is the scheduling time interval; They represent the upward and downward spare capacities of the system during period t respectively.

[0165] 4) Energy storage operation constraints:

[0166]

[0167] E s,0 =E s,T

[0168]

[0169] Among them, η c and η d The charging and discharging efficiency of energy storage; and It is a 0 to 1 variable that characterizes the charging and discharging state of energy storage; and is the maximum charging and discharging power of energy storage; and Indicates the storage energy at the initial and final states. s,t , It is the energy storage capacity, the lower limit of capacity and the upper limit of capacity; Charging and discharging power for energy storage. Upward and downward reserve capacity provided for energy storage.

[0170] 5) System backup constraints:

[0171]

[0172] in, Upward and downward reserve capacity provided for energy storage.

[0173] 6) Grid support power constraints:

[0174]

[0175] Where ξ is the grid support coefficient.

[0176] 7) Constraints on the proportion of new energy in the transmission channel:

[0177]

[0178] Furthermore, based on the constructed optimization model and objective function, the Yalmip toolbox combined with the commercial optimization software GUROBI is used to solve the optimal economic investment capacity of thermal power plants, wind power, photovoltaic power and energy storage that meet the system constraints, as well as the power generation power P of the i-th thermal power plant in period t on a typical day. i,t ; Wind and solar resources power generation P during period t t wt and P t pv , to achieve the effective transmission of new energy through the integrated wind, solar, thermal and storage transmission system.

[0179] Preferably, wind power, photovoltaic and load power scenario curves are selected as follows: Figure 3 , Figure 4 as well as Figure 5 As shown, Figure 3 There are four typical external load power curves in the figure. Figure 4 There are 8 typical wind power generation curves in the figure. Figure 5 There are 6 typical photovoltaic power generation curves. The following two planning schemes are set for analysis and comparison:

[0180] Scheme 1: Planning scheme for the integrated wind, solar, thermal and storage transmission system without considering transient overvoltage constraints.

[0181] Scheme 2: Planning scheme for wind, solar, thermal and storage integrated system considering transient overvoltage constraints. The grid support coefficient is set to 0 (i.e., grid support is not considered).

[0182] Specifically, Table 1 shows the optimization results of different planning schemes. It can be seen from Table 1 that Scheme 2 takes into account transient overvoltage constraints. In order to ensure that the system has sufficient short-circuit capacity and reduce transient overvoltage under DC fault conditions, the total installed capacity of new energy power sources in the planning results increased by 17.89% compared with Scheme 1, among which wind power and photovoltaic installed capacity increased by 1,282MW and 897MW respectively, and the total investment cost increased by 10.712 billion yuan, an increase of 16.9%.

[0183] Table 1

[0184]

[0185] Furthermore, based on the consideration of actual projects, the influence of the fixed ratio of wind and solar on the planning scheme is analyzed, and the optimization results of wind, solar, thermal and energy storage under fixed thermal power installed capacity and different wind and solar configurations are shown in Table 2. As can be seen from Table 2, compared with the configuration results of Scheme 2, excessive increase and decrease of the proportion of photovoltaic will increase the system energy storage configuration power and capacity and the average annual operating cost; and when the wind and solar ratio is close to 1.25:1, it is helpful to give play to the complementary output characteristics of wind power and photovoltaic power and optimize resource allocation.

[0186] Table 2

[0187]

[0188]

[0189] Specifically, Figure 6As shown in the figure, when a bipolar locking fault occurs in the DC transmission system, the transient overvoltage of the wind, solar, thermal and storage integrated system planned in Scheme 2 is relatively low, with a transient overvoltage amplitude of 1.300pu, which is 8.25% lower than that of Scheme 1. It effectively reduces the transient overvoltage level after the DC fault, can ensure the safe operation of the system and reduce the risk of new energy disconnection in the case of severe DC faults, and sacrifice a certain degree of economic efficiency to ensure the safe operation of the system. In terms of wind and solar penetration and consumption, although the economic efficiency of Scheme 2 is inferior to that of Scheme 1, the proportion of wind and solar transmission power (i.e. wind and solar penetration) is 53.86%, and the new energy abandonment rate is 32.96%, which are 8.46% and 2.96% higher than those of Scheme 1, respectively. It effectively increases the proportion of new energy power in the transmission channel and ensures the efficient transmission and cross-regional sharing of new energy. The energy storage power and capacity planning results of Scheme 2 are 1245MW and 6047MWh respectively, and the energy storage duration is 4.85h, which are 399MW and 2222MWh more than Scheme 1 respectively, which can effectively improve the flexibility of the transmission system and expand the space for the larger-scale access of new energy to the transmission system. Therefore, in order to ensure the safety of the system, the proposed planning scheme configures more power sources and energy storage capacity, which reduces the overall economic efficiency, but significantly increases the proportion of wind and solar power in the transmission channel, which can provide guidance for gradually increasing the proportion of new energy electricity transmitted.

[0190] Further, by Figure 7 and Figure 8 It can be seen that both planning schemes can meet the power demand of the transmission system during daily dispatching and operation, and transmit electricity to the interconnected power grid according to the transmission plan. In Scheme 2, transient overvoltage constraints are ensured, so that transient voltage rise is controlled within a safe range. The start-up capacity of thermal power plants is increased, which increases the cost of thermal power generation by 9.6% compared with Scheme 1. However, the penetration rate of new energy is 63.37%, which can ensure a good proportion of green electricity transmission.

[0191] Furthermore, transient overvoltage and short circuit of different schemes such as Fig. 9 and Fig.10 As shown. Fig. 9 , Scheme 2 can meet the transient overvoltage constraint of 1.3pu in all periods of the operating day, which better ensures the operational safety of the transmission system and reduces the risk of nearby renewable energy grid disconnection. Compared with Scheme 2, Scheme 1 did not consider the transient overvoltage constraint during the planning process. In some periods, the short-circuit capacity was small and could not meet the transient overvoltage constraint. Relaxation was required to obtain a feasible solution, resulting in the transient voltage rise exceeding the threshold of 1.3pu from 12:00 to 14:00 when the transmission power was high. It was difficult to ensure the safe operation of the transmission system in the event of a DC fault. Fig.10It can be seen that compared with Scheme 1, Scheme 2 can maintain a higher short-circuit ratio level and improve the voltage support strength while completing the transmission plan, so that the transient overvoltage is maintained within a safe range.

[0192] Specifically, Table 3 shows the wind, solar, thermal and energy storage joint planning under different grid support capabilities. As can be seen from Table 3, as the support level of the grid gradually increases, the energy storage capacity of the transmission system gradually decreases, and the annual average operating cost of the transmission system also decreases. Therefore, by increasing the interconnection and coordination with the power system at the sending end, the new energy consumption space of the integrated transmission system can be increased, the investment and operating costs of the supporting resources of the transmission system can be reduced, and the economy and efficiency of the planned operation can be improved.

[0193] Table 3

[0194]

[0195] The beneficial effects of the present invention are as follows:

[0196] The present invention achieves the description of system uncertainty by obtaining a scene set using quantile regression fitting and Gaussian distribution; determines the minimum short-circuit capacity through the overall short-circuit capacity calculation formula; and optimizes the system cost and daily dispatch cost and plans the optimal capacity through upper-level model constraints and lower-level model constraints.

[0197] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0198] The principles and implementation methods of the present invention are described in this article using specific examples. The description of the above embodiments is only used to help understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A capacity planning method for wind, solar, thermal and storage integrated transmission system taking into account transient overvoltage constraints, characterized in that: include: A linear mapping formula is constructed using a quantile regression fitting method, and the parameters of the linear mapping formula are estimated using quantile regression theory to obtain a non-parametric probability prediction model; The historical actual output sequence is converted into a multi-dimensional Gaussian distribution, and the multi-dimensional Gaussian distribution is clustered using a GMM algorithm to obtain a uniformly distributed vector group; Performing point-by-point linear interpolation using the non-parametric probability prediction model according to the vector group to obtain a scene set; Constructing a power balance relationship formula during normal operation, constructing a reactive power surplus calculation formula during DC blocking based on the power balance relationship formula, and constructing a transient overvoltage calculation formula using the reactive power surplus calculation formula; Constructing short-circuit capacity calculation formulas for the wind farm and the thermal power farm at the commutation busbar respectively, integrating the short-circuit capacity calculation formulas for the wind farm and the thermal power farm to obtain an overall short-circuit capacity calculation formula; Construct transient overvoltage constraints based on short-circuit ratio; Construct the objective function, upper model constraints, and lower model constraints; The upper model constraints include: planning capacity constraints and power generation capacity constraints; the lower model constraints include: power balance constraints, wind, solar and thermal output constraints, ramp and standby constraints, energy storage operation constraints, system standby constraints, grid support power constraints and transmission channel new energy proportion constraints; Based on the upper model constraints and the lower model constraints, the transient overvoltage calculation formula, the overall short-circuit capacity calculation formula and the objective function are solved according to the scenario set to obtain the optimal capacity planning.

2. A method for capacity planning of wind, solar, thermal and storage integrated transmission system taking into account transient overvoltage constraints according to claim 1, characterized in that: The transient overvoltage calculation formula is: Among them, U Lr,pu is the transient overvoltage calculation result; S C is the calculation result of the overall short-circuit capacity; Q Cr_rated is the rated value of the reactive power compensation device in the target converter station; Q ac_N The reactive power fed into the rectifier station for the target AC.

3. A capacity planning method for wind, solar, thermal and storage integrated transmission system taking into account transient overvoltage constraints according to claim 2, characterized in that: The overall short-circuit capacity calculation formula is: in, S T =k ST P T_total ;P T_total =N g P T ; S w_L,i is the short-circuit capacity of the wind farm at the commutation bus; S p_L,j Short-circuit capacity provided for a single photovoltaic power station; N w is the number of wind farms; N p is the number of the photovoltaic power stations; S T is the short-circuit capacity of the thermal power plant at the commutation bus; S w,i The short-circuit capacity provided by the wind farm at the grid connection point; Z P_L,i is the equivalent impedance of the wind farm at the commutation bus; U PCC,i is the rated voltage; k ST The total amount of short-circuit capacity provided for the thermal power plant; P T_total is the total installed capacity of the thermal power plant; N g is the number of thermal power plants; T is the capacity of a single thermal power plant; i represents the i-th wind farm; j represents the j-th photovoltaic power plant.

4. A method for capacity planning of wind, solar, thermal and storage integrated transmission system taking into account transient overvoltage constraints according to claim 3, characterized in that: The transient overvoltage constraint is: in, S Cmin is the minimum short-circuit capacity; k Sw,i The proportion of short-circuit capacity provided for the wind farm; P aw_total,i is the equivalent operating capacity of the wind farm; P w_total,i is the installed capacity of a single wind farm; c w is the wind power average capacity correction factor; P w_cf,i is the wind power output at a given confidence level.

5. The method for capacity planning of wind-solar-thermal-storage integrated transmission system taking into account transient overvoltage constraints according to claim 3 is characterized in that: The planned capacity is approximately: Among them, P w is the capacity of a single wind farm; P p is the capacity of a single photovoltaic power station; N w1 、N p1 are the number of wind turbines and photovoltaic units respectively; c w,min 、c p,min 、c g,min are the lower limits of installed capacity of wind power, photovoltaic power and thermal power in the system; c w,max 、c p,max 、c g,max They are the upper limits of installed capacity of wind power, photovoltaic power and thermal power in the system respectively.

6. A method for capacity planning of wind, solar, thermal and storage integrated transmission system taking into account transient overvoltage constraints according to claim 5, characterized in that: The power generation capacity constraint is: in, k w_e , k p_e are the average output levels of wind power and photovoltaic power, respectively; C g P is the investment and construction cost of a single thermal power plant; dN P is the rated DC transmission power; w_e,i is the equivalent power of the wind farm; P p_e,j is the equivalent power of the photovoltaic power station.

7. A method for capacity planning of wind, solar, thermal and storage integrated transmission system taking into account transient overvoltage constraints according to claim 5, characterized in that: The objective function is: in, F = min() is the minimum value function; N T is the number of scheduling periods; C g,t is the operating cost of the thermal power plant; is the startup and shutdown cost of the thermal power plant; is the cost of wind and solar power curtailment; t represents the tth period; g represents the gth thermal power plant; a g 、b g 、c g are power generation cost coefficients; P g,t is the output of the thermal power plant in period t; is the startup cost of the thermal power plant; is the shutdown cost of the thermal power plant; is the startup state variable; is the shutdown state variable; λ is the wind and solar power abandonment penalty cost coefficient; is the predicted power of the wind farm; is the dispatching power of the wind farm; is the predicted power of the photovoltaic power station; is the dispatching power of the photovoltaic power station.

8. A capacity planning method for wind, solar, thermal and storage integrated transmission system taking into account transient overvoltage constraints according to claim 7, characterized in that: The constraint on the proportion of new energy in the transmission channel is: Among them, δ is the required proportion of renewable energy electricity in the transmission channel; is the external load power.

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