Transmitting-end power grid new energy capacity configuration method and system considering transient overvoltage

Abstract

The invention provides a transmitting-end power grid new energy capacity configuration method and system considering transient overvoltage, and belongs to the technical field of direct current transmission of a multi-energy complementary system. Comprising the following steps: acquiring historical load data, obtaining a typical load curve through clustering analysis, and establishing a sending-end system direct-current transmission plan based on a principle that direct-current transmission power is closest to the load curve; constructing a multi-energy complementary system capacity optimization configuration model considering the direct current bearing capacity by taking the minimum investment cost and operation cost as an optimization target, taking the capacity of various power supplies as variables and taking the bearing capacity of a direct current delivery system and the output limitation of various units as constraint conditions based on transient overvoltage; and optimizing the new energy capacity configuration of the sending-end power grid by using a particle swarm optimization algorithm to obtain the optimal capacity configuration of the new energy, including photovoltaic power generation power, wind power generation power and energy storage power configuration data. The method can comprehensively consider the bearing capacity of the direct current channel, the economical efficiency of power grid operation and the reliability of the multi-energy system.

Description

Technical Field

[0001] The present invention relates to the technical field of direct current transmission of a multi-energy complementary system, and in particular to a method and system for configuring the capacity of new energy in a power grid at the sending end taking transient overvoltage into consideration. Background Art

[0003] The integration of new energy sources into the grid has significantly improved the level of clean electricity supply, but the system voltage support strength has been weakened, and it faces severe challenges of voltage safety issues. DC disturbances may cause serious voltage and frequency problems. If the DC commutation fails or DC locks out in the sending-end power grid, there will be a reactive power surplus in a short time after the fault is cleared, which will cause overvoltage at the DC sending end and affect the safe and stable operation of the power grid. Therefore, when planning and designing the optimal configuration of the capacity of the power system, it is of great significance to reasonably consider the transient overvoltage of the DC transmission commutation busbar to guide the construction of the power grid and maintain the safety and stability of the power grid.

[0004] However, due to the volatility of new energy sources such as wind and solar, the large-scale grid connection of wind power and photovoltaic power will have an adverse impact on the voltage stability and frequency stability of the power system. At the same time, the cost of wind power and photovoltaic power generation is relatively high, and in order to adapt to its volatility, it is necessary to increase energy storage equipment and flexibly adjust the power supply, which will increase the system's power generation cost and investment and operation cost, and bring challenges to the economic efficiency of system operation. Therefore, it is very important to reasonably adjust the configuration of new energy capacity to cope with the randomness of new energy output. Summary of the invention

[0005] In view of this, the present invention provides a method and system for configuring the new energy capacity of a sending-end power grid taking transient overvoltage into consideration, taking into account the impact of transient overvoltage of a DC transmission commutation bus on power grid construction, constructing a multi-energy complementary system to cope with the randomness of new energy output, and rationally configuring multi-energy capacity to give full play to the complementary characteristics of various power sources and improve energy utilization.

[0006] The technical solution adopted by the embodiment of the present invention to solve the technical problem is:

[0007] A method for configuring the capacity of new energy sources in a power grid at the sending end considering transient overvoltages comprises:

[0008] Obtain historical load data, obtain typical load curves through k-means cluster analysis, and establish a DC transmission plan for the sending end system based on the principle that the DC transmission power is closest to the load curve, and obtain the DC transmission plan power;

[0009] Taking the lowest investment cost and operation cost as the optimization goal, the capacity of various power sources as variables, based on transient overvoltage, considering the carrying capacity of the DC transmission system and the output limits of various units as constraints, a capacity optimization configuration model of a multi-energy complementary DC transmission system taking into account the DC carrying capacity is constructed;

[0010] Based on the DC transmission planned power, the particle swarm optimization algorithm is used to solve the capacity optimization configuration model of the DC transmission system with multi-energy complementarity taking into account the DC carrying capacity, and the optimal capacity configuration of the new energy that meets the DC transmission bus transient overvoltage constraint is obtained. The optimal capacity configuration of the new energy that meets the DC transmission bus transient overvoltage constraint is the photovoltaic power generation power P S 、Wind power generation power P W , energy storage power P E Configuration data.

[0011] Preferably, the historical load data is obtained, a typical load curve is obtained by k-means cluster analysis, and a DC transmission plan of the sending-end system is established based on the principle that the DC transmission power is closest to the load curve, and the DC transmission plan power obtained includes:

[0012] Get historical load data as samples. For the sample set X = {x1, x2, ..., x N}, where x i =(x i1 ,x i2 ,...,x id ) is a d-dimensional vector. According to the principle of minimizing the Euclidean distance from all sample points in each cluster to the cluster center of the cluster, X is divided into K clusters, each cluster corresponds to a cluster center c k , all cluster centers constitute a set C = {c1, c2, ..., c K},c k With x n For the same type, calculate the average value within the cluster and update the cluster center;

[0013] The number of clusters K is determined using the elbow point method:

[0014] Before clustering the processed loads, an appropriate number of clusters K should be selected in advance. Usually, the optimal number of clusters is determined by:

[0015]

[0016] Among them, SSE is the sum of squared errors, C i is the i-th cluster, x is C i The sample points in m i is the centroid; the SSE of each K value is plotted as a curve, the point where the SSE starts to slow down is the curve elbow point, and the K corresponding to the curve elbow point is the number of clusters;

[0017] The DC transmission planning model of the sending end system is set as:

[0018] Let the DC transmission power and load curve be infinitely close, construct an optimization problem, and take the square of the difference between the DC planned transmission power and the receiving load as the objective function D v :

[0019]

[0020] In the formula, is the planned DC transmission power at time t, is the load power under scenario i at time t; T is the number of time periods in a single day, and I is the total number of generated scenarios;

[0021] Objective function D v The constraints include:

[0022] Daily DC transmission power continuity constraints:

[0023]

[0024] Constraints on the number of daily DC power adjustments:

[0025]

[0026] Power balance constraints:

[0027]

[0028] DC power switching constraints

[0029]

[0030] In the formula, is the planned power of DC transmission at the initial moment, is the planned power of DC transmission at time T; t ∈{0,1}, when z t = 0 means that the DC transmission power remains unchanged at time t. t =1 is the change of DC transmission power at time t; μ is the coefficient of the large M method; △t is the time interval of the unit period;

[0031] Under the four constraints, the objective function D v To achieve the minimum, we can get the DC transmission plan P d .

[0032] Preferably, the construction of a multi-energy complementary DC transmission system capacity optimization configuration model taking into account the DC carrying capacity includes:

[0033] According to the active power balance condition of the power system, a general model of the multi-energy complementary DC transmission system is established to make the power generation power of the power system equal to the load power at the receiving end:

[0034] ∑PGi =∑P Di

[0035] The relationship between power generation and load power is expressed as:

[0036]

[0037] Among them, P G is the power generation capacity of the thermal power unit, P S is the photovoltaic power generation power, P W is the wind power generation power, P E is the energy storage power, P L is the load demand of the new energy base, P HVDC is the planned power of the DC channel;

[0038] Considering the uncertainty of renewable energy output, wind power and photovoltaic output models are established respectively, among which the wind power output uncertainty model is expressed as:

[0039]

[0040] In the formula, v in is the cut-in wind speed; v out is the cut-out wind speed, v n is the rated wind speed, P N is the rated power of the wind turbine; a, b, c are the parameters of the wind turbine power characteristic curve, which are related to the model parameters of the wind turbine;

[0041] The photovoltaic output uncertainty model is expressed as:

[0042]

[0043] Where P S is the actual output value of the photovoltaic unit; η1 is the photoelectric effect conversion efficiency; A is the total area of ​​the solar panel; s is the total solar radiation, s dir is the direct radiance of solar energy, s lev is the solar surface horizontal radiation, s dif is the solar diffuse radiance;

[0044] Based on the general model of the multi-energy complementary DC transmission system, a capacity optimization configuration model of the multi-energy complementary DC transmission system is established. The objective function is specifically expressed as: the objective function C is the system investment cost C int and operating cost C ope The variable is the wind, solar and storage power capacity P S , P W , P E ;

[0045] C=C int +Cope

[0046]

[0047] C ope =C grid +C om +C fuel

[0048]

[0049] Where η2 is the interest rate; n Z is the operating life of the Zth type of power source, Z∈{wind power, photovoltaic power, energy storage}; c Z P is the unit capacity investment cost of the Z-type power supply; Z Capacity of Class Z power supply; C grid , C om , C fuel They are respectively the cost of electricity purchase and sale, the cost of operation and maintenance of each power source, and the cost of fuel; c grid is the grid electricity price, c om.Z is the unit operation and maintenance cost of the Z-type power supply; c fuel is the unit fuel cost; P buy (t) is the power purchased from the grid at time t, P sell (t) is the power sold to the grid at time t, P Z (t) is the power of the Zth type power supply at time t; P G (t) is the thermal power at time t;

[0050] The constraints of the capacity optimization configuration model of the multi-energy complementary DC transmission system include the transient voltage rise constraint of the DC transmission bus and the power output operation constraint. The minimum value of the objective function C is optimized and solved under the condition of satisfying the constraints to obtain the capacity configuration of the multi-energy complementary DC transmission system.

[0051] Preferably, the output operation constraint expressions of various power sources are:

[0052] Thermal power output constraints:

[0053]

[0054] Where P l f , They are the lower and upper limits of thermal power output respectively;

[0055] Thermal power ramp constraints:

[0056]

[0057] In the formula, They are the upper limits of the upward and downward climbing capabilities of thermal power units respectively;

[0058] New energy output constraints:

[0059]

[0060] In the formula, p W.max It is the maximum power of wind power per unit capacity obtained based on historical data of the project location, p S.max is the maximum power per unit capacity of photovoltaic power, the product P W p W.max , P S p S.max are the actual maximum outputs of wind power and photovoltaic power respectively;

[0061] Energy storage charging and discharging constraints:

[0062]

[0063] Where P dis (t), P ch (t) are the discharge and charging power of the energy storage device; U E (t) is the energy storage state variable at time t, 0 indicates that the energy storage system is discharging, and 1 indicates that the energy storage system is charging;

[0064] SOC constraints of energy storage:

[0065]

[0066] SOC min ≤SOC(t)≤SOC max

[0067] In the formula, η3 is the charge and discharge efficiency of the energy storage system, E(0) is the initial charge, SOCmax and SOCmin are the minimum and maximum state of charge of the energy storage system, respectively;

[0068] Power balance constraints:

[0069] ∑P Gi =∑P Di

[0070]

[0071] Preferably, the evaluation index of the commutation bus transient voltage rise constraint is:

[0072] △U d <δ

[0073] In the formula, △U d represents the transient voltage rise at the commutation busbar at the sending end; δ represents the threshold value of the transient voltage rise;

[0074] After the single-pole DC blocking occurs in the sending-end system, the excess reactive power of the sending-end system is the reactive power absorbed by the single pole, that is, 50% of the reactive power consumed by the converter station. The transient voltage change at the converter station bus is approximately expressed as:

[0075]

[0076] In the formula, S ac It indicates the short-circuit capacity provided by the sending-end AC system to the rectifier bus; △Q indicates the reactive power change; P d It represents the active power transmitted by the multi-energy complementary DC transmission system; Indicates the power factor angle of the rectifier station.

[0077] Preferably, the DC transmission planned power P d , the particle swarm optimization algorithm is used to solve the optimal configuration model of multi-energy complementary DC transmission system capacity taking into account the DC carrying capacity, including:

[0078] First, the new energy capacity optimization particle is initialized, and MATLAB is used to call PSASP to perform system short-circuit calculation to obtain the short-circuit capacity S under the current new energy capacity configuration. ac ,renew Update capacity configuration constraint △U d <δ, where the particle velocity update formula and position update formula are:

[0079] v is (t+1)=ω·v is (t)+c1r1(p is (t)-x is (t))+c2r2(p gs (t)-x gs (t))

[0080] x is (t+1)=x is (t)+v is (t+1)

[0081] Among them, ω represents the inertia factor of the search ability; c1 and c2 represent acceleration constants, c1 represents the individual learning factor of each particle, and c2 represents the social learning factor of each particle; v is (t), v is (t+1) represents the particle speed at the current moment and the particle speed at the next moment respectively; r1 and r2 represent random numbers in the range of [0,1] respectively; p is (t), p gs (t) represents the optimal position of the individual particle and the optimal position of the history;

[0082] Dynamically adjust the three factors of the parameters, the adjustment method is:

[0083]

[0084] In the formula, ω max ,ω min Respectively represent the maximum and minimum values ​​of the inertia factor; t cur Indicates the current iteration number; t max Indicates the total number of iterations; c 1i 、c 2i They represent the initial values ​​of c1 and c2 respectively, i represents the number of iterations; c 1f 、c 2f Represent the final values ​​of c1 and c2 respectively;

[0085] Adjust the position of the optimization particle swarm until the convergence condition is met, and obtain the optimal capacity configuration of new energy that meets the transient overvoltage constraint of the DC transmission commutation bus. The convergence condition is to reach the total number of iterations t max .

[0086] A system for configuring the capacity of new energy sources in a sending-end power grid taking transient overvoltage into consideration is provided, which is used to implement the aforementioned method for configuring the capacity of new energy sources in a sending-end power grid taking transient overvoltage into consideration.

[0087] It can be seen from the above technical solutions that the method and system for configuring the capacity of new energy sources in the sending-end power grid considering transient overvoltage provided by the embodiment of the present invention obtains historical load data, obtains a typical load curve through k-means clustering analysis, and establishes a DC transmission plan for the sending-end system based on the principle that the DC transmission power is closest to the load curve, and obtains the DC transmission plan power; takes the lowest investment cost and operating cost as the optimization goal, and the capacity of various power sources as variables, based on transient overvoltage, considers the carrying capacity of the DC transmission system and the output limitations of various units as constraints, and constructs a multi-energy complementary DC transmission system capacity optimization configuration model taking into account the DC carrying capacity; based on the DC transmission plan power, the particle swarm optimization algorithm is used to optimize the new energy capacity configuration of the sending-end power grid, and the optimal capacity configuration of new energy that meets the transient overvoltage constraint of the DC transmission commutation bus is obtained, and the optimal capacity configuration of new energy that meets the transient overvoltage constraint of the DC transmission commutation bus is the photovoltaic power generation power P S 、Wind power generation power P W , energy storage power P E The present invention considers the impact of transient overvoltage of DC transmission commutation busbar on power grid construction, constructs a multi-energy complementary system to cope with the randomness of new energy output, and reasonably configures multi-energy capacity to give play to the complementary characteristics of various power sources, which can effectively improve the utilization rate of energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0088] Figure 1 It is a flow chart of a method for configuring new energy capacity of a sending-end power grid taking transient overvoltage into consideration according to an example of the present invention.

[0089] Figure 2 It is a schematic diagram of the topological structure of a multi-energy complementary direct current transmission system according to an example of the present invention.

[0090] Figure 3 It is a schematic diagram of the optimization result of the DC transmission plan of the example of the present invention.

[0091] Figure 4 It is a schematic diagram of a typical daily operation plan of the sending end system of an example of the present invention. DETAILED DESCRIPTION

[0092] The technical scheme and technical effects of the present invention are further elaborated in detail below in conjunction with the accompanying drawings of the present invention.

[0093] refer to Figure 1 As shown, the present invention provides a method for configuring the capacity of new energy sources in a power grid at the sending end considering transient overvoltage, which is specifically implemented according to the following steps:

[0094] Step S1, obtaining historical load data, obtaining a typical load curve through k-means cluster analysis, and establishing a DC transmission plan for the sending end system based on the principle that the DC transmission power is closest to the load curve, to obtain the DC transmission plan power;

[0095] Step S2, taking the lowest investment cost and operation cost as the optimization goal, taking the capacity of various power sources as variables, based on transient overvoltage, considering the carrying capacity of the DC transmission system and the output limits of various units as constraints, and constructing a capacity optimization configuration model of a multi-energy complementary DC transmission system taking into account the DC carrying capacity;

[0096] Step S3, based on the planned power of DC transmission, the particle swarm optimization algorithm is used to solve the capacity optimization configuration model of the multi-energy complementary DC transmission system taking into account the DC carrying capacity, and the optimal capacity configuration of the new energy that meets the transient overvoltage constraint of the DC transmission bus is obtained. The optimal capacity configuration of the new energy that meets the transient overvoltage constraint of the DC transmission bus is the photovoltaic power generation power P S 、Wind power generation power P W , energy storage power P E Configuration data.

[0097] Step S1 is specifically implemented according to the following steps:

[0098] Step S11, obtain historical load data as samples, for the sample set X = {x1, x2, ..., x N}, where x i =(x i1 ,xi2 ,...,x id ) is a d-dimensional vector. According to the principle of minimizing the Euclidean distance from all sample points in each cluster to the cluster center of the cluster, X is divided into K clusters, each cluster corresponds to a cluster center c k , all cluster centers constitute a set C = {c1, c2, ..., c K}, c k With x n For the same type, calculate the average value within the cluster and update the cluster center;

[0099] The number of clusters K is determined using the elbow point method:

[0100]

[0101] Among them, SSE is the sum of squared errors, C i is the i-th cluster, x is C i The sample points in m i is the centroid; the SSE of each K value is plotted as a curve, the point where the SSE starts to slow down is the curve elbow point, and the K corresponding to the curve elbow point is the number of clusters;

[0102] Step S12: the DC transmission planning model of the sending-end system is set as:

[0103] Let the DC transmission power and load curve be infinitely close, construct an optimization problem, and take the square of the difference between the DC planned transmission power and the receiving load as the objective function D v :

[0104]

[0105] In the formula, is the planned DC transmission power at time t, is the load power under scenario i at time t; T is the number of time periods in a single day, and I is the total number of generated scenarios; To ensure the stable operation of the DC channel, the constraints of the above model mainly include:

[0106] In order to ensure that the DC transmission power is equal at the beginning (0:00) and the end (24:00) of a day, the daily DC transmission power continuity constraint is established:

[0107]

[0108] In order to improve the stability of system operation, the constraints on the number of daily DC power adjustment are established:

[0109]

[0110] In order to balance the planned DC power and the power required by the receiving system load in all scenarios within a day, the power balance constraint is established:

[0111]

[0112] DC power switching condition constraints:

[0113]

[0114] In the formula, is the planned power of DC transmission at the initial moment, is the planned power of DC transmission at time T; t ∈{0,1}, when z t = 0 means that the DC transmission power remains unchanged at time t. t =1 is the change of DC transmission power at time t; μ is the coefficient of the large M method; △t is the time interval of the unit period;

[0115] Under the above constraints, the objective function D v To achieve the minimum, we can get the DC transmission plan P d .

[0116] Step 2 is implemented as follows:

[0117] Step S21, based on the active power balance condition of the power system, a general model of a multi-energy complementary DC transmission system is established, that is, to ensure that the power generation power of the power system is equal to the load power at the receiving end.

[0118] ∑P Gi =∑P Di (7)

[0119] According to the above topological structure, the relationship between the generated power and the load power is obtained, which is expressed as:

[0120]

[0121] Among them, P G is the power generation capacity of the thermal power unit, P S is the photovoltaic power generation power, P W is the wind power generation power, P E is the energy storage power, P L is the load demand of the new energy base, P HVDC is the planned power of the DC channel.

[0122] Considering the uncertainty of renewable energy output, wind power and photovoltaic output models are established respectively.

[0123] Wind power output uncertainty model:

[0124]

[0125] In the formula, vin is the cut-in wind speed; v out is the cut-out wind speed, v n is the rated wind speed, P N is the rated power of the wind turbine; a, b, c are the parameters of the wind turbine power characteristic curve, which are related to the model parameters of the wind turbine.

[0126] Photovoltaic output uncertainty model:

[0127]

[0128] Where P S is the actual output value of the photovoltaic unit; η is the photoelectric effect conversion efficiency; A is the total area of ​​the solar panel; s is the total solar radiation, s dir is the direct radiance of solar energy, s lev is the solar surface horizontal radiation, s dif is the solar diffuse radiance.

[0129] Step S22, based on the general model of the multi-energy complementary DC transmission system, a capacity optimization configuration model of the multi-energy complementary system is established, and the specific objective function is:

[0130] The objective function C is the system investment cost C int and operating cost C ope sum;

[0131] minC=min{C int +C ope} (11)

[0132]

[0133] C ope =C grid +C om +C fuel (13)

[0134]

[0135] Where η2 is the interest rate; n Z is the operating life of the Zth type of power source, Z∈{wind power, photovoltaic power, energy storage}; c Z P is the unit capacity investment cost of the Z-type power supply; Z Capacity of Class Z power supply; C grid , C om , C fuel They are respectively the cost of electricity purchase and sale, the cost of operation and maintenance of each power source, and the cost of fuel; c grid is the grid electricity price, c om.Z is the unit operation and maintenance cost of the Z-type power supply; c fuelis the unit fuel cost; P buy (t) is the power purchased from the grid at time t, P sell (t) is the power sold to the grid at time t, P Z (t) is the power of the Zth type power supply at time t; P G (t) is the thermal power at time t;

[0136] The constraints of the multi-energy complementary system capacity optimization configuration model include the transient voltage rise constraint of the DC transmission commutation bus and various operation constraints, which are specifically manifested as follows:

[0137] Thermal power output constraints:

[0138]

[0139] Where P l f , They are the lower and upper limits of thermal power output respectively.

[0140] Thermal power ramp constraints:

[0141]

[0142] In the formula, They are respectively the upper limits of the upward and downward climbing capabilities of thermal power units.

[0143] New energy output constraints:

[0144]

[0145]

[0146] In the formula, p W.max It is the unit capacity of wind power obtained based on historical data of the project location, p S.max is the power limit of the unit capacity photovoltaic, the product P W p W.max , P S p S.max They are the actual maximum outputs of wind power and photovoltaic power respectively.

[0147] SOC constraints of energy storage:

[0148]

[0149] SOC min ≤SOC(t)≤SOC max (20)

[0150] In the formula, η is the charge and discharge efficiency of the energy storage system, E(0) is the initial charge, SOCmax and SOCmin are the minimum and maximum state of charge of the energy storage system, respectively.

[0151] Energy storage charging and discharging constraints:

[0152]

[0153] Where P dis (t), P ch (t) are the discharge and charging power of the energy storage device respectively. E (t) is the energy storage state variable at time t, 0 indicates that the energy storage system is discharging, and 1 indicates that the energy storage system is charging.

[0154] Power balance constraints:

[0155] ∑P Gi =∑P Di (twenty two)

[0156]

[0157] The specific evaluation indicators of the transient voltage rise constraint of the commutation bus are:

[0158] △U d <δ (24)

[0159] In the formula, △U d It represents the transient voltage rise at the commutation bus at the sending end; δ represents the threshold value of the transient voltage rise.

[0160] There is only a small amount of reactive power exchange between the DC transmission system and the AC system when the system is in steady state operation. Therefore, the reactive power consumed by the rectifier station can be regarded as basically equal to the reactive power of the reactive power compensation device installed in the rectifier station. d The active power P sent by the DC system d The relationship between can be expressed as:

[0161] Q d =P d tanφ d (25)

[0162] When a DC lockout fault occurs, the reactive power loss of the converter station decreases and the reactive power is excessive, which will cause a sudden increase in the voltage on the AC side of the rectifier station, and then cause a surge in the voltage at the DC near-zone new energy machine end.

[0163] The transient overvoltage caused by excess reactive power will produce a voltage rise, and the table voltage rise △U d The expression is as follows:

[0164]

[0165] Among them, Q dr Indicates the excess reactive power at the rectifier station after DC blocking occurs. When DC blocking is unipolar, Q dr It is equivalent to half of the reactive power consumption of the rectifier station during normal operation, that is, the reactive power absorbed by a single pole.

[0166] After the sending-end system has a single-pole DC blocking, the system excess reactive power is the reactive power absorbed by the single pole, that is, 50% of the reactive power consumed by the converter station. The transient voltage change at the converter station bus can be approximately expressed by the following formula:

[0167]

[0168] In the formula, △U d Represents the transient voltage rise at the commutation bus at the sending end; S ac It indicates the short-circuit capacity provided by the sending-end AC system to the rectifier bus; △Q indicates the reactive power change; P d It represents the active power delivered by the multi-energy complementary DC system; Indicates the power factor angle of the rectifier station.

[0169] The specific solution method for optimizing the new energy capacity of the sending-end power grid considering the transient overvoltage of the DC transmission commutation bus is as follows:

[0170] The DC transmission plan power P is obtained d Based on this, the particle swarm optimization algorithm is used to optimize the new energy capacity of the sending-end power grid. First, the new energy capacity optimization particles are initialized, and MATLAB is used to call PSASP to perform system short-circuit calculations to obtain the short-circuit capacity S under the current new energy capacity configuration. ac ,renew Update capacity configuration constraint △U d <δ, where the particle velocity update formula and position update formula are:

[0171] v is (t+1)=ω·v is (t)+c1r1(p is (t)-x is (t))+c2r2(p gs (t)-x gs (t)) (28)

[0172] x is (t+1)=x is (t)+v is (t+1) (29)

[0173] Among them, ω represents the inertia factor of the search ability. The larger its value, the stronger the global and local search ability. The smaller its value, the stronger the local search ability but the weaker the global search ability. By adjusting the size of ω, the global or local optimization ability can be adjusted; c1 and c2 represent acceleration constants. The former represents the individual learning factor of each particle, and the latter represents the social learning factor of each particle. v is (t), v is (t+1) represents the particle speed at the current moment and the particle speed at the next moment respectively; r1 and r2 represent random numbers in the range of 0 to 1 respectively; p is (t), p gs (t) represent the optimal position of the individual particle and the optimal position of the history respectively.

[0174] In order to improve the optimization performance of the algorithm, dynamically adjusting the size of the three factors in the traditional particle swarm algorithm is conducive to the iterative convergence of the algorithm, thereby making the optimization process more stable and optimal. The dynamic adjustment of the three factors of the method parameters is carried out according to the following formula.

[0175]

[0176] In the formula, ω max ,ω min Respectively represent the maximum and minimum values ​​of the inertia factor; t cur Indicates the current iteration number; t max Indicates the total number of iterations; c 1i 、c 2i They represent the initial values ​​of c1 and c2 respectively, i represents the number of iterations; c 1f 、c 2f Represent the final values ​​of c1 and c2 respectively.

[0177] Adjust the position of the optimization particle swarm until the convergence condition is met, and obtain the optimal capacity configuration of new energy that meets the transient overvoltage constraint of the DC transmission commutation bus. The convergence condition is to reach the total number of iterations t max .

[0178] Furthermore, the present invention provides a system for configuring the capacity of new energy sources in a power grid at the sending end taking into account transient overvoltage, which is used to implement Figure 1 The method for configuring the new energy capacity of the sending-end power grid considering transient overvoltage.

[0179] Acquisition module, used to obtain historical load data;

[0180] The DC transmission plan establishment module is used to obtain the typical load curve through k-means cluster analysis, and to establish the DC transmission plan of the sending end system based on the principle that the DC transmission power is closest to the load curve, and obtain the DC transmission plan power;

[0181] The configuration model building module takes the lowest investment cost and operating cost as the optimization goal, takes the capacity of various power sources as variables, and considers the carrying capacity of the DC transmission system and the output limits of various units as constraints based on transient overvoltage to build a multi-energy complementary DC transmission system capacity optimization configuration model taking into account the DC carrying capacity;

[0182] The optimization module is used to optimize the new energy capacity configuration of the sending-end power grid based on the DC transmission planned power by using a particle swarm optimization algorithm to obtain the optimal new energy capacity configuration that meets the DC transmission bus transient overvoltage constraint. The optimal new energy capacity configuration that meets the DC transmission bus transient overvoltage constraint is the photovoltaic power generation power P S 、Wind power generation power P W , energy storage power P E Configuration data.

[0183] Implementation of the case study

[0184] In order to verify the method of optimizing the configuration of new energy capacity of the sending-end power grid considering the transient overvoltage of the DC transmission commutation busbar of the present invention, a grid structure of a prefecture-level city is selected for simulation analysis. The general model of its topological structure is shown in the appendix of the specification. Figure 2 , Figure 2 This is a schematic diagram of the topological structure of the multi-energy complementary DC transmission system of the present invention. The topological structure consists of four power sources: wind, light, fire, and storage. After rectification at the converter station, the power is sent to the receiving end through a high-voltage DC channel to meet the load demand. The typical annual load data of the prefecture-level city and the local typical day wind and solar output data are obtained, and the DC transmission plan power is simulated as shown in the attached manual. Figure 3 , Figure 3 The figure shows the changes in the planned DC transmission power within a day. The DC transmission power is basically maintained at 1275MW in the interval t∈[10,23] and 965MW in the interval t∈[0,9]. To ensure the safe and stable operation of the DC channel, the channel transmission power can be switched only at t=9h and t=23h.

[0185] For AC and DC systems with a rated operating voltage of ±500kV, the transient voltage rise △U d The threshold δ is set to 0.1pu. On this basis, the improved particle swarm algorithm is used to optimize the configuration of the new energy capacity of the sending power grid. The relevant parameters used in the solution process are shown in Table 1.

[0186] Table 1 Optimization problem related parameters

[0187]

[0188]

[0189] The optimal configuration results of the new energy capacity of the sending-end power grid considering the transient overvoltage of the DC transmission commutation bus are as follows: the wind power capacity is 95MW, the photovoltaic capacity is 75MW, and the energy storage capacity is 33MW. The typical daily operation plan of the sending-end system is shown in the attached manual. Figure 4 , Figure 4 This is a schematic diagram of a typical daily operation plan for the sending end system. Figure 4 The data shows the power changes of four different types of power sources, wind, solar, thermal and energy storage, in one day. The thermal power power fluctuates between 300-1000MW, with obvious power increases and decreases in 10-11 hours and 23-24 hours; the wind power and photovoltaic output fluctuate between 0-100MW, and their output changes are in line with the actual situation; a positive value of energy storage power indicates that the energy storage is in a discharging state, and a negative value of energy storage power indicates that the energy storage is in a charging state.

[0190] The method and system for configuring the capacity of new energy sources in the sending-end power grid considering transient overvoltage of the present invention have significant advantages in many aspects. Based on the load characteristics of the receiving end, a DC transmission plan for the sending-end power grid is established, a typical load curve is obtained through k-means cluster analysis, and a DC transmission plan model is constructed with the square of the difference between the DC planned transmission power and the receiving-end load as the objective function. At the same time, combined with the daily DC transmission power continuity constraint, the daily DC power adjustment number constraint, the power balance constraint and the DC power switching condition constraint, the DC channel is guaranteed to operate stably, and the scientificity and rationality of the transmission plan are improved.

[0191] Taking the lowest investment cost and operation cost as the optimization goal, taking the capacity of wind, solar, thermal and storage power sources as the variable, comprehensively considering the carrying capacity of the DC transmission system, the output limits of various units (including the upper and lower limits of thermal power output, the limit of climbing capacity, the limit of new energy output according to historical data statistics, etc.), and the transient voltage rise constraints of the DC transmission commutation busbar, etc., a capacity optimization configuration model of the multi-energy complementary system is constructed. This model comprehensively and meticulously covers a variety of key elements in the actual power grid operation, optimizes and solves the minimum value of the objective function under various constraints, obtains the reasonable capacity configuration of the multi-energy system, gives full play to the complementary characteristics of various power sources, improves energy utilization, reduces system costs, effectively responds to the challenges brought by the volatility of new energy, and improves the stability and economy of system operation.

[0192] In the solution process, the particle swarm optimization algorithm is used to optimize the new energy capacity of the sending-end power grid. MATLAB calls PSASP to perform system short-circuit calculations to obtain the short-circuit capacity under the current new energy capacity configuration, and then updates the relevant parameters and constraints. The inertia factor, acceleration constant and other parameters in the particle swarm algorithm are dynamically adjusted to make the optimization process more stable and efficient. Finally, the optimal capacity configuration of new energy that meets the transient overvoltage constraint of the DC transmission commutation bus is obtained, which provides an accurate and reliable solution for the optimal configuration of new energy capacity in the sending-end power grid. It is of great significance to guide the construction of power grids, maintain the safe and stable operation of power grids, and promote the efficient application of new energy in power grids. It can provide strong technical support and decision-making basis for the planning, design, operation and dispatch of power systems.

[0193] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.

[0194] Electronic device is intended to represent various forms of digital computers, and may also represent various forms of mobile devices, such as personal digital processing and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit implementations of the invention disclosed herein as described and / or claimed.

[0195] The electronic device includes a computing unit, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) or a computer program loaded from a storage unit into a random access memory (RAM). In the RAM, various programs and data required for the operation of the device can also be stored. The computing unit, ROM and RAM are connected to each other through a bus. An input / output (I / O) interface is also connected to the bus.

[0196] A number of components in the electronic device are connected to the I / O interface 4, including an input unit, an output unit, a storage unit and a communication unit. The communication unit allows the electronic device to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0197] The computing unit may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of computing units include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit performs the various methods and processes described above, such as a method for configuring the capacity of a new energy source in a power grid at the sending end considering transient overvoltages. For example, in some embodiments, the method for configuring the capacity of a new energy source in a power grid at the sending end considering transient overvoltages may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit. In some embodiments, part or all of the computer program may be loaded and / or installed on an electronic device via a ROM and / or a communication unit. When the computer program is loaded into RAM and executed by the computing unit, one or more steps of the method for configuring the capacity of a new energy source in a power grid at the sending end considering transient overvoltages described above may be executed. Alternatively, in other embodiments, the computing unit may be configured to execute the method for configuring the capacity of a new energy source in a power grid at the sending end considering transient overvoltages by any other appropriate means (e.g., by means of firmware).

[0198] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0199] The program code for implementing the method disclosed in the present invention can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code can be executed entirely on the machine, partially on the machine, partially on the machine as a stand-alone software package and partially on a remote machine, or entirely on a remote machine or server.

[0200] In the context disclosed by the present invention, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, 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 of the foregoing.

[0201] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0202] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0203] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship between the client and the server is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0204] What is disclosed above is only a preferred embodiment of the present invention, which certainly cannot be used to limit the scope of rights of the present invention. A person skilled in the art can understand that all or part of the processes of the above embodiments and equivalent changes made according to the claims of the present invention still fall within the scope of the invention.

Claims

1. A method for configuring the capacity of new energy sources in a power grid at the sending end considering transient overvoltage, characterized in that: include: Obtain historical load data, obtain typical load curves through k-means cluster analysis, and establish a DC transmission plan for the sending end system based on the principle that the DC transmission power is closest to the load curve, and obtain the DC transmission plan power; Taking the lowest investment cost and operation cost as the optimization goal, the capacity of various power sources as variables, based on transient overvoltage, considering the carrying capacity of the DC transmission system and the output limits of various units as constraints, a capacity optimization configuration model of a multi-energy complementary DC transmission system taking into account the DC carrying capacity is constructed; Based on the DC transmission planned power, the particle swarm optimization algorithm is used to solve the capacity optimization configuration model of the DC transmission system with multi-energy complementarity taking into account the DC carrying capacity, and the optimal capacity configuration of the new energy that meets the DC transmission bus transient overvoltage constraint is obtained. The optimal capacity configuration of the new energy that meets the DC transmission bus transient overvoltage constraint is the photovoltaic power generation power P S 、Wind power generation power P W , energy storage power P E Configuration data.

2. The method for configuring the capacity of new energy sources in the sending-end power grid considering transient overvoltage according to claim 1, characterized in that: The historical load data is obtained, a typical load curve is obtained through k-means cluster analysis, and a DC transmission plan of the sending end system is established based on the principle that the DC transmission power is closest to the load curve. The DC transmission plan power includes: Get historical load data as samples. For the sample set X = {x1, x2, ..., x N }, where x i =(x i1 ,x i2 ,...,x id ) is a d-dimensional vector. According to the principle of minimizing the Euclidean distance from all sample points in each cluster to the cluster center of the cluster, X is divided into K clusters, each cluster corresponds to a cluster center c k , all cluster centers constitute a set C = {c1, c2, ..., c K }, c k With x n For the same type, calculate the average value within the cluster and update the cluster center; The number of clusters K is determined using the elbow point method: Among them, SSE is the sum of squared errors, C i is the i-th cluster, x is C i The sample points in m i is the centroid; the SSE of each K value is plotted as a curve, the point where the SSE starts to slow down is the curve elbow point, and the K corresponding to the curve elbow point is the number of clusters; The DC transmission planning model of the sending end system is set as: Let the DC transmission power and load curve be infinitely close, construct an optimization problem, and take the square of the difference between the DC planned transmission power and the receiving load as the objective function D v : In the formula, is the planned DC transmission power at time t, is the load power under scenario i at time t; T is the number of time periods in a single day, and I is the total number of generated scenarios; Objective function D v The constraints include: Daily DC transmission power continuity constraints: Constraints on the number of daily DC power adjustments: Power balance constraints: DC power switching constraints In the formula, is the planned power of DC transmission at the initial moment, is the planned power of DC transmission at time T; t ∈{0,1}, when z t = 0 means that the DC transmission power remains unchanged at time t. t =1 is the change of DC transmission power at time t; μ is the coefficient of the large M method; △t is the time interval of the unit period; Under the four constraints, the objective function D v To achieve the minimum, we can get the DC transmission plan P d .

3. The method for configuring the capacity of new energy sources in the sending-end power grid considering transient overvoltage according to claim 2, characterized in that: The construction of the optimal configuration model of the multi-energy complementary DC transmission system capacity taking into account the DC carrying capacity includes: According to the active power balance condition of the power system, a general model of the multi-energy complementary DC transmission system is established to make the power generation power of the power system equal to the load power at the receiving end: ∑P Gi =∑P Di The relationship between power generation and load power is expressed as: Among them, P G is the power generation capacity of the thermal power unit, P S is the photovoltaic power generation power, P W is the wind power generation power, P E is the energy storage power, P L is the load demand of the new energy base, P HVDC is the planned power of the DC channel; Considering the uncertainty of renewable energy output, wind power and photovoltaic output models are established respectively, among which the wind power output uncertainty model is expressed as: In the formula, v in is the cut-in wind speed; v out is the cut-out wind speed, v n is the rated wind speed, P N is the rated power of the wind turbine; a, b, c are the parameters of the wind turbine power characteristic curve; The photovoltaic output uncertainty model is expressed as: Where P S is the actual output value of the photovoltaic unit; η1 is the photoelectric effect conversion efficiency; A is the total area of ​​the solar panel; s is the total solar radiation, s dir is the direct radiance of solar energy, s lev is the solar surface horizontal radiation, s dif is the solar diffuse radiance; Based on the general model of the multi-energy complementary DC transmission system, a capacity optimization configuration model of the multi-energy complementary DC transmission system is established. The objective function is specifically expressed as: the objective function C is the system investment cost C int and operating cost C ope The variable is the wind, solar and storage power capacity P S , P W , P E ; C=C int +C ope C ope =C grid +C om +C fuel Where η2 is the interest rate; n Z is the operating life of the Zth type of power source, Z∈{wind power, photovoltaic power, energy storage}; c Z P is the unit capacity investment cost of the Z-type power supply; Z Capacity of Class Z power supply; C grid , C om , C fuel They are respectively the cost of electricity purchase and sale, the cost of operation and maintenance of each power source, and the cost of fuel; c grid is the grid electricity price, c om.Z is the unit operation and maintenance cost of the Z-type power supply; c fuel is the unit fuel cost; P buy (t) is the power purchased from the grid at time t, P sell (t) is the power sold to the grid at time t, P Z (t) is the power of the Zth type power supply at time t; P G (t) is the thermal power at time t; The constraints of the capacity optimization configuration model of the multi-energy complementary DC transmission system include the transient voltage rise constraint of the DC transmission bus and the power output operation constraint. The minimum value of the objective function C is optimized and solved under the condition of satisfying the constraints to obtain the capacity configuration of the multi-energy complementary DC transmission system.

4. The method for configuring the capacity of new energy sources in the sending-end power grid considering transient overvoltage according to claim 3 is characterized in that: The output operation constraint expressions of various power sources are: Thermal power output constraints: Where P l f , They are the lower and upper limits of thermal power output respectively; Thermal power ramp constraints: In the formula, They are the upper limits of the upward and downward climbing capabilities of thermal power units respectively; New energy output constraints: In the formula, p W.max It is the maximum power of wind power per unit capacity obtained based on historical data of the project location, p S.max is the maximum power per unit capacity of photovoltaic power, the product P W p W.max , P S p S.max are the actual maximum outputs of wind power and photovoltaic power, respectively; Energy storage charging and discharging constraints: Where P dis (t), P ch (t) are the discharge and charging power of the energy storage device; U E (t) is the energy storage state variable at time t, 0 indicates that the energy storage system is discharging, and 1 indicates that the energy storage system is charging; SOC constraints of energy storage: SOC min ≤SOC(t)≤SOC max In the formula, η3 is the charge and discharge efficiency of the energy storage system, E(0) is the initial charge, SOCmax and SOCmin are the minimum and maximum state of charge of the energy storage system, respectively; Power balance constraints: ∑P Gi =∑P Di 5. The method for configuring the capacity of new energy sources in the sending-end power grid considering transient overvoltage according to claim 4, characterized in that: The evaluation index of the commutation bus transient voltage rise constraint is: △U d <d In the formula, △U d represents the transient voltage rise at the commutation busbar at the sending end; δ represents the threshold value of the transient voltage rise; After the single-pole DC blocking occurs in the sending-end system, the excess reactive power of the sending-end system is the reactive power absorbed by the single pole, that is, 50% of the reactive power consumed by the converter station. The transient voltage change at the converter station bus is approximately expressed as: In the formula, S ac Indicates the short-circuit capacity provided by the sending-end AC system to the rectifier bus; △Q represents the reactive power change; P d It represents the active power transmitted by the multi-energy complementary DC transmission system; Indicates the power factor angle of the rectifier station.

6. The method for configuring the capacity of new energy sources in the sending-end power grid considering transient overvoltage according to claim 5, characterized in that: The DC transmission plan power P d , the particle swarm optimization algorithm is used to solve the optimal configuration model of multi-energy complementary DC transmission system capacity taking into account the DC carrying capacity, including: First, the new energy capacity optimization particle is initialized, and MATLAB is used to call PSASP to perform system short-circuit calculation to obtain the short-circuit capacity S under the current new energy capacity configuration. ac ,renew Update capacity configuration constraint △U d <δ, where the particle velocity update formula and position update formula are: v is (t+1)=ω·v is (t)+c1r1(p is (t)-x is (t))+c2r2(p gs (t)-x gs (t)) x is (t+1)=x is (t)+v is (t+1) Among them, ω represents the inertia factor of the search ability; c1 and c2 represent acceleration constants, c1 represents the individual learning factor of each particle, and c2 represents the social learning factor of each particle; v is (t), v is (t+1) represents the particle speed at the current moment and the particle speed at the next moment respectively; r1 and r2 represent random numbers in the range of [0,1] respectively; p is (t), p gs (t) represent the optimal position of the individual particle and the optimal position of the history respectively; the three factors of the dynamic adjustment parameters are adjusted dynamically, and the adjustment method is: In the formula, ω max ,ω min Respectively represent the maximum and minimum values ​​of the inertia factor; t cur Indicates the current iteration number; t max Indicates the total number of iterations; c 1i 、c 2i They represent the initial values ​​of c1 and c2 respectively, i represents the number of iterations; c 1f 、c 2f Represent the final values ​​of c1 and c2 respectively; Adjust the position of the optimization particle swarm until the convergence condition is met, and obtain the optimal capacity configuration of new energy that meets the transient overvoltage constraint of the DC transmission commutation bus. The convergence condition is that the number of iterations reaches the total number of iterations t max .

7. A new energy capacity configuration system for a sending-end power grid considering transient overvoltage, characterized in that: Used to implement the method for configuring the new energy capacity of the sending-end power grid taking transient overvoltage into consideration as described in any one of claims 1-6.

8. An electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method according to any one of claims 1 to 6.

9. A storage medium, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-6.

10. A computer program product, comprising a computer program, which, when executed by a processor, implements the method according to any one of claims 1 to 6.

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