A power distribution network wind and storage combined optimization configuration method and a related device thereof

Abstract

The application discloses a power distribution network wind storage combined optimization configuration method and a related device thereof, obtains index values and index weights of each power quality index according to an actual operation state of a power distribution network system, and calculates power quality comprehensive evaluation values of each node; a wind storage combined double-layer optimization model is constructed, the wind storage combined double-layer optimization model comprises a lower-layer daily operation optimization scheduling model and an upper-layer annual optimization configuration model, the lower-layer daily operation optimization scheduling model takes optimal power quality comprehensive results of each node of the power distribution network system as an objective function, the upper-layer annual optimization configuration model takes annual total income and total abandoned wind rate of a power generation system as an objective function, the wind storage combined double-layer optimization model is solved and optimized, an optimal planning scheme is obtained, and the technical problem that, in the planning process of the prior art, only power quality problems are taken as constraints to calculate a model objective function, the influence of each new energy access on a power grid is not further analyzed, and an optimal planning configuration scheme cannot be obtained is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power distribution networks, in particular to a wind and storage combined optimization configuration method for power distribution networks and a related device thereof. BACKGROUND

[0002] With the proposal of the strategic goal of "carbon peak and carbon neutral", the use and development of non-fossil energy, especially the large-scale development and utilization of clean and efficient energy, and the construction of a clean, low-carbon, safe and efficient energy system have become the trend of current energy transformation development. The large-scale distributed renewable energy power station (Distribution Generate, DG) access changes the original power flow distribution of the power grid, and the random fluctuation and intermittency of its output causes power quality problems that are increasingly prominent. The contradiction between the increasingly improved power quality requirements of modern production equipment in the power distribution network and the power quality (Power Quality, PQ) problems caused by the huge losses has become one of the bottlenecks restricting the large-scale grid connection of new energy.

[0003] Currently, the power quality problem is involved in the new energy grid connection planning process. In the planning process, the power quality problem is only used as a constraint to calculate the model objective function, without further analyzing the impact of each new energy access on the power grid, so the optimal optimization configuration scheme cannot be obtained. SUMMARY

[0004] The present application provides a wind and storage combined optimization configuration method for power distribution networks and a related device thereof, which is used to improve the technical problem that the existing technology only uses the power quality problem as a constraint to calculate the model objective function in the planning process, without further analyzing the impact of each new energy access on the power grid, so the optimal planning configuration scheme cannot be obtained.

[0005] Therefore, the first aspect of the present application provides a wind and storage combined optimization configuration method for power distribution networks, comprising:

[0006] After determining the power quality indicators of each node of the power distribution network system, the index values of each power quality indicator are obtained according to the actual operation state of the power distribution network system;

[0007] The index weights of each power quality indicator are obtained, and the power quality comprehensive evaluation value of each node is calculated according to the index values of each power quality indicator and the index weights;

[0008] A wind and storage combined double-layer optimization model is constructed, which includes a lower layer daily operation optimization scheduling model and an upper layer annual optimization configuration model. The lower layer daily operation optimization scheduling model takes the optimal power quality comprehensive result of each node of the power distribution network system as the objective function, and the upper layer annual optimization configuration model takes the annual total income and total wind curtailment rate of the power generation system as the objective function;

[0009] The access position and capacity of the power distribution network wind turbine and energy storage unit generated in the upper layer are transmitted to the lower layer, the lower layer daily operation optimization scheduling model is optimized, the objective function value of the upper layer annual optimization configuration model is updated according to the obtained lower layer daily optimization scheduling result, and optimization is performed, and the optimal configuration capacity is obtained.

[0010] Optionally, the power quality indicators include voltage deviation, voltage flicker and voltage harmonic;

[0011] The calculation formula of the voltage deviation is:

[0012]

[0013] In the formula, U is the voltage deviation of node i, U i,oc U is the voltage module value of node i, U i,n U is the nominal voltage value of node i;

[0014] The calculation formula of the voltage flicker is:

[0015]

[0016] In the formula, P lt,c U is the voltage flicker value in the continuous operation process of the wind turbine grid-connected point, S k U is the short-circuit capacity of the generator grid-connected point, U is the impedance value of the equivalent impedance of the wind turbine grid-connected point, v a U is the annual average wind speed, U is the flicker coefficient of the wind turbine, S n,i U is the rated apparent power of the wind turbine i, N w,n U is the number of wind turbines;

[0017] The voltage harmonic is measured by the total voltage harmonic distortion rate, and the calculation formula of the total voltage harmonic distortion rate is:

[0018]

[0019] In the formula, THD u U is the total voltage harmonic distortion rate, U h U is the hth harmonic voltage, and U1 is the fundamental voltage.

[0020] Optionally, the index weight of each power quality indicator is obtained, including:

[0021] According to the importance between each power quality indicator, a judgment matrix is constructed;

[0022] The subjective weight of each power quality indicator is calculated through the judgment matrix;

[0023] The values ​​of each power quality index are normalized, and the standard deviation of each normalized power quality index is calculated to obtain the comparative strength of each power quality index.

[0024] The conflict of indicators is calculated based on the correlation coefficients between the normalized power quality indicators.

[0025] The objective weight of each power quality indicator is calculated based on the comparative strength and conflict of each indicator.

[0026] By combining the subjective and objective weights of each power quality indicator, the indicator weights of each power quality indicator are obtained.

[0027] Optionally, the objective function of the lower-level daily operation optimization scheduling model is:

[0028]

[0029] In the formula, f pq S represents the comprehensive power quality results for a typical day's electricity sales in the distribution network system. i,t Let P be the comprehensive power quality assessment value of node i at time t. load,i,t Let be the load power of node i at time t, and n be the number of nodes in the distribution network;

[0030] The constraints of the lower-level daily operation optimization scheduling model during the optimization process include system power flow constraints, energy storage energy constraints, energy storage charging and discharging constraints, wind farm output constraints, and transmission line power constraints.

[0031] Optionally, the objective function of the upper-level annual optimization configuration model is:

[0032]

[0033]

[0034] In the formula, f sum,in Let P be the total annual revenue of the power generation system, N be the number of nodes in the power generation system, and P be the total annual revenue of the power generation system. load,k,t,n Let n be the load power at time t on day k. The total annual operation and maintenance cost of all power supplies. The average annual installation cost of wind turbine units. p represents the average annual installation cost of the energy storage unit. k,t,n Let f be the fluctuating electricity price at node n at time t on day k. wc The total wind curtailment rate, Let P be the maximum output of the nth wind farm at time t. wind,i,t N represents the actual power output of the wind farm at time t. w The number of wind farms;

[0035] The constraints of the upper-level annual optimization configuration model during the optimization process include node wind power installation capacity constraints and total grid system installation capacity constraints.

[0036] The second aspect of this application provides a wind-storage joint optimization configuration device for a distribution network, comprising:

[0037] The acquisition unit is used to acquire the index values ​​of each power quality index according to the actual operating status of the power distribution network system after determining the power quality index of each node in the power distribution network system.

[0038] The calculation unit is used to obtain the index weights of each of the power quality indicators, and to calculate the comprehensive power quality evaluation value of each node based on the index value of each power quality indicator and the index weight.

[0039] A construction unit is used to construct a wind-storage joint two-layer optimization model. The wind-storage joint two-layer optimization model includes a lower-layer daily operation optimization scheduling model and an upper-layer annual optimization configuration model. The lower-layer daily operation optimization scheduling model takes the optimal comprehensive result of the power quality of each node in the distribution network system as the objective function, and the upper-layer annual optimization configuration model takes the annual total revenue of the power generation system and the total wind curtailment rate as the objective functions.

[0040] The optimization unit is used to transmit the access location and capacity of the wind turbine and energy storage units generated in the upper layer to the lower layer, optimize the daily operation optimization scheduling model of the lower layer, update the objective function value of the annual optimization configuration model of the upper layer based on the obtained daily optimization scheduling results of the lower layer, and then perform optimization to obtain the optimal configuration capacity.

[0041] Optionally, the power quality indicators include voltage deviation, voltage flicker, and voltage harmonics;

[0042] The formula for calculating the voltage deviation is:

[0043]

[0044] In the formula, U is the voltage deviation at node i. i,oc Let U be the voltage magnitude at node i. i,n Let be the nominal voltage value of node i;

[0045] The formula for calculating voltage flicker is:

[0046]

[0047] In the formula, P lt,c S represents the voltage flicker value during continuous operation of the wind turbine generator at the grid connection point. k The short-circuit capacity of the generator set's grid connection point. The impedance value of the grid equivalent impedance at the wind turbine connection point, v a The average annual wind speed, S is the flicker coefficient of the wind turbine. n,i N is the rated apparent power of wind turbine i. w,n This refers to the number of wind turbine units;

[0048] The voltage harmonics are measured using the total harmonic distortion (THD), which is calculated using the following formula:

[0049]

[0050] In the formula, THD u U is the total harmonic distortion of voltage. h U1 is the fundamental voltage, where U is the voltage of the h-th harmonic.

[0051] Optionally, the objective function of the lower-level daily operation optimization scheduling model is:

[0052]

[0053] In the formula, f pq S represents the comprehensive power quality results for a typical day's electricity sales in the distribution network system. i,t Let P be the comprehensive power quality assessment value of node i at time t. load,i,t Let be the load power of node i at time t, and n be the number of nodes in the distribution network;

[0054] The constraints of the lower-level daily operation optimization scheduling model during the optimization process include system power flow constraints, energy storage energy constraints, energy storage charging and discharging constraints, wind farm output constraints, and transmission line power constraints.

[0055] The objective function of the upper-level annual optimization configuration model is:

[0056]

[0057]

[0058] In the formula, f sum,in Let P be the total annual revenue of the power generation system, N be the number of nodes in the power generation system, and P be the total annual revenue of the power generation system. load,k,t,n Let n be the load power at time t on day k. The total annual operation and maintenance cost of all power supplies. The average annual installation cost of wind turbine units. p represents the average annual installation cost of the energy storage unit. k,t,n Let f be the fluctuating electricity price at node n at time t on day k. wc The total wind curtailment rate, Let P be the maximum output of the nth wind farm at time t. wind,i,tN represents the actual power output of the wind farm at time t. w The number of wind farms;

[0059] The constraints of the upper-level annual optimization configuration model during the optimization process include node wind power installation capacity constraints and total grid system installation capacity constraints.

[0060] A third aspect of this application provides a wind-storage joint optimization configuration device for a power distribution network, the device including a processor and a memory;

[0061] The memory is used to store program code and transmit the program code to the processor;

[0062] The processor is used to execute any one of the distribution network wind and storage joint optimization configuration methods according to the instructions in the program code.

[0063] The fourth aspect of this application provides a computer-readable storage medium for storing program code, which, when executed by a processor, implements the distribution network wind-storage joint optimization configuration method described in any of the first aspects.

[0064] As can be seen from the above technical solutions, this application has the following advantages:

[0065] This application provides a method for joint optimization and configuration of wind and energy storage in a distribution network, comprising: after determining the power quality indicators of each node in the distribution network system, obtaining the index values ​​of each power quality indicator according to the actual operating status of the distribution network system; obtaining the index weights of each power quality indicator, and calculating the comprehensive power quality assessment value of each node based on the index values ​​and index weights of each power quality indicator; constructing a wind and energy storage joint two-layer optimization model, which includes a lower-layer daily operation optimization scheduling model and an upper-layer annual optimization configuration model. The lower-layer daily operation optimization scheduling model takes the optimal comprehensive power quality result of each node in the distribution network system as the objective function, and the upper-layer annual optimization configuration model takes the annual total revenue and total wind curtailment rate of the power generation system as the objective functions; transferring the access location and capacity of the distribution network wind turbines and energy storage units generated in the upper layer to the lower layer, optimizing the lower-layer daily operation optimization scheduling model, updating the objective function value of the upper-layer annual optimization configuration model based on the obtained lower-layer daily optimization scheduling results, and then performing optimization to obtain the optimal configuration capacity.

[0066] In this application, multiple power quality indicators are used to comprehensively consider the impact of wind turbine grid connection on the distribution network in addressing power quality issues involved in the planning process of new energy grid connection. A comprehensive power quality assessment value is obtained, and a wind-storage joint two-layer planning model is constructed. The lower layer is a daily optimized scheduling operation layer with a short time scale to ensure that the power quality of the distribution network system is at a high level. The upper layer is an annual optimized configuration layer with a long time scale. By introducing a dynamic fluctuating electricity price model based on the comprehensive power quality assessment under the "quality-based electricity price" mechanism, the economic efficiency, reliability, and greenness of wind-storage grid connection in the distribution network system are guaranteed. The planning configuration, absorption operation, and safe operation level of new energy and energy storage are improved. This improves the technical problem of existing technologies that only use power quality issues as constraints to calculate the objective function of the model during the planning process, without further analyzing the impact of each new energy source access on the grid, and thus failing to obtain the optimal planning configuration scheme. Attached Figure Description

[0067] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0068] Figure 1 A flowchart illustrating a method for joint optimization of wind and energy storage configuration in a power distribution network, as provided in this application embodiment;

[0069] Figure 2 A schematic diagram of the structure of the wind-storage combined two-layer optimization model provided in the embodiments of this application;

[0070] Figure 3 This is a schematic diagram of a wind-storage joint optimization configuration device for a power distribution network, provided as an embodiment of this application. Detailed Implementation

[0071] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0072] For easier understanding, please refer to Figure 1 This application provides a method for joint optimization configuration of wind and energy storage in a distribution network, including:

[0073] Step 101: After determining the power quality indicators of each node in the distribution network system, obtain the index values ​​of each power quality indicator based on the actual operating status of the distribution network system.

[0074] This application selects power quality indicators in the context of wind turbine grid connection. In actual operation, under non-light load conditions, distributed generation (DG) configuration can improve voltage deviation of the grid connection lines and enhance power quality. However, under light load conditions, it easily leads to voltage rise at the grid connection point, increased voltage deviation, and reduced grid reliability. Therefore, it is necessary to examine the voltage deviation at the DG grid connection point. Simultaneously, due to the volatility of wind resources and the inherent characteristics of wind turbines, harmonic problems caused by distributed wind power are one of the main negative impacts on grid power quality. Secondly, due to some inherent characteristics of wind turbines, such as wind shear, tower shadow effect, blade gravity error, and yaw error, voltage flicker is a major power quality problem caused by wind power generation. New energy grid connection planning is a long-term decision-making process. Voltage fluctuations and voltage sags occur over short periods, and models are prone to significant errors over long time scales. Therefore, this application selects voltage deviation, voltage flicker, and voltage harmonics as power quality indicators.

[0075] The formula for calculating voltage deviation is:

[0076]

[0077] In the formula, U is the voltage deviation at node i. i,oc Let U be the voltage magnitude at node i. i,n Let be the nominal voltage value of node i. The voltage magnitude of each node can be obtained from the power flow calculation results of the distribution network system.

[0078] Since this application primarily focuses on long-term grid connection planning, voltage flicker is evaluated using only long-term flicker data. The long-term flicker value at the wind turbine grid connection point can be categorized into continuous operation and switching operation processes based on its cause. The long-term flicker value during continuous operation is significantly greater than that during switching operation. Therefore, this application's embodiments only select the long-term flicker value generated during continuous operation. The specific calculation formula is as follows:

[0079]

[0080] In the formula, P lt,c S represents the voltage flicker value during continuous operation of the wind turbine generator at the grid connection point. k The short-circuit capacity of the generator set's grid connection point. The impedance value of the grid equivalent impedance at the wind turbine connection point, v a The average annual wind speed, S is the flicker coefficient of the wind turbine. n,iN is the rated apparent power of wind turbine i. w,n This refers to the number of wind turbine units.

[0081] After obtaining the voltage flicker value at the grid connection point of the wind turbine, the voltage flicker transfer formula can be used to calculate the transfer to other nodes in the system. The voltage flicker transfer formula is as follows:

[0082] P ltA =T BA ·P ltB ;

[0083] In the formula, P is the transfer factor for the voltage flicker value at node B to be transferred to node A. ltA The voltage flicker value at node B is transmitted to node A, and the voltage flicker value caused at node A; P ltB Let S′ be the voltage flicker value at node B. scA S is the short-circuit capacity from node A to node B when node B is short-circuited. scA Let S′ be the short-circuit capacity of node A. scB This represents the short-circuit capacity of the flow from node B to node A when node A is short-circuited.

[0084] Voltage harmonics are measured using the total harmonic distortion (THD), calculated using the following formula:

[0085]

[0086] In the formula, THD u U is the total harmonic distortion of voltage. h Let U be the voltage of the h-th harmonic, and U1 be the fundamental voltage. This application's embodiment uses a linear analysis method based on the harmonic current injected by the wind turbine to determine the harmonic voltage at each node. Specifically, the power output of each wind farm is determined by the scheduling model, the fundamental power flow of the distribution network is calculated, thereby determining the voltage value at the grid connection point of each wind farm. Based on the wind farm output and the corresponding grid connection point voltage value, the current injected into the grid by the wind farm is determined, and the injected harmonic current I is determined. h Size, and then based on the network parameters Y of each harmonic. h Calculate the harmonic voltage U h , among which, I h =Y h U h .

[0087] The above formulas can be used to calculate the values ​​of various power quality indicators.

[0088] Step 102: Obtain the index weights of each power quality index, and calculate the comprehensive power quality assessment value of each node based on the index value and index weight of each power quality index.

[0089] S1021. Construct a judgment matrix based on the importance of each power quality indicator;

[0090] The importance of each power quality indicator is compared in order, and the corresponding scale t is determined using Table 1. i .

[0091] Table 1. Meaning of Scale

[0092]

[0093]

[0094] After determining the values ​​of each scale, calculate the values ​​of other elements according to the transitivity of the importance of each indicator and establish a judgment matrix A:

[0095]

[0096] Where n is the number of power quality indicators.

[0097] S1022. Calculate the subjective weights of each power quality index using the judgment matrix;

[0098] The subjective weights of each power quality indicator are calculated using judgment matrix A, i.e.:

[0099]

[0100] In the formula, s i Let a be the subjective weight of the i-th power quality indicator. ij To determine the element in the i-th row and j-th column of matrix A.

[0101] S1023. Normalize the values ​​of each power quality index, calculate the standard deviation of each normalized power quality index, and obtain the comparative strength of each power quality index.

[0102] The index values ​​x for each power quality indicator ij Normalization, i.e.

[0103] Calculate the standard deviation of each normalized power quality index to obtain the index contrast intensity CI = S for each power quality index. j S j Let be the standard deviation of the j-th power quality index; the index contrast intensity CI reflects the magnitude of the difference in the value of a certain index under different schemes. It is usually represented by the standard deviation of each index in the sample. The larger the standard deviation, the greater the difference in the value of the index, the greater the amount of information it contains, and the greater the weight value should be.

[0104] S1024. Calculate the index conflict based on the correlation coefficients between the normalized power quality indicators;

[0105] Conflict CT (Correlation Coefficient) reflects the strength of correlation between indicators and can be represented by the correlation coefficients of each indicator. The smaller the correlation coefficient between an indicator and other indicators, the greater the conflict between them, the more dissimilar information they contain, and the higher the weight they should be assigned. The formula for calculating Conflict CT is:

[0106]

[0107] In the formula, r ij Let be the correlation coefficient between indicators i and j. The specific calculation process is existing technology and will not be elaborated here. n is the number of indicators.

[0108] S1025. Calculate the objective weight of each power quality indicator based on the comparative strength and conflict of each indicator.

[0109] The amount of information contained in each power quality indicator is calculated based on the comparative strength and conflict of the indicators. Then, the objective weights are calculated. The objective weight of the j-th power quality index can be expressed as:

[0110] S1026. By combining the subjective and objective weights of each power quality indicator, the indicator weights of each power quality indicator are obtained.

[0111] By combining the subjective and objective weights of each power quality indicator, the indicator weights of each power quality indicator are obtained.

[0112] S1027. Calculate the comprehensive power quality assessment value of each node based on the index value and index weight of each power quality index.

[0113] The comprehensive power quality assessment value for each node is obtained by weighting and summing the values ​​of each power quality indicator with their corresponding weights. Assume the vector of measured power quality indicator values ​​is M = [m1, m2, ..., m...]. n The indicator weight vector is Q = [q1, q2, ..., q]. n Then, the comprehensive power quality assessment value S = M × Q T .

[0114] Step 103: Construct a wind-storage joint two-layer optimization model. The wind-storage joint two-layer optimization model includes a lower-layer daily operation optimization scheduling model and an upper-layer annual optimization configuration model. The lower-layer daily operation optimization scheduling model takes the optimal comprehensive result of the electricity sales quality of each node in the distribution network system as the objective function, while the upper-layer annual optimization configuration model takes the annual total revenue of the power generation system and the total wind curtailment rate as the objective functions.

[0115] A joint wind and energy storage two-layer optimization model is constructed, consisting of a lower-layer daily operation optimization scheduling model and an upper-layer annual optimization configuration model, such as... Figure 2 As shown. The lower layer is the daily optimized scheduling operation layer with a short time scale. The objective function is to optimize the comprehensive power quality of each node in the distribution network system. Since a smaller comprehensive evaluation value indicates a higher power quality level for that node, its objective function is:

[0116]

[0117] In the formula, f pq S represents the comprehensive power quality results for a typical day's electricity sales in the distribution network system. i,t Let P be the comprehensive power quality assessment value of node i at time t. load,i,t Let be the load power of node i at time t, and n be the number of nodes in the distribution network;

[0118] The decision variables for the lower-level daily operation optimization scheduling model are the power output of each wind farm, the power output of energy storage units, and the power output of the external power grid. The lower-level daily operation optimization scheduling model is a single-objective optimization problem, and the constraints of the lower-level daily operation optimization scheduling model in the optimization process are as follows:

[0119] System power flow constraints: In the formula, P i.t Q i.t U represents the active power and reactive power injected into node i at time t. i.t U j.t G represents the actual voltages of node i and node j at time t. ij B ij and θ ij Here, represents the conductance, susceptance, and power angle between node i and node j, respectively, and N is the total number of nodes in the system;

[0120] Energy storage energy constraints: In the formula, S es,i.t Let be the state of charge of the energy storage system at time t, and δ be the self-discharge rate of the energy storage system. η represents the charging power and discharging power of the energy storage system at time t, respectively; c η d These are the charging efficiency and discharging efficiency of the energy storage system, respectively; E s For the capacity of the energy storage system, S max S min These are the upper and lower limits of the state of charge of the energy storage system.

[0121] Energy storage charge and discharge constraints: In the formula, These are 0-1 variables, representing the working status of each energy storage station at each moment; These are the minimum and maximum charging power, respectively. These are the minimum and maximum values ​​of the discharge power, respectively.

[0122] Wind farm output constraints: In the formula, P i,t Let i be the power output of wind farm i at time t. Let be the upper limit of the power output of wind farm i at time t;

[0123] Transmission line power constraint: P i-j ≤P i-j,max In the formula, P i-j P i-j,max These represent the transmission line power and the upper limit of the transmission line power between node i and node j, respectively.

[0124] The upper layer is the annual optimization allocation layer on a long-term timescale. The objective function includes the total annual revenue of the power generation system and the total wind curtailment rate. The formula for calculating the total annual revenue of the power generation system is as follows:

[0125]

[0126] In the formula, f sum,in Let P be the total annual revenue of the power generation system, N be the number of nodes in the power generation system, and P be the total annual revenue of the power generation system. load,k,t,n Let n be the load power at time t on day k. The total annual operation and maintenance cost of all power supplies. The average annual installation cost of wind turbine units. p represents the average annual installation cost of the energy storage unit. k,t,n Let p be the fluctuating electricity price at node n at time t on day k. The tiered electricity price based on the power quality assessment results can be set into two parts: one part is the basic grid-connected electricity price, and the other part is determined by the power quality of the electricity provided by the power generator, i.e., p = p0 + Q·p1, where p is the electricity price of each power source, p0 is the basic electricity price, p1 is the power quality reward and penalty price, which can be set to 1 / 2 or 1 / 3 of the basic electricity price, and Q is the weighting coefficient of the fluctuating electricity price.

[0127] The formula for calculating the total wind curtailment rate is:

[0128]

[0129] In the formula, f wc The total wind curtailment rate, Let P be the maximum output of the nth wind farm at time t. wind,i,t N represents the actual power output of the wind farm at time t. w The number of wind farms;

[0130] The upper-level annual optimization configuration model is a multi-objective optimization problem, with decision variables being the installation location and capacity of wind turbine units and energy storage units. Constraints include:

[0131] Node wind power installation capacity constraint: P i min ≤P i ≤P i max In the formula, P i max P i min Set upper and lower limits for the installed capacity of wind turbine units at each node;

[0132] Because wind turbine output is greatly affected by natural conditions and exhibits significant randomness and fluctuation, the total installed capacity of the power grid must be limited. In the formula, This sets the upper and lower limits for the installed capacity of the system's wind turbine units.

[0133] Step 104: Transfer the access location and capacity of the wind turbine and energy storage units generated in the upper layer to the lower layer, optimize the daily operation optimization scheduling model of the lower layer, update the objective function value of the annual optimization configuration model of the upper layer based on the obtained daily optimization scheduling results of the lower layer, and then perform optimization to obtain the optimal configuration capacity.

[0134] After the upper layer generates the access locations and capacities of wind turbines and energy storage units in the distribution network, the configuration parameters (i.e., the access locations and capacities of wind turbines and energy storage units) are passed to the lower layer for optimized scheduling. The lower-level daily operation optimization scheduling model extracts typical daily operation scenarios using the Monte Carlo algorithm and performs optimization calculations based on the optimal comprehensive power quality results of each node in the distribution network system, thereby obtaining the output of each wind farm, the output of energy storage units, and the output of the external power grid. The upper-level annual optimization configuration model updates the objective function value with the lower-level daily optimization scheduling results to find the optimal configuration capacity. By optimizing and solving the wind-storage joint two-layer optimization model, the optimal planning scheme for the wind-storage joint distribution network that meets the constraints is obtained.

[0135] In this embodiment, multiple power quality indicators are used to comprehensively consider the impact of wind turbine grid connection on the distribution network when addressing power quality issues involved in the planning process of new energy grid connection. A comprehensive power quality assessment value is obtained, and a wind-storage joint two-layer planning model is constructed. The lower layer is a daily optimized scheduling operation layer with a short time scale to ensure that the power quality of the distribution network system is at a high level. The upper layer is an annual optimized configuration layer with a long time scale. By introducing a dynamic fluctuating electricity price model based on the comprehensive power quality assessment under the "quality-based electricity price" mechanism, the economy, reliability, and greenness of wind-storage grid connection in the distribution network system are guaranteed. This improves the planning configuration, absorption operation, and safe operation level of new energy and energy storage, thereby addressing the technical problem in existing technologies that only use power quality issues as constraints to calculate the objective function of the model during the planning process, without further analyzing the impact of each new energy source access on the grid, and thus failing to obtain the optimal planning configuration scheme.

[0136] The above is an embodiment of a distribution network wind and energy storage joint optimization configuration method provided by this application. The following is an embodiment of a distribution network wind and energy storage joint optimization configuration device provided by this application.

[0137] Please refer to Figure 3 This application provides a distribution network wind and energy storage joint optimization configuration device, comprising:

[0138] The acquisition unit is used to acquire the index values ​​of each power quality index based on the actual operating status of the power distribution network system after determining the power quality index of each node in the power distribution network system.

[0139] The calculation unit is used to obtain the index weights of each power quality index and calculate the comprehensive power quality assessment value of each node based on the index value and index weight of each power quality index.

[0140] The construction unit is used to build a wind-storage joint two-layer optimization model. The wind-storage joint two-layer optimization model includes a lower-level daily operation optimization scheduling model and an upper-level annual optimization configuration model. The lower-level daily operation optimization scheduling model takes the optimal comprehensive result of the power quality of each node in the distribution network system as the objective function, while the upper-level annual optimization configuration model takes the annual total revenue and total wind curtailment rate of the power generation system as the objective functions.

[0141] The optimization unit is used to transmit the access location and capacity of the wind turbine and energy storage units generated in the upper layer to the lower layer, optimize the daily operation optimization scheduling model of the lower layer, update the objective function value of the annual optimization configuration model of the upper layer based on the obtained daily optimization scheduling results of the lower layer, and then perform optimization to obtain the optimal configuration capacity.

[0142] As a further improvement, power quality indicators include voltage deviation, voltage flicker, and voltage harmonics;

[0143] The formula for calculating voltage deviation is:

[0144]

[0145] In the formula, U is the voltage deviation at node i. i,oc Let U be the voltage magnitude at node i. i,n Let be the nominal voltage value of node i;

[0146] The formula for calculating voltage flicker is:

[0147]

[0148] In the formula, P lt,c S represents the voltage flicker value during continuous operation of the wind turbine generator at the grid connection point. k The short-circuit capacity of the generator set's grid connection point. The impedance value of the grid equivalent impedance at the wind turbine connection point, v a The average annual wind speed, S is the flicker coefficient of the wind turbine. n,i N is the rated apparent power of wind turbine i. w,n This refers to the number of wind turbine units;

[0149] Voltage harmonics are measured using the total harmonic distortion (THD), which is calculated using the following formula:

[0150]

[0151] In the formula, THD u U is the total harmonic distortion of voltage. h U1 is the fundamental voltage, where U is the voltage of the h-th harmonic.

[0152] As a further improvement, the objective function of the lower-level daily operation optimization scheduling model is:

[0153]

[0154] In the formula, f pq S represents the comprehensive power quality results for a typical day's electricity sales in the distribution network system. i,t Let P be the comprehensive power quality assessment value of node i at time t. load,i,t Let be the load power of node i at time t, and n be the number of nodes in the distribution network;

[0155] The constraints of the lower-level daily operation optimization scheduling model during the optimization process include system power flow constraints, energy storage energy constraints, energy storage charging and discharging constraints, wind farm output constraints, and transmission line power constraints.

[0156] The objective function of the upper-level annual optimization configuration model is:

[0157]

[0158]

[0159] In the formula, f sum,in Let P be the total annual revenue of the power generation system, N be the number of nodes in the power generation system, and P be the total annual revenue of the power generation system. load,k,t,n Let n be the load power at time t on day k. The total annual operation and maintenance cost of all power supplies. The average annual installation cost of wind turbine units. p represents the average annual installation cost of the energy storage unit. k,t,n Let f be the fluctuating electricity price at node n at time t on day k. wc The total wind curtailment rate, Let P be the maximum output of the nth wind farm at time t. wind,i,tN represents the actual power output of the wind farm at time t. w The number of wind farms;

[0160] The constraints of the upper-level annual optimization configuration model during the optimization process include the node wind power installation capacity constraint and the total installed capacity constraint of the power grid system.

[0161] In this embodiment, multiple power quality indicators are used to comprehensively consider the impact of wind turbine grid connection on the distribution network when addressing power quality issues involved in the planning process of new energy grid connection. A comprehensive power quality assessment value is obtained, and a wind-storage joint two-layer planning model is constructed. The lower layer is a daily optimized scheduling operation layer with a short time scale to ensure that the power quality of the distribution network system is at a high level. The upper layer is an annual optimized configuration layer with a long time scale. By introducing a dynamic fluctuating electricity price model based on the comprehensive power quality assessment under the "quality-based electricity price" mechanism, the economy, reliability, and greenness of wind-storage grid connection in the distribution network system are guaranteed. This improves the planning configuration, absorption operation, and safe operation level of new energy and energy storage, thereby addressing the technical problem in existing technologies that only use power quality issues as constraints to calculate the objective function of the model during the planning process, without further analyzing the impact of each new energy source access on the grid, and thus failing to obtain the optimal planning configuration scheme.

[0162] This application embodiment also provides a distribution network wind and energy storage joint optimization configuration device, the device including a processor and a memory;

[0163] The memory is used to store program code and transfer the program code to the processor;

[0164] The processor is used to execute the distribution network wind and storage joint optimization configuration method in the foregoing method embodiments according to the instructions in the program code.

[0165] This application also provides a computer-readable storage medium for storing program code, which, when executed by a processor, implements the distribution network wind-storage joint optimization configuration method in the aforementioned method embodiments.

[0166] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the above-described apparatus and unit can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0167] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented, for example, in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0168] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0169] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0170] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0171] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0172] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for executing all or part of the steps of the methods described in the various embodiments of this application through a computer device (which may be a personal computer, server, or network device, etc.). The aforementioned storage medium includes: USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media capable of storing program code.

[0173] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for joint optimization of wind and energy storage configuration in a power distribution network, characterized in that, include: After determining the power quality indicators of each node in the distribution network system, the index values ​​of each power quality indicator are obtained based on the actual operating status of the distribution network system. Obtain the index weights of each power quality index, and calculate the comprehensive power quality assessment value of each node based on the index value of each power quality index and the index weights. A wind-storage joint two-layer optimization model is constructed, comprising a lower-layer daily operation optimization scheduling model and an upper-layer annual optimization configuration model. The lower-layer daily operation optimization scheduling model takes the optimal comprehensive power quality of electricity sold at each node of the distribution network system as its objective function, while the upper-layer annual optimization configuration model takes the total annual revenue and total wind curtailment rate of the power generation system as its objective functions. The objective function of the lower-layer daily operation optimization scheduling model is: ； In the formula, f pq S represents the comprehensive power quality results for a typical day's electricity sales in the distribution network system. i,t Let P be the comprehensive power quality assessment value of node i at time t. load,i,t Let be the load power of node i at time t, and n be the number of nodes in the distribution network; The constraints of the lower-level daily operation optimization scheduling model during the optimization process include system power flow constraints, energy storage energy constraints, energy storage charging and discharging constraints, wind farm output constraints, and transmission line power constraints. The connection locations and capacities of wind turbines and energy storage units generated in the upper-level distribution network are transmitted to the lower-level network. The daily operation optimization scheduling model of the lower-level network is optimized. After updating the objective function value of the annual optimization configuration model of the upper-level network based on the obtained daily optimization scheduling results of the lower-level network, the optimal configuration capacity is obtained.

2. The method for joint optimization of wind and energy storage configuration in a distribution network according to claim 1, characterized in that, The power quality indicators include voltage deviation, voltage flicker, and voltage harmonics. The formula for calculating the voltage deviation is: ； In the formula, U is the voltage deviation at node i. i,oc Let U be the voltage magnitude at node i. i,n Let be the nominal voltage value of node i; The formula for calculating voltage flicker is: ； In the formula, This represents the voltage flicker value during continuous operation of the wind turbine generator at the grid connection point. The short-circuit capacity of the generator set's grid connection point. The impedance value is the equivalent impedance of the power grid at the wind turbine connection point. The average annual wind speed, The flicker coefficient of the wind turbine generator. The rated apparent power of wind turbine i. This refers to the number of wind turbine units; The voltage harmonics are measured using the total harmonic distortion (THD), which is calculated using the following formula: ； In the formula, THD u U is the total harmonic distortion of voltage. h U1 is the fundamental voltage, where U is the voltage of the h-th harmonic.

3. The method for joint optimization configuration of wind and energy storage in a distribution network according to claim 1, characterized in that, The step of obtaining the weights of each of the power quality indicators includes: Construct a judgment matrix based on the importance of each of the power quality indicators; The subjective weights of each power quality indicator are calculated using the judgment matrix. The values ​​of each power quality index are normalized, and the standard deviation of each normalized power quality index is calculated to obtain the comparative strength of each power quality index. The conflict of indicators is calculated based on the correlation coefficients between the normalized power quality indicators. The objective weight of each power quality indicator is calculated based on the comparative strength and conflict of each indicator. By combining the subjective and objective weights of each power quality indicator, the indicator weights of each power quality indicator are obtained.

4. The method for joint optimization configuration of wind and energy storage in a distribution network according to claim 1, characterized in that, The objective function of the upper-level annual optimization configuration model is: ； ； In the formula, Let N be the total annual revenue of the power generation system, and N be the number of nodes in the power generation system. Let n be the load power at time t on day k. The total annual operation and maintenance cost of all power supplies. The average annual installation cost of wind turbine units. The average annual installation cost of energy storage units, The fluctuating electricity price at time t on day k. The total wind curtailment rate, Let n be the maximum output of the nth wind farm at time t. N represents the actual power output of the wind farm at time t. w The number of wind farms; The constraints of the upper-level annual optimization configuration model during the optimization process include node wind power installation capacity constraints and total grid system installation capacity constraints.

5. A wind-storage joint optimization configuration device for power distribution networks, characterized in that, include: The acquisition unit is used to acquire the index values ​​of each power quality index according to the actual operating status of the power distribution network system after determining the power quality index of each node in the power distribution network system. The calculation unit is used to obtain the index weights of each of the power quality indicators, and to calculate the comprehensive power quality evaluation value of each node based on the index value of each power quality indicator and the index weight. A construction unit is used to construct a wind-storage joint two-layer optimization model. This model includes a lower-layer daily operation optimization scheduling model and an upper-layer annual optimization configuration model. The lower-layer daily operation optimization scheduling model takes the optimal comprehensive power quality of electricity sold at each node of the distribution network system as its objective function. The upper-layer annual optimization configuration model takes the total annual revenue and total wind curtailment rate of the power generation system as its objective functions. The objective function of the lower-layer daily operation optimization scheduling model is: ； In the formula, f pq S represents the comprehensive power quality results for a typical day's electricity sales in the distribution network system. i,t Let P be the comprehensive power quality assessment value of node i at time t. load,i,t Let be the load power of node i at time t, and n be the number of nodes in the distribution network; The constraints of the lower-level daily operation optimization scheduling model during the optimization process include system power flow constraints, energy storage energy constraints, energy storage charging and discharging constraints, wind farm output constraints, and transmission line power constraints. The optimization unit is used to transmit the access location and capacity of the wind turbine and energy storage units generated in the upper layer to the lower layer, optimize the daily operation optimization scheduling model of the lower layer, update the objective function value of the annual optimization configuration model of the upper layer based on the obtained daily optimization scheduling results of the lower layer, and then perform optimization to obtain the optimal configuration capacity.

6. The distribution network wind and energy storage joint optimization configuration device according to claim 5, characterized in that, The power quality indicators include voltage deviation, voltage flicker, and voltage harmonics. The formula for calculating the voltage deviation is: ； In the formula, U is the voltage deviation at node i. i,oc Let U be the voltage magnitude at node i. i,n Let be the nominal voltage value of node i; The formula for calculating voltage flicker is: ； In the formula, This represents the voltage flicker value during continuous operation of the wind turbine generator at the grid connection point. The short-circuit capacity of the generator set's grid connection point. The impedance value is the equivalent impedance of the power grid at the wind turbine connection point. The average annual wind speed, The flicker coefficient of the wind turbine generator. The rated apparent power of wind turbine i. This refers to the number of wind turbine units; The voltage harmonics are measured using the total harmonic distortion (THD), which is calculated using the following formula: ； In the formula, THD u U is the total harmonic distortion of voltage. h U1 is the fundamental voltage, where U is the voltage of the h-th harmonic.

7. The distribution network wind-storage joint optimization configuration device according to claim 5, characterized in that, The objective function of the upper-level annual optimization configuration model is: ； ； In the formula, Let N be the total annual revenue of the power generation system, and N be the number of nodes in the power generation system. Let n be the load power at time t on day k. The total annual operation and maintenance cost of all power supplies. The average annual installation cost of wind turbine units. The average annual installation cost of energy storage units, The fluctuating electricity price at time t on day k. The total wind curtailment rate, Let n be the maximum output of the nth wind farm at time t. N represents the actual power output of the wind farm at time t. w The number of wind farms; The constraints of the upper-level annual optimization configuration model during the optimization process include node wind power installation capacity constraints and total grid system installation capacity constraints.

8. A wind-storage combined optimization configuration device for power distribution networks, characterized in that, The device includes a processor and a memory; The memory is used to store program code and transmit the program code to the processor; The processor is used to execute the wind-storage joint optimization configuration method for distribution networks according to any one of claims 1-4, based on the instructions in the program code.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium is used to store program code, which, when executed by a processor, implements the wind and energy storage joint optimization configuration method for power distribution networks as described in any one of claims 1-4.

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