Power guarantee planning method and device for UAV power inspection in multiple weather scenarios

Abstract

The present invention proposes a method and device for planning power supply security for UAV power inspections under multiple weather scenarios, aiming to address the uncertainty issues of UAV energy consumption and renewable energy generation under multiple weather scenarios in the prior art. By classifying weather scenarios into multiple types, the impact of weather scenarios on the energy consumption of the UAV power inspection system is quantified, and a system load model is established. A two-stage microgrid planning model is established using the output of power generation equipment units. A fuzzy set is constructed based on a multi-discrete scenario approach, and the fuzzy set is used to transform the microgrid planning model into a distributionally robust optimization model. The distributionally robust optimization model is solved using a differential evolution-series and constraint generation algorithm, significantly shortening the solution time while ensuring the quality of the solution. Simulation experiments verify that the method can obtain a planning scheme for a UAV power inspection power supply security system under weather uncertainty, while balancing economy, reliability, and efficiency.

Description

Technical Field

[0001] The present invention belongs to the technical field of energy system planning and design, and in particular to a method and device for planning power guarantee for UAV power inspections under multiple weather scenarios. Background Art

[0002] With the widespread use of drones in power inspections, their high efficiency and flexibility have significantly reduced manual inspection costs and improved operational efficiency. However, the continuous operation of drones and their supporting ground equipment (such as control stations, charging stations, and data processing terminals) places high demands on the stability of the power supply. Traditional energy security solutions, such as tethered air vehicles (TUAVs), are limited by cable length, and wireless charging technologies (such as laser charging) have problems such as low transmission efficiency, making them difficult to meet the inspection needs in remote areas or under complex weather conditions. Microgrid systems have become a viable solution to the energy security problem of drones because they can integrate renewable energy sources such as wind power and photovoltaics, achieving local material and energy access. However, existing technologies mostly focus on device-level energy management (such as hybrid architecture optimization) or energy supply optimization in a single scenario, lacking in-depth research on the overall planning and design of ground microgrid systems.

[0003] While existing technologies have proposed microgrids to provide power for drone systems, they fail to consider the impact of various weather scenarios' uncertainties on microgrid system planning. On the one hand, severe weather conditions such as strong winds, rain, and high temperatures can significantly increase drone hovering / flight energy consumption (e.g., increased air resistance and battery degradation), while also impacting the operating load of ground equipment. On the other hand, the output of renewable energy sources (wind power and photovoltaics) is also constrained by fluctuating meteorological conditions. Existing technologies typically employ stochastic programming (SP) or robust optimization (RO) to address uncertainty. However, SP relies on precise probability distributions (which are difficult to obtain in practice), while RO is overly conservative due to its consideration of worst-case scenarios, leading to increased configuration costs or insufficient reliability. Furthermore, most existing technologies only consider the uncertainty of wind and solar power output, ignoring the coupled impact of weather on both the source and load sides, making it difficult to achieve a balance between cost-effectiveness and robustness.

[0004] Microgrid planning involves mixed-integer nonlinear programming (MINLP) problems. Traditional column and constraint generation (CCG) algorithms suffer from computational inefficiency when dealing with large integer variables. While heuristic methods can improve search efficiency, existing research has not fully integrated them with the distributionally robust optimization (DRO) framework, resulting in insufficient solution speed and accuracy in complex scenarios.

[0005] In summary, existing technologies have the following deficiencies: (1) they fail to systematically consider the dual uncertainties of weather on UAV loads and renewable energy generation; (2) traditional SP / RO methods fail to balance economic efficiency and robustness; and (3) the inefficient solution algorithm restricts the practicality of large-scale configuration problems. This paper aims to address these technical deficiencies and fill the corresponding technical gap by designing a two-stage distributed robust optimization model and a hybrid solution algorithm, DECCG. Summary of the Invention

[0006] This paper addresses the significant impact of weather scenario uncertainty, the difficulty in balancing economic efficiency and reliability, and low solution efficiency in existing drone power inspection and power guarantee system planning. By proposing a method and device for planning drone power inspection and power guarantee under multiple weather scenarios, this method models the uncertainty of multiple weather scenarios and employs a differential evolution-column and constraint generation algorithm to achieve efficient planning and reliable operation of microgrid systems.

[0007] The present invention provides a method for planning power supply security for UAV power inspections in multiple weather scenarios, comprising the following steps:

[0008] Get weather data for multiple weather scenarios within a time period;

[0009] Based on weather data from multiple weather scenarios, the impact of multiple weather scenarios on the energy consumption of the UAV power inspection system is quantified using weights, and a system load model is established to calculate the total energy consumption of the UAV power inspection system;

[0010] Using the power generation model of the microgrid power generation equipment unit, the output of the power generation equipment unit is obtained;

[0011] A microgrid planning model is established using the output of power generation equipment units. The model includes a two-stage optimization process: investment cost planning in the first stage and operating cost optimization in the second stage. It also includes a balance constraint on the total energy consumption of the drone power inspection system.

[0012] Based on the multi-discrete scenario method, a fuzzy set is constructed and the microgrid planning model is transformed into a distributed robust optimization model using the fuzzy set. The distributed robust optimization model includes constraints on the two stages of the microgrid planning model.

[0013] The differential evolution-column and constraint generation algorithm is used to solve the distributed robust optimization model, and the planning results of the UAV power inspection power guarantee system under multiple weather scenarios are obtained.

[0014] On the other hand, the present invention provides a UAV power inspection and power guarantee planning device under multiple weather scenarios, comprising:

[0015] The first module is used to obtain weather data for multiple weather scenarios within a time period;

[0016] The second module is used to quantify the impact of multiple weather scenarios on the energy consumption of the UAV power inspection system based on weather data from multiple weather scenarios using weights, establish a system load model, and calculate the total energy consumption of the UAV power inspection system;

[0017] The third module is used to obtain the output of the power generation equipment unit by using the power generation model of the microgrid power generation equipment unit;

[0018] The fourth module is used to establish a microgrid planning model using the output of the power generation equipment units. The microgrid planning model includes a two-stage optimization: investment cost planning in the first stage and operating cost optimization in the second stage, and also includes a balance constraint on the total energy consumption of the drone power inspection system.

[0019] The fifth module is used to construct fuzzy sets based on a multi-discrete scenario method and use the fuzzy sets to transform the microgrid planning model into a distributed robust optimization model; the distributed robust optimization model includes constraints on the two stages of the microgrid planning model;

[0020] The sixth module is used to solve the distributed robust optimization model using differential evolution-column and constraint generation algorithm to obtain the planning results of the UAV power inspection power guarantee system under multiple weather scenarios.

[0021] Compared with the prior art, the technical effects of the present invention are as follows:

[0022] (1) Compared with the previous optimization under a single weather scenario, the present invention not only covers conventional weather, but also targets a variety of adverse weather scenarios. Through a dynamic weight adjustment mechanism, a system load model is established. The UAV power inspection and power guarantee system established in this way is more practical and more robust.

[0023] (2) For the two-stage microgrid planning model and the distributed robust optimization model obtained by fuzzy set transformation, the present invention adopts the DECCG algorithm proposed by fusing the differential evolution algorithm and the column and constraint generation algorithm to solve the distributed robust optimization model, which can effectively shorten the solution time of large-scale configuration problems, and the quality of the solution does not decrease due to the faster speed. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying any creative work.

[0025] Figure 1This is a flowchart of the steps of a method for planning power supply guarantee for UAV power inspection under multiple weather scenarios in the first embodiment of the present invention;

[0026] Figure 2 This is a flowchart of the algorithm framework of a method for planning power supply security for UAV power inspections under multiple weather scenarios in the first embodiment of the present invention;

[0027] Figure 3 This is a flowchart of the algorithm framework of a method for planning power supply security for UAV power inspections under multiple weather scenarios in the second embodiment of the present invention;

[0028] Figure 4 This is a graph of relevant weather data within one year in the simulation experiment of the present invention;

[0029] Figure 5 It is a cluster scatter plot of multiple weather scenes in the simulation experiment of the present invention;

[0030] Figure 6 This is a bar chart of the power generation and load of each power generation equipment unit in the simulation experiment of the present invention. DETAILED DESCRIPTION

[0031] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0032] In the first embodiment, as Figure 1 As shown, the present invention proposes a method for planning power supply guarantee for UAV power inspection under multiple weather scenarios, including:

[0033] The first step is to obtain weather data for multiple weather scenarios within a time period.

[0034] Since different weather scenarios have different effects on the energy consumption and performance of drones, especially in severe weather conditions, this effect is more obvious. Therefore, weather scenarios can be classified into adverse weather scenarios and normal weather scenarios. Adverse weather scenarios mainly refer to various special weather scenarios such as strong wind (SW), rainfall (RF), snowfall (SF), high temperature (HT) and low temperature (LT), while normal weather scenarios refer to other weather scenarios other than adverse weather scenarios. Assume that multiple weather scenarios include weather scenes, among which The first scene is a bad weather scene, Indicates a normal weather scenario.

[0035] In the second step, based on the weather data of multiple weather scenarios, the weights are used to quantify the impact of multiple weather scenarios on the energy consumption of the UAV power inspection system, and a system load model is established to calculate the total energy consumption of the UAV power inspection system.

[0036] The load of the UAV power inspection system consists of two parts. One part is the energy consumption of the UAV. , the other part is the energy consumption of UAV ground-related equipment As the weather scene changes, the energy consumption of the drone will also change. Therefore, the total energy consumption of the drone power inspection system in bad weather scenes and normal weather scenes is different. In normal weather scenes, the total energy consumption of the system is ; Considering multiple weather scenarios with adverse weather conditions, the total energy consumption of the system is To simplify the calculation, we can first calculate the load under normal weather conditions, and then add weights based on the weather type of a particular day. The impacts of different weather types on the same day can be superimposed (for example, the impacts of rainfall and strong winds can be cumulatively calculated). The specific calculation formula is as follows:

[0037] (1)

[0038] (2)

[0039] in: is the total energy consumption of the UAV power inspection system under multiple weather scenarios considering adverse weather scenarios; is the total energy consumption of the UAV power inspection system under normal weather conditions, is the time step, is the weight of the impact of each adverse weather scenario on energy consumption, ; is the total number of weather scenarios, and the weight depends on the meteorological factors in the corresponding weather scenario. For example, the weight of the impact of rainy weather depends on the precipitation, while the weight of the impact of strong wind weather depends on the wind speed, etc. =1, indicating that the weighted impact base of the normal weather scenario is 1; in fact, formula (2) includes the energy consumption of formula (1). When the impact weight values ​​of all adverse weather scenarios in formula (2) are 0, it is converted into the total energy consumption formula (1) of the system under normal weather scenarios.

[0040] Furthermore, in formula (1), the energy consumption of the drone is It is given by:

[0041] (3)

[0042] In the above formula, is the drone's hovering energy consumption, is the energy consumption of direct flight of UAV; let the number of UAVs in the system be , the UAV's shape drag power (the propulsion power required for the UAV to overcome its shape drag) is , the blade induced power is , direct flight propulsion power is ,use Indicates the The drones are performing the entire mission The hovering duration within the time period, Indicates the The direct flight time of a drone is:

[0043] (4)

[0044] (5)

[0045] Since the energy consumption of UAV is highly correlated with the energy consumption of UAV ground-related equipment, in order to simplify the calculation, it is assumed that the energy consumption of ground equipment is linearly related to the energy consumption of UAV. The specific value of can be given by the following formula:

[0046] , (6)

[0047] ;

[0048] In the above formula, Represents the proportional coefficient to the energy consumption of the UAV, , , , They represent the proportional coefficients of the energy consumption of the drone charging station, drone control center, drone monitoring equipment, and data processing device to the drone energy consumption.

[0049] The weights of the aforementioned adverse weather scenarios ( ), can be dynamically set according to the different intervals of the values ​​of the relevant meteorological factors. For example, in a feasible dynamic setting method, for the rainy weather scene, it can be set: when the precipitation is less than the lower limit of the set interval, the weight value is 0; when the precipitation falls within the interval set by the upper and lower limits, the weight value is set. ; When the precipitation is greater than or equal to the upper limit of the set interval, set the weight to Other weather scenarios are set accordingly based on similar rules according to different meteorological factors. By establishing this dynamic weight setting mechanism, the impact weight of each weather type can be calculated, and the total energy consumption in that weather scenario can be obtained. This makes the resulting drone power inspection power guarantee system more in line with actual needs and more robust.

[0050] The third step is to use the power generation model of the microgrid power generation equipment unit to obtain the output of the power generation equipment unit; the power generation equipment unit includes at least a wind turbine, a photovoltaic, an energy storage and a gas turbine.

[0051] The wind turbine (WT) power generation model is as follows:

[0052] (7)

[0053] in, is the wind speed at the center of altitude, Indicates the output (output electric power) of the wind power generation equipment unit, is the air density, is the area swept by the fan rotor, is the fan rotor power coefficient, which is a function of the fan tip speed ratio and blade pitch angle, is the overall efficiency of the fan electrical components and gearbox, is the rated power of the fan, , and They are the cut-out wind speed, rated wind speed and cut-out wind speed of the fan respectively.

[0054] The photovoltaic (PV) power generation model is as follows:

[0055] (8)

[0056] (9)

[0057] in, Indicates the output of the photovoltaic power generation equipment unit, represents the solar irradiance, represents the area of ​​solar photovoltaic panels, represents the efficiency of the entire solar photovoltaic unit, is the temperature coefficient, usually negative, Indicates the current ambient temperature. is the reference temperature, usually set to , Photovoltaic power conversion efficiency at reference temperature.

[0058] The energy storage (ES) power generation model is as follows:

[0059] (10)

[0060] Among them, SOC is the state of charge, and Represents the current time step The charging and discharging power within and Represent the charging efficiency and discharging efficiency respectively, Indicates the maximum energy capacity of a single energy storage system battery pack.

[0061] The microturbine (MT) power generation model is as follows:

[0062] (11)

[0063] (12)

[0064] in, is the output of the MT power generation equipment unit, is the thermal efficiency of MT, which is related to the specific model. is the heat output of natural gas, which depends on the fuel flow consumed and fuel calorific value (Usually 9.78 ).

[0065] The fourth step is to establish a microgrid planning (DRO) model using the output of the power generation equipment unit.

[0066] According to the usage characteristics of the microgrid, the microgrid planning (DRO) model is divided into two stages. The first stage is the investment cost planning stage, which mainly plans the number of power generation equipment units in the microgrid over a long period of time; the second stage is the operating cost optimization stage, which mainly solves the optimization problem of daily scheduling costs.

[0067] The overall objective function of the microgrid planning model is given by:

[0068] (13)

[0069] in, is the total investment cost of power generation equipment, corresponding to the investment cost planning in the first phase; is the system operating cost, corresponding to the second stage of operating cost optimization. Suppose the set of all power generation equipment units in the microgrid is , For collection The A power generation equipment unit, , , , , ,but The specific calculation formula is as follows:

[0070] (14)

[0071] (15)

[0072] Formula (14) represents the calculation method of the total investment cost of power generation equipment, where the investment cost of each type of power generation equipment unit is All can be calculated using formula (15). In formula (15), For a single power generation unit The configuration cost, Equip power generation equipment in microgrids the number of for life cycle, is the discount rate, The time scale for planning and designing the microgrid can be calculated by formula (15). Power generation equipment configuration under time scale, taking into account wear, repair, maintenance, etc. investment costs.

[0073] The system operating cost can be calculated using the following formula:

[0074] (16)

[0075] (17)

[0076] Where, 、 and They represent the equipment operation cost, load unsatisfied penalty cost and wind and solar curtailment cost on a daily time scale, and the sum of the three is multiplied by That means in Total operating costs over time scale, It also includes the operating cost of each power generation equipment, and its specific calculation formula is shown in (17). Represents a single power generation equipment unit The operating cost per unit output power is Indicates the total operating time of the equipment in a day. Indicates that the device is The same goes for the effort of the moment. and The calculation method of is also shown in formula (17), represents the penalty cost per unit power, Indicates time( , in hours). Indicates the cost of curtailing wind and solar power per unit power, and Respectively expressed in The current situation of wind power generation equipment and photovoltaic power generation equipment being abandoned.

[0077] After the power generation equipment unit models of the system are established, the energy security mode of the microgrid for the drone inspection mission can be simulated, and then the established microgrid planning model can be evaluated to provide the microgrid optimization scheduling strategy under the conditions of different power supply priorities of each power generation equipment unit. Figure 2 As shown, first enter the number of drones , the number and configuration of power generation equipment units in each part of the microgrid, some related parameters and historical weather data. On this basis, the daily power generation of wind turbines and photovoltaic panels, as well as the total daily load demand, are calculated on a daily basis. Then, this embodiment performs daily scheduling by setting operation scheduling rules based on power supply priority, including: calculating the relationship between total load demand and supply; giving priority to the use of renewable energy (including wind power and photovoltaic power) for power supply, and determining the remaining power supply. , if this method meets the load demand, that is, , then the excess power generation is used to charge the energy storage system, and calculate , continue to judge the remaining power generation, if there is still , then the cost of curtailing wind and solar power is calculated and returned to the total cost; if this method cannot meet the load demand, that is, it does not meet , the energy storage system is discharged to provide power, and the operating costs of the energy storage system are calculated and included in the total cost. If the load demand can still be met after charging the energy storage, a micro gas turbine is used to generate electricity, and its operating costs are calculated and included in the total cost. In a drone power inspection system, the total cost to be calculated includes the microgrid planning and design costs, the operating costs of each component, the penalty costs for unmet load, and the costs of curtailed wind and solar power.

[0078] The microgrid planning model also includes a balance constraint on the total energy consumption of the drone power inspection system. Specifically, during the operation of the entire drone power inspection system, it is necessary to consider the power balance constraint (i.e., the power generation of the microgrid is equal to the system load demand), which is specifically given by the following formula:

[0079] , (18)

[0080] ;

[0081] in, express In power generation equipment units The value of Is the UAV power inspection system in The total energy consumption at the moment can be calculated using the total energy consumption formula of the system given by formula (2): Represents the reduction in renewable energy generation (renewable generation minus utilization).

[0082] The fifth step is to construct a fuzzy set based on a multi-discrete scenario approach and use it to transform the microgrid planning model into a distributionally robust optimization model. This distributionally robust optimization model includes constraints on the total energy consumption of the drone power inspection system and constraints on the two stages of the microgrid planning model.

[0083] First, a fuzzy set is constructed based on the multi-discrete scenario method, including:

[0084] (19)

[0085] in, represents the constructed fuzzy set, Represents the construction Weather scenes, Representative The probability of a weather scenario occurring, Indicates the The basic probability value of the weather scene probability, 、 are 1-norm and Probability limit range parameter under norm constraint.

[0086] Next, considering the uncertainty, the microgrid planning model is transformed into the following distributed robust optimization model:

[0087] (20)

[0088] (twenty one)

[0089] (twenty two)

[0090] (twenty three)

[0091] (twenty four)

[0092] In the above formula (20), It is the planning decision variable of the first stage, which represents the number of power generation equipment units equipped, which is determined by the previous The vector represented by are the operational decision variables for the second stage, including the power output of the gas turbine, the charge and discharge power of the energy storage system, the reduction in renewable energy power generation, and the load reduction (total energy consumption minus total power generation). represents the investment cost coefficient vector The transpose of (the same upper index symbol for a vector or matrix indicates the transpose); is the objective function of the first stage, i.e., planning cost, corresponding to formula (14); is the objective function of the second stage, i.e., operating cost, corresponding to formula (16), is the operating cost coefficient vector, It is The uncertainty parameters under each scenario are represents the feasible domain of the decision variables in the second stage. Formula (21) represents the planning constraints in the first stage, namely the capacity constraints of power generation equipment, is the coefficient matrix corresponding to the constraints of the first stage; Formula (22) represents the inequality constraints of the second stage, is the coefficient matrix corresponding to the inequality constraint of the second stage; Formula (23) expresses the equality constraint condition of the second stage, is the coefficient matrix corresponding to the equality constraint of the second stage; Formula (24) represents the decision variables and The coupling constraints, 、 is the coupling constraint matrix; 、 、 and g are the constant terms in the corresponding constraints.

[0093] In the sixth step, the distributed robust optimization model is solved based on the differential evolution-column and constraint generation (DECCG) algorithm to obtain the planning results of the UAV power inspection power guarantee system under multiple weather scenarios considering the uncertainty of weather scenarios.

[0094] By integrating the differential evolution (DE) algorithm with the column and constraint generation (CCG) algorithm, this paper proposes a new differential evolution-column and constraint generation (DECCG) algorithm. This algorithm can effectively shorten the solution time for large-scale configuration problems similar to the aforementioned distributed robust optimization model, and the quality of the solution does not decrease due to the increased speed. Specifically, the original problem in formula (20) is first divided into a main problem (MP) and a subproblem (SP). The original min-max-min optimization problem (including the objective function optimization problem of the first-stage investment cost and the objective function optimization problem of the second-stage operating cost) is decoupled. The improved DECCG algorithm is then used to solve the MP and SP. The specific algorithm flow is shown in the pseudo code in Listing 1 below:

[0095] Table 1: Algorithms for solving MP and SP based on the DECCG method

[0096]

[0097] In the above algorithm, the input is the main problem and sub-problems and related parameters, and the output is the decision variables of the first stage , which is the capacity configuration plan of the microgrid. First, in step 1 of the algorithm, set the parameters of differential evolution, including the population size , the maximum number of generations of evolution , the variation factor and crossover rate , set the upper bound of column and constraint generation ,Down and the interval width constraint thresholds of the upper and lower bounds , used to determine the boundary convergence conditions of the main loop, and the initial population randomly generated within the boundary range determined by the upper and lower bounds Then, in steps 3 to 13 of the algorithm, the differential evolution algorithm is used to solve the integer programming problem of the first stage, including: in each round of evolution ( to ), by traversing the population, the decision variables Apply mutation operators and mutation factors Perform mutation operation, applying crossover rate The crossover operator performs the crossover operation; in the selection operation, by solving the problem , for the candidate solution , get the target value ,like , then Assign to Then in steps 14 to 15 of the algorithm, the best solution in the first stage population is Return to the second stage and use Update the Nether ,in, is the first-stage cost estimation coefficient, is the lower bound estimate of the cost of the second stage in the current problem MP. In steps 16-17 of the algorithm, the second stage is solved using a commercial solver, including: Solving the problem , get the probability value in the worst case and the objective function value ,use Update upper bound In steps 18 to 20 of the algorithm, a convergence check is performed. If If it holds, the algorithm is terminated and new scenarios and constraints are added in steps 21-22: Update the worst-case scenario , and add new variables and related constraints to the problem MP, and update the iteration counter Repeat this cycle (the main loop of algorithm steps 3 to 22) until a solution that meets the termination condition is found. After solving the problem, the loop is exited and the optimal solution for microgrid planning and design is obtained, which is used as the result of the power guarantee planning for drone power inspection under multiple weather scenarios.

[0098] In the second embodiment of the present invention, the UAV is a quadcopter model, and the adverse weather scene is selected from five special weather types: strong wind (SW), rain (RF), snowfall (SF), high temperature (HT) and low temperature (LT). That is, the multi-weather scene includes a total of weather scenes, among which The scenarios correspond to adverse weather scenarios: SW, RF, SF, HT and LT. Indicates a normal weather scenario.

[0099] Then, in the second step, the total energy consumption formula (2) of the UAV power inspection system can be expressed as:

[0100] ; (25)

[0101] in: is the time step, is the weight of the impact of rainfall, which depends on the amount of precipitation , is the weight of the snowfall effect, which depends on the snow intensity , and are the weights of high and low temperature effects, depending on the temperature , is the weight of the impact of strong wind weather, which depends on the wind speed The rules for setting each weight are as follows:

[0102] Table 2: Weighting rules table

[0103]

[0104] As shown in Table 2, each weight can be calculated according to the numerical range of its related meteorological factors, where: 、 、 、 、 They represent the lower limit of precipitation, the lower limit of snowfall, the lower limit of high temperature weather, the lower limit of low temperature weather and the lower limit of wind speed respectively. When the corresponding variable values ​​of all meteorological influencing factors on a certain day are less than the lower limit, the weight can be considered to be 0, that is, the weather on that day is a normal weather scene. 、 、 、 、 They represent the upper limit of the corresponding influencing factors. When the corresponding variable value is greater than the upper limit, the weight is When the value is between the upper and lower limits, the weight is By using the rules of this dynamic weight adjustment mechanism, the impact weight of each weather type can be calculated, and then the total energy consumption in that weather scenario can be obtained, making the drone power inspection power guarantee system obtained in this way more in line with actual needs and more robust.

[0105] (25) In formula, the total energy consumption of the system under normal weather conditions is Still given by the following formula:

[0106] ; (26)

[0107] Among them, the energy consumption of drones According to the above formulas (3)-(5), the energy consumption of the UAV and the energy consumption of the ground-related equipment are given as follows: Given by the above formula (6), and the UAV type resistance power in formulas (3)-(5) , blade induced power , direct flight propulsion power , respectively given by the following formula:

[0108] ; (27)

[0109] ; (28)

[0110] ; (29)

[0111] In the above formula, , represents the average induced velocity; , represents the tip linear velocity of the rotor angle; It represents the type resistance coefficient, represents the air density, Indicates the solidity of the propeller disc, which is related to the number of blades, blade chord length and rotor radius of the drone related, It represents the area formed when the rotor rotates. is the blade angular velocity, represents the induced power increment correction factor, is the weight of the drone, is the fuselage drag ratio, It is the direct flight speed of the UAV and the decisive variable affecting the direct flight power. According to the first to sixth steps of the above method, the power guarantee planning of the UAV power inspection is completed. The corresponding algorithm framework process is as follows: Figure 3 shown.

[0112] Specifically for the power supply planning method for drone power inspection under multiple weather scenarios proposed in the second embodiment, the present invention verifies the effectiveness of the method proposed by the present invention by simulating and solving actual data. The relevant weather data of a certain place (35.8°N, 90.6°E) over a period of time is selected, specifically the original data from 0:00 on January 1, 2022 to 0:00 on December 31, 2022, with a resolution of one hour. The specific data is as follows: Figure 4 The multi-scene classification method proposed in this invention is used to cluster the weather scenes into Figure 5 As shown in the figure, all historical scenarios are first reduced in dimension using principal component analysis, and then four types of weather day scenarios are obtained using the clustering method. The horizontal axis represents principal component one, and the vertical axis represents principal component two. The distributed robust optimization method and the improved DECCG algorithm proposed in the present invention are used to plan and design the UAV power guarantee system under multiple weather scenarios, and the results are shown in Table 3. It can be seen from Table 3 that the two-stage microgrid planning (DRO) model proposed in the present invention and the microgrid planning scheme obtained by solution are reasonable, the capacity configuration is also within a reasonable range, the utilization rate of renewable energy is high, and the penalty cost of unsatisfied load is almost 0. In important scenarios such as power inspections, it is necessary to ensure that the entire load system has reliable energy security in order to complete the inspection task and avoid significant losses.

[0113] Table 3 Solution results of the two-stage microgrid planning model

[0114]

[0115] In order to deeply analyze the daily optimization scheduling situation, the optimization scheduling results of a typical day are selected for analysis. Figure 6 Analysis of the power generation and load histograms for each power generation unit shows that ES and MT are the primary sources of power during periods of low renewable energy generation, while WT and PV are the primary sources of power during other periods. Excess renewable energy can be stored in the ES for subsequent discharge. The relationship between load and power generation on that day demonstrates that this microgrid system fully meets the load requirements of the drone inspection system, ensuring the successful completion of the inspection mission.

[0116] In a third embodiment, the present invention provides a device for planning power supply security for UAV power inspections in multiple weather scenarios, comprising:

[0117] The first module is used to obtain weather data for multiple weather scenarios within a time period;

[0118] The second module is used to quantify the impact of multiple weather scenarios on the energy consumption of the UAV power inspection system based on weather data from multiple weather scenarios using weights, establish a system load model, and calculate the total energy consumption of the UAV power inspection system;

[0119] The third module is used to obtain the output of the power generation equipment unit by using the power generation model of the microgrid power generation equipment unit;

[0120] The fourth module is used to establish a microgrid planning model using the output of the power generation equipment units. The microgrid planning model includes a two-stage optimization: investment cost planning in the first stage and operating cost optimization in the second stage, and also includes a balance constraint on the total energy consumption of the drone power inspection system.

[0121] The fifth module is used to construct fuzzy sets based on a multi-discrete scenario method and use the fuzzy sets to transform the microgrid planning model into a distributed robust optimization model; the distributed robust optimization model includes constraints on the two stages of the microgrid planning model;

[0122] The sixth module is used to solve the distributed robust optimization model using differential evolution-column and constraint generation algorithm to obtain the planning results of the UAV power inspection power guarantee system under multiple weather scenarios.

[0123] On the other hand, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the method for planning power supply security for UAV power inspections under multiple weather scenarios provided in any of the above embodiments are implemented. The computer device may be a server. The computer device comprises a processor, a memory, a network interface, and a database connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device comprises a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store sample data. The network interface of the computer device is used to communicate with an external terminal via a network connection.

[0124] On the other hand, the present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the method for planning power supply security for UAV power inspections in multiple weather scenarios provided in any of the above embodiments are implemented.

[0125] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0126] Matters not covered by the present invention are known technologies.

[0127] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0128] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention. It should be noted that a person skilled in the art may make various modifications and improvements without departing from the spirit of the present application, and such modifications and improvements are all within the scope of protection of the present application.

[0129] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that the present invention is susceptible to various modifications and variations. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A method for planning power supply security for UAV power inspections under multiple weather scenarios, characterized by: include: Get weather data for multiple weather scenarios within a time period; Based on weather data from multiple weather scenarios, the impact of multiple weather scenarios on the energy consumption of the UAV power inspection system is quantified using weights, and a system load model is established to calculate the total energy consumption of the UAV power inspection system; Using the power generation model of the microgrid power generation equipment unit, the output of the power generation equipment unit is obtained; A microgrid planning model is established using the output of power generation equipment units. The model includes a two-stage optimization process: investment cost planning in the first stage and operating cost optimization in the second stage. It also includes a balance constraint on the total energy consumption of the drone power inspection system. Based on the multi-discrete scenario method, a fuzzy set is constructed and the microgrid planning model is transformed into a distributed robust optimization model using the fuzzy set. The distributed robust optimization model includes constraints on the two stages of the microgrid planning model. The differential evolution-column and constraint generation algorithm is used to solve the distributed robust optimization model, and the planning results of the power guarantee system for UAV power inspection under multiple weather scenarios are obtained. The differential evolution-column and constraint generation algorithm solves the distributed robust optimization model, including: Decomposing the distributed robust optimization model into a main problem and sub-problems; Decouple the objective function optimization problem of the first-stage investment cost and the objective function optimization problem of the second-stage operating cost in the distributed blue stick optimization model; The differential evolution-column and constraint generation algorithm is used to solve the main problem and sub-problems respectively. The specific algorithm process includes: Step 1.1 Input the main problem, sub-problems and related parameters, including: Set the parameters for differential evolution, including population size , the maximum number of generations of evolution , the variation factor and crossover rate ; Set upper bounds for column and constraint generation ,Down and the interval width constraint thresholds of the upper and lower bounds ; The initial population is randomly generated within the boundaries determined by the upper and lower bounds ; Step 1.2 Use the differential evolution algorithm to solve the first stage integer programming problem, including: In each round of evolution, by traversing the population, the decision variables Apply mutation operators and mutation factors Perform mutation operations; The crossover rate is The crossover operator performs the crossover operation; In the selection operation, by solving the problem , for the candidate solution , get the target value ; like , then Assign to ; Step 1.3 The best solution in the first stage population Return to the second stage and use Update the Nether ,in, is the first-stage cost estimation coefficient, is the lower bound estimate of the second-stage cost in the current problem MP; Step 1.4: Solve the second phase using a commercial solver, including: fixed Solving the problem , get the probability value in the worst case and the objective function value ; use Update upper bound ; Step 1.5: Check convergence. If If it holds, the algorithm is terminated; Step 1.6: Add new scenarios and constraints: Update the worst-case scenario , and add new variables and related constraints to the problem MP, and update the iteration counter ; Step 1.7 Repeat steps 1.2 to 1.6 until the main loop termination condition is reached. , jump out of the main loop and obtain the optimal solution for microgrid planning and design as the result of power guarantee planning for UAV power inspection under multiple weather scenarios.

2. The method for planning power supply guarantee for UAV power inspection under multiple weather scenarios according to claim 1 is characterized in that: Weather scenes are classified into adverse weather scenes and normal weather scenes.

3. The method for planning power supply guarantee for UAV power inspection under multiple weather scenarios according to claim 2 is characterized in that: The system load model is given by the following formula: ， ； in, is the total energy consumption of the UAV power inspection system under multiple weather scenarios considering adverse weather scenarios; is the total energy consumption of the UAV power inspection system under normal weather conditions, is the time step, is the energy consumption of the drone, is the energy consumption of the UAV’s ground-related equipment; is the weight of the impact of each adverse weather scenario on energy consumption, ; is the total number of weather scenes, weight Depends on the meteorological factors in the corresponding weather scenario; The energy consumption of the UAV , the energy consumption of the drone is Energy consumption of direct flights with drones Sum obtained; Energy consumption of the UAV ground-related equipment ,in, Represents the proportional coefficient to the energy consumption of the UAV, , , , They represent the proportional coefficients of the energy consumption of the drone charging station, drone control center, drone monitoring equipment, and data processing device to the drone energy consumption.

4. The method for planning power supply guarantee for UAV power inspection under multiple weather scenarios according to claim 3 is characterized in that: The power generation equipment unit includes at least a wind turbine, photovoltaic, energy storage and micro gas turbine, which are represented by WT, PV, ES and MT respectively, and Represents a collection of power generation equipment units; The method of using the power generation model of the microgrid power generation equipment unit to obtain the output of the power generation equipment unit includes: Using wind turbine power generation model to obtain the output of WT equipment unit , It is about the wind speed at the center of the altitude and time function; Use photovoltaic power generation models to obtain the output of PV power generation equipment units ; Use the energy storage power generation model to obtain the output of the ES power generation equipment unit , Indicates the state of charge of the energy storage, which is about time function; Using the micro gas turbine power generation model to obtain the output of the MT power generation equipment unit , It's about time function.

5. The method for planning power supply guarantee for UAV power inspection under multiple weather scenarios according to claim 4 is characterized in that: The overall objective function of the microgrid planning model is given by: ； in, is the total investment cost of power generation equipment, corresponding to the first phase of investment cost planning, It is the system operating cost, corresponding to the second stage of operating cost optimization, specifically: It is given by: ； ； In the above formula, Representing a collection The A power generation equipment unit, ,include: , , , ; For a single power generation unit The configuration cost, Equip power generation equipment in microgrids the number of for life cycle, is the discount rate, The planning and design timescale of the microgrid; It is given by: ； In the above formula, 、 and They represent the equipment operating cost, load unsatisfied penalty cost, and wind and solar curtailment cost on a daily time scale, respectively, and are given by the following formula: ， ， ； in, Represents a single power generation equipment unit The operating cost per unit output power is Indicates the total operating time of the equipment in a day. Indicates that the device is The effort of every moment; represents the penalty cost per unit power, Indicates within one day The load reduction at the time, It is a 24-hour system, and the unit is hour; Indicates the cost of curtailing wind and solar power per unit power, and Respectively expressed in The current situation of wind power generation equipment and photovoltaic power generation equipment being abandoned; The microgrid planning model also includes a balance constraint on the total energy consumption of the UAV power inspection system, which is given by the following formula: ； in, express In power generation equipment units The value of Is the UAV power inspection system in The total energy consumption at the moment is calculated by the total energy consumption formula of the UAV power inspection system under multiple weather scenarios considering adverse weather scenarios. Represents the current time step in the energy storage power generation model The charging power inside Represents the current time step in the energy storage power generation model The discharge power inside, It represents the reduction in renewable energy generation, which is equal to the amount of renewable electricity generated minus the amount utilized.

6. The method for planning power supply guarantee for UAV power inspection under multiple weather scenarios according to claim 5 is characterized in that: The multi-discrete scenario method is used to construct a fuzzy set, and the fuzzy set is used to transform the microgrid planning model into a distributed robust optimization model, including: Based on the multi-discrete scenario method, the following fuzzy sets are constructed: ； in, represents the constructed fuzzy set, Indicates the Weather scenes, is the total number of weather scenes, Indicates the The probability of a weather scenario occurring, Indicates the The basic probability value of each weather scenario, 、 are 1-norm and Probability limit range parameters under norm constraints; Using the above fuzzy sets, the microgrid planning model is transformed into the following distributed robust optimization model: in, is the planning decision variable of the first stage, which represents the number of power generation equipment units. The operational decision variables in the second stage include the power generation output of the gas turbine, the charge and discharge power of the energy storage system, the power generation reduction of renewable energy, and the load reduction. The renewable energy includes wind turbines and photovoltaics. The load reduction is equal to the total energy consumption minus the total power generation. represents the transpose of a vector or matrix, represents the investment cost coefficient vector, is the objective function corresponding to the investment cost of the first stage; is the objective function corresponding to the second stage operating cost, is the operating cost coefficient vector, It is The uncertainty parameters under each scenario are represents the feasible region of the second-stage decision variables; represents the planning constraints of the first stage, corresponding to the capacity constraints of the power generation equipment units, is the coefficient matrix corresponding to the constraints in the first stage; , represents the inequality constraints of the second stage, is the coefficient matrix corresponding to the second-stage inequality constraints; , represents the equality constraint of the second stage, is the coefficient matrix corresponding to the second-stage equality constraints; , represents the decision variable and The coupling constraints, is the coupling constraint matrix; and g are the constant terms in the corresponding constraints.

7. The method for planning power supply guarantee for UAV power inspection under multiple weather scenarios according to claim 6 is characterized in that: The UAV is a quadrotor model, and the adverse weather scenarios include five types of weather: rain, snow, high temperature, low temperature and strong wind; In the system load model, the total energy consumption of the UAV power inspection system under multiple weather scenarios considering adverse weather scenarios is: ； in, is the total energy consumption of the UAV power inspection system under normal weather conditions, is the time step, 、 、 、 and are the weights of the impact of rain, snow, high temperature, low temperature and strong wind weather scenarios; set up and is the preset weight value, Depends on precipitation , is given by: ； in, and represent the lower and upper limits of precipitation respectively; described Depends on snow intensity , is given by: ； in, and They represent the lower and upper limits of snowfall respectively; described Depends on temperature , is given by: ； in, and Respectively represent the lower and upper limits of high temperature weather temperature; described Depends on temperature , is given by: ； in, and Respectively represent the lower and upper limits of low temperature weather temperatures; Depends on wind speed , is given by: ； in, and represent the lower and upper limits of precipitation, respectively.

8. A power supply planning device for UAV power inspection in multiple weather scenarios, characterized by: include: The first module is used to obtain weather data for multiple weather scenarios within a time period; The second module is used to quantify the impact of multiple weather scenarios on the energy consumption of the UAV power inspection system based on weather data from multiple weather scenarios using weights, establish a system load model, and calculate the total energy consumption of the UAV power inspection system; The third module is used to obtain the output of the power generation equipment unit by using the power generation model of the microgrid power generation equipment unit; The fourth module is used to establish a microgrid planning model using the output of the power generation equipment units. The microgrid planning model includes a two-stage optimization: investment cost planning in the first stage and operating cost optimization in the second stage, and also includes a balance constraint on the total energy consumption of the drone power inspection system. The fifth module is used to construct fuzzy sets based on a multi-discrete scenario method and use the fuzzy sets to transform the microgrid planning model into a distributed robust optimization model; the distributed robust optimization model includes constraints on the two stages of the microgrid planning model; The sixth module is used to solve the distributed robust optimization model using the differential evolution-column and constraint generation algorithm to obtain the planning results of the UAV power inspection power guarantee system under multiple weather scenarios; the differential evolution-column and constraint generation algorithm for solving the distributed robust optimization model includes the following submodules: Submodule 1 is used to decompose the distributed robust optimization model into a main problem and subproblems; Submodule 2 is used to decouple the objective function optimization problem of the first-stage investment cost and the objective function optimization problem of the second-stage operating cost in the distributed blue stick optimization model; Submodule 3 is used to solve the main problem and subproblems respectively using differential evolution-column and constraint generation algorithms. The specific algorithm flow includes the following submodules: Submodule 3.1 is used to input the main question, sub-questions and related parameters, including: Set the parameters for differential evolution, including population size , the maximum number of generations of evolution , the variation factor and crossover rate ; Set upper bounds for column and constraint generation ,Down and the interval width constraint thresholds of the upper and lower bounds ; The initial population is randomly generated within the boundaries determined by the upper and lower bounds ; Submodule 3.2 uses the differential evolution algorithm to solve the integer programming problem in the first stage, including: In each round of evolution, by traversing the population, the decision variables Apply mutation operators and mutation factors Perform mutation operations; The crossover rate is The crossover operator performs the crossover operation; In the selection operation, by solving the problem , for the candidate solution , get the target value ; like , then Assign to ; Submodule 3.3 is used to convert the best solution in the first stage population into Return to the second stage and use Update the Nether ,in, is the first-stage cost estimation coefficient, is the lower bound estimate of the second-stage cost in the current problem MP; Submodule 3.4 solves the second stage using a commercial solver, including: fixed Solving the problem , get the probability value in the worst case and the objective function value ; use Update upper bound ; Submodule 3.5 is used to perform convergence check. If If it holds, the algorithm is terminated; Submodule 3.6 is used to perform the addition of new scenarios and constraints: update the worst-case scenario , and add new variables and related constraints to the problem MP, and update the iteration counter ; Submodule 3.7 is used to repeat the steps of submodule 3.2 to submodule 3.6 until the main loop termination condition is reached , jump out of the main loop and obtain the optimal solution for microgrid planning and design as the result of power guarantee planning for UAV power inspection under multiple weather scenarios.

Images