Capability assessment method and system based on new energy consumption

Abstract

The invention relates to the technical field of power system operation and planning, and discloses a capability assessment method and system based on new energy consumption, and the method comprises the steps: obtaining wind power data, photovoltaic data, load data and actual consumption data; wind speed simulation is carried out to carry out wind power plant evaluation, and the maximum wind power output and the minimum wind power output are obtained; illumination simulation is carried out to carry out photovoltaic field evaluation, and photovoltaic maximum output and photovoltaic minimum output are obtained; calculating a consumption capability coefficient based on a preset strategy space model; carrying out consumption capability calculation on the consumption capability coefficient to obtain the maximum consumption capability; on the basis of a preset consumption potential model, performing calculation to obtain consumption potential; searching an optimal configuration point in the strategy space to obtain an optimal absorption capability; and performing verification evaluation to obtain an evaluation coefficient so as to evaluate the new energy consumption capability. According to the method, the absorption capability of new energy is improved by considering the wind and light output characteristics.

Description

Technical Field

[0001] The present invention relates to the technical field of power system operation and planning, and particularly to a method and system for evaluating the capacity of new energy consumption. Background Art

[0002] Currently, in the context of the continuous increase in the penetration rate of new energy, the problems of wind curtailment in wind power and "valley" in photovoltaic power seriously restrict the healthy development of new energy. In order to ensure the power supply reliability and voltage quality of the power grid, it is necessary to effectively regulate the output of new energy, which poses a greater challenge to the evaluation of new energy consumption capacity.

[0003] However, there is little research on the evaluation of the consumption capacity of wind power. Currently, there is no method for evaluating the consumption capacity by comprehensively considering the output characteristics of wind and light according to different operating modes of the power grid in the time domain.

[0004] Existing methods often consider wind power or photovoltaic power alone. The single evaluation method of consumption capacity cannot adapt to the multi-energy system dominated by new energy, resulting in a low new energy consumption rate. Summary of the Invention

[0005] The present invention provides a method and system for evaluating the capacity of new energy consumption to improve the consumption capacity of new energy.

[0006] In a first aspect, to solve the above technical problems, the present invention provides a method for evaluating the capacity of new energy consumption, including:

[0007] Obtaining wind power data, photovoltaic data, load data, and actual consumption data;

[0008] According to the wind power data, performing wind speed simulation to evaluate the wind farm, and obtaining the maximum wind power output and the minimum wind power output;

[0009] According to the photovoltaic data, performing light intensity simulation to evaluate the photovoltaic farm, and obtaining the maximum photovoltaic power output and the minimum photovoltaic power output;

[0010] Based on the maximum wind power output, the minimum wind power output, the maximum photovoltaic power output, and the minimum photovoltaic power output, calculating a consumption capacity coefficient based on a preset policy space model;

[0011] Performing consumption capacity calculation on the consumption capacity coefficient to obtain the maximum consumption capacity;

[0012] According to the load data, calculating a consumption potential based on a preset consumption potential model;

[0013] According to the consumption potential and the maximum consumption capacity, searching for an optimal configuration point in the policy space to obtain the optimal consumption capacity;

[0014] Based on the optimal accommodation capacity and the actual accommodation data, perform verification and evaluation to obtain an evaluation coefficient for evaluating the new energy accommodation capacity.

[0015] In an alternative embodiment, the wind speed simulation is performed based on the wind power data for wind farm evaluation to obtain the maximum wind power output and the minimum wind power output, including:

[0016] Calculate the maximum wind power output and the minimum wind power output through the following formula:

[0017]

[0018] where, P wmax is the maximum wind power output, P wmin is the minimum wind power output, ρ is the air density, A is the swept area of the wind turbine blades, C p is the power coefficient, v max is the maximum wind speed, v min is the minimum wind speed.

[0019] In an alternative embodiment, the light intensity simulation is performed based on the photovoltaic data for photovoltaic field evaluation to obtain the maximum photovoltaic output and the minimum photovoltaic output, including:

[0020] Calculate the maximum photovoltaic output and the minimum photovoltaic output through the following formula:

[0021] P lmax = P inmax ·η·E

[0022] P lmin = P inmin ·η·E

[0023] where, P lmax is the maximum photovoltaic output, P lmin is the minimum photovoltaic output, P inmax is the maximum solar radiation power, P inmin is the minimum solar radiation power, η is the photoelectric conversion efficiency, and E is the system efficiency.

[0024] In an alternative embodiment, based on the maximum wind power output, the minimum wind power output, the maximum photovoltaic output, and the minimum photovoltaic output, calculate the accommodation capacity coefficient based on a preset policy space model, including:

[0025] Calculate the accommodation capacity coefficient through the following formula:

[0026]

[0027] where, EI is the accommodation capacity coefficient, P wyis the predicted power of wind power for the day ahead, P ws is the actual power of wind power, P ly is the predicted power of photovoltaic for the day ahead, P ls is the actual power of photovoltaic, P wmax is the maximum output of wind power, P wmin is the minimum output of wind power, P lmax is the maximum output of photovoltaic, P lmin is the minimum output of photovoltaic.

[0028] In an alternative embodiment, the step of calculating the maximum accommodation capacity by performing accommodation capacity calculation on the accommodation capacity coefficient includes:

[0029] Comparing the coefficients for each time period according to the accommodation capacity coefficient to obtain the maximum accommodation capacity coefficient;

[0030] Performing accommodation capacity calculation according to the maximum accommodation capacity coefficient to obtain the maximum accommodation capacity;

[0031] Among them, the maximum accommodation capacity is calculated by the following formula:

[0032] C max =P load,max ·EI max

[0033] Among them, C max is the maximum accommodation capacity, EI max is the maximum accommodation capacity coefficient, P load,max is the maximum load demand in a day.

[0034] In an alternative embodiment, the step of calculating the accommodation potential based on the preset accommodation potential model according to the load data includes:

[0035] The accommodation potential is calculated by the following formula:

[0036]

[0037] Among them, G is the accommodation potential, L i is the load data at time i, PW t is the wind power output at time t, Pl t is the photovoltaic output at time t, and T is the total number of time periods.

[0038] In an alternative embodiment, the step of finding the optimal configuration point in the strategy space according to the accommodation potential and the maximum accommodation capacity to obtain the optimal accommodation capacity includes:

[0039] The optimal accommodation capacity is calculated by the following formula:

[0040]

[0041] Among them, Y * is the optimal accommodation capacity, G is the accommodation potential, K is the proportion of wind power, and C max is the maximum accommodation capacity, D is the proportion of photovoltaic power, and φ is the weight coefficient.

[0042] In an alternative embodiment, the method of performing verification and evaluation based on the optimal accommodation capacity and the actual accommodation data to obtain an evaluation coefficient for evaluating the new energy accommodation capacity includes:

[0043] According to the evaluation coefficient, determine whether the evaluation coefficient is greater than a preset threshold. If so, output an evaluation unqualified result; if not, output an evaluation qualified result;

[0044] Among them, the evaluation coefficient is calculated by the following formula:

[0045]

[0046] Among them, ∈ is the evaluation coefficient, Y * is the optimal accommodation capacity, and S is the actual accommodation data.

[0047] In a second aspect, the present invention provides a capacity evaluation system based on new energy accommodation, including:

[0048] A data acquisition module, configured to acquire wind power data, photovoltaic data, load data, and actual accommodation data;

[0049] A wind power calculation module, configured to perform wind speed simulation based on the wind power data to evaluate a wind farm, and obtain the maximum wind power output and the minimum wind power output;

[0050] A photovoltaic calculation module, configured to perform light intensity simulation based on the photovoltaic data to evaluate a photovoltaic power station, and obtain the maximum photovoltaic power output and the minimum photovoltaic power output;

[0051] An accommodation coefficient module, configured to calculate an accommodation capacity coefficient based on the maximum wind power output, the minimum wind power output, the maximum photovoltaic power output, and the minimum photovoltaic power output, based on a preset policy space model;

[0052] A maximum accommodation module, configured to perform accommodation capacity calculation on the accommodation capacity coefficient to obtain the maximum accommodation capacity;

[0053] An accommodation potential module, configured to calculate an accommodation potential based on the load data, based on a preset accommodation potential model;

[0054] An optimal accommodation module, configured to find an optimal configuration point in the policy space based on the accommodation potential and the maximum accommodation capacity, and obtain the optimal accommodation capacity;

[0055] An evaluation and verification module, configured to perform verification and evaluation based on the optimal accommodation capacity and the actual accommodation data to obtain an evaluation coefficient for evaluating the new energy accommodation capacity.

[0056] In a third aspect, the present invention further provides an electronic device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the method for evaluating the capacity based on new energy accommodation described in any one of the above is implemented.

[0057] In a fourth aspect, the present invention further provides a computer-readable storage medium, which includes a stored computer program. When the computer program runs, it controls the device where the computer-readable storage medium is located to execute the method for evaluating the capacity based on new energy accommodation described in any one of the above.

[0058] Compared with the prior art, the present invention has the following beneficial effects: The present invention discloses a method for evaluating the capacity based on new energy accommodation, including obtaining wind power data, photovoltaic data, load data, and actual accommodation data; performing wind speed simulation based on the wind power data to evaluate the wind farm and obtaining the maximum wind power output and the minimum wind power output; performing light simulation based on the photovoltaic data to evaluate the photovoltaic farm and obtaining the maximum photovoltaic output and the minimum photovoltaic output; calculating an accommodation capacity coefficient based on the maximum wind power output, the minimum wind power output, the maximum photovoltaic output, and the minimum photovoltaic output based on a preset strategy space model; performing accommodation capacity calculation on the accommodation capacity coefficient to obtain the maximum accommodation capacity; calculating the accommodation potential based on the load data and a preset accommodation potential model; finding an optimal configuration point in the strategy space based on the accommodation potential and the maximum accommodation capacity to obtain the optimal accommodation capacity; performing verification and evaluation based on the optimal accommodation capacity and the actual accommodation data to obtain an evaluation coefficient for evaluating the new energy accommodation capacity.

[0059] The present invention obtains the maximum and minimum outputs through simulation evaluation of wind power and photovoltaic to set reasonable constraints on the wind power and photovoltaic outputs, calculates an accommodation potential coefficient based on a preset strategy space model to measure the size of new energy accommodation, then obtains the maximum accommodation capacity through accommodation capacity calculation, calculates the accommodation potential based on a preset accommodation potential model to provide more evaluation references for new energy accommodation, and finally finds the optimal configuration point and uses the optimal accommodation capacity and actual accommodation data to evaluate the new energy accommodation capacity. By considering the accommodation potential of both wind and light aspects, the new energy accommodation capacity is improved. Description of the Drawings

[0060] Figure 1It is a schematic flow chart of the capacity evaluation method based on new energy consumption provided by the first embodiment of the present invention;

[0061] Figure 2 It is a schematic structural diagram of the capacity evaluation system based on new energy consumption provided by the second embodiment of the present invention. Detailed implementation manners

[0062] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0063] Refer to Figure 1 , the first embodiment of the present invention provides a capacity evaluation method based on new energy consumption, including the following steps:

[0064] S11, obtain wind power data, photovoltaic data, load data and actual consumption data;

[0065] S12, according to the wind power data, perform wind speed simulation to evaluate the wind farm, and obtain the maximum wind power output and the minimum wind power output;

[0066] S13, according to the photovoltaic data, perform light simulation to evaluate the photovoltaic field, and obtain the maximum photovoltaic output and the minimum photovoltaic output;

[0067] S14, according to the maximum wind power output, the minimum wind power output, the maximum photovoltaic output and the minimum photovoltaic output, calculate the consumption capacity coefficient based on a preset strategy space model;

[0068] S15, perform consumption capacity calculation on the consumption capacity coefficient to obtain the maximum consumption capacity;

[0069] S16, according to the load data, calculate the consumption potential based on a preset consumption potential model;

[0070] S17, according to the consumption potential and the maximum consumption capacity, find the optimal configuration point in the strategy space to obtain the optimal consumption capacity;

[0071] S18, according to the optimal consumption capacity and the actual consumption data, perform verification evaluation to obtain an evaluation coefficient to evaluate the new energy consumption capacity.

[0072] In step S11, obtaining wind power data, photovoltaic data, load data and actual consumption data includes:

[0073] In this embodiment, data is directly collected from the monitoring systems of wind farms, which can provide the real-time operating status of wind turbines, including power output, rotational speed, blade angle, etc. Satellite or drone remote sensing technology is used to monitor wind speed and direction, and data such as wind speed, direction, temperature, and humidity are obtained from weather stations. The photovoltaic power station is equipped with a monitoring system that can provide real-time operating data of photovoltaic panels, including power output, voltage, current, etc. Satellite images are used to analyze cloud cover and solar radiation patterns. Power load data, including historical load data and load forecasts, is obtained from grid operators, and power consumption data at the user end is collected through smart meters. The actual consumption situation of new energy power generation, including the actual output data of wind power and photovoltaic power, is obtained from the grid dispatching center, new energy trading and consumption data is obtained through the power market trading platform, and the consumption data of new energy power generation is collected using the real-time monitoring system of the grid. Specifically, wind power data for the past year is collected from the SCADA systems of each wind farm, photovoltaic power generation data is collected from the monitoring systems of photovoltaic power stations, historical load data and real-time load data are obtained from grid operators, and actual consumption data is obtained from the grid dispatching center. Except for outliers and missing values, for example, data during the maintenance of wind turbines needs to be excluded, ensuring that all data is recorded according to a unified timestamp (per hour) and unit (kilowatt-hour). For missing data points, interpolation methods or other statistical methods are used for estimation. A central database is created using a database management system (MySQL, PostgreSQL), and all the collected data is imported into it. The database schema is designed to facilitate the storage and query of different types of data. Different tables can be created for wind power data, photovoltaic data, load data, and consumption data, and the relevant data is associated through SQL queries. The wind power data is associated with the load data at the corresponding timestamp.

[0074] It should be noted that the wind power data is the historical and real-time data of the wind farm, such as wind speed, direction, and the operating status of wind turbines. The photovoltaic data involves the relevant data of the photovoltaic power station, including solar radiation intensity, temperature, tilt angle and direction of photovoltaic panels, etc. The output of photovoltaic power generation is affected by solar radiation intensity and temperature. The load data refers to the power demand data in the power system, including power load forecasts and historical load data for different time periods. The actual consumption data includes the actual consumption situation of new energy power generation in the power system, such as the actual output of wind power and photovoltaic power generation, and the dispatching situation of the grid.

[0075] In step S12, according to the wind power data, wind speed simulation is performed for wind farm assessment to obtain the maximum wind power output and the minimum wind power output, including:

[0076] The maximum wind power output and the minimum wind power output are calculated through the following formula:

[0077]

[0078] Among them, P wmax is the maximum wind power output, P wmin is the minimum wind power output, ρ is the air density, A is the swept area of the wind turbine blades, C p is the power coefficient, v max is the maximum wind speed, v min is the minimum wind speed.

[0079] In a specific embodiment, assume there is a wind farm located in Inner Mongolia. This wind farm has installed 1 wind turbine, and the rated power of each wind turbine is 2.5 megawatts. There are the following data:

[0080] The swept area of the wind turbine blades A = 3000m 2 , the power coefficient C p = 0.4 (this is a typical value, representing the maximum energy conversion efficiency of the turbine under ideal conditions), the air density ρ = 1.225kg / m 3 (value under standard atmospheric conditions). Wind speed data for the past year was collected from a meteorological station, and the following statistical data was obtained: the maximum wind speed v max = 12m / s, the minimum wind speed v min = 3m / s.

[0081] Calculate the maximum wind power output:

[0082]

[0083] Calculate the minimum wind power output:

[0084]

[0085] It should be noted that the formula is based on the following physical principle: the kinetic energy of the wind is proportional to the cube of its speed. Therefore, an increase in wind speed will result in an increase in the kinetic energy of the wind, thereby increasing the output power of the turbine; under ideal conditions, a wind turbine can capture at most 16 / 27 (about 59.3%) of the wind's kinetic energy. This theory provides an upper limit for the design and performance evaluation of wind turbines. Under different altitude, temperature and humidity conditions, the air density will vary. At high altitudes, the air density is lower, which will affect the output power of the wind turbine. The design of the turbine blades directly affects its swept area, thereby affecting the ability to capture wind energy. A larger swept area means more energy capture. The power coefficient depends on the design and operating conditions of the turbine. Modern turbines are designed to maximize the power coefficient, but in actual operation, the power coefficient will be affected by various factors, such as wind speed, blade angle and the control strategy of the turbine. The power coefficient is an indicator to measure the energy conversion efficiency of a wind turbine. It represents the efficiency of the turbine in converting wind energy into mechanical energy. The power coefficient is used as a correction factor in the formula, reflecting the energy conversion efficiency of the actual turbine.

[0086] In step S13, according to the photovoltaic data, perform a light simulation to evaluate the photovoltaic field, and obtain the maximum photovoltaic output and the minimum photovoltaic output, including:

[0087] Calculate the maximum photovoltaic output and the minimum photovoltaic output through the following formula:

[0088] P lmax =P inmax ·η·E

[0089] P lmin =P inmin ·η·E

[0090] Wherein, P lmax is the maximum photovoltaic output, P lmin is the minimum photovoltaic output, P inmax is the maximum solar radiation power, P inmin is the minimum solar radiation power, η is the photoelectric conversion efficiency, and E is the system efficiency.

[0091] It should be noted that the photoelectric conversion efficiency represents the efficiency of the photovoltaic panel in converting light energy into electrical energy. This is the ability of the photovoltaic panel to convert the received light energy into electrical energy, which directly affects the performance of the photovoltaic panel. The higher the photoelectric conversion efficiency, the more electrical energy the photovoltaic panel generates under the same lighting conditions. The system efficiency takes into account all losses from the photovoltaic panel to the power grid, including cable losses, inverter losses, etc., and is the efficiency of the entire photovoltaic system (including photovoltaic panels, inverters, cables, etc.). It takes into account all energy losses in the system. The lower the system efficiency, the greater the energy loss in the system. The solar radiation power is provided by solar radiation and represents the solar radiation intensity per unit area (unit: W / m 2 ), and the maximum and minimum values reflect the fluctuation range of solar radiation over a day or under different weather conditions.

[0092] In step S14, according to the maximum wind power output, the minimum wind power output, the maximum photovoltaic power output, and the minimum photovoltaic power output, a consumption capacity coefficient is calculated based on a preset policy space model, including:

[0093] The consumption capacity coefficient is calculated by the following formula:

[0094]

[0095] where EI is the consumption capacity coefficient, P wy is the predicted wind power output for the day, P ws is the actual wind power, P ly is the predicted photovoltaic power output for the day, P ls is the actual photovoltaic power, P wmax is the maximum wind power output, P wmin is the minimum wind power output, P lmax is the maximum photovoltaic power output, P lmin is the minimum photovoltaic power output.

[0096] It should be noted that the predicted wind power output for the day is estimated using a prediction model based on meteorological forecast data and the historical output data of the wind farm. The actual wind power is directly obtained from the SCADA (Supervisory Control And Data Acquisition) system or real-time monitoring equipment of the wind farm. The predicted photovoltaic power output for the day is calculated using a prediction model with meteorological forecast data (solar radiation intensity) and the historical output data of the photovoltaic power station. The actual photovoltaic power obtains the actual power data from the SCADA system or real-time monitoring equipment of the photovoltaic power station. The numerator part of the formula P wy P ly -P ws P ls represents the difference between the predicted power and the actual power. A positive value indicates that the predicted power is higher than the actual power, and a negative value indicates the opposite. The denominator part P wmax P lmax -Pwmin P lmin Indicates the potential power difference between wind power and photovoltaic power under extreme conditions (maximum and minimum output). This is a standardized comparison that makes the accommodation capacity coefficient independent of scale. The accommodation capacity coefficient can reflect the grid's ability to accommodate wind power and photovoltaic power. An accommodation capacity coefficient close to 1 indicates that the accommodation capacity is close to the maximum, close to -1 indicates that the accommodation capacity is close to the minimum, and close to 0 indicates that the accommodation capacity is close to the minimum output level. By comparing the difference between the predicted and actual power with the difference between the maximum and minimum output, a standardized accommodation capacity assessment is provided.

[0097] In a specific embodiment, assume there is a power grid that connects a wind farm and a photovoltaic power station, and it is necessary to evaluate the grid's ability to accommodate wind power and photovoltaic power.

[0098] The predicted power of wind power for the day ahead, P wt = 80 MW, the predicted power of photovoltaic power for the day ahead, P ly = 40 MW, the actual power of wind power, P ws = 70 MW, the actual power of photovoltaic power, P ls = 35 MW, the maximum output of wind power, P wmax = 100 MW, the minimum output of wind power, P wmin = 10 MW, the maximum output of photovoltaic power, P lmax = 50 MW, the minimum output of photovoltaic power, P lmin = 5 MW.

[0099] Calculate the accommodation capacity coefficient:

[0100]

[0101] The calculated accommodation capacity coefficient is 0.1515, which means that the grid's ability to accommodate wind power and photovoltaic power is slightly higher than the minimum output level but much lower than the maximum output level.

[0102] In step S15, calculating the maximum accommodation capacity by performing accommodation capacity calculation on the accommodation capacity coefficient includes:

[0103] Comparing the coefficients for each time period according to the accommodation capacity coefficient to obtain the maximum accommodation capacity coefficient;

[0104] Calculating the maximum accommodation capacity according to the maximum accommodation capacity coefficient;

[0105] Among them, the maximum accommodation capacity is calculated by the following formula:

[0106] C max = P load,max ·EI max

[0107] Among them, C max is the maximum accommodation capacity, EI max is the maximum accommodation capacity coefficient, and P load,max is the maximum load demand in a day.

[0108] It should be noted that the maximum accommodation capacity is the maximum new energy (wind power and photovoltaic) power that the power grid can accommodate under the best conditions, and is used to evaluate the power grid's ability to accommodate and utilize new energy. The maximum accommodation capacity coefficient is the maximum accommodation capacity coefficient calculated in all time periods, reflecting the maximum accommodation efficiency of the power grid for new energy. The maximum load demand in a day refers to the maximum power demand faced by the power grid in a day, and the maximum load demand appears during the peak hours of the day, such as in the morning or evening.

[0109] In step S16, according to the load data, based on a preset accommodation potential model, the accommodation potential is calculated, including:

[0110] The accommodation potential is calculated through the following formula:

[0111]

[0112] Among them, G is the accommodation potential, and L i is the load data at the i-th moment, PW t is the wind power output at the t-th moment, Pl t is the photovoltaic output at the t-th moment, and T is the total number of moments.

[0113] It should be noted that by calculating the average value within a day, the formula can smooth out short-term fluctuations caused by weather changes or load fluctuations, thereby providing a stable and reliable estimate of the accommodation potential. It reflects the capacity size of new energy accommodation within the total time period of the day. When the load data at the i-th moment is greater than the sum of the wind power output and the photovoltaic output at the t-th moment, it means that the power grid load is greater than the renewable energy output, and the power grid needs more electric energy to be accommodated. When the load data at the i-th moment is less than the sum of the wind power output and the photovoltaic output at the t-th moment, it means that the load is fully met or exceeded, and no additional accommodation capacity is required.

[0114] In a specific embodiment, assume that the total number of moments T = 4 hours, and the load, wind power output, and photovoltaic output data per hour are as follows:

[0115] The first moment t = 1, the load data L1 at the first moment = 50 MW, the wind power output PW1 at the first moment = 10 MW, and the photovoltaic output Pl1 at the first moment = 20 MW;

[0116] The second moment \(t = 2\), the load data at the second moment \(L2 = 60MW\), the wind power processing at the second moment \(PW2 = 15MW\), and the photovoltaic output at the second moment \(Pl2 = 25MW\);

[0117] The third moment \(t = 3\), the load data at the third moment \(L3 = 55MW\), the wind power processing at the third moment \(PW3 = 20MW\), and the photovoltaic output at the third moment \(Pl3 = 30MW\);

[0118] The fourth moment \(t = 4\), the load data at the fourth moment \(L4 = 65MW\), the wind power processing at the fourth moment \(PW4 = 25MW\), and the photovoltaic output at the fourth moment \(Pl4 = 35MW\).

[0119] Calculate the interpolation value per hour:

[0120] When \(t = 1\), \(L1-(PW1 + PV1)=50-(10 + 20)=20\);

[0121] When \(t = 2\), \(L2-(PW2 + PV2)=60-(15 + 25)=20\);

[0122] When \(t = 3\), \(L3-(PW3 + PV3)=55-(20 + 30)=5\);

[0123] When \(t = 4\), \(L4-(PW4 + PV4)=65-(25 + 35)=20\).

[0124] Calculate the accommodation potential:

[0125]

[0126] During this 4-hour period, the average accommodation potential of the power grid is 12.5MW. This means that even considering the power generation capabilities of wind power and photovoltaics, the power grid still needs to provide an additional 12.5MW of load accommodation space to meet the demand.

[0127] In step S17, according to the accommodation potential and the maximum accommodation capacity, search for the optimal configuration point in the strategy space to obtain the optimal accommodation capacity, including:

[0128] Calculate the optimal accommodation capacity through the following formula:

[0129]

[0130] Among them, \(Y\) * is the optimal accommodation capacity, \(G\) is the accommodation potential, \(K\) is the proportion of wind power, \(C\) max is the maximum accommodation capacity, \(D\) is the proportion of photovoltaics, and \(\varphi\) is the weight coefficient.

[0131] It should be noted that the power generation ratios of wind power and photovoltaic directly affect the overall performance of the system. In the formula, the absorption capacity is adjusted by the ratio of the wind power ratio to the photovoltaic ratio, reflecting the demand for different energy types in the optimal configuration of the system. The maximum absorption capacity of the power grid limits the total power that the system can handle. Therefore, the maximum absorption capacity C max is used as the denominator in the formula to reflect this physical limitation. The weight coefficients and constants are used for the balance and scaling of the absorption capacity. The contribution of wind power and photovoltaic can be weighed by adjusting the value. For example, if the wind power ratio is high, a higher weight coefficient is required to enhance the absorption capacity. The optimal absorption capacity refers to the best load balancing ability achieved by the power grid system under specific conditions by adjusting the power generation ratios of wind power and photovoltaic, making full use of its absorption potential and combining with the maximum absorption capacity. It reflects the best operating state of the power grid when coordinating the operation among wind power, photovoltaic and load. If the optimal absorption capacity Y * is high, it indicates that the ratio distribution of wind power and photovoltaic power generation is relatively reasonable, and the power grid can maximize the utilization of these renewable energies; if the optimal absorption capacity Y * is low, it indicates that the operation of the power grid fails to fully explore the absorption potential, and there are problems such as resource waste or load mismatch.

[0132] In step S18, according to the optimal absorption capacity and the actual absorption data, a verification evaluation is carried out to obtain an evaluation coefficient for evaluating the new energy absorption capacity, including:

[0133] According to the evaluation coefficient, it is judged whether the evaluation coefficient is greater than a preset threshold. If so, an evaluation unqualified result is output; if not, an evaluation qualified result is output;

[0134] Among them, the evaluation coefficient is calculated by the following formula:

[0135]

[0136] Among them, ∈ is the evaluation coefficient, Y * is the optimal absorption capacity, and S is the actual absorption data.

[0137] It should be noted that the evaluation coefficient is a measure of the difference between the optimal absorption capacity and the actual absorption data, and is used to evaluate whether the new energy absorption capacity reaches the expected goal.

[0138] In a specific embodiment, assume that the preset threshold is 1, the optimal absorption capacity Y * is 1000MW, and the actual absorption data S is 900MW, then When the actual absorption capacity fully reaches the theoretical optimal value, it indicates that the new energy configuration and grid operation are highly matched, and the system is in the best state. If the evaluation coefficient ∈ < 1, the actual absorption capacity exceeds the theoretical optimal value because the parameter weight design is relatively conservative, or there are unexpected load fluctuations that result in more utilization of new energy, indicating that there are certain problems with the current new energy configuration or grid operation and the absorption potential is not fully utilized.

[0139] The working process of the present invention will be described below by taking a relatively common scenario as an example. Please also refer to Figure 2 , which is Figure 1 a schematic diagram of the working scenario of the method.

[0140] Suppose a regional power grid company hopes to evaluate the absorption capacity of its power grid for wind power and photovoltaic power generation in order to carry out power grid planning and optimize the dispatching strategy. The power grid in this region is connected to multiple wind farms and photovoltaic power plants, and the proportion of new energy generation increases year by year.

[0141] First, collect the historical and real-time data of wind farms and photovoltaic power plants, including wind speed, solar radiation intensity, the operating status of wind turbines and photovoltaic panels, etc. Collect the load data of the power grid, including the power demand in different time periods. Collect the actual absorption data, that is, the actual absorption amount of wind power and photovoltaic power generation in the power grid. Secondly, use the wind power data for wind speed simulation to evaluate the maximum and minimum output of the wind farm. The maximum output of wind power is 200 MW, and the minimum output of wind power is 20 MW. Use the photovoltaic data for light simulation to evaluate the maximum and minimum output of the photovoltaic power plant. The maximum output of photovoltaic power is 100 MW, and the minimum output of photovoltaic power is 10 MW. Based on the preset strategy space model, calculate the absorption capacity coefficient to obtain an absorption coefficient of 0.75, which reflects the absorption capacity of the power grid for wind power and photovoltaic power generation. According to the load data and the absorption capacity coefficient, calculate the absorption potential to be 180 MW to evaluate the absorption capacity of the power grid under different conditions. Find the optimal configuration point in the strategy space and calculate the optimal absorption capacity to be 120 MW. Finally, according to the optimal absorption capacity and the actual absorption data (110 MW), calculate the evaluation coefficient to be 1.09 to evaluate the new energy absorption capacity. The evaluation coefficient is greater than the preset threshold (1), and the evaluation qualified result is output.

[0142] In summary, the present invention discloses a method for evaluating the capacity of new energy consumption, including obtaining wind power data, photovoltaic data, load data, and actual consumption data; performing wind speed simulation based on the wind power data to evaluate the wind farm and obtaining the maximum wind power output and the minimum wind power output; performing light simulation based on the photovoltaic data to evaluate the photovoltaic farm and obtaining the maximum photovoltaic output and the minimum photovoltaic output; calculating the consumption capacity coefficient based on the maximum wind power output, the minimum wind power output, the maximum photovoltaic output, and the minimum photovoltaic output according to a preset policy space model; performing consumption capacity calculation on the consumption capacity coefficient to obtain the maximum consumption capacity; calculating the consumption potential based on the load data according to a preset consumption potential model; finding the optimal configuration point in the policy space according to the consumption potential and the maximum consumption capacity to obtain the optimal consumption capacity; and performing verification and evaluation according to the optimal consumption capacity and the actual consumption data to obtain an evaluation coefficient for evaluating the new energy consumption capacity. The present invention improves the new energy consumption capacity by considering the characteristics of the wind and light output.

[0143] Referring to Figure 2 , the second embodiment of the present invention provides a capacity evaluation system for new energy consumption, including:

[0144] A data acquisition module for obtaining wind power data, photovoltaic data, load data, and actual consumption data;

[0145] A wind power calculation module for performing wind speed simulation based on the wind power data to evaluate the wind farm and obtaining the maximum wind power output and the minimum wind power output;

[0146] A photovoltaic calculation module for performing light simulation based on the photovoltaic data to evaluate the photovoltaic farm and obtaining the maximum photovoltaic output and the minimum photovoltaic output;

[0147] A consumption coefficient module for calculating the consumption capacity coefficient based on the maximum wind power output, the minimum wind power output, the maximum photovoltaic output, and the minimum photovoltaic output according to a preset policy space model;

[0148] A maximum consumption module for performing consumption capacity calculation on the consumption capacity coefficient to obtain the maximum consumption capacity;

[0149] A consumption potential module for calculating the consumption potential based on the load data according to a preset consumption potential model;

[0150] An optimal consumption module for finding the optimal configuration point in the policy space according to the consumption potential and the maximum consumption capacity to obtain the optimal consumption capacity;

[0151] An evaluation and verification module is used to perform verification and evaluation based on the optimal accommodation capacity and the actual accommodation data to obtain an evaluation coefficient for evaluating the new energy accommodation capacity.

[0152] Preferably, the wind power calculation module is specifically configured to perform wind speed simulation based on the wind power data to evaluate the wind farm and obtain the maximum wind power output and the minimum wind power output, including:

[0153] The maximum wind power output and the minimum wind power output are calculated by the following formula:

[0154]

[0155] where P wmax is the maximum wind power output, P wmin is the minimum wind power output, ρ is the air density, A is the swept area of the wind turbine blades, C p is the power coefficient, v max is the maximum wind speed, v min is the minimum wind speed.

[0156] Preferably, the photovoltaic calculation module is specifically configured to perform light irradiation simulation based on the photovoltaic data to evaluate the photovoltaic field and obtain the maximum photovoltaic output and the minimum photovoltaic output, including:

[0157] The maximum photovoltaic output and the minimum photovoltaic output are calculated by the following formula:

[0158] P lmax = P inmax ·η·E

[0159] P lmin = P inmin ·η·E

[0160] where P lmax is the maximum photovoltaic output, P lmin is the minimum photovoltaic output, P inmax is the maximum solar radiation power, P inmin is the minimum solar radiation power, η is the photoelectric conversion efficiency, and E is the system efficiency.

[0161] Preferably, the accommodation coefficient module is specifically configured to calculate an accommodation capacity coefficient based on the maximum wind power output, the minimum wind power output, the maximum photovoltaic output, and the minimum photovoltaic output based on a preset policy space model, including:

[0162] The accommodation capacity coefficient is calculated by the following formula:

[0163]

[0164] where EI is the accommodation capacity coefficient, Pwy is the predicted power of wind power for the day ahead, P ws is the actual power of wind power, P ly is the predicted power of photovoltaic for the day ahead, P ls is the actual power of photovoltaic, P wmax is the maximum output of wind power, P wmin is the minimum output of wind power, P lmax is the maximum output of photovoltaic, P lmin is the minimum output of photovoltaic.

[0165] Preferably, the maximum accommodation module is specifically configured to perform accommodation capacity calculation on the accommodation capacity coefficient to obtain the maximum accommodation capacity, including:

[0166] Compare the coefficients for each time period according to the accommodation capacity coefficient to obtain the maximum accommodation capacity coefficient;

[0167] Perform accommodation capacity calculation according to the maximum accommodation capacity coefficient to obtain the maximum accommodation capacity;

[0168] Among them, the maximum accommodation capacity is calculated by the following formula:

[0169] C max = P load,max ·EI max

[0170] Among them, C max is the maximum accommodation capacity, EI max is the maximum accommodation capacity coefficient, P load,max is the maximum load demand in a day.

[0171] Preferably, the accommodation potential module is specifically configured to calculate the accommodation potential based on the load data and a preset accommodation potential model, including:

[0172] Calculate the accommodation potential by the following formula:

[0173]

[0174] Among them, G is the accommodation potential, L i is the load data at time i, PW t is the wind power output at time t, Pl t is the photovoltaic output at time t, and T is the total number of times.

[0175] Preferably, the optimal accommodation module is specifically configured to find the optimal configuration point in the strategy space according to the accommodation potential and the maximum accommodation capacity to obtain the optimal accommodation capacity, including:

[0176] Calculate the optimal accommodation capacity by the following formula:

[0177]

[0178] Among them, Y * is the optimal accommodation capacity, G is the accommodation potential, K is the proportion of wind power, and C max is the maximum accommodation capacity, D is the proportion of photovoltaic power, and φ is the weight coefficient.

[0179] Preferably, the evaluation and verification module is specifically configured to perform verification and evaluation based on the optimal accommodation capacity and the actual accommodation data to obtain an evaluation coefficient for evaluating the new energy accommodation capacity, including:

[0180] Judging whether the evaluation coefficient is greater than a preset threshold according to the evaluation coefficient. If so, output an evaluation unqualified result; if not, output an evaluation qualified result;

[0181] Among them, the evaluation coefficient is calculated by the following formula:

[0182]

[0183] Among them, ∈ is the evaluation coefficient, Y * is the optimal accommodation capacity, and S is the actual accommodation data.

[0184] It should be noted that a capacity evaluation system based on new energy accommodation provided in an embodiment of the present invention is used to execute all the process steps of a capacity evaluation method based on new energy accommodation in the above embodiment. The working principles and beneficial effects of the two correspond one by one, so they will not be elaborated here.

[0185] An embodiment of the present invention also provides an electronic device. The electronic device includes: a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a data acquisition program. When the processor executes the computer program, it implements the steps in each of the above embodiments of the capacity evaluation method based on new energy accommodation, such as Figure 1 the step S11 shown. Alternatively, when the processor executes the computer program, it implements the functions of each module / unit in each of the above device embodiments, such as the data acquisition module.

[0186] Exemplarily, the computer program can be divided into one or more modules / units. The one or more modules / units are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units can be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer program in the electronic device.

[0187] The electronic device can be a computing device such as a desktop computer, notebook, handheld computer, and smart tablet. The electronic device may include, but is not limited to, a processor and a memory. Those skilled in the art can understand that the above components are only examples of the electronic device and do not constitute a limitation on the electronic device. It may include more or fewer components than the above, or combine some components, or different components. For example, the electronic device may also include input / output devices, network access devices, buses, etc.

[0188] The so-called processor may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The processor is the control center of the electronic device and connects various parts of the entire electronic device through various interfaces and lines.

[0189] The memory can be used to store the computer program and / or module. The processor realizes various functions of the electronic device by running or executing the computer program and / or module stored in the memory, and by calling the data stored in the memory. The memory may mainly include a program storage area and a data storage area. Among them, the program storage area may store an operating system, application programs required for at least one function (such as a sound playback function, an image playback function, etc.); the data storage area may store data created according to the use of the mobile phone (such as audio data, phone book, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one magnetic disk storage device, flash device, or other volatile solid-state storage devices.

[0190] Among them, if the modules / units integrated in the electronic device are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, to implement all or part of the processes in the above-described embodiment methods of the present invention, it can also be completed by a computer program instructing relevant hardware. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-described various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content included in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0191] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. In addition, in the attached drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which can be specifically implemented as one or more communication buses or signal lines. Those of ordinary skill in the art can understand and implement it without creative efforts.

[0192] The specific embodiments described above further elaborate on the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only the specific embodiments of the present invention and is not used to limit the protection scope of the present invention. It is particularly pointed out that for those skilled in the art, any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for evaluating the capacity based on new energy consumption, characterized in that, Including: Obtain wind power data, photovoltaic data, load data and actual consumption data; According to the wind power data, conduct wind speed simulation for wind farm assessment to obtain the maximum wind power output and the minimum wind power output; According to the photovoltaic data, conduct light intensity simulation for photovoltaic farm assessment to obtain the maximum photovoltaic output and the minimum photovoltaic output; According to the maximum wind power output, the minimum wind power output, the maximum photovoltaic output and the minimum photovoltaic output, calculate the consumption capacity coefficient based on a preset policy space model; Perform consumption capacity calculation on the consumption capacity coefficient to obtain the maximum consumption capacity; According to the load data, calculate the consumption potential based on a preset consumption potential model; According to the consumption potential and the maximum consumption capacity, search for the optimal configuration point in the policy space to obtain the optimal consumption capacity; According to the optimal consumption capacity and the actual consumption data, conduct verification assessment to obtain an assessment coefficient for evaluating the new energy consumption capacity.

2. The method for evaluating the capacity for new energy consumption according to claim 1, wherein The step of according to the wind power data, conducting wind speed simulation for wind farm assessment to obtain the maximum wind power output and the minimum wind power output includes: Calculate the maximum wind power output and the minimum wind power output through the following formula: Among them, P wmax is the maximum output of wind power, P wmin is the minimum output of wind power, ρ is the air density, A is the swept area of the wind turbine blades, C p is the power coefficient, v max is the maximum wind speed, v min is the minimum wind speed.

3. The method for evaluating the ability based on new energy consumption according to claim 1, characterized in that The step of according to the photovoltaic data, conducting light intensity simulation for photovoltaic farm assessment to obtain the maximum photovoltaic output and the minimum photovoltaic output includes: Calculate the maximum photovoltaic output and the minimum photovoltaic output through the following formula: P lmax = P inmax · η · E P lmin = P inmin ·η·E Among them, P lmax is the maximum PV output, P lmin is the minimum PV output, P inmax is the maximum solar radiation power, P inmin is the minimum solar radiation power, η is the photoelectric conversion efficiency, and E is the system efficiency.

4. The method for evaluating the capacity for new energy accommodation according to claim 1, wherein The step of according to the maximum wind power output, the minimum wind power output, the maximum photovoltaic output and the minimum photovoltaic output, calculating the consumption capacity coefficient based on a preset policy space model includes: Calculate the consumption capacity coefficient through the following formula: Among them, EI is the absorption capacity coefficient, P wy is the predicted power of wind power for the day-ahead, P ws is the actual power of wind power, P ly is the predicted power of photovoltaic power for the day-ahead, P ls is the actual power of photovoltaic power, P wmax is the maximum output of wind power, P wmin is the minimum output of wind power, P lmax is the maximum output of photovoltaic power, P lmin is the minimum output of photovoltaic power.

5. The method for evaluating the capacity based on new energy consumption according to claim 1, wherein The step of performing consumption capacity calculation on the consumption capacity coefficient to obtain the maximum consumption capacity includes: Compare the coefficients for each time period according to the consumption capacity coefficient to obtain the maximum consumption capacity coefficient; Perform consumption capacity calculation according to the maximum consumption capacity coefficient to obtain the maximum consumption capacity; Among them, calculate the maximum consumption capacity through the following formula: c max = P load,max ·EI max Among them, C max is the maximum accommodation capacity, EI max is the maximum accommodation capacity coefficient, and P load,max is the maximum load demand during a day.

6. The method for evaluating the capacity for new energy consumption according to claim 2, wherein The step of according to the load data, calculating the consumption potential based on a preset consumption potential model includes: Calculate the consumption potential through the following formula: Among them, G is the absorption potential, and L i is the load data at time i, PW t is the wind power output at time t, Pl t is the photovoltaic output at time t, and T is the total time.

7. The method for evaluating the capacity based on new energy consumption according to claim 1, wherein The step of according to the consumption potential and the maximum consumption capacity, searching for the optimal configuration point in the policy space to obtain the optimal consumption capacity includes: Calculate the optimal consumption capacity through the following formula: Among them, Y * is the optimal accommodation capacity, G is the accommodation potential, K is the proportion of wind power, C max is the maximum accommodation capacity, D is the proportion of photovoltaic power, and φ is the weight coefficient.

8. The method for evaluating the ability based on new energy consumption according to claim 1, wherein The step of according to the optimal consumption capacity and the actual consumption data, conducting verification assessment to obtain an assessment coefficient for evaluating the new energy consumption capacity includes: According to the assessment coefficient, judge whether the assessment coefficient is greater than a preset threshold. If so, output an assessment unqualified result; if not, output an assessment qualified result; Among them, calculate the assessment coefficient through the following formula: Among them, ∈ is the evaluation coefficient, Y * is the optimal accommodation capacity, and S is the actual accommodation data.

9. An ability evaluation system based on new energy consumption, characterized in that, Including: A data acquisition module for obtaining wind power data, photovoltaic data, load data and actual consumption data; A wind power calculation module for conducting wind speed simulation for wind farm assessment according to the wind power data to obtain the maximum wind power output and the minimum wind power output; A photovoltaic calculation module, configured to perform light simulation based on the photovoltaic data for photovoltaic field evaluation, and obtain the maximum photovoltaic output and the minimum photovoltaic output; An accommodation coefficient module, configured to calculate an accommodation capacity coefficient based on the maximum wind power output, the minimum wind power output, the maximum photovoltaic output, and the minimum photovoltaic output, based on a preset policy space model; A maximum accommodation module, configured to perform accommodation capacity calculation on the accommodation capacity coefficient to obtain the maximum accommodation capacity; An accommodation potential module, configured to calculate an accommodation potential based on the load data, based on a preset accommodation potential model; An optimal accommodation module, configured to find an optimal configuration point in the policy space according to the accommodation potential and the maximum accommodation capacity, and obtain the optimal accommodation capacity; An evaluation and verification module, configured to perform verification and evaluation according to the optimal accommodation capacity and the actual accommodation data, and obtain an evaluation coefficient to evaluate the new energy accommodation capacity.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein when the computer program runs, it controls the device where the computer-readable storage medium is located to execute the new energy accommodation-based capacity evaluation method according to any one of claims 1 to 7.

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