Renewable energy power generation confidence capacity evaluation method and system based on Monte Carlo simulation

Abstract

The invention relates to a renewable energy power generation confidence capacity evaluation method and system based on Monte Carlo simulation. The method comprises the following steps: step 1, collecting photovoltaic and wind power meteorological data and power grid load data; 2, establishing a photovoltaic and wind power generation model, and generating a year-round power generation time sequence; step 3, selecting the EENS which does not provide expected energy as an index of the power generation reliability of the system, and calculating the EENS in the current year by adopting time sequence Monte Carlo simulation; 4, calculating the confidence capacity of the renewable energy sources by adopting an ELCC (Effective Load Carrying Capacity) method, and passing the confidence capacity of the newly added renewable energy sources; 5, calculating the maximum load capable of being supported by the newly-added renewable energy power generation capacity by adopting a secant method; and step 6, based on the change trends in different scenes, evaluating the stability and feasibility of the power grid under different renewable energy power generation capacities. The accuracy and the dynamic adaptability of the evaluation result can be improved.

Description

Technical Field

[0001] The invention belongs to the technical field of renewable energy power generation, and relates to a renewable energy power generation confidence capacity evaluation method, in particular to a renewable energy power generation confidence capacity evaluation method based on Monte Carlo simulation. Background Art

[0002] To promote sustainable energy development, China has formulated a series of carbon emission control strategies to achieve a transition from "fossil energy as the main source" to "fossil energy as the auxiliary source and clean energy as the main source". Wind power and photovoltaic power are clean energy sources that are currently being developed on a large scale. However, wind and photovoltaic power generation are characterized by randomness and intermittency. Large-scale centralized renewable energy development and grid connection bring major challenges to the operation and control of the power system, profoundly affecting the safety, stability, dispatching capability and controllability of large power grids. With the rapid development of renewable energy-related technologies and the continuous increase in grid-connected capacity, the concept of confidence capacity has received increasing attention in the uncertainty planning of power systems containing renewable energy. Objectively evaluating the contribution of renewable energy to the power system can help planning decision makers reduce investment in redundant power generation capacity. Therefore, efforts need to be made to carry out relevant research to improve the accuracy and efficiency of confidence capacity evaluation methods.

[0003] In terms of background technology, based on relevant research results at home and abroad, many scholars have proposed confidence capacity to evaluate the contribution of renewable energy to the power system. However, most of these methods are based on non-sequential algorithms and non-sequential indicators for evaluation. Non-sequential indicators have been widely used due to their significant research results in algorithm acceleration. However, such algorithms usually ignore the temporal autocorrelation (or time continuity characteristics) of generator state transitions and wind speed changes, which limits their ability to accurately model and evaluate dynamic processes.

[0004] With the rapid development of computing power and technology, how to accelerate timing algorithms has also become an important research direction for experts in related fields.

[0005] After searching, no public documents of the prior art that are identical or similar to the present invention were found. Summary of the invention

[0006] The purpose of the present invention is to overcome the shortcomings of the prior art and propose a renewable energy power generation confidence capacity evaluation method based on Monte Carlo simulation. The confidence capacity of renewable energy is calculated by adopting a time series algorithm to improve the accuracy and dynamic adaptability of the evaluation results.

[0007] The present invention solves the practical problem by adopting the following technical solutions:

[0008] A method for evaluating the confidence capacity of renewable energy power generation based on Monte Carlo simulation includes the following steps:

[0009] Step 1: Collect meteorological data and grid load data of photovoltaic and wind power;

[0010] Step 2: Establish a photovoltaic and wind power generation model, and generate a full-year power generation time series based on the data collected in step 1;

[0011] Step 3: Using the annual power generation time series generated in step 2 and combined with load data, the expected energy not provided (EENS) is selected as the indicator of system power generation reliability. The EENS of the year is calculated using time series Monte Carlo simulation to evaluate the reliability of the system under different meteorological conditions.

[0012] Step 4: Based on the calculation results of the EENS of the year calculated by the time series Monte Carlo simulation in step 3, the confidence capacity of renewable energy is calculated by using the effective load carrying capacity ELCC method, and the confidence capacity of the newly added renewable energy is used to evaluate its carrying capacity for the grid load;

[0013] Step 5: Use the secant method to calculate the maximum load that the new renewable energy generation capacity can support while maintaining a specific reliability level;

[0014] Step 6: According to the confidence capacity of the newly added renewable energy and the maximum load that the newly added renewable energy generation capacity can support calculated in steps 4 and 5, based on the changing trends in different scenarios, evaluate the stability and feasibility of the power grid under different renewable energy generation capacities.

[0015] Moreover, the specific method of step 1 is:

[0016] Collect hourly data on light radiation intensity, temperature and wind speed in the experimental area;

[0017] Collect grid load data, including total installed capacity of renewable and traditional energy sources, load levels, and historical load data;

[0018] The collected data is cleaned and preprocessed to ensure the consistency of time series data and consider the processing of missing values ​​and outliers in the data.

[0019] Moreover, the specific steps of step 2 include:

[0020] 2.1 The photovoltaic power generation model is:

[0021]

[0022] 2.2 The wind power generation model is as follows:

[0023]

[0024] 2.3 Based on the data collected in step 1, substitute the wind power generation model and photovoltaic power generation model to generate the annual power generation time series.

[0025] Moreover, the specific steps of step 3 include:

[0026] Step 3.1: Using the annual power generation time series generated in step 2 and combined with load data, select the expected energy not provided EENS as the indicator of system power generation reliability. The calculation formula is as follows:

[0027]

[0028] This indicator represents the severity of system failure;

[0029] Step 3.2, use the time series Monte Carlo simulation to calculate EENS, and the sampling method is:

[0030] t 1 =-t MTTF lnγ (5)

[0031] t 2 =-t MTTR lnγ (6)

[0032] In the formula, γ is a random number uniformly distributed between 0 and 1; t MTTF is the average working time; t MTTR is the mean time to repair;

[0033] After sampling is completed, the annual available capacity sequence of conventional units is generated, and the annual available capacity per hour on the power generation side is compared with the annual hourly load to calculate the EENS for the year:

[0034]

[0035] Among them, N Y is the total number of simulated years; N is the power outage state that occurs; C j represents the available capacity at the jth hour; L j is the load level at the jth hour;

[0036] 3.3 Based on the EENS calculation results of the year, evaluate the reliability of the system under different meteorological conditions.

[0037] Moreover, the specific steps of step 4 include:

[0038] 4.1 Based on the calculation results of the EENS of the year calculated by the sequential Monte Carlo simulation in step 3, the effective load carrying capacity (ELCC) method is used to calculate the confidence capacity under different photovoltaic and wind power installed capacities. The calculation formula is shown in formula (8):

[0039] ELCC=EENS(C+ΔC,L+ΔL) (8)

[0040] 4.2 The confidence capacity of new renewable energy is defined as the ratio of the ideal conventional generator capacity to the renewable energy power capacity that provides the same additional power, and the formula is as follows:

[0041]

[0042] Among them, C R is the capacity of the ideal conventional generator. The additional capacity of renewable energy power required to provide the same additional load ΔL is C WP The additional renewable energy capacity required to provide additional load refers to the contribution of renewable energy under the additional load.

[0043] 4.3 The confidence capacity of ELCC and newly added renewable energy calculated through the above steps is combined with the load support capacity under different installed capacities to further evaluate the load carrying capacity of the power grid under different renewable energy installed capacities.

[0044] Moreover, the specific steps of step 5 include:

[0045] Step 5.1, if the peak load of the original load is L pk0 , the deviation of the final result is ε, the reliability of the original system is calculated, the original system reliability is R, and the renewable energy capacity C is added to the original system WP System and increase the capacity of conventional generators C WP The system reliability is R1 and R2 respectively;

[0046] Step 5.2: Calculate the passing point X(L pk0 ,R) and point Y(L pkθ +C WP ,R 2 ) 0 The peak load L corresponding to the intersection point with the line f(x)=R 1 , using the time series Monte Carlo simulation to add a load duration curve L a1 =S+L 1 -L pk0 , calculate the reliability R 3 ;

[0047] Step 5.3: If | R 3 -R|>ε, calculate the point X(L 1 ,R 3 ) and point Y(L pkθ +C WP ,R 2 )1与 The peak load L corresponding to the intersection of line f(x) = R 2 , using the time series Monte Carlo simulation to simulate the new load duration curve L a2 =S+L 2 -L pk0 , and obtain the reliability index R 4 ;

[0048] Step 5.4: If the new reliability index R 4 If the difference with R is still greater than ε, continue the previous level process until the calculated reliability index R i The difference from the original reliability is less than ε. The maximum load that the newly added renewable energy generation capacity can support when the reliability level R is obtained.

[0049] A renewable energy power generation confidence capacity evaluation system based on Monte Carlo simulation, comprising:

[0050] Data acquisition module, collecting meteorological data of photovoltaic and wind power and grid load data;

[0051] The module for generating the annual power generation time series establishes the power generation models of photovoltaic and wind power and generates the annual power generation time series based on the collected data;

[0052] The EENS calculation module uses the generated annual power generation time series and load data to select the EENS of unprovided expected energy as an indicator of system power generation reliability. It uses time-series Monte Carlo simulation to calculate the EENS of the year and evaluate the reliability of the system under different meteorological conditions.

[0053] The newly added confidence capacity calculation module for renewable energy uses the effective load carrying capacity ELCC method to calculate the confidence capacity of renewable energy based on the calculation results of the EENS of the current year through time-series Monte Carlo simulation, and evaluates its carrying capacity for the grid load through the newly added confidence capacity of renewable energy;

[0054] The maximum load calculation module uses the secant method to calculate the maximum load that the newly added renewable energy generation capacity can support while maintaining a specific reliability level;

[0055] The evaluation module evaluates the stability and feasibility of the power grid under different renewable energy generation capacities based on the calculated confidence capacity of the newly added renewable energy and the maximum load that the newly added renewable energy generation capacity can support, based on the changing trends under different scenarios.

[0056] Advantages and beneficial effects of the present invention:

[0057] 1. The present invention uses the annual power generation time series generated in step 2, combined with load data, to select EENS (expected energy not provided) as an indicator of system power generation reliability. In step 3, the EENS of the year is calculated by using time series Monte Carlo simulation to evaluate the reliability of the system under different meteorological conditions, improve the reliability evaluation accuracy of renewable energy power generation, and accurately simulate the reliability of the system based on photovoltaic, wind power and other power generation data and grid load data. The EENS (expected energy not provided) indicator in step 3.1 is used to accurately quantify the reliability of the renewable energy power generation system. This method takes into account the influence of multiple meteorological factors and provides a more reliable basis for grid planning and renewable energy access.

[0058] 2. The present invention uses the effective load carrying capacity (ELCC) method to calculate the confidence capacity of renewable energy through step 4, and evaluates its load carrying capacity for the grid through the confidence capacity of the newly added renewable energy, so as to quantify the load carrying capacity of renewable energy for the grid. Step 4.1 uses the effective load carrying capacity (ELCC) method to calculate the confidence capacity under different photovoltaic and wind power installed capacities, and step 4.2 quantifies the confidence capacity of the newly added renewable energy, and then analyzes the load carrying capacity of renewable energy for the grid, optimizing the capacity planning and energy configuration of the grid.

[0059] 3. The present invention uses the Secant Method in step 5 to iteratively analyze different load and generation capacity scenarios, and calculates the maximum load that can be supported by the newly added renewable energy generation capacity while maintaining a specific reliability level. The calculation process from step 5.1 to step 5.5 helps optimize grid planning, improve grid stability and feasibility, and provide a scientific basis for decision-making by simulating the load support capacity of the grid under the condition of newly added renewable energy capacity. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 It is a general flow chart of a method for evaluating the confidence capacity of renewable energy power generation based on Monte Carlo simulation of the present invention;

[0061] Figure 2 It is a relationship diagram between capacity and reliability under different systems in a renewable energy power generation confidence capacity evaluation method based on Monte Carlo simulation of the present invention;

[0062] Figure 3 It is a flow chart of evaluating reliability by the secant method in a method for evaluating the confidence capacity of renewable energy power generation based on Monte Carlo simulation according to the present invention;

[0063] Figure 4The invention discloses a graph showing the relationship between peak load and reliability in a confidence capacity evaluation method for renewable energy power generation based on Monte Carlo simulation and a secant method diagram. DETAILED DESCRIPTION

[0064] The embodiments of the present invention are further described in detail below with reference to the accompanying drawings:

[0065] A confidence capacity evaluation method for renewable energy generation based on Monte Carlo simulation, such as Figure 1 As shown, the following steps are included:

[0066] Step 1: Collect meteorological data and grid load data of photovoltaic and wind power to provide key inputs for model establishment and subsequent Monte Carlo simulation to ensure the accuracy and timeliness of the data;

[0067] The specific method of step 1 is:

[0068] Collect hourly data on light radiation intensity, temperature and wind speed in the experimental area; ensure that the data comes from reliable weather stations, satellite remote sensing data or high-precision meteorological prediction models. The data should have sufficient accuracy and take into account regional climate characteristics and seasonal changes.

[0069] Collect grid load data, including total installed capacity, load level, and historical load data of renewable energy (such as photovoltaic and wind power) and traditional energy (thermal power, nuclear power, natural gas, etc.). Ensure that the data source is legitimate and can be time-series aligned with meteorological data.

[0070] The collected data is cleaned and preprocessed to ensure the consistency of time series data and consider the processing of missing values ​​and outliers in the data.

[0071] In this embodiment, the light radiation intensity and temperature data of the experimental object area are collected hour by hour as the output parameter values ​​of the photovoltaic power station. The main factors affecting the photovoltaic output are light radiation intensity, temperature, photoelectric conversion efficiency, photovoltaic panel inclination, etc. The change of light radiation intensity directly determines the photovoltaic output curve, and the temperature has a greater impact on the photovoltaic output curve. The reason why temperature affects the power generation of the photovoltaic power station is mainly because the change of temperature will change the performance of the photovoltaic cell;

[0072] Collect hourly wind speed data in the experimental area as the value of setting wind power output parameters. The output power of the wind farm is directly related to the wind speed, so the time series of power can be calculated from the time series of wind speed.

[0073] The required grid load data includes renewable energy, the total installed capacity of power sources in the experimental area, and the installed capacity of thermal power, nuclear power, natural gas, etc.

[0074] Step 2: Establish the power generation model of photovoltaic and wind power, and generate the annual power generation time series based on the data collected in step 1 as the basis for Monte Carlo simulation;

[0075] The specific steps of step 2 include:

[0076] 2.1 The photovoltaic power generation model is:

[0077]

[0078] 2.2 The wind power generation model is as follows:

[0079]

[0080] 2.3 Based on the data collected in step 1, substitute the wind power generation model and photovoltaic power generation model to generate the annual power generation time series.

[0081] In this embodiment, step 2.1, although the output of the photovoltaic power station is related to many factors, by using the maximum power tracking technology of photovoltaic cells and the dual-axis rear tracking device of the National Renewable Energy Laboratory (NREL) of the United States, it can be obtained that the output of the photovoltaic module is only affected by the light radiation intensity and temperature. The output power of the photovoltaic cell is as follows:

[0082]

[0083] Where P PV is the actual output power of the photovoltaic cell; Y PV is the rated power of the photovoltaic cell under standard test conditions; f PV is the photovoltaic cell loss factor; R T is the actual light radiation intensity; α P is the photovoltaic cell power temperature coefficient, generally -0.35% / ℃; R STC The light radiation intensity under standard test conditions is 1kW / m 2 ; T C is the actual ambient temperature; T STC The ambient temperature under standard test conditions is 25°C.

[0084] Step 2.2, the wind power generation model is as follows:

[0085]

[0086] Where P r is wind force; A, B, C are wind turbine power characteristic curve parameters, and the parameters of different wind turbines are slightly different; V cl 、V r 、V co , P rThey are the starting wind speed, rated wind speed, cut-off wind speed and rated power of the wind farm respectively. A, B and C are:

[0087]

[0088] Step 2.3: Based on the data collected in step 1, substitute it into the wind power and photovoltaic power generation model to generate the annual power generation time series.

[0089] Step 3: Using the annual power generation time series generated in step 2 and combined with load data, the expected energy not provided (EENS) is selected as the indicator of system power generation reliability. The EENS of the year is calculated using time series Monte Carlo simulation to evaluate the reliability of the system under different meteorological conditions.

[0090] The specific steps of step 3 include:

[0091] Step 3.1: Using the annual power generation time series generated in step 2 and combined with load data, select the expected energy not provided EENS as the indicator of system power generation reliability. The calculation formula is as follows:

[0092]

[0093] This indicator characterizes the severity of system failures, because the reliability of the power system is not only limited by power shortages, but power shortages can also have a great impact on the system;

[0094] Step 3.2, use the time series Monte Carlo simulation to calculate EENS, and the sampling method is:

[0095] t 1 =-t MTTF lnγ (5)

[0096] t 2 =-t MTTR lnγ (6)

[0097] In the formula, γ is a random number uniformly distributed between 0 and 1; t MTTF is the average working time; t MTTR is the mean time to repair;

[0098] After sampling is completed, the annual available capacity sequence of conventional units is generated, and the annual available capacity per hour on the power generation side is compared with the annual hourly load to calculate the EENS for the year:

[0099]

[0100] Among them, N Y is the total number of simulated years; N is the power outage state that occurs; C j represents the available capacity at the jth hour; L jis the load level at the jth hour;

[0101] 3.3 Based on the EENS calculation results of the year, evaluate the reliability of the system under different meteorological conditions.

[0102] In this embodiment, in step 3.1, for different subsystems of the power system, the specific evaluation index may be different. The present invention selects EENS (expected energy not provided) as the index of system power generation reliability. The calculation formula is as follows:

[0103]

[0104] Where N is the number of hours in a year; C i is the available power in the ith hour; L i is the load level at the i-th hour; P is the probability of power shortage. EENS represents the expected value of the power system due to the forced shutdown of the unit and the insufficient power of the user. This indicator can characterize the severity of the system failure, because the reliability of the power system is not only limited by the power shortage, but also has a great impact on the system;

[0105] Step 3.2, use the time series Monte Carlo simulation to calculate EENS. In general, the working time and maintenance time follow the exponential distribution. The time series state of the conventional unit is sampled. The conventional unit adopts a two-state model, namely the normal operation state and the fault state. The normal operation time t 1 and maintenance time t 2 The sampling method is:

[0106] t 1 =-t MTTF lnγ (5)

[0107] t 2 =-t MTTR lnγ (6)

[0108] In the formula, γ is a random number uniformly distributed between 0 and 1; t MTTF is the average working time; t MTTR is the mean time to repair;

[0109] The status of all units is sampled to obtain the hourly sequence of the grid status throughout the year, and the annual available capacity sequence of conventional units is generated. The hourly power generation of renewable energy is added to the hourly power generation of conventional units to obtain the hourly annual available capacity of the power generation side, which is then compared with the hourly load throughout the year to calculate the EENS for the year:

[0110]

[0111] Among them, N Yis the total number of simulated years; N is the power outage state that occurs; C j represents the available capacity at the jth hour; L j is the load level at the jth hour;

[0112] Step 4: Based on the calculation results of the EENS of the year calculated by the time series Monte Carlo simulation in Step 3, the confidence capacity of renewable energy is calculated by the Effective Load Carrying Capacity (ELCC) method, and the confidence capacity of the newly added renewable energy is used to evaluate its carrying capacity for the grid load;

[0113] The ELCC method quantifies the contribution of renewable energy to grid stability by analyzing the acceptable renewable energy capacity in the system at a specific reliability level.

[0114] The specific steps of step 4 include:

[0115] 4.1 Based on the calculation results of the EENS of the year calculated by the sequential Monte Carlo simulation in step 3, the effective load carrying capacity (ELCC) method is used to calculate the confidence capacity under different photovoltaic and wind power installed capacities. The calculation formula is shown in formula (8):

[0116] ELCC=EENS(C+ΔC,L+ΔL) (8)

[0117] 4.2 The confidence capacity of new renewable energy is defined as the ratio of the ideal conventional generator capacity to the renewable energy power capacity that provides the same additional power, and the formula is as follows:

[0118]

[0119] Among them, C R is the capacity of the ideal conventional generator. The additional capacity of renewable energy power required to provide the same additional load ΔL is C WP The additional renewable energy capacity required to provide additional load refers to the contribution of renewable energy under the additional load.

[0120] 4.3 The confidence capacity of ELCC and newly added renewable energy calculated through the above steps is combined with the load support capacity under different installed capacities to further evaluate the load carrying capacity of the power grid under different renewable energy installed capacities.

[0121] In this embodiment, Figure 2 It can reflect the relationship between capacity and reliability under different systems. It can be seen that the relationship between load L and reliability level R is monotonically increasing. This shows that when the load is added, the reliability level of the system is also improving.

[0122] The effective load carrying capacity (ELCC) method is used to evaluate the confidence capacity under different photovoltaic and wind power installed capacities. The meaning of ELCC is as shown in formula (8):

[0123] ELCC=EENS(C+ΔC,L+ΔL) (8)

[0124] Where ΔL is Figure 1 The additional load power that the new ideal power plant can provide is shown in the figure. In general, ΔC≠ΔL, because faults in the generator set and the transmission line may cause power system interruption.

[0125] The additional power that the new renewable energy can provide to meet the load demand can also be provided by the capacity of the installed ideal conventional generators. Both cases can maintain system reliability. The confidence capacity is defined as the ratio of the ideal conventional generator capacity to the renewable energy power capacity that provides the same additional power. The formula is as follows:

[0126]

[0127] Among them, C R is the capacity of the ideal conventional generator. The additional capacity of renewable energy power required to provide the same additional load ΔL is C WP The additional renewable energy capacity required to provide additional load refers to the contribution of renewable energy under the additional load.

[0128] In this embodiment, Figure 2 It can reflect the relationship between capacity and reliability under different systems. It can be seen that the relationship between load L and reliability level R is monotonically increasing.

[0129] The ELCC and the confidence capacity of the newly added renewable energy calculated through the above steps, combined with the load support capacity under different installed capacities, can further evaluate the load carrying capacity of the power grid under different installed capacities of renewable energy. Through these analyses, it is possible to scientifically quantify the support role of renewable energy on the power grid load, evaluate its stability and contribution in the power grid, and provide a strong decision-making basis for power grid planning, load scheduling, and the access of renewable energy.

[0130] Step 5: Use the secant method to calculate the maximum load that the new renewable energy generation capacity can support while maintaining a specific reliability level;

[0131] The specific steps of step 5 include:

[0132] Step 5.1, if the peak load of the original load is L pk0, the deviation of the final result is ε, the reliability of the original system is calculated, the original system reliability is R, and the renewable energy capacity C is added to the original system WP System and increase the capacity of conventional generators C WP The system reliability is R1 and R2 respectively;

[0133] Step 5.2: Calculate the passing point X(L pk0 ,R) and point Y(L pkθ +C WP ,R 2 ) 0 The peak load L corresponding to the intersection point with the line f(x)=R 1 , using the time series Monte Carlo simulation to add a load duration curve L a1 =S+L 1 -L pk0 , calculate the reliability R 3 ;

[0134] Step 5.3: If | R 3 -R|>ε, calculate the point X(L 1 ,R 3 ) and point Y(L pkθ +C WP ,R 2 ) 1与 The peak load L corresponding to the intersection of line f(x) = R 2 , using the time series Monte Carlo simulation to simulate the new load duration curve L a2 =S+L 2 -L pk0 , and obtain the reliability index R 4 ;

[0135] Step 5.4: If the new reliability index R 4 If the difference with R is still greater than ε, continue the previous level process until the calculated reliability index R i The difference from the original reliability is less than ε. The maximum load that the newly added renewable energy generation capacity can support when the reliability level R is obtained.

[0136] The flowchart of the secant method for evaluating reliability is as follows Figure 3 As shown in Figure 2, it is easier to calculate the reliability level under different loads using Monte Carlo simulation, but the calculation of the inverse process is more complicated. Figure 2 , the Secant Method is used to solve the additional load that the newly added renewable energy generation capacity can support. In the calculation process, it is necessary to consider the relationship between each load level and system reliability. For the convenience of calculation, Figure 2 The load duration curve in is changed to numerical form, such as Figure 4As shown, the modified x-axis is the peak load, and the figure reflects the relationship between the peak load and reliability and the secant method diagram.

[0137] Step 6: Based on the confidence capacity of the newly added renewable energy and the maximum load that the newly added renewable energy generation capacity can support calculated in steps 4 and 5, and based on the changing trends in different scenarios, evaluate the stability and feasibility of the power grid under different renewable energy generation capacities, and provide a decision-making basis for power grid planning and renewable energy access.

[0138] 6.1 Analyze the changing trends under different scenarios. According to the effective load carrying capacity (ELCC) calculated in step 4 and the maximum load that the newly added renewable energy generation capacity can support obtained by the secant method in step 5, analyze the system performance under different photovoltaic and wind power installed capacities, load levels, and reliability requirements. Evaluate the load carrying capacity and stability of the power grid under different conditions and identify the optimal renewable energy capacity configuration plan.

[0139] 6.2 Calculate the confidence capacity of renewable energy generation. After completing the evaluation of the load and generation capacity support capabilities under different scenarios, combine the ELCC method and the confidence capacity of the newly added renewable energy (as described in step 4) to calculate the confidence capacity of renewable energy generation under each scenario. This process will combine different load and generation capacity configurations to ultimately derive the confidence capacity index of renewable energy generation, providing the most reliable load support capacity data for the power grid.

[0140] 6.3 Evaluate the stability and feasibility of the power grid and provide a decision-making basis for power grid planning. Based on the calculated confidence capacity of renewable energy generation, evaluate whether the power grid can maintain stable operation after connecting to renewable energy of different scales, and analyze the changes in the reliability of the system under different renewable energy capacities. Through a detailed evaluation of the stability and feasibility of the power grid, combined with the confidence capacity of renewable energy generation, propose a suitable power grid planning scheme, provide a scientific and reasonable decision-making basis for the access of renewable energy, and ensure the safety, stability and economy of power grid operation.

[0141] A renewable energy power generation confidence capacity evaluation system based on Monte Carlo simulation, comprising:

[0142] Data acquisition module, collecting meteorological data of photovoltaic and wind power and grid load data;

[0143] The module for generating the annual power generation time series establishes the power generation models of photovoltaic and wind power and generates the annual power generation time series based on the collected data;

[0144] The EENS calculation module uses the generated annual power generation time series and load data to select the EENS of unprovided expected energy as an indicator of system power generation reliability. It uses time-series Monte Carlo simulation to calculate the EENS of the year and evaluate the reliability of the system under different meteorological conditions.

[0145] The newly added confidence capacity calculation module for renewable energy uses the effective load carrying capacity ELCC method to calculate the confidence capacity of renewable energy based on the calculation results of the EENS of the current year through time-series Monte Carlo simulation, and evaluates its carrying capacity for the grid load through the newly added confidence capacity of renewable energy;

[0146] The maximum load calculation module uses the secant method to calculate the maximum load that the newly added renewable energy generation capacity can support while maintaining a specific reliability level;

[0147] The evaluation module evaluates the stability and feasibility of the power grid under different renewable energy generation capacities based on the calculated confidence capacity of the newly added renewable energy and the maximum load that the newly added renewable energy generation capacity can support, based on the changing trends under different scenarios.

[0148] In this embodiment, generally speaking, the confidence capacities of the two are consistent with the trend of penetration power level. As the penetration power level decreases, the confidence capacities of the two new energy sources decrease accordingly. Analyze the calculation results of the confidence capacity: high capacity credit represents the stable power provided by renewable energy during peak load, which can effectively replace traditional energy and reduce the grid's dependence on conventional generators; low capacity credit represents that even if the installed capacity of renewable energy increases, it has limited load support for the grid at critical moments, and may require additional energy storage systems or backup conventional power sources.

[0149] It should be emphasized that the embodiments of the present invention are illustrative rather than restrictive. Therefore, the present invention includes but is not limited to the embodiments described in the specific implementation modes. Any other implementation modes derived by those skilled in the art based on the technical solutions of the present invention also fall within the scope of protection of the present invention.

Claims

1. A method for evaluating the confidence capacity of renewable energy power generation based on Monte Carlo simulation, characterized in that: The following steps are involved: Step 1: Collect meteorological data and grid load data of photovoltaic and wind power; Step 2: Establish a photovoltaic and wind power generation model, and generate a full-year power generation time series based on the data collected in step 1; Step 3: Using the annual power generation time series generated in step 2 and combined with load data, select the expected energy not provided EENS as the indicator of system power generation reliability, calculate the EENS of the year by using time series Monte Carlo simulation, and evaluate the reliability of the system under different meteorological conditions; Step 4: Based on the calculation results of the EENS of the year calculated by the time series Monte Carlo simulation in step 3, the confidence capacity of renewable energy is calculated by using the effective load carrying capacity ELCC method, and the confidence capacity of the newly added renewable energy is used to evaluate its carrying capacity for the grid load; Step 5: Use the secant method to calculate the maximum load that the new renewable energy generation capacity can support while maintaining a specific reliability level; Step 6: Based on the confidence capacity of the newly added renewable energy calculated in Steps 4 and 5 and the maximum load that the newly added renewable energy generation capacity can support, and based on the changing trends in different scenarios, evaluate the stability and feasibility of the power grid under different renewable energy generation capacities.

2. The method for evaluating the confidence capacity of renewable energy power generation based on Monte Carlo simulation according to claim 1, characterized in that: The specific method of step 1 is: Collect hourly data on light radiation intensity, temperature and wind speed in the experimental area; Collect grid load data, including total installed capacity of renewable and traditional energy sources, load levels, and historical load data; The collected data is cleaned and preprocessed to ensure the consistency of time series data and consider the processing of missing values ​​and outliers in the data.

3. The method for evaluating the confidence capacity of renewable energy power generation based on Monte Carlo simulation according to claim 1, characterized in that: The specific steps of step 2 include: 2.1 The photovoltaic power generation model is: 2.2 The wind power generation model is as follows: 2.3 Based on the data collected in step 1, substitute the wind power generation model and photovoltaic power generation model to generate the annual power generation time series.

4. The method for evaluating the confidence capacity of renewable energy power generation based on Monte Carlo simulation according to claim 1, characterized in that: The specific steps of step 3 include: Step 3.1: Using the annual power generation time series generated in step 2 and combined with load data, select the expected energy not provided EENS as the indicator of system power generation reliability. The calculation formula is as follows: This indicator represents the severity of system failure; Step 3.2, use the time series Monte Carlo simulation to calculate EENS, and the sampling method is: t1=-t MTTF lnγ (5) t2=-t MTTR lnγ (6) In the formula, γ is a random number uniformly distributed between 0 and 1; t MTTF is the average working time; t MTTR is the mean time to repair; After sampling is completed, the annual available capacity sequence of conventional units is generated, and the annual available capacity per hour on the power generation side is compared with the annual hourly load to calculate the EENS for the year: Among them, N Y is the total number of simulated years; N is the power outage state that occurs; C j represents the available capacity at the jth hour; L j is the load level at the jth hour; 3.3 Based on the EENS calculation results of the year, evaluate the reliability of the system under different meteorological conditions.

5. The method for evaluating the confidence capacity of renewable energy power generation based on Monte Carlo simulation according to claim 1, characterized in that: The specific steps of step 4 include: 4.1 Based on the calculation results of the EENS of the year calculated by the sequential Monte Carlo simulation in step 3, the effective load carrying capacity ELCC method is used to calculate the confidence capacity under different photovoltaic and wind power installed capacities. The calculation formula is shown in formula (8): ELCC=EENS(C+ΔC,L+ΔL) (8) 4.2 The confidence capacity of new renewable energy is defined as the ratio of the ideal conventional generator capacity to the renewable energy power capacity that provides the same additional power, and the formula is as follows: Among them, C R is the capacity of the ideal conventional generator. The additional capacity of renewable energy power required to provide the same additional load ΔL is C WP , the additional renewable energy capacity required to provide the additional load refers to the contribution of renewable energy under the additional load; 4.3 The confidence capacity of ELCC and newly added renewable energy calculated through the above steps is combined with the load support capacity under different installed capacities to further evaluate the load carrying capacity of the power grid under different renewable energy installed capacities.

6. The method for evaluating the confidence capacity of renewable energy power generation based on Monte Carlo simulation according to claim 1, characterized in that: The specific steps of step 5 include: Step 5.1, if the peak load of the original load is L pk0 , the deviation of the final result is ε, the reliability of the original system is calculated, the original system reliability is R, and the renewable energy capacity C is added to the original system WP System and increase the capacity of conventional generators C WP The system reliability is R1 and R2 respectively; Step 5.2: Calculate the passing point X(L pk0 ,R) and point Y(L pkθ +C WP , R2) and the intersection of the straight line S0 and the line f(x) = R corresponds to the peak load L1, and the time series Monte Carlo simulation is used to simulate the additional load duration curve L a1 =S+L1-L pk0 , calculate reliability R3; Step 5.3: If |R3-R|>ε, calculate the distance between point X(L1,R3) and point Y(L pkθ +C WP ,R2) of the straight line S 1与 The peak load L2 corresponding to the intersection of line f(x) = R is simulated by using time series Monte Carlo to simulate the new load duration curve L a2 =S+L2-L pk0 , and obtain the reliability index R4; Step 5.4: If the difference between the new reliability index R4 and R is still greater than ε, continue the previous process until the calculated reliability index R i The difference with the original reliability is less than ε; when the reliability level R is obtained, the maximum load that the newly added renewable energy generation capacity can support.

7. A renewable energy power generation confidence capacity evaluation system based on Monte Carlo simulation, characterized by: include: Data acquisition module, collecting meteorological data of photovoltaic and wind power and grid load data; The module for generating the annual power generation time series establishes the power generation models of photovoltaic and wind power and generates the annual power generation time series based on the collected data; The EENS calculation module uses the generated annual power generation time series and load data to select the EENS of unprovided expected energy as an indicator of system power generation reliability, and uses time-series Monte Carlo simulation to calculate the EENS of the year to evaluate the reliability of the system under different meteorological conditions; The newly added confidence capacity calculation module for renewable energy uses the effective load carrying capacity ELCC method to calculate the confidence capacity of renewable energy based on the calculation results of the EENS of the current year through time-series Monte Carlo simulation, and evaluates its carrying capacity for the grid load through the newly added confidence capacity of renewable energy; The maximum load calculation module uses the secant method to calculate the maximum load that the newly added renewable energy generation capacity can support while maintaining a specific reliability level; The evaluation module evaluates the stability and feasibility of the power grid under different renewable energy generation capacities based on the calculated confidence capacity of the newly added renewable energy and the maximum load that the newly added renewable energy generation capacity can support, based on the changing trends under different scenarios.

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