A method for quantifying the flexibility requirements of power grids across multiple time scales

Abstract

This invention belongs to the field of power system dispatching and discloses a method for quantifying the flexibility requirements of power grids across multiple time scales. It uses seasonal generation demand fluctuation indicators and uncertainty indicators to describe the monthly power balance pressure of the power grid; it uses the number of extreme fluctuation days and the volatility of generation demand under extreme conditions to quantify the medium-term power compensation pressure faced by new energy sources due to continuous multi-day extreme fluctuations; and it uses anti-peak-shaving characteristic indicators and intraday fluctuation indicators to describe the regulation pressure caused by the hourly and minute-level intermittent output of new energy sources. By coupling long-term, medium-term, and short-term quantitative indicators, a comprehensive flexibility quantification index is constructed. Analysis of the East China region and its subordinate provincial power grids shows that this invention can effectively identify the flexibility requirements of dispatching operations at different time scales in different power grids, providing accurate decision support for inter-provincial power exchange under a large power grid platform.

Description

Technical Field

[0001] This invention belongs to the field of power system dispatching and relates to a method for quantifying the flexibility requirements of power grids across multiple time scales. Background Technology

[0002] With the energy transition, the integration of a high proportion of renewable energy, and the continuous growth of electricity demand, higher demands are being placed on grid flexibility. The proportion of renewable energy sources such as wind and solar power in China's power structure continues to increase, but their power output is significantly affected by external factors such as weather and seasons. Assessing flexibility requirements helps quantify the impact of uncertainties in renewable energy output and improve grid stability. The distribution of renewable energy varies greatly across different provinces in China. For example, the "Three Norths" region (Northeast, North China, and Northwest China) has abundant wind and solar resources, while the southeast coast has high load demand. Assessing flexibility requirements helps promote inter-provincial and inter-regional power dispatch and resource optimization. Therefore, quantifying grid flexibility across multiple time scales has become an urgent technical problem to be solved in the field of power system dispatch. Summary of the Invention

[0003] The technical problem to be solved by this invention is the quantification of power grid flexibility requirements based on multiple time scales. By analyzing the sources of power grid flexibility at different time scales, different power grid flexibility quantification indicators are proposed for different time scales, and the changes in power grid flexibility requirements at different time scales are analyzed.

[0004] One aspect of the present invention provides a method for quantifying the flexibility requirements of a power grid across multiple time scales, comprising the following steps:

[0005] 1) To assess the long-term flexibility requirements of the power grid, seasonal generation demand fluctuation indicators and generation demand uncertainty indicators are introduced, as follows;

[0006] Among them, seasonal power generation demand fluctuation indicators include:

[0007]

[0008] In the formula: E r,m The monthly electricity demand for region r in month m; P r,m For the total output of region r in month m; T represents the total power output for scenic effects in region r in month m; T represents the number of hours in a month. The average monthly electricity demand in region r; Let be the difference between the monthly electricity demand in region r and the annual average monthly electricity demand.

[0009] Among them, the indicators of seasonal power generation demand uncertainty include:

[0010]

[0011] In the formula: This represents the upper limit of monthly electricity demand in region r for month m. This represents the lower limit of monthly electricity demand in region r for month m. This represents the difference between the upper and lower limits of the monthly electricity demand fluctuation in region r.

[0012] 2) Calculate the long-term flexibility requirement index of the power grid. The specific steps are as follows:

[0013] A1: Using a monthly step size, select the monthly output data of each power source in the power grid for one year, and solve for the power generation E required by the flexible power sources of the power grid in each month according to equation (1). r,m ;

[0014] A2: Solve the annual average monthly electricity demand of the grid's flexible power source according to equation (2). Then, the power grid's power fluctuation flexibility adjustment requirement is solved according to equation (3).

[0015] A3: Select several years of data and repeat step A1 to obtain the power generation data required by the grid's flexible power sources over those years; plot the data from each year on the same power-time line graph, and take the upper and lower envelopes of the graph to obtain the upper limit of monthly power demand. and monthly electricity demand lower limit

[0016] A4: Subtract the lower limit of monthly electricity demand from the upper limit of monthly electricity demand according to formula (6) to obtain the grid uncertainty flexibility adjustment demand.

[0017] In one possible implementation, the method further includes:

[0018] 3) Taking long-term power control as the boundary condition, it is necessary to further analyze the daily-scale flexibility requirements brought about by the continuous extreme fluctuations of new energy in the power grid over multiple days within the month. To this end, an index of the number of days of continuous extreme fluctuations of new energy and an index of the volatility of power generation demand under extreme conditions are proposed, as follows.

[0019] Among them, the indicator of consecutive extreme fluctuation days in new energy includes:

[0020]

[0021] Where: MA t The average power output of the wind and solar energy over days t; x i Contribute to the beauty of i day; E t θ represents the allowable fluctuation range of wind and solar power output, and C represents the 0-1 variable used to determine whether it is extreme weather. t Use 0-1 variables to determine whether there are continuous extreme fluctuations; k is the maximum number of days that extreme fluctuations are allowed to occur.

[0022] Among them, the indicators of power generation demand volatility under extreme conditions include:

[0023]

[0024] In the formula: L represents the average power generation demand. i For the power generation needs of i days; L t To meet the world's extreme weather power generation needs, F t M This represents the extreme volatility value in the medium term;

[0025] 4) Calculate the medium-term daily-scale flexibility requirements of the power grid. The specific steps are as follows:

[0026] B1: Using the day as the step size, select the daily output data of each power source in the power grid throughout the year, and calculate the average output of the next n days of each day throughout the year according to formula (7), where n is a positive integer;

[0027] B2: Determine the normal daily fluctuation range of wind and solar power, draw the daily wind and solar power output map of the power grid, and define the number of days with daily power output outside the normal fluctuation range as the extreme days;

[0028] B3: Calculate the average output of flexible power supplies for each month. The power generation demand during extreme weather is statistically analyzed, and the flexibility demand F during extreme weather is calculated according to equation (11). t M .

[0029] In one possible implementation, the method further includes:

[0030] 5) To address the system regulation pressure caused by the intermittent output of new energy sources at the hourly and minute levels, anti-peak shaving characteristic indicators and intraday fluctuation indicators are used to quantify short-term flexibility requirements, as detailed below;

[0031] Among them, the indicators of new energy anti-peak shaving characteristics include:

[0032] The objective is to reflect the phenomenon where, when traditional power generation cannot flexibly adjust the load in a certain region within a day, the power generation characteristics of new energy sources exhibit a completely opposite trend to those of traditional load adjustment. Therefore, an index for the anti-peak-shaving characteristics of new energy sources is introduced, defined as:

[0033]

[0034] L net =LP (13)

[0035]

[0036] In the formula: L peak For peak load; L valley This represents the load trough value; Lnet L represents net load; P represents raw load; P represents renewable energy output; PV peak Peak-to-valley ratio of load; PV net-peak The peak-to-valley ratio of net load; L net-peak Peak net load; L net-valley Net load trough value; FI is the peak-shaving index for renewable energy output;

[0037] Among them, the intraday volatility indicators for new energy include:

[0038] To reflect the intraday volatility of new energy sources, an intraday volatility index for new energy sources is introduced, defined as:

[0039]

[0040] In the formula: For the average daily output of new energy; L i,j (t) represents the daily output at time j; T d For 24 hours; Contribute to short-term fluctuations;

[0041] 6) Calculate the short-term flexibility demand index of the power grid. The specific steps are as follows:

[0042] C1: Select daily grid load data and renewable energy output data, and calculate the net grid load excluding renewable energy output based on the raw data;

[0043] C2: Solve for the peak-to-valley ratio PV of the original load of the power grid according to equations (12) and (14). peak and the peak-to-valley ratio of net load (PV) net-peak Then, according to formula (15), the peak-shaving index FI of new energy output is calculated;

[0044] C3: Calculate the average daily output of new energy according to formula (16), and calculate the fluctuation of new energy output over multiple days according to formula (17);

[0045] C4: Statistically analyze the power output fluctuation values ​​at different time periods to obtain the probability distribution of power output fluctuations. Take different probability quantiles to obtain the new energy intraday fluctuation flexibility demand index with different confidence levels.

[0046] In one possible implementation, the method further includes:

[0047] 7) Couple long-term, medium-term, and short-term quantitative indicators to comprehensively describe the power grid's flexibility requirements across multiple time scales, and construct a comprehensive quantitative indicator for power grid flexibility, as detailed below:

[0048]

[0049] In the formula: F stThe result is the data after standardization; F represents the calculated long-term, medium-term, and short-term flexibility datasets; μ is the mean of the data; σ is the standard deviation of the data; F MAV The average of the absolute values ​​of standardized data; for short- and long-term indicators, n0 = n; for medium-term indicators, n = 365; ω L ω M ω S Weighting coefficients for long-term, medium-term, and short-term flexibility; These represent the absolute averages of long-term, medium-term, and short-term standardized data, respectively; F ALL A comprehensive index for quantifying power grid flexibility;

[0050] 8) Calculate the comprehensive quantitative index of power grid flexibility and analyze the flexibility of power balance. The specific steps are as follows:

[0051] D1: Select long-term fluctuation data, medium-term extreme fluctuation data and short-term 50th percentile daily fluctuation data to form a power grid fluctuation flexibility dataset;

[0052] D2: Standardize the power grid fluctuation flexibility dataset according to Equation (18) to obtain the standard values ​​of the power grid's long-term, medium-term, and short-term fluctuation flexibility requirements;

[0053] D3: Calculate the quantitative average values ​​of the long-term, medium-term, and short-term flexibility standards of the power grid according to formula (19);

[0054] D4: Determine the short-, medium-, and long-term flexibility coefficients based on the power grid power source attributes and actual needs;

[0055] D5: Calculate the comprehensive index of power grid flexibility according to formula (20).

[0056] In a second aspect, a computing device is provided, comprising a processor and a memory, wherein the memory stores a computer program, and the processor is configured to run the computer program to perform the power grid multi-timescale flexibility demand quantification method described in any of the preceding claims.

[0057] Thirdly, a storage medium is provided that stores a computer program, wherein the computer program is configured to execute the power grid multi-timescale flexibility requirement quantification method described in any of the preceding claims at runtime.

[0058] Compared with existing methods, the advantages of this invention are as follows: By introducing multi-timescale flexibility demand quantification indicators (such as seasonal demand volatility, extreme condition volatility, and intraday volatility of renewable energy), it achieves comprehensive coverage of medium- and long-term fluctuations and the impact of short-term extreme conditions, while refining the analysis of the reverse impact of renewable energy output on traditional load regulation. This systematic quantitative method not only makes up for the shortcomings of existing research in time scale and demand characteristic analysis, but also provides more targeted theoretical support and practical guidance for dispatch optimization and stability assessment of high-proportion renewable energy grid integration. Attached Figure Description

[0059] Figure 1 This is a flowchart for calculating the long-term flexibility requirements of the power grid;

[0060] Figure 2(a) is a diagram illustrating the principle of calculating the number of days with extreme fluctuations;

[0061] Figure 2(b) is a schematic diagram of the calculation principle for extreme fluctuation power generation demand;

[0062] Figures 3(a) to 3(e) These are the calculation results of seasonal power generation demand volatility indicators for Shanghai, Zhejiang, Anhui, Fujian, and Jiangsu, respectively.

[0063] Figures 4(a) to 4(e) These are the calculation results of seasonal power generation demand uncertainty indicators for Shanghai, Zhejiang, Anhui, Fujian, and Jiangsu, respectively.

[0064] Figures 5(a) to 5(e) These are charts showing the calculation results of the number of consecutive extreme fluctuation days of new energy in Shanghai, Zhejiang, Anhui, Fujian, and Jiangsu.

[0065] Figures 6(a) to 6(e) These are the calculation results of power generation demand volatility indicators under extreme conditions in Shanghai, Zhejiang, Anhui, Fujian, and Jiangsu, respectively.

[0066] Figures 7(a) to 7(e) These are the calculation results of the new energy anti-peak-shaving characteristic indicators for Shanghai, Zhejiang, Anhui, Fujian, and Jiangsu, respectively.

[0067] Figures 8(a) to 8(e) These are charts showing the calculation results of the intraday volatility index for new energy sources in Shanghai, Zhejiang, Anhui, Fujian, and Jiangsu. Detailed Implementation

[0068] The specific embodiments of the present invention will be further described below with reference to the accompanying drawings and technical solutions.

[0069] To accurately describe the flexibility requirements of the power grid, existing technologies often use the following indicators: 1-hour ramp rate reflecting short-term load fluctuations, 3-hour ramp rate reflecting medium-term load fluctuations, ramp factor reflecting the proportion of load change in total power generation, and second-order load volatility reflecting short-term net load fluctuations; maximum residual load demand reflecting the extreme value of positive grid flexibility requirements, and minimum residual demand reflecting the extreme value of negative grid flexibility requirements; wind power output volatility reflecting the grid's specific power source regulation requirements, and power balance correction reflecting the grid's hydropower deviation flexibility requirements. Existing indicators focus more on the total fluctuation amplitude (such as dispatch change rate or net load volatility), lacking in-depth quantification of fluctuation uncertainty and distribution patterns. Furthermore, most of the above studies concentrate on short-term fluctuations (such as intraday or hourly levels), with less attention paid to medium- and long-term volatility. Flexibility requirements in scenarios with low probability but extremely high risk under extreme conditions have not been systematically quantified.

[0070] To address the aforementioned issues, this invention proposes a method for quantifying the flexibility demand of power grids across multiple time scales, and its application has been tested using the East China Power Grid and its subordinate provincial power grids. Results show that this invention can significantly reduce the deviation between power transmission plans and receiving-end demand while ensuring the total power generation of the basin, thus reducing power curtailment. Verification results show that, with power generation remaining essentially constant, the calculation scheme of this invention reduces the power transmission deviation during the dry season by 88.6% and the power transmission deviation during the flood season by 69.9% compared to the original method, demonstrating good practicality.

[0071] This invention provides a method for quantifying the flexibility requirements of power grids across multiple time scales, with the following specific implementation steps:

[0072] 1) Evaluating the long-term flexibility requirements of the power grid helps to assess power shortages and curtailment over a longer timescale, which is beneficial for optimizing the power grid's power supply structure. To evaluate the long-term flexibility requirements of the power grid, seasonal generation demand fluctuation indicators and seasonal generation demand uncertainty indicators are introduced, as follows;

[0073] Among them, seasonal power generation demand fluctuation indicators include:

[0074]

[0075] In the formula: E r,m The monthly electricity demand for region r in month m; P r,m For the total output of region r in month m; T represents the total power output for scenic effects in region r in month m; T represents the number of hours in a month. The average monthly electricity demand in region r; Let be the difference between the monthly electricity demand in region r and the annual average monthly electricity demand.

[0076] Among them, the indicators of seasonal power generation demand uncertainty include:

[0077]

[0078] In the formula: This represents the upper limit of monthly electricity demand in region r for month m. This represents the lower limit of monthly electricity demand in region r for month m. This represents the difference between the upper and lower limits of the monthly electricity demand fluctuation in region r.

[0079] 2) Calculate the long-term flexibility demand index of the power grid based on historical data, see [reference]. Figure 1 The specific steps are as follows:

[0080] A1: Using a monthly step size, select the monthly output data of each power source in the power grid for one year, and solve for the power generation E required by the flexible power sources of the power grid in each month according to equation (1). r,m ;

[0081] A2: Solve for the annual average monthly power consumption of the flexible power source of the power grid according to equation (2). Then, the power grid's power fluctuation flexibility adjustment requirement is solved according to equation (3).

[0082] A3: Select several years of data and repeat step A1 to obtain the power generation data required by the grid's flexible power sources over those years; plot the data from each year on the same power-time line graph, and take the upper and lower envelopes of the graph to obtain the upper limit of monthly power demand. and monthly electricity demand lower limit

[0083] A4: Subtract the lower limit of monthly electricity demand from the upper limit of monthly electricity demand according to formula (6) to obtain the grid uncertainty flexibility adjustment demand.

[0084] This application provides one possible implementation method, which may further include the following steps:

[0085] 3) After determining the long-term flexibility requirements, it is necessary to further subdivide the time scale. Quantifying the medium-term flexibility requirements of new energy sources helps to indicate the adaptability and resilience of the power system in the face of continuous fluctuations and extreme weather events on longer time scales (e.g., days to weeks) of new energy sources. Therefore, the following indicators are proposed to quantify the medium-term flexibility requirements of the power grid.

[0086] Among them, the indicator of consecutive extreme fluctuation days in new energy includes:

[0087]

[0088] Where: MA t The average power output of the wind and solar energy over days t; x iContribute to the beauty of i day; E t θ represents the allowable fluctuation range of wind and solar power output, and C represents the 0-1 variable used to determine whether it is extreme weather. t Use 0-1 variables to determine whether there are continuous extreme fluctuations; k is the maximum number of days that extreme fluctuations are allowed to occur.

[0089] Among them, the indicators of power generation demand volatility under extreme conditions include:

[0090]

[0091] In the formula: L represents the average power generation demand. i For the power generation needs of i days; L t To meet the world's extreme weather power generation needs, F t M This represents the extreme volatility value in the medium term;

[0092] 4) Calculate the daily-scale medium-term flexibility requirements of the power grid based on historical data. The specific steps are as follows:

[0093] B1: Using the day as the step size, select the power output data of each power source in the power grid for a year, and solve the average power output of the next n days of each day in the whole year according to the formula (7). n is a positive integer, for example, n is 7, that is, solve the 7-day average power output of each day in the whole year.

[0094] B2: θ is set to 30%, the normal daily fluctuation range of wind and solar power is determined, and a daily power output diagram of the power grid is drawn. The number of days with daily power output outside the normal fluctuation range is defined as the extreme number of days. The calculation principle is shown in Figure 2(a); in Figure 2(a), the horizontal axis is the number of days, and the vertical axis is the power output, with the unit being MW (megawatts). D t It was the scenery of each day that contributed, MA t It is the average output over 7 days;

[0095] B3: Calculate the average output of flexible power supplies for each month. The power generation demand during extreme weather is statistically analyzed, and the flexibility demand F during extreme weather is calculated according to equation (11). t M When M is 7, it is represented as V t The calculation principle is shown in Figure 2(b); in Figure 2(b), the horizontal axis represents the number of days, and the vertical axis represents the output, with units of MW and L. t This represents the actual power generation demand for each day. It is the 7-day average output, that is, the average power generation demand over 7 days, V t It is a fluctuation value.

[0096] This application provides one possible implementation method, which may further include the following steps:

[0097] 5) As the proportion of new energy in the power system gradually increases, the challenges faced by the power system not only come from long-term seasonal changes and medium-term continuous extreme fluctuations, but also from the surge in flexibility demand caused by short-term power output fluctuations of new energy. Therefore, the following indicators are proposed to quantify the short-term flexibility demand of new energy.

[0098] Among them, the indicators of new energy anti-peak shaving characteristics include:

[0099] The objective is to reflect the phenomenon where, when traditional power generation cannot flexibly adjust the load in a certain region within a day, the power generation characteristics of new energy sources exhibit a completely opposite trend to those of traditional load adjustment. Therefore, an index for the anti-peak-shaving characteristics of new energy sources is introduced, defined as:

[0100]

[0101] L net =LP (16)

[0102]

[0103] In the formula: L peak For peak load; L valley This represents the load trough value; L net L represents net load; P represents raw load; P represents renewable energy output; PV peak Peak-to-valley ratio of load; PV net-peak The peak-to-valley ratio of net load; L net-peak Peak net load; L net-valley Net load trough value; FI is the peak-shaving index for renewable energy output;

[0104] Among them, the intraday volatility indicators for new energy include:

[0105] To reflect the intraday volatility of new energy sources, an intraday volatility index for new energy sources is introduced, defined as:

[0106]

[0107] In the formula: For the average daily output of new energy; L i,j (t) represents the daily output at time j; T d For 24 hours; Contribute to short-term fluctuations;

[0108] (6) Calculate the short-term flexibility requirements of the power grid based on historical data. The specific steps are as follows:

[0109] C1: Select daily grid load data and renewable energy output data, and calculate the net grid load excluding renewable energy output based on the raw data;

[0110] C2: Solve for the peak-to-valley ratio PV of the original load of the power grid according to equations (12) and (14). peak and the peak-to-valley ratio of net load (PV) net-peak Then, according to formula (15), the peak-shaving index FI of new energy output is calculated;

[0111] C3: Calculate the average daily output of new energy according to formula (16), and calculate the fluctuation of new energy output over multiple days according to formula (17);

[0112] C4: Statistically analyze the power output fluctuation values ​​at different time periods to obtain the probability distribution of power output fluctuations. Take different probability quantiles to obtain the new energy intraday fluctuation flexibility demand index with different confidence levels.

[0113] This application provides one possible implementation method, which may further include the following steps:

[0114] 7) Couple long-term, medium-term, and short-term quantitative indicators to comprehensively describe the power grid's flexibility requirements across multiple time scales, and construct a comprehensive quantitative indicator for power grid flexibility, as detailed below:

[0115]

[0116] In the formula: F st The result is the data after standardization; F represents the calculated long-term, medium-term, and short-term flexibility datasets; μ is the mean of the data; σ is the standard deviation of the data; F MAV The average of the absolute values ​​of standardized data; for short- and long-term indicators, n0 = n; for medium-term indicators, n = 365; ω L ω M ω S Weighting coefficients for long-term, medium-term, and short-term flexibility; These represent the absolute averages of long-term, medium-term, and short-term standardized data, respectively; F ALL A comprehensive index for quantifying power grid flexibility;

[0117] 8) Calculate the comprehensive quantitative index of power grid flexibility and analyze the flexibility of power balance. The specific steps are as follows:

[0118] D1: Select long-term fluctuation data, medium-term extreme fluctuation data and short-term 50th percentile daily fluctuation data to form a power grid fluctuation flexibility dataset;

[0119] D2: Standardize the power grid fluctuation flexibility dataset according to Equation (18) to obtain the standard values ​​of the power grid's long-term, medium-term, and short-term fluctuation flexibility requirements;

[0120] D3: Calculate the quantitative average values ​​of the long-term, medium-term, and short-term flexibility standards of the power grid according to formula (19);

[0121] D4: Determine the short-, medium-, and long-term flexibility quantification coefficients based on the power grid's power source attributes and actual needs. For example, if the number of days with extreme power grid fluctuations is large, ω can be appropriately increased. M .

[0122] D5: Calculate the comprehensive index of power grid flexibility according to formula (20).

[0123] The rationality of the flexibility requirement index of this method is judged by taking the four provinces and one municipality (including Shanghai, Zhejiang, Anhui, Fujian and Jiangsu) of the East China power grid as an example.

[0124] We used five-year hourly actual load data and renewable energy output data of the power grids in four provinces and one municipality in East China from 2019 to 2023 to calculate grid flexibility. When calculating coarse-grained indicators such as medium- and long-term flexibility, we can average the hourly indicators on monthly and daily scales to obtain the corresponding coarse-grained calculation data.

[0125] Long-term flexibility requirements

[0126] This study calculates seasonal power generation demand fluctuation indicators and seasonal power generation demand uncertainty indicators to quantify the long-term flexibility of new energy sources in East China. Figures 3(a) to 3(e) It is evident that the power generation demand varies considerably across provinces and cities, exhibiting distinct seasonal characteristics. Summer (June-September) is the peak period for power generation demand, accounting for 38.2% of the annual total. This is primarily due to higher summer temperatures, leading to a significant increase in cooling demand and consequently a substantial rise in electricity load. Furthermore, summer is also the rainy season or typhoon season in some regions, with frequent overcast and rainy weather significantly impacting the power generation capacity of photovoltaic power systems.

[0127] Depend on Figures 4(a) to 4(e) It can be seen that the uncertainty index of power generation demand varies greatly among provinces and cities, reflecting the fluctuations and seasonal differences in electricity load among provinces and cities in East China over many years. Shanghai has the lowest uncertainty index at only 220 million kWh, indicating that its electricity demand fluctuates less and its load forecast is relatively stable. Jiangsu has the highest uncertainty index at 4.7 billion kWh, indicating that its electricity demand fluctuates more drastically. The maximum and minimum values ​​of the uncertainty index of power generation demand occurred in November and August, respectively, with a difference of 78.8%.

[0128] Medium-term flexibility requirements

[0129] This study calculates the number of consecutive days of extreme fluctuations in new energy sources and the volatility of power generation demand under extreme conditions to quantify the medium-term flexibility of new energy sources in East China. Figures 5(a) to 5(e)It is known that Fujian has the most days of extreme fluctuations in annual renewable energy generation, at 234 days, while Zhejiang has the fewest, at 126 days. Fujian's annual number of days with extreme fluctuations is significantly higher than Zhejiang's, indicating that renewable energy is in a state of extreme fluctuation for most of the time (approximately 64%), while Zhejiang only experiences extreme fluctuations for 34% of the time. Furthermore, in both Fujian and Zhejiang, the number of days with extreme fluctuations is lower in the second half of the year than in the first half, indicating that this indicator is affected by seasonal factors. This is because photovoltaic power generation is relatively low in winter, while wind power may be more concentrated in winter. Therefore, the second half of the year may see some more stable weather conditions, resulting in relatively smaller fluctuations in renewable energy generation. Fujian has the most consecutive days of extreme fluctuations in annual renewable energy generation, at 120 days, while Zhejiang has the fewest, at 35 days. Therefore, Fujian's renewable energy generation faces greater challenges in terms of volatility and continuity, requiring more flexible resource adjustments to ensure the stable operation of the power system.

[0130] Depend on Figures 6(a) to 6(e) It can be seen that Zhejiang has the largest extreme power generation demand fluctuation, with an upper limit of 30,145 MW and a lower limit of -34,795 MW, indicating that its power demand fluctuates greatly and its load forecast is poor. Fujian has the smallest fluctuation, with an upper limit of 6,688 MW and a lower limit of -7,063 MW, indicating that its power demand fluctuates less and its load forecast is more stable. Meanwhile, Shanghai's fluctuation index quantiles are relatively close, and the distribution of extreme cases is relatively concentrated with high similarity. Anhui's fluctuation index quantiles are relatively dispersed, and the distribution of extreme cases is relatively dispersed with low similarity, indicating that it faces greater pressure to ensure supply and absorb power in the medium term.

[0131] Short-term flexibility needs

[0132] The peak-shaving characteristic index and intraday fluctuation index of renewable energy are calculated to quantify the short-term renewable energy flexibility in East China. Figures 7(a) to 7(e) It can be seen that the anti-peak shaving characteristics of Shanghai, Zhejiang, and Fujian are not obvious, with all anti-peak shaving indices being negative and less than -16%, indicating that the wind and solar power output process is highly consistent with the load trend, and no anti-peak shaving phenomenon has occurred. Anti-peak shaving phenomena exist in Jiangsu and Anhui provinces, with Anhui exhibiting the most obvious characteristics. All anti-peak shaving indices are positive, with the highest reaching 128.8% and an average of 54.8%.

[0133] Depend on Figures 8(a) to 8(e)It can be seen that Jiangsu Province experienced the largest intraday fluctuation in renewable energy, ranging from 23,580 MW to -24,774 MW. This significant positive and negative fluctuation in Jiangsu's renewable energy resources on that day was related to the strong volatility of wind and solar energy resources and significant weather changes. Shanghai experienced the smallest intraday fluctuation, ranging from 6,812 MW to -8,511 MW, indicating relatively stable weather conditions that day. The East China region exhibits a characteristic of "relatively dense in the middle and relatively loose at both ends" in its fluctuation quantiles. This suggests that the fluctuations in renewable energy generation in these regions are mostly concentrated around a certain intermediate value, while the fluctuations at both ends are relatively less. Under normal circumstances, the fluctuations in renewable energy generation are concentrated within a moderate range, but under extreme weather conditions, the fluctuation range can become larger.

[0134] Compared to traditional single-scale methods for quantifying flexibility demand, this method comprehensively quantifies the characteristics of grid flexibility demand, from long-term seasonal fluctuations and medium-term extreme condition demands to short-term intraday fluctuations in renewable energy. By refining the time scale and designing indicators, it fully reveals the regional differences and key influencing factors of flexibility demand under high renewable energy penetration in various provinces, providing important references for grid planning and flexibility optimization.

[0135] Flexibility Comprehensive Index

[0136] To verify the reliability of the method and comprehensively evaluate the overall flexibility of the power grid, this study selected equal weights for long-term, medium-term, and short-term flexibility requirements, and calculated the overall flexibility of the power grid over multiple time periods.

[0137] The results of the flexibility calculation for the four provinces and one municipality are summarized in Table 1:

[0138] Table 1 Comprehensive Grid Flexibility Requirements

[0139]

[0140] As shown in Table 1, Fujian and Shanghai have significant overall flexibility requirements, making power balancing more challenging for both. Nested planning should consider the characteristics of renewable energy output in both provinces. However, the sources of their flexibility needs differ. Fujian's medium-term flexibility is particularly pronounced, characterized by numerous days of extreme power output and significant variations in daily renewable energy output. Therefore, when calculating the start-up and shutdown of thermal power plants, the flexibility configuration within Fujian should be emphasized. Shanghai's flexibility needs, on the other hand, are primarily intraday. The volatility of intraday output necessitates more flexible power sources for balancing, and this volatility may even alter the peak-valley characteristics of the original load. Therefore, when making short-term plans, the flexible power source configuration in Shanghai should be prioritized.

[0141] Based on the same inventive concept, this application also provides a computing device, including a processor and a memory, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the power grid multi-timescale flexibility demand quantification method of any of the above embodiments.

[0142] Based on the same inventive concept, this application also provides a storage medium storing a computer program, wherein the computer program is configured to execute the power grid multi-timescale flexibility requirement quantification method of any of the above embodiments at runtime.

[0143] Those skilled in the art will clearly understand that the specific working process of the systems, devices, and modules described above can be referred to the corresponding process in the foregoing method embodiments. For the sake of brevity, it will not be repeated here.

[0144] Those skilled in the art will understand that the technical solution of this application, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several program instructions to cause an electronic device (e.g., a personal computer, server, or network device) to execute all or part of the steps of the methods described in the embodiments of this application when running the program instructions. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, portable hard drive, read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0145] Alternatively, all or part of the steps of the foregoing method embodiments can be implemented by hardware (such as electronic devices like personal computers, servers, or network devices) associated with program instructions. The program instructions can be stored in a computer-readable storage medium. When the program instructions are executed by the processor of the electronic device, the electronic device executes all or part of the steps of the methods described in the embodiments of this application.

[0146] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that within the spirit and principles of this application, modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein; and these modifications or substitutions do not cause the corresponding technical solutions to leave the protection scope of this application.

Claims

1. A method for quantifying the flexibility requirements of a power grid across multiple time scales, characterized in that, Includes the following steps: 1) To assess the long-term flexibility requirements of the power grid, seasonal generation demand fluctuation indicators and generation demand uncertainty indicators are introduced, as follows; Among them, seasonal power generation demand fluctuation indicators include: (1) (2) (3) In the formula: for r area m Monthly electricity demand; for r area m Total output of the month; for r area m The moon's scenery always contributes; Hours per month; for r The region's average monthly electricity demand; for r area m The difference between monthly electricity demand and annual average monthly electricity demand; Among them, the indicators of seasonal power generation demand uncertainty include: (4) (5) (6) In the formula: This represents the upper limit of monthly electricity demand in region r for month m. This represents the lower limit of monthly electricity demand in region r for month m. This represents the difference between the upper and lower limits of the monthly electricity demand fluctuation in region r. 2) Calculate the long-term flexibility requirement index of the power grid. The specific steps are as follows: A1: Using a monthly step size, select the monthly output data of each power source in the power grid for one year, and solve according to formula (1) to obtain the power generation required by the flexible power sources of the power grid in each month. ; A2: Solve the annual average monthly electricity demand of the grid's flexible power source according to equation (2). Then, the power grid's power fluctuation flexibility adjustment requirement is solved according to equation (3). ; A3: Select several years of data and repeat step A1 to obtain the power generation data required by the flexible power sources of the power grid for several years; plot the data of each year on the same power-time line graph, and take the upper and lower envelopes of the graph to obtain the upper limit of monthly power demand. and monthly electricity demand lower limit ; A4: Subtract the lower limit of monthly electricity demand from the upper limit of monthly electricity demand according to formula (6) to obtain the grid uncertainty flexibility adjustment demand. ; 3) Taking long-term power control as the boundary condition, it is necessary to further analyze the daily-scale flexibility requirements brought about by the continuous extreme fluctuations of new energy in the power grid over multiple days within the month. To this end, we propose an index for the number of days of continuous extreme fluctuations of new energy and an index for the volatility of power generation demand under extreme conditions, as follows. Among them, the indicator of consecutive extreme fluctuation days in new energy includes: (7) (8) (9) In the formula: for t The average output of the scenery throughout the day; for i The scenery of the sky contributes to the effort; Use 0-1 variables to determine whether it is extreme weather; Allowable fluctuation range for wind and solar power output; Determine whether a variable with values ​​of 0 and 1 represents continuous extreme fluctuations; This represents the maximum number of days that extreme fluctuations are allowed to occur. Among them, the indicators of power generation demand volatility under extreme conditions include: (10) (11) In the formula: This represents the average power generation demand. for i Daily power generation demand; for t The world's demand for power generation during extreme weather events This represents the extreme volatility value in the medium term. 4) Calculate the medium-term daily-scale flexibility requirements of the power grid. The specific steps are as follows: B1: Using the day as the step size, select the daily output data of each power source in the power grid throughout the year, and solve the adjacent previous days of each day throughout the year according to the formula (7). n Average daily output, n It is a positive integer; B2: Determine the normal daily fluctuation range of wind and solar power, draw the daily wind and solar power output map of the power grid, and define the number of days with daily power output outside the normal fluctuation range as the extreme days; B3: Calculate the average output of flexible power supplies for each month. The power generation demand during extreme weather is statistically analyzed, and the flexibility demand during extreme weather is calculated according to formula (11). ; 5) To address the system regulation pressure caused by the intermittent output of new energy sources at the hourly and minute levels, anti-peak shaving characteristic indicators and intraday fluctuation indicators are used to quantify short-term flexibility requirements, as detailed below; Among them, the indicators of new energy anti-peak shaving characteristics include: The objective is to reflect the phenomenon where, when traditional power generation cannot flexibly adjust the load in a certain region within a day, the power generation characteristics of new energy sources exhibit a completely opposite trend to those of traditional load adjustment. Therefore, an index for the anti-peak-shaving characteristics of new energy sources is introduced, defined as: (12) (13) (14) (15) In the formula: This represents the peak load. This represents the load trough value. Net load; L This is the original load; Contribute to new energy; The peak-to-valley ratio of the load; This refers to the peak-to-valley ratio of net load. This represents the peak net load. This represents the net load trough value. Peak-shaving indicators that contribute to new energy sources; Among them, the intraday volatility indicators for new energy include: To reflect the intraday volatility of new energy sources, an intraday volatility index for new energy sources is introduced, defined as: (16) (17) In the formula: For the average daily output of new energy sources; for j Efforts must be made within the specified timeframe; For 24 hours; Contribute to short-term fluctuations; 6) Calculate the short-term flexibility demand index of the power grid. The specific steps are as follows: C1: Select daily grid load data and renewable energy output data, and calculate the net grid load excluding renewable energy output based on the raw data; C2: Solve for the peak-to-valley ratio of the original load of the power grid according to equations (12) and (14). and the peak-to-valley ratio of net load Then, according to equation (15), the peak-shaving index of new energy output is calculated. ; C3: Calculate the average daily output of new energy according to formula (16), and calculate the fluctuation of new energy output over multiple days according to formula (17); C4: Statistically analyze the power output fluctuation values ​​in different time periods to obtain the probability distribution of power output fluctuations. Take different probability quantiles to obtain new energy intraday fluctuation flexibility demand indicators with different confidence levels. 7) By coupling long-term, medium-term, and short-term quantitative indicators to comprehensively describe the power grid's flexibility requirements across multiple time scales, a comprehensive quantitative indicator for power grid flexibility has been formed, as follows: (18) (19) (20) In the formula: This is the result after data standardization. These are separate datasets for calculating long-term, medium-term, and short-term flexibility. The mean of the data; The standard deviation of the data; The average of the absolute values ​​of standardized data, for both long-term and short-term indicators. For the medium-term indicator n=365; , , Weighting coefficients for long-term, medium-term, and short-term flexibility; , , These represent the average absolute values ​​of standardized data for long, medium, and short periods, respectively. A comprehensive index for quantifying power grid flexibility; 8) Calculate the comprehensive quantitative index of power grid flexibility and analyze the flexibility of power balance. The specific steps are as follows: D1: Select long-term fluctuation data, medium-term extreme fluctuation data and short-term 50th percentile daily fluctuation data to form a power grid fluctuation flexibility dataset; D2: Standardize the power grid fluctuation flexibility dataset according to Equation (18) to obtain the standard values ​​of the power grid's long-term, medium-term, and short-term fluctuation flexibility requirements; D3: Calculate the quantitative average values ​​of the long-term, medium-term, and short-term flexibility standards of the power grid according to formula (19); D4: Determine the short-, medium-, and long-term flexibility coefficients based on the power grid power source attributes and actual needs; D5: Calculate the comprehensive index of power grid flexibility according to formula (20).

2. A computing device, characterized in that, It includes a processor and a memory, wherein the memory stores a computer program, and the processor is configured to run the computer program to perform the power grid multi-timescale flexibility demand quantification method of claim 1.

3. A storage medium, characterized in that, The storage medium stores a computer program, which is configured to execute the power grid multi-timescale flexibility requirement quantification method of claim 1 at runtime.

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