Power grid multi-time scale flexibility demand quantification method

Abstract

The invention belongs to the field of power system dispatching, and discloses a power grid multi-time-scale flexibility demand quantification method. The monthly electric quantity balance pressure of a power grid is described by adopting a seasonal power generation demand fluctuation index and an uncertainty index, and the medium-term electric quantity compensation pressure faced by continuous multi-day extreme fluctuation of new energy is quantified by adopting the number of days of extreme fluctuation and an extreme condition power generation demand fluctuation index. Regulation pressure caused by hour-level and minute-level output intermittency of new energy is described by adopting an anti-peak-shaving characteristic index and an intraday fluctuation index, the multi-time scale flexibility requirement of a power grid is comprehensively described by coupling long, medium and short-term quantitative indexes, and a flexible quantitative comprehensive index is constructed. By analyzing the East China region and the governed provincial power grids, the result shows that the method can effectively identify the flexibility requirements of scheduling operation of different power grids at different time scales, and provides accurate decision support for trans-provincial power and electric quantity interaction under a large power grid platform.

Description

Technical Field

[0001] The present invention belongs to the field of power system dispatching and relates to a method for quantifying the flexibility requirements of a power grid on multiple time scales. Background Art

[0002] With the energy transition, the high proportion of renewable energy access, and the continuous growth of electricity demand, higher requirements are put forward for the flexibility of the power grid. The proportion of renewable energy such as wind power and photovoltaic power in the Chinese power structure continues to increase, but their power generation output is greatly affected by external factors such as weather and seasons. Evaluating the flexibility requirements helps to quantify the impact of the uncertainty of new energy output and improve the stability of the power grid. The distribution of renewable energy varies greatly in different provinces of China. For example, in the "Three-North Regions", including Northeast China, northern North China, and Northwest China, there are rich wind and solar resources, while the load demand in the southeast coast is high. Evaluating the flexibility requirements helps to promote cross-provincial and cross-regional power dispatching and resource optimization. Therefore, quantifying the flexibility of the power grid on multiple time scales has become a technical problem to be solved urgently in the field of power system dispatching. Summary of the Invention

[0003] The technical problem to be solved by the present invention is the problem of quantifying the flexibility requirements of the power grid 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 corresponding to different time scales, and the changes in the flexibility requirements of the power grid at different time scales are analyzed.

[0004] On the one hand, the present invention provides a method for quantifying the flexibility requirements of a power grid on multiple time scales, including the following steps:

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

[0006] Among them, the seasonal power generation demand fluctuation indicator includes:

[0007]

[0008] In the formula: E r,m is the monthly power demand of region r in month m; P r,m is the total output of region r in month m; is the total wind and solar output of region r in month m; T is the monthly hour number; is the annual average monthly power demand of region r; is the difference between the power demand of region r in month m and the annual average monthly power demand;

[0009] Among them, the seasonal power generation demand uncertainty indicator includes:

[0010]

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

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

[0013] A1: Taking a month as the step, select the monthly output data of each power source of the power grid in a year, and solve according to formula (1) to obtain the power generation E that the flexible power sources of the power grid need to provide each month r,m ;

[0014] A2: Solve the annual average monthly electricity demand of the flexible power sources of the power grid according to formula (2) Then solve the flexible regulation demand for electricity fluctuation of the power grid according to formula (3)

[0015] A3: Select data for several years, repeat step A1, and obtain the power generation data that the flexible power sources of the power grid need to provide in several years; plot the data of each year on the same electricity - time line graph, and take the upper and lower envelopes of the graph to obtain the upper limit of the monthly electricity demand and the lower limit of the monthly electricity demand

[0016] A4: Subtract the lower limit of the monthly electricity demand from the upper limit of the monthly electricity demand for each month according to formula (6) to obtain the flexible regulation demand for grid uncertainty

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

[0018] 3) Taking long-term electricity control as the boundary condition, it is necessary to further analyze the daily-scale flexibility demand brought by the extreme fluctuations of new energy in the power grid for consecutive days within a month. For this purpose, the index of the number of consecutive extreme fluctuation days of new energy and the index of the volatility of power generation demand under extreme conditions are proposed, as follows;

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

[0020]

[0021] In the formula: MA t is the average output of wind and light in t days; x i is the output of wind and light on the i-th day; E t is a 0 - 1 variable to judge whether it is an extreme weather; θ is the allowable fluctuation range of wind and light output; C t is a 0 - 1 variable to judge whether it is a continuous extreme fluctuation; k is the maximum number of days when extreme fluctuations are allowed to occur;

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

[0023]

[0024] In the formula: is the average value of power generation demand; L i is the power generation demand on the i-th day; L t is the power generation demand for extreme weather on the t-th day, F t M is the medium-term extreme fluctuation value;

[0025] 4) Calculate the flexibility demand of the power grid on a medium-term daily scale, and the specific steps are as follows:

[0026] B1: Taking one day as the step, select the daily output data of each power source in the power grid for one year, and roll and solve the average output of the adjacent previous n days for each day of the whole year according to formula (7), where n is a positive integer;

[0027] B2: Determine the normal daily fluctuation range of wind and light, draw the daily output diagram of wind and light in the power grid, and define the number of days when the daily output is outside the normal fluctuation range as the extreme days;

[0028] B3: Calculate the average output of flexible power sources in each month Statistical power generation demand for extreme weather, and calculate the flexibility demand F for extreme weather according to formula (11) t M .

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

[0030] 5) For the system regulation pressure brought by the intermittency of new energy output at the hourly and minute levels, adopt the reverse peak shaving characteristic index and the intra-day fluctuation index to quantify the short-term flexibility demand, specifically as follows;

[0031] Among them, the new energy reverse peak shaving characteristic index includes:

[0032] The goal is to reflect that when traditional power generation in a certain area cannot flexibly regulate the load within a day, the power generation characteristics of new energy show a trend completely opposite to that of traditional load regulation. Introduce the new energy reverse peak shaving characteristic index, which is defined as:

[0033]

[0034] L net = L - P (13)

[0035]

[0036] In the formula: L peak is the load peak; L valley is the load valley; Lnet is the net load; L is the original load; P is the new energy output; PV peak is the load peak-valley ratio; PV net-peak is the net load peak-valley ratio; L net-peak is the net load peak value; L net-valley is the net load valley value; FI is the reverse peak-shaving index of the new energy output;

[0037] Among them, the new energy intra-day fluctuation index includes:

[0038] To reflect the new energy intra-day fluctuation situation, a new energy intra-day fluctuation index is introduced and defined as:

[0039]

[0040] In the formula: is the average new energy output within a day; L i,j (t) is the output within a day at time j; T d is 24h; is the short-term moment fluctuation output;

[0041] 6) Calculate the short-term grid flexibility demand index, and the specific steps are as follows:

[0042] C1: Select the daily load data of the grid and the new energy output data, and calculate the grid net load except for the new energy output according to the original data;

[0043] C2: Solve the grid original load peak-valley ratio PV according to formulas (12) and (14) peak and the net load peak-valley ratio PV net-peak , and then calculate the reverse peak-shaving index FI of the new energy output according to formula (15);

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

[0045] C4: Statistically calculate the output fluctuation values in different time periods by time period, obtain the probability distribution of the output fluctuation, and take the quantiles of different probabilities to obtain the new energy intra-day fluctuation flexibility demand index with different confidence levels.

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

[0047] 7) Couple the long-term, medium-term, and short-term quantization indexes to comprehensively describe the grid flexibility demand at multiple time scales, and construct a comprehensive grid flexibility quantization index, specifically as follows:

[0048]

[0049] In the formula: F stis the result after data standardization processing; F is the set of long-, medium- and short-term flexibility data calculated respectively; μ is the mean value of the data; σ is the standard deviation of the data; F MAV is the absolute value average of the standardized data. For long- and short-term indicators, n0 = n, and for medium-term indicators, n = 365; ω L 、ω M 、ω S are the long-, medium- and short-term flexibility weight coefficients; are the absolute value averages of the long-, medium- and short-term standardized data respectively; F ALL is the comprehensive index for quantifying grid flexibility;

[0050] 8) Calculate the comprehensive index for quantifying 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 50%-quantile daily fluctuation data to form the grid fluctuation flexibility data set;

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

[0053] D3: Calculate the standard quantification averages of the long-, medium- and short-term grid flexibility respectively according to Equation (19);

[0054] D4: Determine the long-, medium- and short-term flexibility quantification coefficients according to the grid power source attributes and actual requirements;

[0055] D5: Calculate the comprehensive index for quantifying grid flexibility according to Equation (20).

[0056] In the second aspect, a computing device is provided. The computing device includes a processor and a memory. Among them, a computer program is stored in the memory, and the processor is configured to run the computer program to execute the grid multi-time scale flexibility requirement quantification method described in any one of the above.

[0057] In the third aspect, a storage medium is provided. The storage medium stores a computer program. Among them, the computer program is configured to execute the grid multi-time scale flexibility requirement quantification method described in any one of the above when running.

[0058] Beneficial effects of the present invention compared with existing methods: By introducing flexibility requirement quantification indicators at multiple time scales (such as seasonal demand volatility, extreme condition volatility, and intraday volatility of new energy), comprehensiveness is achieved in covering medium- and long-term fluctuations and the impact of short-term extreme conditions, while refining the analysis of the reverse impact of new energy output on traditional load regulation. This systematic quantification method not only makes up for the deficiencies in the existing research on time scales and demand characteristic analysis, but also provides more targeted theoretical support and practical guidance for the dispatching optimization and stability assessment of power grids with a high proportion of new energy connected. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0060] Fig. 2(a) is a schematic diagram of the calculation principle of the number of extreme fluctuation days;

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

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

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

[0064] Figures 5(a) to 5(e) are respectively the calculation result diagrams of the continuous extreme fluctuation days indicators of new energy in Shanghai, Zhejiang, Anhui, Fujian, and Jiangsu;

[0065] Figures 6(a) to 6(e) are respectively the calculation result diagrams of the extreme condition power generation demand volatility indicators of Shanghai, Zhejiang, Anhui, Fujian, and Jiangsu;

[0066] Figures 7(a) to 7(e) are respectively the calculation result diagrams of the reverse peak shaving characteristic indicators of new energy in Shanghai, Zhejiang, Anhui, Fujian, and Jiangsu;

[0067] Figures 8(a) to 8(e) are respectively the calculation result diagrams of the intraday volatility indicators of new energy in Shanghai, Zhejiang, Anhui, Fujian, and Jiangsu. DETAILED DESCRIPTION OF THE INVENTION

[0068] The following further illustrates the specific implementation manners of the present invention in conjunction with the drawings and technical solutions.

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

[0070] To address the above problems, the present invention proposes a method for quantifying the flexibility requirements of the power grid at multiple time scales and conducts application tests relying on the East China Power Grid and its affiliated provincial power grids. The results show that the results of the present invention can significantly reduce the deviation between the power transmission plan and the receiving-end demand and reduce curtailment while ensuring the total power generation of the basin. The verification results show that, with the power generation remaining basically unchanged, the power transmission deviation of the calculation scheme of the present invention is reduced by 88.6% during the dry season and 69.9% during the flood season compared with the original method, showing good practicability.

[0071] The present invention provides a method for quantifying the flexibility requirements of the power grid at multiple time scales, and the specific implementation steps are as follows:

[0072] 1) Evaluating the long-term flexibility requirements of the power grid helps to evaluate the power shortage and curtailment situations of the power grid on a relatively long time scale and is conducive to optimizing the power grid power source structure. To evaluate the long-term flexibility requirements of the power grid, seasonal power generation demand fluctuation indicators and seasonal power generation demand uncertainty indicators are introduced, and the specific indicators are as follows;

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

[0074]

[0075] In the formula: E r,m is the monthly power demand of region r in month m; P r,m is the total output of region r in month m; is the total wind and solar output of region r in month m; T is the monthly hour number; is the annual average monthly power demand of region r; is the difference between the power demand of region r in month m and the annual average monthly power demand;

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

[0077]

[0078] Wherein: is the upper limit of the monthly electricity demand in the r region in month m; is the lower limit of the monthly electricity demand in the r region in month m; is the difference between the upper and lower limits of the electricity demand fluctuation in the r region in month m;

[0079] 2) Calculate the long-term flexibility demand index of the power grid according to historical data respectively, see Figure 1 , and the specific steps are as follows:

[0080] A1: Taking a month as the step length, select the monthly output data of each power source of the power grid in one year, and solve the generated electricity E that the flexible power source of the power grid needs to provide each month according to formula (1) r,m ;

[0081] A2: Solve the annual average monthly electricity of the flexible power source of the power grid according to formula (2) Then solve the flexible regulation demand for electricity fluctuation of the power grid according to formula (3)

[0082] A3: Select data for several years, repeat step A1, and obtain the generated electricity data that the flexible power source of the power grid needs to provide in several years; plot the data of each year on the same electricity-time broken line graph, and take the upper and lower envelopes of the graph to obtain the upper limit of the monthly electricity demand and the lower limit of the monthly electricity demand

[0083] A4: Subtract the lower limit of the monthly electricity demand from the upper limit of the monthly electricity demand for each month according to formula (6) to obtain the flexible regulation demand for grid uncertainty

[0084] In the embodiment of the present application, a possible implementation manner is provided, and the following steps may further be included:

[0085] 3) Considering that after determining the long-term flexibility demand, it is necessary to further subdivide the time scale. Quantifying the medium-term flexibility demand of new energy helps to prompt the adaptability and resilience of the power system in the face of continuous fluctuations and extreme weather of new energy on a relatively long time scale (such as several days to several weeks). Therefore, the following indicators are proposed to quantify the medium-term flexibility demand of the power grid.

[0086] Among them, the new energy continuous extreme fluctuation days index includes:

[0087]

[0088] Wherein: MA t is the average output of wind and light within t days; x iThe wind and solar power output for day i; E t A 0-1 variable to determine whether it is extreme weather; θ is the allowable fluctuation range of wind and solar power output; C t A 0-1 variable to determine whether it is continuous extreme fluctuation; k is the maximum number of days that extreme fluctuations are allowed to occur;

[0089] Among them, the extreme condition power generation demand volatility index includes:

[0090]

[0091] In the formula: The average power generation demand; L i The power generation demand for day i; L t The extreme weather power generation demand on day t, F t M The medium-term extreme fluctuation value;

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

[0093] B1: Taking one day as the step length, select the power output data of each power source in the power grid for one year, and solve the average output of the adjacent previous n days of each day of the whole year according to formula (7). n is a positive integer. For example, when n is 7, that is, solve the 7-day average output of each day of the whole year;

[0094] B2: When θ is taken as 30%, determine the normal daily fluctuation range of wind and solar power, draw the wind and solar power output diagram of the power grid, and define the number of days when the daily output is outside the normal fluctuation range as the extreme days. The calculation principle is shown in Figure 2(a); in Figure 2(a), the abscissa is the number of days, the ordinate is the output, and the unit is MW (megawatt). D t Is the wind and solar power output of each day, MA t Is the 7-day average output;

[0095] B3: Calculate the average output of the flexible power sources in each month Statistical extreme weather power generation demand, and calculate the flexibility demand F of extreme weather according to formula (11) t M When M is taken as 7, it is expressed as V t The calculation principle is shown in Figure 2(b); in Figure 2(b), the abscissa is the number of days, the ordinate is the output, and the unit is MW. L t Is the actual power generation demand of each day, Is the 7-day average output, that is, the 7-day average power generation demand, V t Is the fluctuation value.

[0096] In the embodiment of the present application, a possible implementation manner is provided, and it 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 involve the surge in flexibility demand brought about by the output fluctuations of new energy in the short term. Therefore, the following indicators are proposed to quantify the short-term flexibility demand of new energy.

[0098] Among them, the new energy reverse peak shaving characteristic indicators include:

[0099] The goal is to reflect the power generation characteristics of new energy showing a trend completely opposite to that of traditional load regulation when traditional power generation cannot flexibly regulate the load in a certain area within a day. The new energy reverse peak shaving characteristic indicator is introduced and defined as:

[0100]

[0101] L net =L - P (16)

[0102]

[0103] In the formula: L peak is the load peak; L valley is the load valley; L net is the net load; L is the original load; P is the output of new energy; PV peak is the load peak-to-valley ratio; PV net-peak is the net load peak-to-valley ratio; L net-peak is the net load peak; L net-valley is the net load valley; FI is the reverse peak shaving index of the output of new energy;

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

[0105] To reflect the intraday fluctuation of new energy, the new energy intraday fluctuation indicator is introduced and defined as:

[0106]

[0107] In the formula: is the average intraday output of new energy; L i,j (t) is the intraday output at the jth moment; T d is 24h; is the short-term moment fluctuation output;

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

[0109] C1: Select the daily load data of the power grid and the output data of new energy, and calculate the net load of the power grid except for the output of new energy according to the original data;

[0110] C2: Solve the peak-valley ratio PV of the original grid load according to formulas (12) and (14), peak as well as the peak-valley ratio PV of the net load, net-peak and then calculate the reverse peak-shaving index FI of the new energy output according to formula (15);

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

[0112] C4: Statistically analyze the output fluctuation values in different time periods to obtain the probability distribution of the output fluctuation, and obtain the new energy intra-day fluctuation flexibility demand indicators with different confidence levels by taking the quantiles with different probabilities.

[0113] A possible implementation manner is provided in the embodiment of the present application, and the following steps may further be included:

[0114] 7) Couple long-term, medium-term, and short-term quantization indicators to comprehensively describe the flexibility requirements of the grid at multiple time scales, and construct a comprehensive quantization indicator for the grid flexibility, specifically as follows:

[0115]

[0116] In the formula: F st is the result after data standardization processing; F is the set of long-term, medium-term, and short-term flexibility data calculated respectively; μ is the mean value of the data; σ is the standard deviation of the data; F MAV is the absolute value average of the standardized data. For long-term and short-term indicators, n0 = n, and for medium-term indicators, n = 365; ω L , ω M , ω S are the long-term, medium-term, and short-term flexibility weight coefficients; are the absolute value averages of the long-term, medium-term, and short-term standardized data respectively; F ALL is the comprehensive quantization indicator for the grid flexibility;

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

[0118] D1: Select the long-term fluctuation data, medium-term extreme fluctuation data, and short-term 50% quantile daily fluctuation data to form a grid fluctuation flexibility data set;

[0119] D2: Standardize the grid fluctuation flexibility data set according to formula (18) to obtain the standard values of the long-term, medium-term, and short-term fluctuation flexibility requirements of the grid;

[0120] D3: Calculate the long-term, medium-term, and short-term flexibility standard quantization averages of the grid according to formula (19) respectively;

[0121] D4: Determine the long, medium, and short-term flexibility quantization coefficients according to the grid power source attributes and actual requirements. For example, if the number of days of extreme grid fluctuations is large, ω can be appropriately increased. M 。

[0122] D5: Calculate the comprehensive grid flexibility quantization index according to Equation (20).

[0123] Take the four provinces and one municipality in the East China Power Grid (including Shanghai, Zhejiang, Anhui, Fujian, and Jiangsu) as an example to judge the rationality of the flexibility demand index of this method.

[0124] Obtain the actual load data and new energy output data of the four provinces and one municipality in the East China Power Grid at the hourly scale from 2019 to 2023, and conduct grid flexibility calculations. When calculating coarse-grained indicators such as medium- and long-term flexibility, the hourly-scale indicators can be averaged within the monthly and daily scales to obtain the corresponding coarse-grained calculation data.

[0125] Long-term flexibility demand

[0126] Calculate the seasonal power generation demand fluctuation index and the seasonal power generation demand uncertainty index to quantify the long-term flexibility of new energy in the East China region. As Figures 3(a) to 3(e) can be seen, the power generation demand fluctuations in each province and municipality are relatively large, and the seasonal characteristics are obvious. Summer (June - September) is the peak period of power generation demand, accounting for 38.2% of the whole year. This is mainly because the temperature is relatively high in summer, the cooling demand increases significantly, resulting in a substantial increase in the electricity load. At the same time, summer is also the plum rain season or typhoon season in some areas, with frequent rainy weather, which significantly affects the power generation capacity of the photovoltaic power generation system.

[0127] As Figures 4(a) to 4(e) can be seen, the differences in the power generation demand uncertainty indicators of each province and municipality are relatively large, reflecting the power load fluctuations and seasonal differences among the provinces and municipalities in the East China region over the years. Among them, Shanghai is the smallest, only 220 million kWh, indicating that its power demand fluctuations are relatively small and the load forecast is relatively stable; Jiangsu is the largest, reaching 4.7 billion kWh, indicating that its power demand fluctuations are relatively intense. The maximum and minimum values of the power generation demand uncertainty index appear in November and August respectively, with a gap of 78.8%.

[0128] Medium-term flexibility demand

[0129] Calculate the new energy continuous extreme fluctuation days index and the extreme condition power generation demand volatility index to quantify the medium-term flexibility of new energy in the East China region. As Figures 5(a) to 5(e)It can be seen that the number of days with extreme fluctuations in new energy in a year is the highest in Fujian, which is 234 days, and the lowest in Zhejiang, which is 126 days. The number of days with annual extreme fluctuations in Fujian is significantly higher than that in Zhejiang. New energy is in an extreme fluctuation state for most of the time (about 64%), while extreme fluctuations occur only 34% of the time in Zhejiang. At the same time, in Fujian and Zhejiang, the number of days with extreme fluctuations in the second half of the year is less than that in the first half of the year, indicating that this indicator is affected by seasonal factors. This is because photovoltaic power generation is relatively low in winter, while wind energy may be more concentrated in winter. Therefore, in the second half of the year, there may be some relatively stable weather conditions, and the power generation fluctuations of new energy are relatively small. The number of consecutive days with extreme fluctuations in new energy in a year is the highest in Fujian, which is 120 days; the lowest in Zhejiang, which is 35 days. Therefore, the power generation of new energy in Fujian faces greater challenges in terms of volatility and continuity, and higher flexibility regulation resources are required to ensure the stable operation of the power system.

[0130] It can be seen from Figures 6(a) to 6(e) that the extreme power generation demand fluctuation value is the largest in Zhejiang, with an upper fluctuation limit of 30145 MW and a lower fluctuation limit of -34795, indicating that its power demand fluctuates greatly and the load forecasting is poor; the smallest upper fluctuation limit in Fujian is 6688 MW and the lower fluctuation limit is -7063 MW, indicating that its power demand fluctuates less and the load forecasting is relatively stable. At the same time, the quantile points of the fluctuation index in Shanghai are relatively close, the extreme situations are relatively concentrated, and the similarity is relatively high; the quantile points of the fluctuation index in Anhui are relatively scattered, the extreme situations are relatively dispersed, and the similarity is relatively low, facing greater pressure in ensuring power supply and consumption in the medium-term scale.

[0131] Short-term flexibility demand

[0132] Calculate the new energy reverse peak regulation characteristic index and the new energy intraday fluctuation index to quantify the short-term new energy flexibility in the East China region. It can be seen from Figures 7(a) to 7(e) that the reverse peak regulation characteristics of Shanghai, Zhejiang, and Fujian are not obvious, and the reverse peak regulation indexes are all negative and less than -16%, indicating that the consistency between the wind and light output process and the load trend is relatively high, and there is no reverse peak regulation phenomenon. There are reverse peak regulation phenomena in both Jiangsu and Anhui provinces. Among them, the reverse peak regulation characteristics in Anhui are the most obvious, and the reverse peak regulation indexes are all positive, and the highest is 128.8%, with an average value of 54.8%.

[0133] It can be seen from Figures 8(a) to 8(e)It can be seen that the intraday fluctuations of new energy are the largest in Jiangsu, reaching 23,580 MW and -24,774 MW. On this day, there are significant positive and negative fluctuations in new energy in Jiangsu Province, which is related to the strong volatility of wind and solar energy resources and the large weather changes on that day. The intraday fluctuations are the smallest in Shanghai, at 6,812 MW and -8,511 MW. The weather conditions in Shanghai are relatively stable on that day. The East China region shows the characteristic of "relatively dense in the middle and relatively loose on both sides" in terms of fluctuation quantiles, indicating that the fluctuations of new energy power generation in these regions mostly concentrate around a certain intermediate value, while the fluctuations on both sides are relatively small. Under normal circumstances, the fluctuations of new energy power generation concentrate in a certain medium range, but under extreme weather conditions, the fluctuation range will become larger.

[0134] Compared with the traditional single-scale flexibility demand quantification method, this method comprehensively quantifies the characteristics of power grid flexibility demand from long-term seasonal fluctuations, medium-term extreme condition demands to short-term intraday fluctuations of new energy. By refining the time scale and index design, it fully reveals the regional differences and key influencing factors of flexibility demand under the condition of high new energy penetration in each province, providing an important reference for power grid planning and flexibility optimization.

[0135] Comprehensive flexibility index

[0136] In order to verify the reliability of the method and comprehensively evaluate the flexibility of the overall power grid this time, the weights of long, medium and short-term flexibility demands are selected to be the same, and the flexibility of the overall power grid at multiple times is calculated.

[0137] In summary, the flexibility solution results for the four provinces and one municipality are shown in Table 1:

[0138] Table 1 Comprehensive flexibility demand of the power grid

[0139]

[0140] It can be seen from Table 1 that the comprehensive flexibility demands of Fujian and Shanghai are relatively large. Therefore, it is more difficult for them to balance power, and the characteristics of new energy output in the two provinces should be considered when making nested plans. However, at the same time, the sources of their flexibility demands are different. The medium-term flexibility of Fujian is particularly prominent, specifically manifested by the large number of extreme output days and the significant difference in new energy daytime output. Therefore, when calculating the start-up and shutdown of thermal power, the flexibility configuration within Fujian Province should be emphasized. The flexibility demand in Shanghai is mainly reflected in the intraday period. The volatility of intraday output requires more flexible power sources to balance, and this volatility may even change the peak-valley characteristics of the original load. Therefore, when making short-term plans, the flexibility power source configuration in Shanghai should be considered as the key point.

[0141] Based on the same inventive concept, an embodiment of the present application further provides a computing device, including a processor and a memory. A computer program is stored in the memory, and the processor is configured to run the computer program to execute the method for quantifying the flexibility requirements of the power grid on multiple time scales in any one of the above embodiments.

[0142] Based on the same inventive concept, an embodiment of the present application further provides a storage medium, in which a computer program is stored. The computer program is configured to execute the method for quantifying the flexibility requirements of the power grid on multiple time scales in any one of the above embodiments when running.

[0143] Those skilled in the art can clearly understand the specific working processes of the above-described systems, devices, and modules, and can refer to the corresponding processes in the foregoing method embodiments. For the sake of brevity, they will not be described in detail herein.

[0144] Those of ordinary skill in the art can understand that the technical solution of the present application can essentially or all or part of the technical solution be embodied in the form of a software product. The computer software product is stored in a storage medium, which includes several program instructions for causing an electronic device (such as a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the embodiments of the present application when running the program instructions. The foregoing storage medium includes: various media that can store program codes such as USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs.

[0145] Alternatively, all or part of the steps of implementing the foregoing method embodiments can be completed by hardware related to program instructions (such as an electronic device such as a personal computer, a server, or a network device). 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 the present application.

[0146] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that within the spirit and principles of the present application, they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not cause the corresponding technical solutions to deviate from the protection scope of the present application.

Claims

1. A method for quantifying the multi-time scale flexibility demand of a power grid, characterized in that: The steps include: 1) In order to evaluate the long-term flexibility demand of the power grid, seasonal power generation demand fluctuation index and power generation demand uncertainty index are introduced as follows; Among them, seasonal power generation demand fluctuation indicators include: Where: E r,m is the monthly electricity demand in region r in month m; P r,m is the total output of region r in month m; is the total wind and solar power output in region r in month m; T is the number of hours per month; is the annual average monthly electricity demand in region r; is the difference between the electricity demand in month m of region r and the annual average monthly electricity demand; Among them, seasonal power generation demand uncertainty indicators include: Where: is the upper limit of monthly electricity demand in region r in month m; is the lower limit of monthly electricity demand in region r in month m; is the difference between the upper and lower limits of the fluctuation of electricity demand in region r in month m; 2) Calculate the long-term flexibility demand index of the power grid. The specific steps are as follows: A1: Taking the month as the step length, select the monthly output data of each power source in the power grid for one year, and solve the equation (1) to obtain the power generation E that the flexible power source of the power grid needs to provide in each month r,m ; A2: According to formula (2), solve the average monthly power demand of the flexible power source of the power grid Then, according to formula (3), the power grid power fluctuation flexibility regulation demand is solved A3: Select data from several years and repeat step A1 to obtain the required power generation data of the flexible power source of the power grid for several years; plot the data of each year on the same power-time line graph, take the upper and lower envelopes of the graph, and obtain the upper limit of the monthly power demand and monthly power demand minimum A4: According to formula (6), the monthly electricity demand upper limit is subtracted from the monthly electricity demand lower limit to obtain the grid uncertainty flexibility regulation demand:

2. The method according to claim 1, characterized in that: The method further comprises: 3) Taking long-term power control as the boundary condition, it is necessary to further analyze the daily flexibility demand caused by the extreme fluctuations of renewable energy in the power grid for many consecutive days within a month. For this purpose, the indicators of continuous extreme fluctuation days of renewable energy and the volatility index of power generation demand under extreme conditions are proposed, as follows; Among them, the indicators of consecutive extreme fluctuation days of new energy include: Where: MA t is the average wind and solar power output within t days; x i Contribute to the scenery of i days; t is a 0-1 variable to determine whether it is extreme weather; θ is the allowable fluctuation range of wind and solar power output; C t is a 0-1 variable to determine whether it is a continuous extreme fluctuation; k is the maximum number of days that extreme fluctuations are allowed to occur; Among them, the extreme conditions power generation demand volatility indicators include: Where: is the average power generation demand; L i is the power generation demand for day i; L t To meet the power generation needs of extreme weather in the world, t M It is the medium-term extreme volatility value; 4) Calculate the medium-term daily flexibility demand of the power grid. The specific steps are as follows: B1: With the day as the step length, select the daily output data of each power source in the power grid for one year, and solve the average output of the previous n consecutive days of each day in the whole year according to formula (7), where n is a positive integer; B2: Determine the normal daily fluctuation range of wind and solar power, draw the wind and solar power daily power diagram of the power grid, and define the days with daily power outside the normal fluctuation range as extreme days; B3: Calculate the average output of flexible power sources in each month Statistics on extreme weather power generation demand, and calculate extreme weather flexibility demand F according to formula (11) t M .

3. The method according to claim 2, characterized in that The method further comprises: 5) In view of the system regulation pressure caused by the intermittent output of renewable energy at the hour and minute levels, the anti-peak characteristic index and intraday volatility index are used to quantify the short-term flexibility demand, as follows; Among them, the new energy anti-peak regulation characteristic indicators include: The goal is to reflect that when traditional power generation cannot flexibly adjust the load in a certain area within a day, the power generation characteristics of new energy show a trend completely opposite to traditional load regulation. The new energy anti-peak regulation characteristic index is introduced, which is defined as: L net =L-P (13) Where: L peak is the load peak value; L valley is the load valley value; L net is the net load; L is the original load; P is the output of new energy; PV peak is the load peak-to-valley ratio; PV net-peak is the net load peak-to-valley ratio; L net-peak is the net load peak value; L net-valley is the net load valley value; FI is the anti-peak load index of renewable energy output; Among them, the new energy intraday volatility indicators include: To reflect the intraday fluctuation of new energy, the intraday fluctuation index of new energy is introduced, which is defined as: Where: is the average daily output of renewable energy; L i,j (t) is the daily output at time j; T d 24h; Contribute to short-term volatility; 6) Calculate the short-term flexibility demand index of the power grid. The specific steps are as follows: C1: Select the daily load data of the power grid and the output data of renewable energy, and calculate the net load of the power grid excluding the output of renewable energy based on the original data; C2: Solve the original peak-to-valley ratio of the power grid load PV according to equations (12) and (14): peak and net load peak-to-valley ratio PV net-peak , and then according to formula (15), the anti-peak index FI of the new energy output is calculated; C3: Calculate the average output of renewable energy on the day according to formula (16), and calculate the output fluctuation of renewable energy over multiple days according to formula (17); C4: Count the output fluctuation values ​​in different time periods to obtain the probability distribution of output fluctuations, and take quantiles with different probabilities to obtain the new energy daily fluctuation flexibility demand indicators with different confidence levels.

4. The method according to claim 3, characterized in that The method further comprises: 7) The long-term, medium-term and short-term quantitative indicators are coupled to comprehensively describe the multi-time scale flexibility requirements of the power grid, forming a comprehensive quantitative indicator of power grid flexibility, as follows: Where: F st is the result after data standardization; F is the long-term, medium-term and short-term flexibility data set calculated separately; μ is the mean of the data; σ is the standard deviation of the data; F MAV is the absolute average of the standardized data, n0=n for long-term and short-term indicators, and n=365 for medium-term indicators; ω L ,ω M ,ω S is the weight coefficient of long-term, medium-term and short-term flexibility; are the absolute averages of long-term, medium-term and short-term standardized data respectively; F ALL Quantify comprehensive indicators for grid flexibility; 8) Calculate the quantitative comprehensive index of 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 50% quantile daily fluctuation data to form a power grid fluctuation flexibility data set; D2: Standardize the grid fluctuation flexibility data set according to formula (18) to obtain the standard value of the grid's long-, medium- and short-term fluctuation flexibility demand; 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 long-, medium- and short-term flexibility quantitative coefficients based on the power source attributes of the grid and actual demand; D5: Calculate the quantitative comprehensive index of grid flexibility according to formula (20).

5. A computing device, characterized in that: The method comprises 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 method for quantifying multi-timescale flexibility demand of a power grid according to any one of claims 1 to 4.

6. A storage medium, characterized in that: The storage medium stores a computer program, wherein the computer program is configured to execute the method for quantifying multi-time-scale flexibility demand of a power grid according to any one of claims 1 to 4 when running.

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