Method for quantitatively evaluating flexibility of multi-energy complementary system of coal power and clean energy

Abstract

The invention discloses a coal power and clean energy multi-energy complementary system flexibility quantitative evaluation method comprising the following steps: collecting wind / light output historical power prediction errors of a wind power station / photovoltaic station as an error set, and sampling the error set; superposing the extracted wind / light output historical power prediction error value on the input wind and light data to obtain a wind and light load prediction curve; based on the minimum value of the total loss cost of the abandoned electricity quantity and the load shedding quantity in the coal power and clean energy power generation system as a target function, forming an optimal economic dispatching model in combination with constraint conditions; and calculating a flexibility quantitative evaluation index by taking the abandoned electricity quantity and the load shedding quantity as operation intermediate quantities, setting a threshold value of the flexibility quantitative evaluation index, and performing evaluation. Quantitative evaluation of the flexibility of the coal-water wind-light power generation system is realized, and subsequent guidance of setting of the reserve capacity of the power generation system according to the evaluation result is facilitated.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy power generation, and in particular to a method for quantitatively evaluating the flexibility of a multi-energy complementary system of coal power and clean energy. Background Art

[0002] With the large-scale access of new energy, the randomness and volatility of new energy have an increasing impact on the power grid. As the cornerstone of the power system, traditional thermal power provides auxiliary services such as reliable capacity, peak load regulation and frequency regulation to the power system, which has an important impact on social stability, economic development and energy security. Overall, considering the impact of the volatility and uncertainty of new energy access to the power system, evaluating the flexible adjustment capabilities of coal-fired power and various types of clean energy power generation systems is the key to ensuring the safe and reliable operation of the system, and is also of great significance to promoting the transformation of the energy structure and accelerating the construction of energy bases.

[0003] At present, the multi-dimensional quantitative evaluation of the flexibility of coal-fired power and various types of clean energy power generation systems has the following main deficiencies:

[0004] (1) The research on flexibility assessment fails to take into account the assessment of system flexibility when multiple energy sources such as coal power, hydropower, wind power, and photovoltaic power are combined;

[0005] (2) The quantitative evaluation indicators for the flexibility of coal-fired power and various types of clean energy power generation systems have few dimensions, and fail to achieve a quantitative evaluation of system flexibility from a multi-dimensional perspective. Summary of the invention

[0006] In view of the above-mentioned deficiencies in the prior art, the present invention provides a method for quantitatively evaluating the flexibility of a multi-energy complementary system of coal-fired power and clean energy, so as to solve the defects in the prior art that the flexibility quantitative evaluation dimensions of coal-fired power and multi-type clean energy power generation systems are few, and the flexible adjustment capabilities of the four power generation energies of coal-fired power, hydropower, wind power and photovoltaic power cannot be evaluated after being combined.

[0007] In order to achieve the above-mentioned object of the invention, the technical solution adopted by the present invention is:

[0008] A method for quantitatively evaluating the flexibility of a multi-energy complementary system of coal power and clean energy is provided, which comprises the following steps:

[0009] S1: Collect the historical power prediction errors of wind / solar output of wind power stations / photovoltaic stations as an error set, which includes several historical power prediction error values ​​of wind / photovoltaic output. Build a wind power / photovoltaic power prediction model based on the Monte Carlo method, sample the error set, and superimpose the extracted historical power prediction error values ​​of wind / photovoltaic output on the input wind / photovoltaic data;

[0010] Sampling is performed several times in the error set, and the extracted wind / solar output historical power prediction error value is superimposed on the wind / solar output time series curve to obtain the wind / solar output prediction curve. The wind / solar output historical power prediction error value obeys the normal distribution. The load prediction error generated based on the normal distribution is superimposed on the load time series curve to obtain the load prediction curve;

[0011] S2: Take the load forecast curve and wind-solar forecast curve as input, and combine the constraints of the multi-energy complementary system of coal-fired power and clean energy to obtain the wind-solar power abandonment and load shedding; and take the minimum total cost of abandoned power and load shedding losses in the coal-fired power and clean energy power generation systems as the objective function to form the optimal economic dispatch model;

[0012] S3: Calculate the flexibility quantitative evaluation index based on the wind and solar power curtailment and load shedding as the intermediate operation quantities, set the threshold of the flexibility quantitative evaluation index, compare the flexibility quantitative evaluation index with the threshold, and evaluate the flexibility of coal-fired power and clean energy power generation systems.

[0013] Further, step S1 includes:

[0014] S11: Collect the historical power prediction errors of wind / solar output of wind power stations / photovoltaic stations as an error set, which includes several historical power prediction error values ​​of wind / photovoltaic output. A wind power / photovoltaic power prediction model is built based on the Monte Carlo method, and the historical power prediction error values ​​of wind / photovoltaic output in the error set are sampled. The wind power / photovoltaic power prediction model is:

[0015]

[0016] Among them, X t,K is the error set consisting of the historical power prediction error of wind / solar output of the Kth wind power station / photovoltaic station at time t, α w is the w-th wind / solar output historical power prediction error value sampled from the error set, is the probability distribution of the wind / PV power prediction value of the Kth wind power station / PV station at time t; is the wind / PV power forecast value at time t for time t+k;

[0017] S12: Based on the correlation between the historical wind / solar power prediction error values ​​at adjacent times, a differential sequence h of the wind / solar power prediction error values ​​at adjacent historical times t and t-1 is established. t,k 、m t,k , get the error set X in step S11 t,K ;

[0018]

[0019]

[0020]

[0021] in, is the measured value of wind power at time t, is the predicted value of wind power at time t, is the measured value of photovoltaic power at time t, is the predicted value of photovoltaic power at time t, is the measured value of wind power at time t-1, is the predicted value of wind power at time t-1, is the measured value of photovoltaic power at time t-1, is the predicted value of photovoltaic power at time t-1, h t,k 、m t,k are the difference sets of wind power and photovoltaic power prediction errors at time t; W t,K , S t,K are the wind power and photovoltaic power prediction error sets respectively;

[0022] S13: Based on the difference sequence h t,k 、m t,k Establish a load forecasting model that obeys normal distribution, and transform the error set X t,K The historical power forecast error value of wind / solar output extracted from the load forecast is superimposed on the input wind / solar data, and the load forecast model is:

[0023]

[0024] in, is the load forecast value at time t, is the load forecast correction value at time t, ΔP Lerro,t is the load forecast error value at time t;

[0025] S14: In the error set X t,K Several samplings are performed in the process, and the extracted historical power prediction error values ​​of wind / solar output are superimposed on the wind-solar timing curve to obtain the wind-solar prediction curve. The historical power prediction error values ​​of wind / solar output obey the normal distribution. The load prediction error generated based on the normal distribution is superimposed on the load timing curve to obtain the load prediction curve.

[0026] Further, step S2 includes:

[0027] S21: Taking the load forecast curve and the wind and solar forecast curve as inputs, combined with the constraints of the multi-energy complementary system of coal-fired power and clean energy, the wind and solar power curtailment and load shedding are obtained;

[0028] The constraints of the multi-energy complementary system of coal power and clean energy include:

[0029] S211: Construct the residual load mean square error constraint condition:

[0030]

[0031]

[0032]

[0033] Among them, C G,t is the system residual load corresponding to time t, is the average value of the system residual load, is the grid load at time t, is the sum of the hydropower output values ​​of M hydropower stations at time t, m is the number of the hydropower station, is the hydropower output value of the mth hydropower station at time t, H is the hydropower, is the wind power output value corresponding to time t, W is wind power; is the photovoltaic output value corresponding to time t, PV is photovoltaic; N t is the capacity of the outbound channel corresponding to time t; N tmax is the maximum capacity of the transmission channel corresponding to time t, ε is the set residual load mean square error threshold, and F is the residual load mean square error;

[0034] S212: Construct the upper and lower limits of wind power station / photovoltaic station output:

[0035]

[0036] Among them, Δp wind,t is the wind power output value at time t, Δp solar,t is the photovoltaic output value at time t, They are the upper limits of wind power and photovoltaic power output forecasts respectively;

[0037] S213: Construct the upper and lower limits of wind / solar abandonment for wind power stations / photovoltaic stations:

[0038]

[0039] Among them, ΔP W,t is the wind power curtailment value at time t, ΔP S,t is the photovoltaic abandonment value at time t;

[0040] S214: Construct the upper and lower limits of thermal power output: in, are the upper and lower limits of the thermal power output of the m1th thermal power station at time t; is the thermal power output value of the m1th thermal power station at time t;

[0041] S215: Constructing upper and lower limit constraints of load shedding; in, is the upper limit of load forecast at time t;

[0042] S216: Constraints for constructing hydropower stations in coal-fired power and multi-type clean energy power generation systems, including:

[0043] Water balance constraints: Among them, V t i is the storage capacity of the i-th reservoir at time t, is the storage capacity of the i-th reservoir at time t-1, is the inflow of the i-th reservoir at time t, is the outflow of the i-th reservoir at time t;

[0044] Hydraulic constraints of upstream and downstream reservoirs: in, is the power generation flow of the upstream i-1 hydropower station in the period (t-τ), r t i is the interval flow between hydropower station i-1 and hydropower station i, τ is the water flow lag time between hydropower station i-1 and hydropower station i;

[0045] Water level constraints: in, Z i , are the water level in front of the dam and the upper and lower limits of the water level of the i-th reservoir at time t respectively;

[0046] Initial and final water level constraints: in, are the target control water levels of the reservoir where the i-th hydropower station is located at the initial and final stages of dispatch, is the water level of the reservoir where the i-th hydropower station is located at the initial stage of dispatch, is the water level of the reservoir where the i-th hydropower station is located at the end of the dispatch period, and T is the final value of the water level;

[0047] Reservoir outflow constraints: in, q i , are the upper and lower limits of the power generation flow of the i-th hydropower station, is the power generation flow of the i-th hydropower station at time t;

[0048] Constraints on generating head of hydropower station units: in, is the water head of the i-th hydropower station at time t, is the tailwater level of the i-th hydropower station at time t, is the water level of the i-th hydropower station at time t-1, is the water level of the hydropower station at the i-th hydropower station at time t;

[0049] Tailwater level-discharge constraint conditions: in, is the tailwater level-discharge relationship function of the reservoir where the i-th hydropower station is located, is the discharge of the hydropower station at the i-th hydropower station at time t;

[0050] Water level-storage capacity constraints: in, is the water level-storage capacity relationship function of the reservoir where the i-th hydropower station is located, V t i is the storage capacity of the reservoir where the i-th hydropower station is located at time t;

[0051] Hydropower output-power head constraints: in, is the binary relationship function between the output of unit g of the i-th hydropower station and the power generation flow and power generation head; The power output of the unit g of the i-th hydropower station at time t; is the discharge of the hydropower station of unit g at time t, is the water head of unit g of the i-th hydropower station at time t;

[0052] Constraints on the unit dynamic characteristics of the power station: in, is the binary relationship function between the output of the i-th hydropower station and the power generation flow and power generation head, P t i is the power output of the ith power station at time t, is the discharge of the i-th hydropower station at time t;

[0053] S22: According to the maximum and minimum head interval of the i-th hydropower station The interval Split into three continuous water head sub-intervals and in, The power generation head of hydropower station i at any time satisfies the following constraints:

[0054]

[0055]

[0056] in, is the indicator variable of the kth water head sub-interval of unit g of hydropower station i at time t, k is the number of three consecutive water head sub-intervals, Indicates that the generating head of unit g is within the kth head sub-interval, is the head height of unit g of hydropower station i in the k-1 head sub-interval; The head height of unit g of hydropower station i in the kth head sub-interval;

[0057] S23: Get the water head height interval of the unit of hydropower station i When the water head height of hydropower station i When , the unit power characteristic curve under the average water head is selected as the representative curve, the representative curve is linearized, and the mathematical model of the relationship between the unit power characteristic curve and water head of hydropower station i is constructed:

[0058]

[0059]

[0060]

[0061]

[0062] in, is the functional relationship between the output and power generation flow of hydropower station i in the kth head sub-interval, is the relationship coefficient of the unit power characteristic curve of hydropower station i at the kth head section, the relationship coefficient Only one is equal to 1, f1 i,pq|h (.), They are the power characteristic curves and water head correlation functions of the 1st, 2nd and 3rd sections of the unit respectively;

[0063] S24: Based on the minimum total cost of power abandonment and load shedding loss in coal-fired power and clean energy power generation systems as the objective function, an optimal economic dispatch model is formed; the objective function is:

[0064] min[a elec ΔP E,t +a load ΔP L,t ]Δt;

[0065] ΔP E,t =ΔP W,t +ΔP S,t ;

[0066] Among them, a elec 、a loadare the penalty amounts for unit power abandonment and unit load shedding, Δt is the set time step, and are the power abandonment and load shedding at time t, ΔP L,t is the load shedding amount at time t, ΔP E,t is the amount of power abandoned at time t, ΔP W,t is the wind abandonment at time t, ΔP S,t is the wind abandonment volume at time t, T is the total number of time periods;

[0067] Further, step S3 includes:

[0068] S31: Calculate the amount of power abandonment and load shedding according to the optimal economic dispatch model, and set the initial values ​​of the intermediate quantities μ for upward flexibility calculation, downward flexibility calculation, wind power flexibility calculation, and photovoltaic flexibility calculation in the coal-fired power and clean energy power generation systems respectively. up , =0, the abandoned wind volume ΔP solved by the optimal economic dispatch model W,t , Amount of abandoned light ΔP S,t and load shedding capacity ΔP L,t , calculate the quantitative evaluation index of the flexibility of coal-fired power and clean energy power generation systems, where the quantitative evaluation index of flexibility includes upward and downward adjustment of the expected index of insufficient flexibility;

[0069]

[0070]

[0071] Among them, e up,h is the upward flexibility index value of the h-th coal-fired power and clean energy power generation system, e down,h They are the wind power downward adjustment flexibility index value, photovoltaic downward adjustment flexibility index value, and total downward adjustment flexibility index value of the h-th coal-fired power and clean energy power generation systems respectively;

[0072] S32: The air abandonment volume ΔP at each moment is obtained W,t , abandoned light amount, ΔP S,t and load shedding capacity ΔP L,t Matrix, calculating the probability of insufficient upward or downward flexibility of coal-fired and clean energy generation systems;

[0073]

[0074] in, When the load shedding or power abandonment of coal-fired power and clean energy power generation systems is not all zero, the total number of times the load shedding or power abandonment value is non-zero is counted; ΔP (·) The wind abandonment volume ΔP of coal-fired power and clean energy power generation systems W,t , abandoned light amount, ΔPS,t and load shedding capacity ΔP L,t The 0-1 state matrix with non-zero values ​​from t = 1 to t = T takes 1 when it is non-zero and takes 0 when it is zero; station The probability of insufficient upward or downward flexibility of the power generation system, including load shedding, wind power curtailment and solar power curtailment;

[0075] S33: Calculate the expected standard deviation coefficient V according to the upward or downward flexibility deficiency index of the coal-fired power generation system and the clean energy power generation system obtained in step S31 and step S32 σ ;

[0076]

[0077]

[0078] Where, ζ is the set flexibility threshold; [Y k ] is an array, including the upward / downward flexibility index value and the probability of insufficient upward / downward flexibility under multiple cycles of coal-fired power and clean energy power generation systems; For the array [Y k ], which represents the average value of the upward / downward flexibility index value and the probability of insufficient upward / downward flexibility; They represent the expected standard deviation coefficient of the upward / downward flexibility index value and the expected standard deviation coefficient of the upward / downward flexibility insufficiency probability respectively;

[0079] S34: If It is judged that the flexibility of coal-fired power and clean energy power generation systems is good; otherwise, the flexibility is poor.

[0080] The beneficial effects of the present invention are:

[0081] 1. In the coal-fired power and multi-type clean energy power generation system of the present invention, the multi-type clean energy includes three new energy forms: hydropower, photovoltaics, and wind power. At the same time, the volatility and uncertainty of wind power and photovoltaics and the impact of the wet and dry characteristics of hydropower on the flexibility of the power generation system are comprehensively considered. By building a Monte Carlo-based wind and solar prediction model, after multiple samplings and simulations of various uncertainty scenarios, the prediction of wind and solar output is finally achieved.

[0082] 2. The present invention takes the minimum cost of power abandonment and load shedding in coal-fired power and multi-type clean energy power generation systems as the objective function, combines the system power constraints and new energy power generation constraints, and obtains the power abandonment and load shedding of the new energy power generation system by solving the economic dispatch model. The flexibility index is calculated using the power and load shedding amount, and finally the system flexibility evaluation is realized.

[0083] 3. The present invention constructs a flexibility quantitative evaluation system from multiple dimensions such as speed, range and direction, realizes the quantitative evaluation of the flexibility of coal, water, wind and solar power generation systems, and facilitates the subsequent guidance of the setting of the backup capacity of the power generation system according to the evaluation results. BRIEF DESCRIPTION OF THE DRAWINGS

[0084] Figure 1 This is a flow chart of the method for quantitatively evaluating the flexibility of the multi-energy complementary system of coal power and clean energy of the present invention.

[0085] Figure 2 It is a schematic diagram of the wind and solar power prediction data and the measured data of the present invention.

[0086] Figure 3 It is a schematic diagram of the flexibility scenario of the coal-fired power generation system and multiple types of clean energy power generation systems of the present invention.

[0087] Figure 4 This is a wind power and photovoltaic input curve diagram for different water inflow periods of the present invention.

[0088] Figure 5 It is the load input curve diagram of different water inflow periods of the present invention.

[0089] Figure 6 This is a diagram showing that the flexibility of the present invention is less than expected and the probability changes with increasing climbing rate.

[0090] Figure 7 This is a diagram showing the change in probability of the flexibility of the present invention being less than expected as the spare capacity increases.

[0091] Figure 8 This is a diagram showing the flexibility of the present invention being less than expected and the probability of that changing with the proportion of new energy access. DETAILED DESCRIPTION

[0092] The specific implementation modes of the present invention are described below so that those skilled in the art can understand the present invention. However, it should be clear that the present invention is not limited to the scope of the specific implementation modes. For those of ordinary skill in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the attached claims, these changes are obvious, and all inventions and creations utilizing the concept of the present invention are protected.

[0093] As shown in the figure, a method for quantitatively evaluating the flexibility of a multi-energy complementary system of coal power and clean energy includes the following steps:

[0094] S1: Collect the historical power prediction errors of wind / solar output of wind power stations / photovoltaic stations as an error set, which includes several historical power prediction error values ​​of wind / photovoltaic output. Build a wind power / photovoltaic power prediction model based on the Monte Carlo method, sample the error set, and superimpose the extracted historical power prediction error values ​​of wind / photovoltaic output on the input wind / photovoltaic data;

[0095] Sampling is performed several times in the error set, and the extracted wind / solar output historical power prediction error value is superimposed on the wind / solar output time series curve to obtain the wind / solar output prediction curve. The wind / solar output historical power prediction error value obeys the normal distribution. The load prediction error generated based on the normal distribution is superimposed on the load time series curve to obtain the load prediction curve;

[0096] Step S1 specifically includes:

[0097] S11: Collect the historical power prediction errors of wind / solar output of wind power stations / photovoltaic stations as an error set, which includes several historical power prediction error values ​​of wind / photovoltaic output. A wind power / photovoltaic power prediction model is built based on the Monte Carlo method, and the historical power prediction error values ​​of wind / photovoltaic output in the error set are sampled. The wind power / photovoltaic power prediction model is:

[0098]

[0099] Among them, X t,K is the error set consisting of the historical power prediction error of wind / solar output of the Kth wind power station / photovoltaic station at time t, α w is the w-th wind / solar output historical power prediction error value sampled from the error set, is the probability distribution of the wind / PV power prediction value of the Kth wind power station / PV station at time t; is the wind / PV power forecast value at time t for time t+k;

[0100] S12: Based on the correlation between the historical wind / solar power prediction error values ​​at adjacent times, a differential sequence h of the wind / solar power prediction error values ​​at adjacent historical times t and t-1 is established. t,k 、m t,k , get the error set X in step S11 t,K ;

[0101]

[0102]

[0103]

[0104] in, is the measured value of wind power at time t, is the predicted value of wind power at time t, is the measured value of photovoltaic power at time t, is the predicted value of photovoltaic power at time t, is the measured value of wind power at time t-1, is the predicted value of wind power at time t-1, is the measured value of photovoltaic power at time t-1, is the predicted value of photovoltaic power at time t-1, h t,k 、m t,k are the difference sets of wind power and photovoltaic power prediction errors at time t, respectively; W t,K , S t,K are the wind power and photovoltaic power prediction error sets respectively;

[0105] S13: Based on the difference sequence h t,k 、m t,k Establish a load forecasting model that obeys normal distribution, and transform the error set X t,K The historical power forecast error value of wind / solar output extracted from the load forecast is superimposed on the input wind / solar data, and the load forecast model is:

[0106]

[0107] in, is the load forecast value at time t, is the load forecast correction value at time t, ΔP Lerro,t is the load forecast error value at time t;

[0108] S14: In the error set X t,K Several samplings are performed in the process, and the extracted historical power prediction error values ​​of wind / solar output are superimposed on the wind-solar timing curve to obtain the wind-solar prediction curve. The historical power prediction error values ​​of wind / solar output obey the normal distribution. The load prediction error generated based on the normal distribution is superimposed on the load timing curve to obtain the load prediction curve.

[0109] like Figure 2As shown in the figure, the wind and solar forecast data and measured data schematic diagram established according to the load forecast model, before point A, the forecast data is greater than the measured data. In order to accept more wind and solar energy, the system will appropriately reduce the output of the unit. At this time, due to the small measured value, the system may lack flexibility in upward adjustment, and in order to meet the source-load balance, the system will experience load shedding events; between points AB, the forecast data is less than the measured data, and the unit output is more. In order to meet the balance, the system will abandon excess clean energy, resulting in insufficient flexibility in downward adjustment of the system, and wind and solar abandonment events will occur; when a large climbing rate appears in the curve, if the system's downward adjustment capacity cannot keep up at this time, wind and solar abandonment events will also occur. Therefore, analyzing the period of insufficient flexibility of coal-fired power and multi-type clean energy power generation systems is crucial to quantitatively evaluate system flexibility.

[0110] S2: Take the load forecast curve and wind-solar forecast curve as input, and combine the constraints of the multi-energy complementary system of coal-fired power and clean energy to obtain the wind-solar power abandonment and load shedding; and take the minimum total cost of abandoned power and load shedding losses in the coal-fired power and clean energy power generation systems as the objective function to form the optimal economic dispatch model;

[0111] Step S2 specifically includes:

[0112] S21: Taking the load forecast curve and the wind and solar forecast curve as inputs, combined with the constraints of the multi-energy complementary system of coal-fired power and clean energy, the wind and solar power curtailment and load shedding are obtained;

[0113] The constraints of the multi-energy complementary system of coal power and clean energy include:

[0114] S211: Construct the residual load mean square error constraint condition:

[0115]

[0116]

[0117]

[0118] Among them, C G,t is the system residual load corresponding to time t, is the average value of the system residual load, is the grid load at time t, is the sum of the hydropower output values ​​of M hydropower stations at time t, m is the number of the hydropower station, is the hydropower output value of the mth hydropower station at time t, H is the hydropower, is the wind power output value corresponding to time t, W is wind power; is the photovoltaic output value corresponding to time t, PV is photovoltaic; N t is the capacity of the outbound channel corresponding to time t; Ntmax is the maximum capacity of the transmission channel corresponding to time t, ε is the set residual load mean square error threshold, and F is the residual load mean square error;

[0119] S212: Construct the upper and lower limits of wind power station / photovoltaic station output:

[0120]

[0121] Among them, Δp wind,t is the wind power output value at time t, Δp solar,t is the photovoltaic output value at time t, They are the upper limits of wind power and photovoltaic power output forecasts respectively;

[0122] S213: Construct the upper and lower limits of wind / solar abandonment for wind power stations / photovoltaic stations:

[0123]

[0124] Among them, ΔP W,t is the wind power curtailment value at time t, ΔP S,t is the photovoltaic abandonment value at time t;

[0125] S214: Construct the upper and lower limits of thermal power output: in, are the upper and lower limits of the thermal power output of the m1th thermal power station at time t; is the thermal power output value of the m1th thermal power station at time t;

[0126] S215: Constructing upper and lower limit constraints of load shedding; in, is the upper limit of load forecast at time t;

[0127] S216: Constraints for constructing hydropower stations in coal-fired power and multi-type clean energy power generation systems, including:

[0128] Water balance constraints: Among them, V t i is the storage capacity of the i-th reservoir at time t, is the storage capacity of the i-th reservoir at time t-1, is the inflow of the i-th reservoir at time t, is the outflow of the i-th reservoir at time t;

[0129] Hydraulic constraints of upstream and downstream reservoirs: in, is the power generation flow of the upstream i-1 hydropower station in the (t-τ) period, is the interval flow between hydropower station i-1 and hydropower station i, τ is the water flow lag time between hydropower station i-1 and hydropower station i;

[0130] Water level constraints: in, Z i , are the water level in front of the dam and the upper and lower limits of the water level of the i-th reservoir at time t respectively;

[0131] Initial and final water level constraints: in, are the target control water levels of the reservoir where the i-th hydropower station is located at the initial and final stages of dispatch, is the water level of the reservoir where the i-th hydropower station is located at the initial stage of dispatch, is the water level of the reservoir where the i-th hydropower station is located at the end of the dispatch period, and T is the final value of the water level;

[0132] Reservoir outflow constraints: in, q i , are the upper and lower limits of the power generation flow of the i-th hydropower station, is the power generation flow of the i-th hydropower station at time t;

[0133] Constraints on generating head of hydropower station units: in, is the water head of the i-th hydropower station at time t, is the tailwater level of the i-th hydropower station at time t, is the water level of the i-th hydropower station at time t-1, is the water level of the hydropower station at the i-th hydropower station at time t;

[0134] Tailwater level-discharge constraint conditions: in, is the tailwater level-discharge relationship function of the reservoir where the i-th hydropower station is located, is the discharge of the hydropower station at the i-th hydropower station at time t;

[0135] Water level-storage capacity constraints: in, is the water level-storage capacity relationship function of the reservoir where the i-th hydropower station is located, V t i is the storage capacity of the reservoir where the i-th hydropower station is located at time t;

[0136] Hydropower output-power head constraints: in, is the binary relationship function between the output of unit g of the i-th hydropower station and the power generation flow and power generation head; The power output of the unit g of the i-th hydropower station at time t; is the discharge of the hydropower station of unit g at time t, is the water head of unit g of the i-th hydropower station at time t;

[0137] Constraints on the unit dynamic characteristics of the power station: in, is the binary relationship function between the output of the i-th hydropower station and the power generation flow and power generation head, P t i is the power output of the ith power station at time t, is the discharge of the i-th hydropower station at time t;

[0138] S22: According to the maximum and minimum head interval of the i-th hydropower station The interval Split into three continuous water head sub-intervals and in, The power generation head of hydropower station i at any time satisfies the following constraints:

[0139]

[0140]

[0141] in, is the indicator variable of the kth water head sub-interval of unit g of hydropower station i at time t, k is the number of three consecutive water head sub-intervals, Indicates that the generating head of unit g is within the kth head sub-interval, is the head height of unit g of hydropower station i in the k-1 head sub-interval; The head height of unit g of hydropower station i in the kth head sub-interval;

[0142] S23: Get the water head height interval of the unit of hydropower station i When the water head height of hydropower station i When , the unit power characteristic curve under the average water head is selected as the representative curve, the representative curve is linearized, and the mathematical model of the relationship between the unit power characteristic curve and water head of hydropower station i is constructed:

[0143]

[0144]

[0145]

[0146]

[0147] in, is the functional relationship between the output and power generation flow of hydropower station i in the kth head sub-interval, is the relationship coefficient of the unit power characteristic curve of hydropower station i at the kth head section, the relationship coefficient Only one is equal to 1, f1 i,pq|h (.), They are the power characteristic curves and water head correlation functions of the 1st, 2nd and 3rd sections of the unit respectively;

[0148] S24: Based on the minimum total cost of power abandonment and load shedding losses in coal-fired power and clean energy power generation systems as the objective function, an optimal economic dispatch model is formed; the objective function is:

[0149] min[a elec ΔP E,t +a load ΔP L,t ]Δt;

[0150] ΔP E,t =ΔP W,t +ΔP S,t ;

[0151] Among them, a elec 、a load are the penalty amounts for unit power abandonment and unit load shedding, Δt is the set time step, and are the power abandonment and load shedding at time t, ΔP L,t is the load shedding amount at time t, ΔP E,t is the amount of power abandoned at time t, ΔP W,t is the wind abandonment at time t, ΔP S,t is the wind abandonment volume at time t, and T is the total number of time periods.

[0152] S3: Calculate the flexibility quantitative evaluation index based on the wind and solar power curtailment and load shedding as the intermediate operation quantities, set the threshold of the flexibility quantitative evaluation index, compare the flexibility quantitative evaluation index with the threshold, and evaluate the flexibility of coal-fired power and clean energy power generation systems.

[0153] like Figure 3The figure shows the flexibility of coal-fired power and multiple types of clean energy power generation systems. In scenario 1, if the system adjustment speed is limited, the system flexibility will be affected and the flexibility resources will be insufficient. Therefore, the key to the study is to search for the period of insufficient system flexibility and quantify its severity. In scenario 2, when the net load curve exceeds the upper limit of output, the system power generation cannot be supplied, which may lead to load shedding events and insufficient flexibility resources for the system to adjust upward. In scenario 4, the net load curve exceeds the lower limit of output, and the system will appropriately abandon wind and solar power to match the net load. At this time, the system has insufficient downward adjustment capacity.

[0154] Step S3 specifically includes:

[0155] S31: Calculate the amount of power abandonment and load shedding according to the optimal economic dispatch model, and set the initial values ​​of the intermediate quantities μ for upward flexibility calculation, downward flexibility calculation, wind power flexibility calculation, and photovoltaic flexibility calculation in the coal-fired power and clean energy power generation systems respectively. up , =0, the abandoned wind volume ΔP solved by the optimal economic dispatch model W,t , Amount of abandoned light ΔP S,t and load shedding capacity ΔP L,t , calculate the quantitative evaluation index of the flexibility of coal-fired power and clean energy power generation systems, where the quantitative evaluation index of flexibility includes upward and downward adjustment of the expected index of insufficient flexibility;

[0156]

[0157]

[0158] Among them, e up,h is the upward flexibility index value of the h-th coal-fired power and clean energy power generation system, e down,h They are the wind power downward adjustment flexibility index value, photovoltaic downward adjustment flexibility index value, and total downward adjustment flexibility index value of the h-th coal-fired power and clean energy power generation systems respectively;

[0159] S32: The air abandonment volume ΔP at each moment is obtained W,t , abandoned light amount, ΔP S,t and load shedding capacity ΔP L,t Matrix, calculating the probability of insufficient upward or downward flexibility of coal-fired and clean energy generation systems;

[0160]

[0161] in, When the load shedding or power abandonment of coal-fired power and clean energy power generation systems is not all zero, the total number of times the load shedding or power abandonment value is non-zero is counted; ΔP (·)The wind abandonment volume ΔP of coal-fired power and clean energy power generation systems W,t , abandoned light amount, ΔP S,t and load shedding capacity ΔP L,t The 0-1 state matrix with non-zero values ​​from t = 1 to t = T takes 1 when it is non-zero and takes 0 when it is zero; station The probability of insufficient upward or downward flexibility of the power generation system, including load shedding, wind power curtailment and solar power curtailment;

[0162] S33: Calculate the expected standard deviation coefficient V according to the upward or downward flexibility deficiency index of the coal-fired power generation system and the clean energy power generation system obtained in step S31 and step S32 σ ;

[0163]

[0164]

[0165] Where, ζ is the set flexibility threshold; [Y k ] is an array, including the upward / downward flexibility index value and the probability of insufficient upward / downward flexibility under multiple cycles of coal-fired power and clean energy power generation systems; For the array [Y k ], which represents the average value of the upward / downward flexibility index value and the probability of insufficient upward / downward flexibility; They represent the expected standard deviation coefficient of the upward / downward flexibility index value and the expected standard deviation coefficient of the upward / downward flexibility insufficiency probability respectively;

[0166] S34: If It is judged that the flexibility of coal-fired power and clean energy power generation systems is good; otherwise, the flexibility is poor.

[0167] In the coal-fired power and multi-type clean energy power generation system of the present invention, the multi-type clean energy includes three new energy forms: hydropower, photovoltaics, and wind power. At the same time, the volatility and uncertainty of wind power and photovoltaics and the impact of the wet and dry characteristics of hydropower on the flexibility of the power generation system are comprehensively considered. By building a Monte Carlo-based wind and solar prediction model, multiple samplings are carried out to simulate multiple uncertainty scenarios, and finally the prediction of wind and solar output is realized.

[0168] The present invention takes the minimum cost of power abandonment and load shedding in coal-fired power and multi-type clean energy power generation systems as the objective function, combines the system power constraints and new energy power generation constraints, and obtains the power abandonment and load shedding of the new energy power generation system by solving the economic dispatch model. The flexibility index is calculated using the power and load shedding amount, and finally the system flexibility is evaluated.

[0169] The present invention constructs a flexibility quantitative evaluation system from multiple dimensions such as speed, range and direction, realizes the quantitative evaluation of the flexibility of coal, water, wind and solar power generation systems, and facilitates the subsequent guidance of the setting of the backup capacity of the power generation system according to the evaluation results.

[0170] This example takes coal-fired power and multi-type clean energy power generation systems in a certain place in southwest China as the research objects, among which wind power installed capacity is 2160MW, photovoltaic installed capacity is 4077MW, and thermal power installed capacity is 1500MW; four typical days in the flood season (September), dry season (January), flood-dry season (November) and dry-flood season (May) are selected for analysis respectively, and the corresponding hydropower, wind power, photovoltaic and load conditions are as follows: Figure 4 , Figure 5 shown.

[0171] According to the multi-dimensional quantitative evaluation method for the flexibility of coal-fired power and multiple types of clean energy power generation systems introduced above, a quantitative evaluation of the multi-dimensional flexibility of a certain place in the southwest was conducted, and the values ​​of the flexibility deficiency index in different water inflow periods were obtained, as shown in Table 1.

[0172] Table 1 Insufficient flexibility index (probability, expectation) in different water inflow periods

[0173]

[0174] Based on this quantitative evaluation scheme, the flexibility index results of coal-fired power and multi-type clean energy power generation systems in different water inflow periods are obtained under four scenarios: dry season, wet season, wet-dry transition period, and dry-wet transition period. It can be seen that the flexibility of coal-fired power and multi-type clean energy power generation systems varies in different water inflow periods. For the wet season and wet-dry transition period with large water inflow, the system down-regulation capacity required to be set will be smaller than that of the dry season and dry-wet transition period with small water inflow, and as the water volume decreases, the downregulation capacity of hydropower in coal-fired power and multi-type clean energy power generation systems gradually increases.

[0175] Since the system ramp performance will also affect flexibility, the influence of different ramp rates on system flexibility is studied. The specific calculation results of the probability index of insufficient flexibility are shown in Table 2. The expected value of insufficient flexibility is as follows: Figure 7 shown.

[0176] Table 2 Probability index of insufficient system flexibility under different ramp rates

[0177]

[0178] As the climbing rate increases, the probability of insufficient system flexibility decreases. This is because as the climbing rate increases, the system's restrictions on unit output decrease, and the unit can respond to load changes more quickly. Therefore, the system's flexible adjustment capability improves and flexibility becomes better. However, due to system capacity limitations, flexibility cannot be increased indefinitely. When the climbing rate increases to a certain level, the flexible adjustment capability will be limited.

[0179] At the same time, from Figure 6 It can be seen that no matter what the climbing rate is, the expected change curve of the system flexibility deficiency tends to converge with the increase of simulation times, which means that the flexibility quantification method adopted in the study can fully cope with various working conditions that may be encountered during the operation of the system and can be better evaluated.

[0180] Under the premise that the system ramp rate remains unchanged, the influence of the change of reserve capacity on the flexibility deficiency index is studied, and the reserve capacity is set to 15%, 20%, 25%, and 30% respectively. The probability index of system flexibility deficiency is shown in Table 3, and the change trend of the flexibility deficiency expectation index with the number of simulations is shown in Figure 7 shown.

[0181] Table 3 Different proportions of installed capacity of new energy sources (%) Insufficient flexibility indicators (probability, expectation)

[0182]

[0183] As the spare capacity increases, the probability of insufficient system flexibility gradually decreases. This is because the increase in spare capacity will increase the number of resources that can be flexibly adjusted within the system, which can improve the system's flexible adjustment capabilities to a certain extent, thereby increasing flexibility.

[0184] The proportion of new energy access will affect the flexibility of the system. This section studies the changes in the system's flexible adjustment capabilities under different proportions of new energy access. Table 4 shows the results of the flexibility deficiency index under different proportions of new energy installed capacity. Figure 8 The flexibility is less than expected and the probability changes with the proportion of new energy installed capacity.

[0185] Table 4 Different proportions of installed capacity of new energy sources (%) Insufficient flexibility indicators (probability, expectation)

[0186]

[0187] Can be obtained from Figure 8 It can be seen that before 30%, the expected and probability values ​​of insufficient flexibility are both 0, but once it exceeds 30%, the flexibility index begins to increase as the installed capacity increases to 40%. This is because as the proportion of new energy access increases, the uncertainty in the system also increases accordingly, which in turn affects the system's flexible adjustment ability and reduces flexibility.

Claims

1. A method for quantitatively evaluating the flexibility of a multi-energy complementary system of coal power and clean energy, characterized by: The following steps are involved: S1: Collect the historical power prediction errors of wind / solar output of wind power stations / photovoltaic stations as an error set, which includes several historical power prediction error values ​​of wind / photovoltaic output. Build a wind power / photovoltaic power prediction model based on the Monte Carlo method, sample the error set, and superimpose the extracted historical power prediction error values ​​of wind / photovoltaic output on the input wind / photovoltaic data; Sampling is performed several times in the error set, and the extracted wind / solar output historical power prediction error value is superimposed on the wind / solar output time series curve to obtain the wind / solar output prediction curve. The wind / solar output historical power prediction error value obeys the normal distribution. The load prediction error generated based on the normal distribution is superimposed on the load time series curve to obtain the load prediction curve; S2: Take the load forecast curve and wind-solar forecast curve as input, and combine the constraints of the multi-energy complementary system of coal-fired power and clean energy to obtain the wind-solar power abandonment and load shedding; and take the minimum total cost of abandoned power and load shedding losses in the coal-fired power and clean energy power generation systems as the objective function to form the optimal economic dispatch model; S3: Calculate the flexibility quantitative evaluation index based on the wind and solar power curtailment and load shedding as the intermediate operation quantities, set the threshold of the flexibility quantitative evaluation index, compare the flexibility quantitative evaluation index with the threshold, and evaluate the flexibility of coal-fired power and clean energy power generation systems.

2. The method for quantitatively evaluating the flexibility of a multi-energy complementary system of coal power and clean energy according to claim 1 is characterized in that: The step S1 comprises: S11: Collect the historical power prediction errors of wind / solar output of wind power stations / photovoltaic stations as an error set, which includes several historical power prediction error values ​​of wind / photovoltaic output. A wind power / photovoltaic power prediction model is built based on the Monte Carlo method, and the historical power prediction error values ​​of wind / photovoltaic output in the error set are sampled. The wind power / photovoltaic power prediction model is: Among them, X t,K is the error set consisting of the historical power prediction error of wind / solar output of the Kth wind power station / photovoltaic station at time t, α w is the w-th wind / solar output historical power prediction error value sampled from the error set, is the probability distribution of the wind / PV power prediction value of the Kth wind power station / PV station at time t; is the wind / PV power forecast value at time t for time t+k; S12: Based on the correlation between the historical wind / solar power prediction error values ​​at adjacent times, a differential sequence h of the wind / solar power prediction error values ​​at adjacent historical times t and t-1 is established. t,k 、m t,k , get the error set X in step S11 t,K ; W t,K ={h t,k },S t,K ={m t,k }; in, is the measured value of wind power at time t, is the predicted value of wind power at time t, is the measured value of photovoltaic power at time t, is the predicted value of photovoltaic power at time t, is the measured value of wind power at time t-1, for t-1 The predicted value of wind power at the moment, is the measured value of photovoltaic power at time t-1, is the predicted value of photovoltaic power at time t-1, h t,k 、m t,k are the difference sets of wind power and photovoltaic power prediction errors at time t; W t,K , S t,K are the wind power and photovoltaic power prediction error sets respectively; S13: Based on the difference sequence h t,k 、m t,k Establish a load forecasting model that obeys normal distribution, and transform the error set X t,K The historical power forecast error value of wind / solar output extracted from the load forecast is superimposed on the input wind / solar data, and the load forecast model is: in, is the load forecast value at time t, is the load forecast correction value at time t, ΔP Lerro,t is the load forecast error value at time t; S14: In the error set X t,K Several samplings are performed in the process, and the extracted historical power prediction error values ​​of wind / solar output are superimposed on the wind-solar timing curve to obtain the wind-solar prediction curve. The historical power prediction error values ​​of wind / solar output obey the normal distribution. The load prediction error generated based on the normal distribution is superimposed on the load timing curve to obtain the load prediction curve.

3. The method for quantitatively evaluating the flexibility of a multi-energy complementary system of coal power and clean energy according to claim 2 is characterized in that: The step S2 comprises: S21: Taking the load forecast curve and the wind and solar forecast curve as inputs, combined with the constraints of the multi-energy complementary system of coal-fired power and clean energy, the wind and solar power curtailment and load shedding are obtained; The constraints of the multi-energy complementary system of coal power and clean energy include: S211: Construct the residual load mean square error constraint condition: Among them, C G,t is the system residual load corresponding to time t, is the average value of the system residual load, is the grid load at time t, is the sum of the hydropower output values ​​of M hydropower stations at time t, m is the number of the hydropower station, is the hydropower output value of the mth hydropower station at time t, H is the hydropower, is the wind power output value corresponding to time t, W is wind power; is the photovoltaic output value corresponding to time t, PV is photovoltaic; N t is the capacity of the outbound channel corresponding to time t; N tmax is the maximum capacity of the transmission channel corresponding to time t, ε is the set residual load mean square error threshold, and F is the residual load mean square error; S212: Construct the upper and lower limits of wind power station / photovoltaic station output: Among them, Δp wind,t is the wind power output value at time t, Δp solar,t is the photovoltaic output value at time t, They are the upper limits of wind power and photovoltaic power output forecasts respectively; S213: Construct the upper and lower limits of wind / solar abandonment for wind power stations / photovoltaic stations: Among them, ΔP W,t is the wind power curtailment value at time t, ΔP S,t is the photovoltaic abandonment value at time t; S214: Construct the upper and lower limits of thermal power output: in, are the upper and lower limits of the thermal power output of the m1th thermal power station at time t; is the thermal power output value of the m1th thermal power station at time t; S215: Constructing upper and lower limit constraints of load shedding; in, is the upper limit of load forecast at time t; S216: Constraints for constructing hydropower stations in coal-fired power and multi-type clean energy power generation systems, including: Water balance constraints: Among them, V t i is the storage capacity of the i-th reservoir at time t, is the storage capacity of the i-th reservoir at time t-1, is the inflow of the i-th reservoir at time t, is the outflow of the i-th reservoir at time t; Hydraulic constraints of upstream and downstream reservoirs: in, is the power generation flow of the upstream i-1 hydropower station in the (t-τ) period, is the interval flow between hydropower station i-1 and hydropower station i, τ is the water flow lag time between hydropower station i-1 and hydropower station i; Water level constraints: in, Z i , are the water level in front of the dam and the upper and lower limits of the water level of the i-th reservoir at time t respectively; Initial and final water level constraints: in, are the target control water levels of the reservoir where the i-th hydropower station is located at the initial and final stages of dispatch, is the water level of the reservoir where the i-th hydropower station is located at the initial stage of dispatch, is the water level of the reservoir where the i-th hydropower station is located at the end of the dispatch period, and T is the final value of the water level; Reservoir outflow constraints: Among them, q i , are the upper and lower limits of the power generation flow of the i-th hydropower station, is the power generation flow of the i-th hydropower station at time t; Constraints on generating head of hydropower station units: in, is the water head of the i-th hydropower station at time t, is the tailwater level of the i-th hydropower station at time t, is the water level of the i-th hydropower station at time t-1, is the water level of the hydropower station at the i-th hydropower station at time t; Tailwater level-discharge constraint conditions: in, is the tailwater level-discharge relationship function of the reservoir where the i-th hydropower station is located, is the discharge of the hydropower station at the i-th hydropower station at time t; Water level-storage capacity constraints: in, is the water level-storage capacity relationship function of the reservoir where the i-th hydropower station is located, V t i is the storage capacity of the reservoir where the i-th hydropower station is located at time t; Hydropower output-power head constraints: in, is the binary relationship function between the output of unit g of the i-th hydropower station and the power generation flow and power generation head; P t i,g The power output of the unit g of the i-th hydropower station at time t; is the discharge of the hydropower station of unit g at time t, is the water head of unit g of the i-th hydropower station at time t; Constraints on the unit dynamic characteristics of the power station: in, is the binary relationship function between the output of the i-th hydropower station and the power generation flow and power generation head, P t i is the power output of the ith hydropower station at time t, is the discharge of the ith hydropower station at time t; S22: According to the maximum and minimum head interval of the i-th hydropower station The interval Split into three continuous water head sub-intervals and in, The power generation head of hydropower station i at any time satisfies the following constraints: in, is the indicator variable of the kth water head sub-interval of unit g of hydropower station i at time t, k is the number of three consecutive water head sub-intervals, Indicates that the generating head of unit g is within the kth head sub-interval, is the head height of unit g of hydropower station i in the k-1 head sub-interval; The head height of unit g of hydropower station i in the kth head sub-interval; S23: Get the water head height interval of the unit of hydropower station i When the water head height of hydropower station i When , the unit power characteristic curve under the average water head is selected as the representative curve, the representative curve is linearized, and the mathematical model of the relationship between the unit power characteristic curve and water head of hydropower station i is constructed: in, is the functional relationship between the output and power generation flow of hydropower station i in the kth head sub-interval, is the relationship coefficient of the unit power characteristic curve of hydropower station i at the kth head section, the relationship coefficient Only one is equal to 1, f1 i,pq|h (.), They are the power characteristic curves and water head correlation functions of the 1st, 2nd and 3rd sections of the unit respectively; S24: Based on the minimum total cost of power abandonment and load shedding losses in coal-fired power and clean energy power generation systems as the objective function, an optimal economic dispatch model is formed; the objective function is: min[a elec ΔP E,t +a load ΔP L,t ]Δt; ΔP E,t =ΔP W,t +ΔP S,t ; Among them, a elec 、a load are the penalty amounts for unit power abandonment and unit load shedding, Δt is the set time step, and are the power abandonment and load shedding at time t, ΔP L,t is the load shedding amount at time t, ΔP E,t is the amount of power abandoned at time t, ΔP W,t is the wind abandonment at time t, ΔP S,t is the wind abandonment volume at time t, and T is the total number of time periods.

4. The method for quantitatively evaluating the flexibility of a multi-energy complementary system of coal power and clean energy according to claim 3 is characterized in that: The step S3 comprises: S31: Calculate the amount of power abandonment and load shedding according to the optimal economic dispatch model, and set the initial values ​​of the intermediate quantities μ for upward flexibility calculation, downward flexibility calculation, wind power flexibility calculation, and photovoltaic flexibility calculation in the coal-fired power and clean energy power generation systems respectively. up , =0, the abandoned wind volume ΔP solved by the optimal economic dispatch model W,t , Amount of abandoned light ΔP S,t and load shedding capacity ΔP L,t , calculate the quantitative evaluation index of the flexibility of coal-fired power and clean energy power generation systems, where the quantitative evaluation index of flexibility includes upward and downward adjustment of the expected index of insufficient flexibility; Among them, e up,h is the upward flexibility index value of the h-th coal-fired power and clean energy power generation system, e down,h They are the wind power downward adjustment flexibility index value, photovoltaic downward adjustment flexibility index value, and total downward adjustment flexibility index value of the h-th coal-fired power and clean energy power generation systems respectively; S32: The air abandonment volume ΔP at each moment is obtained W,t , abandoned light amount, ΔP S,t and load shedding capacity ΔP L,t Matrix, calculating the probability of insufficient upward or downward flexibility of coal-fired and clean energy generation systems; in, When the load shedding or power abandonment of coal-fired power and clean energy power generation systems is not all zero, the total number of times the load shedding or power abandonment value is non-zero is counted; ΔP (·) The wind abandonment volume ΔP of coal-fired power and clean energy power generation systems W,t , abandoned light amount, ΔP S,t and load shedding capacity ΔP L,t The 0-1 state matrix with non-zero values ​​from t = 1 to t = T takes 1 when it is non-zero and takes 0 when it is zero; station The probability of insufficient flexibility in the upward or downward regulation of the power generation system, including load shedding, wind power curtailment and solar power curtailment; S33: Calculate the expected standard deviation coefficient V according to the upward or downward flexibility deficiency index of the coal-fired power generation system and the clean energy power generation system obtained in step S31 and step S32 σ ; Where, ζ is the set flexibility threshold; [Y k ] is an array, including the upward / downward flexibility index value and the probability of insufficient upward / downward flexibility under multiple cycles of coal-fired power and clean energy power generation systems; For the array [Y k ], which represents the average value of the upward / downward flexibility index value and the probability of insufficient upward / downward flexibility; They represent the expected standard deviation coefficient of the upward / downward flexibility index value and the expected standard deviation coefficient of the upward / downward flexibility insufficiency probability respectively; S34: If It is judged that the flexibility of coal-fired power and clean energy power generation systems is good; otherwise, the flexibility is poor.

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