Sufficiency assessment method suitable for power system with high water-electricity ratio

Abstract

The invention relates to the technical field of random production simulation, and discloses an adequacy assessment method suitable for a high-hydropower-ratio electric power system, which comprises the following steps: carrying out statistical analysis on historical output data of wind power photovoltaic and radial flow type hydropower; obtaining a time sequence net load upper limit curve; obtaining a time sequence net load lower limit curve; utilizing a successive load shedding method to process reservoir capacity type water and electricity so as to correct a time sequence net load curve; and performing random production simulation by using an improved equivalent electric quantity function method. According to the method, three states of shutdown, minimum technical output and maximum technical output of the thermal power generating unit, the maximum technical output constraint of the unit and the climbing constraint of the unit are considered, and meanwhile, the uncertainty of wind power photovoltaic and radial flow type hydropower output is processed by applying a confidence number statistical method; and a successive load shedding method is applied to determine the power generation amount and the on-load position of the storage capacity type hydropower station, so that the peak regulation cost of thermal power is reduced, the adequacy and flexibility of the power generation capacity of the system are improved, and the safety and stability of a power grid can be effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of random production simulation, and in particular to a redundancy assessment method applicable to a power system with a high proportion of hydropower. Background Art

[0002] The random production simulation of the power system is a method that optimizes the production of the generating units, takes into account the random failures of the units and the volatility of the power load, and calculates the power generation of each power plant under the optimal operation mode, the production cost of the system and the system reliability index. It can provide a basis for the power system power supply planning and the formulation of the unit operation plan. How to use the random production simulation method to guide the planning and operation of the new power system with a high proportion of new energy access is a topic of great concern to power companies such as the State Grid Corporation of China.

[0003] Stochastic production simulation can calculate the output of each unit under different operating modes. It is used for research that only considers the output characteristics of the generator set and the system load curve, and can provide a basis for power system power planning and unit operation plan formulation. Stochastic production simulation of new power systems can be divided into simulation method and analytical method. Among them, the simulation method is mainly the Monte Carlo method, which simulates the system state by statistically analyzing the system failure frequency. The analytical method can construct clear and simple mathematical models, such as the equivalent power function method, sequence operation and universal generating function (UGF) method, which have been widely used in power system production simulation. However, these two methods also have certain defects: Monte Carlo simulation has the advantage that the number of sampling times is independent of the system scale, so it has been widely used in modern power system stochastic production simulation. However, in order to achieve higher calculation accuracy, Monte Carlo simulation needs to extract a large number of samples, which leads to slower convergence speed, less accurate calculation accuracy and difficulty in accurately evaluating the system's new energy absorption capacity. Although the traditional equivalent electricity function method in the analytical method has certain guarantees in terms of calculation accuracy and speed, the calculation results are mainly based on the economic and reliability indicators of the power system, which cannot cope with the challenges brought by the new power system; both methods cannot effectively evaluate the flexibility of the new power system with a high proportion of new energy access; existing research has not considered the impact of a high proportion of hydropower on the adequacy and flexibility of the new power system. The above problems will affect the assessment of the adequacy of the power system to a certain extent. Therefore, how to solve the above problems and improve the accuracy of the assessment of the adequacy and flexibility of the power system has become an urgent problem to be solved. Summary of the invention

[0004] The purpose of the present invention is to provide a method for assessing the adequacy of a power system with a high proportion of hydropower, so as to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solution: a method for assessing the adequacy of a power system with a high proportion of hydropower, comprising the following steps:

[0006] S1. Use confidence number statistics to conduct statistical analysis on historical output data of wind power, photovoltaic power and run-of-river hydropower;

[0007] S2. Using the time-series output curve of wind, solar and water, the time-series net load upper limit curve is obtained; the formula is as follows: Using the original load L t Deduct the lower limit of wind, solar and hydro output at each moment to obtain the upper limit of the system's sequential net load

[0008] S3. Using the time-series output curve of wind, solar and water, obtain the time-series net load lower limit curve;

[0009] S4. Using the successive load shedding method to handle reservoir-type hydropower to correct the time-series net load curve;

[0010] S5. Use the improved equivalent electricity function method to perform random production simulation.

[0011] Optionally, the S1 further comprises the following steps:

[0012] S11. Take the wind power output of multiple operating days, including the predicted value and the actual value, and calculate the error rate of the wind and solar power output in each period;

[0013] S12. Arrange the system wind and solar power output forecast error rates for the same period of all historical operating days from small to large and from negative to positive into an error rate set. The number of error samples is the number of elements in the set. Calculate the upper and lower limits of the error rate for the period corresponding to a certain confidence level.

[0014] S13. By using the upper and lower limits of the system wind power output forecast error rate for time period t corresponding to a certain confidence level, the estimation formula of the system wind power output forecast error is obtained:

[0015] e +,W,t =max(r o,t K t ,0)#(4)

[0016] e -,W,t =min(r b,t K t ,0)#(5)

[0017] Where: e +,W,t 、e -,W,t are the positive and negative errors of the system wind power output forecast for the time period estimated based on the confidence number statistical method, MW;

[0018] S14. Obtain the upper limit curve of system wind power output based on the predicted positive and negative errors of system wind power processing And wind power output lower limit curve W t ;

[0019] S15. Similarly, the upper and lower limit curves of the system photovoltaic output can be obtained S t and run-of-river hydropower output upper and lower limit curves and D t ;

[0020]

[0021] S t =P S,t +e -,S,t #(9)

[0022]

[0023] D t =P D,t +e -,D,t #(11)

[0024] Where: P S,t is the predicted wind power output at time t obtained based on historical data, P D,t is the run-of-river hydropower output forecast at time t based on historical data, MW; e +,S,t 、e -,S,t are the positive and negative errors of the system photovoltaic output prediction based on the confidence number statistical method during period t, e +,D,t 、e -,D,t They are the positive and negative errors of the run-of-river hydropower output forecast for period t based on the confidence number statistics method, MW.

[0025] Optionally, the error rate formula of S11 is:

[0026]

[0027] Where: r h,t is the system wind and solar power output forecast error rate for the period calculated based on historical data, %; K a,t , K t They are the actual and predicted values ​​of the system wind and solar power output in MW for each period.

[0028] Optionally, the calculation formula of S12 is:

[0029]

[0030] Where: r o,t 、r b,tare the upper and lower limits of the system wind power output forecast error rate for a certain confidence level in time period t, %; C is the confidence level, R t is the number of elements in the system wind power output forecast error rate set for the time period; The symbols for the rounding up and down operators.

[0031] Optionally, the formula in S14 is:

[0032]

[0033] W t =P W,t +e -,W,t #(7)

[0034] Where: P W,t is the predicted wind power output based on historical data, MW.

[0035] Optionally, S3 further includes: using the original load to deduct the upper limit of wind, solar and hydropower output at each moment to obtain the lower limit of the system time series net load, and the formula is as follows:

[0036] Optionally, the S4 further includes:

[0037] S41. The output upper limit of the hydropower unit is set to the maximum value of the daily load of the time-series net load upper limit curve. The output lower limit of the hydropower unit is determined by subtracting the maximum capacity of the hydropower unit from the output upper limit, which can be expressed as:

[0038] H iup =P Lmax #(14)

[0039] H idoun =H iup -N g #(15)

[0040] Where: P Lmax Indicates the maximum daily load of the time series net load upper limit curve, H iup Indicates the upper limit of the hydropower unit output, H idoun Indicates the lower limit of the output of the hydropower unit;

[0041] S42, calculate the power generation Eg of the reservoir-type hydropower unit on the selected typical day, and calculate the actual power generation E of the hydropower unit according to the position of the hydropower unit in the time series net load upper limit curve, that is, Figure 1 The shaded area in , and then the iteration step length Δx is calculated according to the following formula;

[0042] Δx=(EE g ) / T#(16)

[0043] Where: T represents the simulation period;

[0044] S43, setting a very small ε value, and comparing the iteration step length Δx obtained by formula (16) with ε;

[0045] If |Δx|≤ε, Δx satisfies the convergence criterion, and the process goes to step S44;

[0046] Otherwise, go to step S42 to correct the working position of the hydropower unit;

[0047] The correction formula for correcting the working position of the hydropower unit is as follows:

[0048] H iup =H iup +Δx#(17)

[0049] H idoun =H idoun +Δx#(18)

[0050] S44, the iteration ends, and the time series net load upper limit curve is corrected according to the upper and lower limit positions determined by the hydropower unit i;

[0051] If the failure of the unit needs to be considered, the generating capacity of the hydropower unit should be corrected accordingly.

[0052] Compared with the prior art, the present invention provides a method for assessing the adequacy of a power system with a high proportion of hydropower, which has the following beneficial effects:

[0053] 1. This method for assessing the adequacy of power systems with a high proportion of hydropower takes into account the shutdown, minimum technical output and maximum technical output of thermal power units, as well as the maximum technical output constraints and ramp constraints of the units. At the same time, it uses the confidence number statistical method to deal with the uncertainty of wind power, photovoltaic power and run-of-river hydropower output, and uses the successive load shedding method to determine the power generation and load position of reservoir-type hydropower, which reduces the peak-shaving cost of thermal power, improves the adequacy and flexibility of the system's power generation capacity, and can effectively improve the safety and stability of the power grid.

[0054] 2. This method of redundancy assessment, which is applicable to power systems with a high proportion of hydropower, not only takes into account the various states of thermal power units and the ramping rates of units under different states, but also describes the impact of uncertain factors by applying confidence number statistics. At the same time, it considers the new challenges that valley peak regulation and the ramping of thermal power units bring to the stability and safety of the system in new power systems, thereby improving the accuracy of power system generation capacity assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 It is a schematic diagram of the time sequence net load upper limit diagram of the present invention;

[0056] Figure 2 It is a schematic diagram of the continuous load curve of the present invention. DETAILED DESCRIPTION

[0057] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0058] like Figure 1-Figure 2 As shown, the present invention provides a technical solution: a method for assessing the adequacy of a power system with a high proportion of hydropower, comprising the following steps:

[0059] S1. Use the confidence number statistical method to conduct statistical analysis on the historical output data of wind power, photovoltaic power and run-of-river hydropower. The processing methods for photovoltaic power and run-of-river hydropower are the same as those for wind power. The specific steps are as follows:

[0060] S11. Take the wind power output of multiple operating days, including the predicted value and the actual value, and calculate the error rate of wind and solar power output in each period. The formula is:

[0061]

[0062] Where: r h,t is the system wind and solar power output forecast error rate for the period calculated based on historical data, %; K a,t , K t They are the actual and predicted values ​​of the system wind and solar power output in MW for each period.

[0063] S12. Arrange the system wind and solar output forecast error rates of the same period of all historical operation days from small to large and from negative to positive into an error rate set. The number of error samples is the number of elements in the set. Calculate the upper and lower limits of the error rate for the period corresponding to a certain confidence level. The calculation formula is:

[0064]

[0065] Where: r o,t 、r b,t are the upper and lower limits of the system wind power output forecast error rate for a certain confidence level in time period t, %; C is the confidence level (which can be 98%, 95% and 90%, etc.), %; r n,t is the error rate corresponding to the nth element in the system wind and solar output prediction error rate set for the time period, %; r t is the system wind and solar power output prediction error rate set for the time period; R tis the number of elements in the system wind power output forecast error rate set for the time period; For the symbols of the rounding up and rounding down operations;

[0066] S13. By using the upper and lower limits of the system wind power output forecast error rate for time period t corresponding to a certain confidence level, the estimation formula of the system wind power output forecast error is obtained:

[0067] e +,W,t =maxr o,t K t ,0)#(4)

[0068] e -,W,t =min(r b,t K t ,0)#(5)

[0069] Where: e +,W,t 、e -,W,t They are the positive and negative errors of the system wind power output forecast for the time period estimated based on the confidence number statistical method, MW.

[0070] S14. Obtain the upper limit curve of system wind power output based on the predicted positive and negative errors of system wind power processing And wind power output lower limit curve W t :

[0071]

[0072] W t =P W,t +e -,W,t #(7)

[0073] Where: P W,t is the predicted wind power output based on historical data, MW.

[0074] S15. Similarly, the upper and lower limit curves of the system photovoltaic output can be obtained S t and run-of-river hydropower output upper and lower limit curves and D t .

[0075]

[0076] S t =P S,t +e -,S,t #(9)

[0077]

[0078] D t =P D,t +e -,D,t #(11)

[0079] Where: P S,t is the predicted wind power output at time t obtained based on historical data, P D,t is the run-of-river hydropower output forecast at time t based on historical data, MW. +,S,t 、e -,S,t are the positive and negative errors of the system photovoltaic output prediction based on the confidence number statistical method during period t, e +,D,t 、e -,D,t They are the positive and negative errors of the run-of-river hydropower output forecast for period t based on the confidence number statistics method, MW.

[0080] S2. Using the time-series output curve of wind, solar and water, the time-series net load upper limit curve is obtained; the formula is as follows: Using the original load L t Deduct the lower limit of wind, solar and hydro output at each moment to obtain the upper limit of the system's sequential net load

[0081] S3. Using the time-series output curve of wind, solar and water, obtain the time-series net load lower limit curve;

[0082] The specific method is: use the original load to deduct the upper limit of wind, solar and hydropower output at each moment to obtain the lower limit of the system time series net load. The formula is as follows:

[0083]

[0084] S4. Using the successive load shedding method to handle reservoir-type hydropower to correct the time-series net load curve;

[0085] Arrange the reservoir capacity hydropower for peak regulation to reduce the peak-to-valley difference of the net load curve, and use the successive load shedding method to determine the load period and power generation of the hydropower unit. In the embodiment, it is necessary to use the time series net load upper limit curve and the time series net load lower limit curve to correct the output of the reservoir capacity hydropower. Here, only the correction process of the time series net load upper limit curve is introduced; Figure 1 As shown;

[0086] Assume that the simulated daily power consumption of the hydropower unit is E g , unit MWh, capacity N g , unit MW, the successive load shedding method is used to arrange the load of the hydropower unit, that is, the maximum load is obtained according to the time series net load upper limit curve, and the maximum allowable output of the unit is subtracted from the maximum load to determine the initial working position of the unit.

[0087] The steps of the load shedding method are as follows:

[0088] S41. The output upper limit of the hydropower unit is set to the maximum value of the daily load of the time-series net load upper limit curve. The output lower limit of the hydropower unit is determined by subtracting the maximum capacity of the hydropower unit from the output upper limit, which can be expressed as:

[0089] H iup =PL max #(14)

[0090] H idoun =H iup -N g #(15)

[0091] Where: P Lmax Indicates the maximum daily load of the time series net load upper limit curve, H iup Indicates the upper limit of the hydropower unit output, H idoun Indicates the lower limit of the output of the hydropower unit;

[0092] S42, calculate the power generation Eg of the reservoir-type hydropower unit on the selected typical day, and calculate the actual power generation E of the hydropower unit according to the position of the hydropower unit in the time series net load upper limit curve, that is, Figure 1 The shaded area in , and then the iteration step length Δx is calculated according to the following formula;

[0093] Δx=(EE g ) / T#(16)

[0094] Where: T represents the simulation period;

[0095] S43, setting a very small ε value, and comparing the iteration step length Δx obtained by formula (16) with ε;

[0096] If |Δx|≤ε, Δx satisfies the convergence criterion, and the process goes to step S44;

[0097] Otherwise, go to step S42 to correct the working position of the hydropower unit;

[0098] The correction formula for correcting the working position of the hydropower unit is as follows:

[0099] H iup =H iup +Δx#(17)

[0100] H idoun =H idoun +Δx#(18)

[0101] S44, the iteration ends, and the time series net load upper limit curve is corrected according to the upper and lower limit positions determined by the hydropower unit i;

[0102] If the failure of the unit needs to be considered, the generating capacity of the hydropower unit should be corrected accordingly.

[0103] S5. Use the improved equivalent electricity function method to perform random production simulation;

[0104] The equivalent power function method of random production simulation mainly arranges the power generation of the units according to the economic priority order and calculates the power produced. This method can treat the failure and shutdown of the unit as equivalent to the increase of load. Assuming that there are n generating units in the system, the continuous load curve of each unit put into operation will be convolved once. When all units are put into operation, the power generation of each unit, the power shortage probability LOLP of the system and the expected value of power shortage EENS can be calculated.

[0105] The continuous load curve is the core of the analytical random production simulation. It is a curve F(x) formed by rearranging the original load curve according to the cumulative duration of different load values ​​in the research period. Figure 2 As shown, the horizontal axis is load x (MW); the vertical axis is duration t (h); the research period is T (h); CS represents the total installed capacity of the system.

[0106] Assuming the total study time is T, dividing the continuous load curve by T, we can get:

[0107]

[0108] f(x) can be viewed as the probability that the system load is greater than or equal to x.

[0109] However, this traditional equivalent electricity function method does not take into account the minimum capacity constraints of thermal power units and the impact of the climbing ability of thermal power units on system flexibility under the new power system. The improved equivalent electricity function law proposed in this embodiment solves these problems well and is more reliable in evaluating the redundancy of the power system.

[0110] After processing the storage capacity hydropower, the corrected time series net load upper limit curve and time series net load lower limit curve are obtained. The improved equivalent power function method is used to perform random production simulation through the two time series net load curves, and the margin evaluation index of the power system under optimal operation is provided (here only the random production simulation steps of the time series net load upper limit curve are described). The specific steps are as follows:

[0111] S51. For the time series net load upper limit curve, convert the time series net load upper limit curve at each moment into a continuous load curve in chronological order. Divide the horizontal axis of the continuous load curve into segments at equal intervals according to the greatest common divisor Δx of the minimum output and rated power of each unit. The integral of each segment is the power function E(J), and convert the convolution operation of the equivalent continuous load curve into the convolution operation of the equivalent power function.

[0112]

[0113] In the formula, E(J) corresponds to the area under the load curve from X to X+Δx, that is, the amount of electricity corresponding to this load.

[0114] S52. According to the principle of economic optimization, the simulation is arranged with the minimum output state of the unit, that is, the first segment output, and the continuous load curve at each moment is corrected accordingly.

[0115] f I-1 (x) represents the load curve corrected after the I-1th unit is arranged, and the corresponding power function is E I-1 (J). Then the first unit is put into operation, and its capacity is C i The forced outage rate is denoted by q i The corrected load curve can be expressed as:

[0116] f I (x) = p i f I-1 (x)+q i f I-1 (xc i )#(twenty one)

[0117] In the formula, p i =1-q i

[0118] According to the definition of formula (20), the corresponding power function is:

[0119] E I (J) = p i E I-1 (J)+q i E I-1 (JK i )#(twenty two)

[0120] In the formula, K i =C i / Δx

[0121] At this time, the amount of electricity that the system has not yet met should be:

[0122]

[0123] Assume that there are N generators in the system, then the expected value of power shortage in the system is E DN .

[0124] The amount of electricity generated by the first unit can be expressed as:

[0125] E Gi =E D(i-1) -E Di #(twenty four)

[0126] The load loss probability LOLP can be expressed as:

[0127]

[0128] S53. According to the unmet power demand of the system and based on the principle of optimal economy, the second stage of unit output is arranged, and the expected power shortage and load loss probability indicators of the system are calculated.

[0129] When calculating the amount of electricity that the increased output unit faces, the influence of the convolution operation of the minimum technical output segment of the unit must be removed. At this time, deconvolution calculation is required.

[0130] The calculation formula of deconvolution can be derived from the convolution formula.

[0131] Assume that the output of unit b needs to be increased to the rated output. When the output of unit b is increased, E should have been formed. I (J)

[0132] E I (J) = E 0 (J)*G1*G2*···*G b *···G i #(26)

[0133] Where * represents the convolution operation, G1G2···G b ···G i is the original equivalent electric quantity function E 0 (J) For the unit that performs convolution calculation, equation (26) can be written in the form of recursive operation:

[0134] E I (J) = E I-1 (J)*G i #(27)

[0135] In the formula

[0136] E I-1 (J) = E 0 (J)*G1*G2*···*G b *···G i-1 #(28)

[0137] Since the convolution operation obeys the commutative law, equation (26) can be rewritten as:

[0138] E I (J) = E 0 (J)*G1*G2*···*G i *G b #(29)

[0139] Or written as:

[0140] E I (J) = E' I-1 (J)*G b #(30)

[0141] Where:

[0142] E' I-1 (J) = E 0 (J)*G1*G2*···*G i #(31)

[0143] That is E' I-1 (J) is the original E 0 (J) Except for Unit G b The result of convolution operation of all previous I units except .

[0144] Since we need to arrange G b They are different sections of the same unit, so G is rearranged b When the unit is b The impact of random failures when the unit is at its minimum technical output, so the newly arranged G b The power of the unit should be in E' represented by formula (30) I-1 (J) instead of E I (J) is calculated, requiring that G b When the unit is used, first calculate E' I-1 (J) To this end, E' can be obtained by deconvolution calculation using formula (31): I-1 (J).

[0145] Set unit G b The minimum technical output of the power generation capacity is C b , the corresponding discrete variable is K b , and its forced outage rate is q b , expand formula (30):

[0146] E I (J) = p b E' I-1 (J)+q b E' I-1 (JK b )#(32)

[0147] Where: p b =1-q b , K b =C b / Δx

[0148] The following deconvolution calculation formula can be derived from formula (32):

[0149] E' I-1 (J)=[E I (J)-E' I-1 (JK b )q b ] / p b #(33)

[0150] From formula (33), we can get E' I-1 (J), Unit G after increasing output b Rated power generation capacity is C' b , the corresponding discrete variable is K' b , its forced outage rate is still q b , and then unit G b Performing convolution operation yields:

[0151] E'I(J)=p b E' I-1 (J)+q b E' I-1 (JK' b )#(34)

[0152] In the formula, K' b =C' b / Δx, and then perform the above calculations for other required segmented output units in turn, and use formula (23) to calculate the power generation E of the unit Gi , and then use formula (22) to recalculate the expected power shortage of the system E DN And use formula (23) to calculate the load loss probability LOLP.

[0153] S54, maximum technical output constraints of the unit;

[0154] In the process of random production simulation, each time the output data of a unit is obtained, it is judged whether it meets the maximum technical output constraint. If not, the load is cut, which is equivalent to increasing the net load. After the load is updated, random production simulation is performed again until the constraint is met. The specific algorithm is:

[0155] P Gi (t) = E Gi (t) / Δt#(35)

[0156]

[0157] P new (t) = P(t) - P cut (t) / +P discard (t)#(37)

[0158] In the formula, E Gi(t) represents the amount of electricity generated by the i-th unit at the t-th time, Δt represents the time scale, P Gi (t) represents the output of the i-th unit at the t-th time, P U (i) represents the maximum output constraint of the i-th unit, P cut (t) represents the load shedding value required at the tth moment, P new P(t) represents the load value after updating at time t, and P(t) represents the load value before updating at time t.

[0159] S55, unit climbing constraint: According to the maximum / minimum technical output of N units in operation, 2N+1 power generation output intervals are divided, and the upward / downward climbing speed of each unit in the corresponding power generation output interval is determined. Table 1 shows the 2N+1 power generation output intervals of N units in operation and their corresponding upward / downward climbing speeds, where P(i,t) is the power generation output of unit i in period t.

[0160] As shown in Table 1, the upward / downward climbing rate of the system in different power generation output ranges.

[0161]

[0162] According to the system's climbing rate and load change speed in different output ranges in the above table, it is judged whether the climbing rate constraint is met, so as to perform load shedding or wind and solar abandonment operations. The specific algorithm is as follows:

[0163] V(t+1)=P(t+1)-P(t)#(38)

[0164]

[0165] P new (t+1)=P(t+1)-P cut (t+1) / +P discard (t+1)#(41)

[0166] In the formula, V(t+1) represents the load change rate from time t to time t+1, R U (k) represents the upward climbing rate of the kth load interval, R D (k) represents the downward climbing rate of the kth load interval, P cut (t+1) represents the load shedding value at the t+1th moment, P discard (t+1) represents the value of wind power that needs to be abandoned at the t+1th moment, P new (t+1) represents the load value after the update at time t+1, and P(t+1) represents the load value before the update at time t+1.

[0167] The above generally describes the present invention in detail, but it is obvious to a person skilled in the art that some modifications or improvements can be made to the present invention. Therefore, modifications or improvements that do not depart from the spirit of the present invention are within the scope of protection of the present invention.

Claims

1. A method for assessing the adequacy of a power system with a high proportion of hydropower, characterized by: The steps include: S1. Use confidence number statistics to conduct statistical analysis on historical output data of wind power, photovoltaic power and run-of-river hydropower; S2. Using the time-series output curve of wind, solar and water, the time-series net load upper limit curve is obtained; the formula is as follows: Using the original load L t Deduct the lower limit of wind, solar and hydro output at each moment to obtain the upper limit of the system's sequential net load S3. Using the time-series output curve of wind, solar and water, obtain the time-series net load lower limit curve; S4. Using the successive load shedding method to handle reservoir-type hydropower to correct the time-series net load curve; S5. Use the improved equivalent electricity function method to perform random production simulation.

2. The method for assessing the adequacy of a power system with a high proportion of hydropower according to claim 1, characterized in that: The S1 further comprises the following steps: S11. Take the wind power output of multiple operating days, including the predicted value and the actual value, and calculate the error rate of the wind and solar power output in each period; S12. Arrange the system wind and solar power output forecast error rates for the same period of all historical operating days from small to large and from negative to positive into an error rate set. The number of error samples is the number of elements in the set. Calculate the upper and lower limits of the error rate for the period corresponding to a certain confidence level. S13. By using the upper and lower limits of the system wind power output forecast error rate for time period t corresponding to a certain confidence level, the estimation formula of the system wind power output forecast error is obtained: e +,W,t =max(r o,t K t ,0)#(4) e -,W,t =min(r b,t K t ,0)#(5) Where: e +,W,t 、e -,W,t are the positive and negative errors of the system wind power output forecast for the time period estimated based on the confidence number statistical method, MW; S14. Obtain the upper limit curve of system wind power output based on the predicted positive and negative errors of system wind power processing And wind power output lower limit curve W t ; S15. Similarly, the upper and lower limit curves of the system photovoltaic output can be obtained S t and run-of-river hydropower output upper and lower limit curves and D t ; S t =P S,t +e -,S,t #(9) D t =P D,t +e -,D,t #(11) Where: P S,t is the predicted wind power output at time t obtained based on historical data, P D,t is the run-of-river hydropower output forecast at time t based on historical data, MW; e +,S,t 、e -,S,t are the positive and negative errors of the system photovoltaic output prediction based on the confidence number statistical method during period t, e +,D,t 、e -,D,t They are the positive and negative errors of the run-of-river hydropower output forecast for period t based on the confidence number statistics method, MW.

3. The method for assessing the adequacy of a power system with a high proportion of hydropower according to claim 2, characterized in that: The error rate formula of S11 is: Where: r h,t is the system wind and solar power output forecast error rate for the period calculated based on historical data, %; K a,t , K t They are the actual and predicted values ​​of the system wind and solar power output in MW for each period.

4. The method for assessing the adequacy of a power system with a high proportion of hydropower according to claim 2, characterized in that: The calculation formula of S12 is: Where: r o,t 、r b,t are the upper and lower limits of the system wind power output forecast error rate for a certain confidence level in time period t, %; C is the confidence level, R t is the number of elements in the system wind power output forecast error rate set for the time period; The symbols for the rounding up and down operators.

5. The method for assessing the adequacy of a power system with a high proportion of hydropower according to claim 2, characterized in that: The formula in S14 is: W t =P W,t +e -,W,t #(7) Where: P W,t is the predicted wind power output based on historical data, MW.

6. The method for assessing the adequacy of a power system with a high proportion of hydropower according to claim 1, characterized in that: The S3 further comprises: The lower limit of the system net load is obtained by deducting the upper limit of wind, solar and hydropower output at each moment from the original load. The formula is as follows:

7. The method for assessing the adequacy of a power system with a high proportion of hydropower according to claim 1, characterized in that: The S4 further comprises: S41. The output upper limit of the hydropower unit is set to the maximum value of the daily load of the time-series net load upper limit curve. The output lower limit of the hydropower unit is determined by subtracting the maximum capacity of the hydropower unit from the output upper limit, which can be expressed as: H iup =P Lmax #(14) H idoun =H iup -N g #(15) Where: P Lmax Indicates the maximum daily load of the time series net load upper limit curve, H iup Indicates the upper limit of the hydropower unit output, H idoun Indicates the lower limit of the output of the hydropower unit; S42, calculating the power generation Eg of the reservoir-type hydropower unit on the selected typical day, calculating the actual power generation E of the hydropower unit according to the position of the hydropower unit in the time series net load upper limit curve, that is, the shaded area in Figure 1, and then calculating the iteration step length Δx according to the following formula; Δx=(E-E g ) / T#(16) Where: T represents the simulation period; S43, setting a very small ε value, and comparing the iteration step length Δx obtained by formula (16) with ε; If |Δx|≤ε, Δx satisfies the convergence criterion, and the process goes to step S44; Otherwise, go to step S42 to correct the working position of the hydropower unit; The correction formula for correcting the working position of the hydropower unit is as follows: H iup =H iup +Δx#(17) H idoun =H idoun +Δx#(18) S44, the iteration ends, and the time series net load upper limit curve is corrected according to the upper and lower limit positions determined by the hydropower unit i; If the failure of the unit needs to be considered, the generating capacity of the hydropower unit should be corrected accordingly.

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