CN111598388A - Online evaluation method for frequency modulation resource demand of real-time market of power grid - Google Patents

Online evaluation method for frequency modulation resource demand of real-time market of power grid Download PDF

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CN111598388A
CN111598388A CN202010274223.2A CN202010274223A CN111598388A CN 111598388 A CN111598388 A CN 111598388A CN 202010274223 A CN202010274223 A CN 202010274223A CN 111598388 A CN111598388 A CN 111598388A
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肖艳炜
李继红
涂孟夫
孙珂
朱炳铨
蒋正威
曹建伟
杨力强
鲁文
昌力
吴继平
王文
王阳英夫
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State Grid Corp of China SGCC
State Grid Zhejiang Electric Power Co Ltd
NARI Group Corp
Nari Technology Co Ltd
Huzhou Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
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State Grid Zhejiang Electric Power Co Ltd
NARI Group Corp
Nari Technology Co Ltd
Huzhou Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
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Abstract

The invention discloses an online evaluation method for frequency modulation resource demand of a real-time market of a power grid, which comprises the following steps: step S1: calculating the frequency modulation capacity requirement caused by the load change component of the power grid system; step S2: calculating the frequency modulation capacity requirement caused by the new energy output of the power grid; step S3: calculating the capacity requirement of the planned component frequency modulation of the tie line; step S4: calculating the unit plan component frequency modulation capacity requirement; step S5: obtaining a preliminary AGC frequency modulation capacity requirement according to the steps S1, S2, S3 and S4; step S6, index correction is carried out; step S7: and obtaining the final AGC frequency modulation capacity requirement. According to the method, the total frequency modulation capacity demand of the power grid is obtained by respectively calculating the frequency modulation capacity demands caused by system load, new energy output, a tie line plan and a unit plan component, and meanwhile, capacity correction is carried out according to the assessment indexes, so that the problem of online quantitative evaluation and analysis of the frequency modulation resource demand in the real-time market is solved, and the economy of the power grid market is improved.

Description

Online evaluation method for frequency modulation resource demand of real-time market of power grid
Technical Field
The invention relates to the technical field of power market resource allocation, in particular to an online evaluation method for frequency modulation resource requirements of a real-time market of a power grid.
Background
With the advancement of the innovation of the electric power system, the provincial electric power spot market starts to run on test points, and the real-time electric power market is a key component of the spot market. The real-time market trading clearance needs to consider the market price, the power grid safety and the power supply and demand balance, and also needs to consider the demand constraint of reserved frequency modulation capacity. The reserved frequency modulation capacity has great influence on the real-time market electricity price and market benefit, and the excessive reservation of the frequency modulation capacity wastes the adjusting capacity of a unit, causes the market price to rise and damages the benefit of power users; the reserved frequency modulation capacity is too small, and the capacity of the power grid for dealing with real-time load fluctuation and emergency faults is insufficient, so that the safe operation of the power grid is damaged. Therefore, accurate online assessment and analysis of real-time power market capacity modulation needs is needed.
The domestic and foreign scholars develop deep research on quantitative evaluation and analysis of the demand of the frequency modulation capacity of the power grid, and the current domestic and foreign power systems generally adopt a mode of reserving the frequency modulation capacity according to the percentage of the system load, for example, the frequency modulation capacity of the American PJM market is reserved according to 0.7 percent of the demand of the system load. The domestic power grid can not completely follow the foreign experience because the power grid architecture and the power supply composition are obviously different from those of foreign countries. Under the condition that the scale of the current extra-high voltage interconnected power grid and new energy grid connection is rapidly enlarged, if system load fluctuation, extra-high voltage tie line exchange power fluctuation and new energy output fluctuation are combined, real-time market frequency modulation capacity requirements are quantitatively evaluated and analyzed, and the problem needs to be solved in real-time electric power market construction.
Prior art 1, AGC demand analysis based on electric power market support service (35 th volume 7 of china power 2007) analyzes a demand calculation method of an AGC in a foreign electric power market, taking texas and california as an example. For Texas, the basic idea is to estimate the deviation of the maximum load, the minimum load and the average load within 5min based on the analysis of the historical load data and to count the distribution rule. Therefore, the dispatcher can estimate the AGC capacity requirement within 5min according to the load deviation distribution rule only by giving a load deviation coverage rate; the California power market predicts AGC regulation capacity more likely to meet performance standard assessment, and the specific method is as follows: and (3) counting the maximum value of the actual use value of the regulating capacity in each 24-hour rolling in the first 7 days of the predicted target day, taking the average value of the values as a predicted base value of the AGC, and then adjusting the base value by using a tie line deviation checking CPS (control Performance Standard), thereby realizing the self-balance of the predicted capacity and the demand of the AGC market.
In the prior art 2, the AGC capacity demand and control strategy research of interconnected power systems (university of university, 2009) analyzes the components of the AGC capacity demand, and predicts the demand more accurately for different capacity components. Through analyzing the historical load data, the unit tracking capacity, the power generation plan, the standby tracking and other data, the real-time required purchasing curve of the system AGC at the historical moment is obtained, and the required purchasing capacity of the AGC at each period can be directly determined. The AGC capacity is finally and accurately calculated by considering the removal of some untraceable high-frequency load parts, and considering the influence of plan tracking, rotary standby, low-frequency load parts of real-time markets, parts which are not timely followed by the rotary standby and unit retuning.
The above documents discuss methods for calculating the AGC frequency modulation capacity from the aspects of load change, unit planning, evaluation criteria, etc., and these methods are not considered comprehensively enough, do not consider all factors influencing the frequency modulation capacity from the actual distribution and utilization balance of the power grid, do not have the requirements of on-line and real-time performance, and cannot meet the requirements of the frequency modulation real-time market.
Disclosure of Invention
The invention mainly solves the problem of poor real-time performance caused by insufficient comprehensive consideration of power grid capacity frequency modulation in the prior art; according to the method, frequency modulation reserve capacity correction is carried out according to assessment indexes from provincial power grid real-time market frequency modulation requirements caused by different components of power grid system load prediction deviation, new energy output deviation, tie line plan deviation and unit plan deviation, and finally total AGC frequency modulation reserve capacity requirements of the provincial power grid real-time market are obtained, and the economy of the power grid market is improved.
The technical problem of the invention is mainly solved by the following technical scheme: a power grid real-time market frequency modulation resource demand online evaluation method comprises the following steps:
step S1: calculating the frequency modulation capacity requirement caused by the load change component of the power grid system; the Automatic Generation Control (AGC) mainly adjusts the power shortage between the system generation output and the actual power consumption, the historical load data is combined to preliminarily predict the frequency modulation capacity requirement of the AGC, and the frequency modulation capacity requirement caused by the system load change component is the frequency modulation capacity requirement caused by the system ultra-short-term load prediction deviation;
step S2: calculating the frequency modulation capacity requirement caused by the new energy output of the power grid; the output of the new energy has certain fluctuation, and the output change of the new energy is difficult to predict, so that the power grid needs more frequency modulation requirements;
step S3: calculating the capacity requirement of the planned component frequency modulation of the tie line; in an interconnected power grid, a power generation part of a provincial power grid meets the balance requirement of local loads, inter-provincial channel variation is also considered, inter-provincial market influence is also considered under the condition of developing the inter-provincial market, and the gateway deviation of a tie line is controlled within a certain range, so that the frequency modulation requirement of a system is also influenced by the change of a tie line exchange plan;
step S4: calculating the unit plan component frequency modulation capacity requirement;
step S5: obtaining a preliminary AGC frequency modulation capacity requirement according to the steps S1, S2, S3 and S4;
step S6, judging whether the preliminary AGC unit frequency modulation capacity requirement obtained in the step S5 meets the index, if not, performing index correction, repeating the step S6, otherwise, entering the step S7;
step S7: and obtaining the final AGC frequency modulation capacity requirement.
Preferably, a historical day d is set, the past 4-hour system load of the current time of the historical day d is compared with the actual similarity of the system in the corresponding time period of the current day, the Euclidean distance is used as a judgment basis of the similarity degree, and the calculation formula is as follows:
Figure BDA0002444205230000031
wherein, the system load of the current day and the system load of the historical day d are normalized in the formula. Central european distance ΔdRepresenting the similarity between the system load of 4 hours before the current time of the historical day d and the system load of 4 hours after the current time of the current day of the course day, wherein the current time is t0, and the next 5-minute planning time of t0 is t; (t0-47, t0-46, … … t0-1, t0) is a time series every 5 minutes for the past 4 hours;
Figure BDA0002444205230000032
actual system load for the last 4 hours per 15 minutes;
Figure BDA0002444205230000033
Figure BDA0002444205230000034
the historical day d corresponds to the actual system load every 5 minutes for the past 4 hours at the current time. DeltadThe smaller the value is, the greater the similarity of the system load trend is, the given threshold value Z is, if deltadIf the date d is less than or equal to Z, the date d is considered as the similar date of the current date, otherwise, the historical date d is reselected;
calculating the maximum error between the load forecast and the actual load of the ultra-short term system at each moment of the historical similar day:
Figure BDA0002444205230000035
wherein, (t, t +1, …, t +23) is every 5 minutes within 2 hours after the next 5 minutes at the current time;
Figure BDA0002444205230000036
Figure BDA0002444205230000037
the ultra-short-term system load prediction is carried out every 5 minutes within 2 hours after the next 5 minutes point of the current time of the historical similar day d;
Figure BDA0002444205230000038
is the actual system load of every 5 minutes within 2 hours after the next 5 minutes point of the current time of the historical similar day d;
calculating the frequency modulation requirement caused by system load fluctuation in the planning time t according to the error, wherein the calculation formula is as follows:
ΔPt load＝fed·LFt a(1-3)
in the formula: delta Pt loadThe capacity requirement of frequency modulation caused by system load change at the moment t; LF (Low frequency)t aAnd predicting the load of the ultra-short-term system at the time t. The method comprises the steps that an error between ultra-short-term load prediction of one or more similar days selected by the method and actual load of a system can be used as a frequency modulation capacity demand at the t moment of the current day, based on the relation that a frequency modulation standby demand of a power grid operation plan operation day is similar to the actual frequency modulation capacity of a historical day, the method firstly selects a historical day which is similar to the plan operation day based on the similarity condition of the load demand of the system, and selects the most effective similar day for accuracy, the method compares the actual load 4 hours before the current moment with the load of the same period of the historical day to determine the optimal historical similar day, the real-time market planning period is generally 5 minutes or 15 minutes, the smallest 5 minutes are used as a calculation period, the current moment is t0, and the next 5-minute planning moment of t0 is t; the time series every 5 minutes for the past 4 hours can be represented as (t0-47, t0-46, … … t0-1, t 0); the actual system load per 15 minutes for the past 4 hours can be expressed as
Figure BDA0002444205230000039
The actual system load every 5 minutes for the past 4 hours of the current time corresponding to the historical day d may be expressed as
Figure BDA0002444205230000041
The most similar historical day is determined.
Preferably, the requirement of the frequency modulation capacity caused by the new energy output is an adjustment requirement caused by the amount deviation between the new energy ultra-short term power prediction and the new energy actual output in each time period, and the calculation method of the Euclidean distance of the ultra-short term new energy prediction is as follows:
Figure BDA0002444205230000042
in the formula (I), the compound is shown in the specification,
Figure BDA0002444205230000043
is the installed capacity of new energy at the time t of the historical day,
Figure BDA0002444205230000044
is the installed capacity of the new energy at the moment t of the current day. Suppose (t, t +1, …, t +23) is every 5 minutes within 2 hours after the next 5 minutes of the current time,
Figure BDA0002444205230000045
for ultra-short term new energy power prediction every 5 minute period for two hours in the future of the current day,
Figure BDA0002444205230000046
the ultra-short term power for the corresponding period of historical day d is predicted as,
Figure BDA0002444205230000047
actual output of new energy, delta N, for a corresponding period of time of historical day ddThe similarity between the actual measurement of the new energy ultra-short term power two hours after the current time of d days and the prediction of the ultra-short term power two hours after the current time of the d days, namely delta NdThe smaller the value is, the larger the similarity of the output trend of the new energy is, the given threshold value ZN is, and if delta N isdIf the current date is less than or equal to ZN, the date d is considered to be a similar date of the new energy output on the current date, otherwise, the historical date d is selected again;
calculating the average error between the ultra-short term new energy power prediction and the actual output at each moment of the historical similar days of the new energy output:
Figure BDA0002444205230000048
wherein, fndFor the average error between the ultra-short term new energy power prediction and the actual output at each moment on the historical similar day, the method for calculating the frequency modulation capacity required by the new energy output fluctuation in the time period of the moment t is as follows:
Figure BDA0002444205230000049
wherein, Δ Pt energyAnd the capacity requirement of frequency modulation required by the output fluctuation of the new energy is met. If the grid-connected scale of new energy (such as wind power, photovoltaic and the like) of the power grid is large, the output of the new energy has certain fluctuation, the output change of the new energy is difficult to predict, so that the power grid needs more frequency modulation requirements, and supposing that the power of the ultra-short term new energy at every 5 minutes (t, t +1, …, t +23) within 2 hours after the next 5 minutes at the current moment is predicted to be
Figure BDA00024442052300000410
Ultra-short term power prediction of corresponding time period of historical day d is
Figure BDA00024442052300000411
Figure BDA00024442052300000412
The actual output of the new energy in the corresponding time period of the historical day d is
Figure BDA00024442052300000413
And (4) calculating the frequency modulation capacity requirement required by the output fluctuation of the new energy according to the history day which is the most similar to the history day.
Preferably, the method for calculating the frequency modulation capacity requirement of the tie line plan component comprises:
Figure BDA0002444205230000051
wherein, Δ Pt lineScheduling components for the time interval linksA frequency capacity requirement;
Figure BDA0002444205230000052
planning to output force for the connecting line at the current day t in real time,
Figure BDA0002444205230000053
planning the maximum error between the power and the actual power in real time for the tie line at the moment t;
the method for calculating the maximum error between the real-time planned power and the actual power of the tie line at each moment of the historical similar day comprises the following steps:
Figure BDA0002444205230000054
wherein, (t, t +1, …, t +23) is every 5 minutes within 2 hours after the next 5 minutes at the current time;
Figure BDA0002444205230000055
Figure BDA0002444205230000056
the real-time planning power of the connecting line is 2 hours per 5 minutes after the next 5 minutes point of the current time of the historical similar day d;
Figure BDA0002444205230000057
is the actual tie line power every 5 minutes within 2 hours after the next 5 minutes point of the current time on the historical similar day d. In an interconnected power grid, a power generation part of a provincial power grid meets the balance requirement of local loads, inter-provincial channel change needs to be considered, inter-provincial market influence needs to be considered under the condition of developing the inter-provincial market, and gateway deviation of a tie is controlled within a certain range, so that the frequency modulation requirement of a system can be influenced by the change of a tie exchange plan.
Preferably, the method for calculating the frequency modulation capacity of the planned component of the unit comprises the following steps: the SCHEO mode machine set and the non-AGC controlled machine set are provided with m sets, the frequency modulation capacity requirement caused by the output deviation of the machine set is calculated according to the average deviation of the real-time plan and the actual output every 5 minutes in the past 1 hour, and the calculation method comprises the following steps:
Figure BDA0002444205230000058
in the formula:
Figure BDA0002444205230000059
the capacity requirements of m SCHEO mode machine sets and non-AGC machine sets at the time of t frequency modulation are met;Δtis the deviation of the past time period from the current time period; PF (particle Filter)i,t0-ΔtReal-time planned output for the ith unit at the time t 0-delta t; pi,t0-ΔtThe real-time output of the unit at the time t 0-delta t is realized. For a provincial power grid, generally, unit control modes within a regulation range mainly include three major types, wherein the first type is an automatic control (AUTOR) mode and is responsible for regulating the frequency of the power grid; the second type is a tracking plan (SCHEO) mode, in which the output of the unit tracks a corresponding power generation plan; the third category is non-AGC controlled units, which are also tracked, day-ahead power generation plans, and which are not revised after being delivered to the plant in the day-ahead. The division can be continued with the crew in the planning mode, one tracking a day-ahead plan that is revisable within a day, and the other tracking a plan that is scrolled in real time, typically once in 5 or 15 minutes. Because the AUTOR mode unit is originally used for frequency modulation, the demand of the unit plan on the frequency modulation capacity is mainly the demand of frequency modulation resources caused by output deviation of the SCHEO mode unit and the non-AGC untracked real-time plan, and in an actual power grid, the average deviation of the real-time plan and the actual output of the unit in the past hour can be replaced.
Preferably, the calculation formula of the obtained preliminary AGC frequency modulation capacity requirement is:
Pt AGC＝(ΔPt load+ΔPt line+ΔPt energy+ΔPt gen) (1-10)
wherein, Pt AGCThe total regulation requirement of AGC at the time t; delta Pt loadA total adjustment amount for system load fluctuations; delta Pt energyThe capacity requirement of frequency modulation caused by the output of new energy is met; delta Pt linePlanning the frequency modulation capacity requirement caused by output for the tie line; delta Pt genThe capacity requirements of m SCHEO mode machine sets and non-AGC machine sets at the time t are met.
Preferably, the fm capacity demand changes with load demand and the like in one day, from the perspective of real-time market trading and scheduling control, if the fm capacity demand fluctuates at each time, it is difficult to arrange the reserved capacity of the generator set in the market trading clearing and regulation execution, so in the real-time market, the fm capacity demand in the future 1 to 2 hours can be regarded as a value, no consideration is given to the time change, the maximum fm capacity demand at each 5-minute planning time in two hours after the current time can be taken as the fm capacity demand in the future 1 to 2 hours, and the calculation formula is:
Figure BDA0002444205230000061
wherein, PAGCFor a total demand of fm capacity of 1 to 2 hours in the future.
Preferably, in step S6, a CPS standard is used to perform index determination on the preliminary AGC frequency modulation capacity, where the CPS standard includes a CPS1 index and a CPS2 index, and the index correction method includes the following steps:
step S61: counting the number N of times of violating the assessment indexes in the assessment evaluation results, if N is less than or equal to 10, finishing the correction, and if N is greater than 10, entering the step S62;
step S62, judging whether the CPS1 index or the CPS2 index is violated, and if the CPS1 index is violated, entering step S63; if the CPS2 index is violated, the method goes to step S64;
step S63: when the CPS1 index is violated, the adopted correction method comprises the following steps: p'AGC＝(1+λ)*PAGCWherein λ is a correction coefficient, λ is 0.1;
step S64: the degree of violation of the CPS2 index is judged, and the index correction is performed according to different violation degrees.
Preferably, in step S64, if
L10＜|CPS2|＜2*L10(1-12)
Wherein L is10Averaging a limit value of the control area ACE within 10 min; then P'AGC＝(1+λ)*PCGATaking the correction coefficient lambda to be 0.05; if it is
|CPS2|≥2*L10(1-13)
The correction method is as follows:
P’AGC＝PAGC+max{2*L10,PAGC*10％} (1-14)
wherein, P'AGCIs the corrected total capacity requirement.
Preferably, if no condition violating the relevant assessment indexes is found in the statistical results, it indicates that the frequency modulation requirement obtained by calculation may far exceed the frequency modulation capacity actually required by the system, that is, the calculation result has redundancy, in this case, to avoid resource waste occurring during frequency modulation and improve the economy of frequency modulation service in the market environment, the AGC capacity should be reduced to some extent on the basis of the calculation result, and the calculation formula is:
P’AGC＝(1-λ)*PAGC(1-15)
after correction, in order to prevent the capacity from remaining redundant or insufficient, the fm capacity value after correction should be reevaluated and cyclically corrected.
The invention has the beneficial effects that: the total frequency modulation capacity demand of the power grid is obtained by respectively calculating the frequency modulation capacity demand caused by system load, the frequency modulation capacity demand caused by new energy output, the link plan frequency modulation capacity demand and the unit plan component frequency modulation capacity demand, meanwhile, capacity correction is carried out according to assessment indexes, further correction is carried out according to actual economic conditions, the problem of online quantitative assessment and analysis of frequency modulation resource demands in a real-time market is solved, and the economy of the power grid market is improved.
Drawings
Fig. 1 is a flow chart of calculation of demand for frequency modulation resources in a real-time market of a power grid according to the first embodiment.
Detailed Description
The technical scheme of the invention is further specifically described by the following embodiments and the accompanying drawings.
The first embodiment is as follows: a method for online evaluation of frequency modulation resource demand of a real-time market of a power grid is disclosed, as shown in FIG. 1, and comprises the following steps:
step 1: according to the actual system load of every 5 minutes in 4 hours past the current day, comparing the similarity of the historical day system load and the current day system, using Euclidean distance as the judgment basis of the similarity degree, selecting a historical similar day d with the system load trend consistent with the current day, and the calculation formula is as follows:
Figure BDA0002444205230000071
wherein, the system load of the current day and the system load of the historical day d are normalized in the formula. Central european distance ΔdRepresenting the similarity between the system load of 4 hours before the current time of the historical day d and the system load of 4 hours after the current time of the current day of the course day, wherein the current time is t0, and the next 5-minute planning time of t0 is t; (t0-47, t0-46, … … t0-1, t0) is a time series every 5 minutes for the past 4 hours;
Figure BDA0002444205230000072
actual system load for the last 4 hours per 15 minutes;
Figure BDA0002444205230000073
Figure BDA0002444205230000081
the historical day d corresponds to the actual system load every 5 minutes for the past 4 hours at the current time. DeltadThe smaller the value is, the greater the similarity of the system load trend is, the given threshold value Z is, if deltadAnd if the date d is less than or equal to Z, the date d is considered as the similar date of the current date, otherwise, the historical date d is reselected.
Step 2: counting the maximum error of the ultra-short term system load prediction and the actual load prediction every 5 minutes within 2 hours after the current time of the historical similar dayfedThe calculation formula is as follows:
Figure BDA0002444205230000082
wherein, (t, t +1, …, t +23) is every 5 minutes within 2 hours after the next 5 minutes at the current time;
Figure BDA0002444205230000083
Figure BDA0002444205230000084
the ultra-short-term system load prediction is carried out every 5 minutes within 2 hours after the next 5 minutes point of the current time of the historical similar day d;
Figure BDA0002444205230000085
the actual system load is every 5 minutes within 2 hours after the next 5 minutes point of the current time of the historical similar day d, and the frequency modulation capacity demand delta P caused by the system load prediction deviation at the future time of the current day is calculated based on the load prediction of the super-short term system at the current dayt loadThe calculation formula is as follows:
ΔPt load＝fed·LFt a(1-3)
in the formula: delta Pt loadThe capacity requirement of frequency modulation caused by system load change at the moment t; LF (Low frequency)t aAnd predicting the load of the ultra-short-term system at the time t.
And step 3: counting the maximum error fline between the real-time plan and the actual power of the tie line every 5 minutes within 2 hours after the current time of the historical similar daydThe calculation method comprises the following steps:
Figure BDA0002444205230000086
wherein, (t, t +1, …, t +23) is every 5 minutes within 2 hours after the next 5 minutes at the current time;
Figure BDA0002444205230000087
Figure BDA0002444205230000088
the real-time planning power of the connecting line is 2 hours per 5 minutes after the next 5 minutes point of the current time of the historical similar day d;
Figure BDA0002444205230000089
the method comprises the steps of calculating the actual tie line power of every 5 minutes within 2 hours after the next 5 minutes point of the current time of the historical similar day d, calculating the maximum error between the daily plan and the actual exchange power of the tie line of the historical similar day, and calculating the frequency modulation capacity demand delta P caused by the deviation of the tie line plan at the future time of the current dayt lineThe calculation method comprises the following steps:
Figure BDA00024442052300000810
wherein, Δ Pt linePlanning the frequency modulation capacity requirement of the component for the time interval tie line;
Figure BDA00024442052300000811
planning to output force for the connecting line at the current day t in real time,
Figure BDA0002444205230000091
the maximum error between planned power and actual power is real-time for the tie at time t.
And 4, step 4: according to the ultra-short term power prediction and the actual new energy output trend of the historical day every 5 minutes in the next two hours at the current moment, the Euclidean distance is used as the judgment basis of the similarity degree, the historical similar day d of the new energy output is selected, and the calculation formula is as follows:
Figure BDA0002444205230000092
in the formula (I), the compound is shown in the specification,
Figure BDA0002444205230000093
is the installed capacity of new energy at the time t of the historical day,
Figure BDA0002444205230000094
is the installed capacity of the new energy at the moment t of the current day. Suppose (t, t +1, …, t +23) is every 5 minutes within 2 hours after the next 5 minutes of the current time,
Figure BDA0002444205230000095
for ultra-short term new energy power prediction every 5 minute period for two hours in the future of the current day,
Figure BDA0002444205230000096
the ultra-short term power for the corresponding period of historical day d is predicted as,
Figure BDA0002444205230000097
actual output of new energy, delta N, for a corresponding period of time of historical day ddThe similarity between the actual measurement of the new energy ultra-short term power two hours after the current time of d days and the prediction of the ultra-short term power two hours after the current time of the d days, namely delta NdThe smaller the value is, the larger the similarity of the output trend of the new energy is, the given threshold value ZN is, and if delta N isdAnd if not more than ZN, considering the d day as the similar day of the new energy output on the current day, and otherwise, reselecting the historical d day.
And 5: calculating the average error between the ultra-short term new energy power prediction and the actual output at each moment of the historical similar days of the new energy outputfndThe calculation formula is as follows:
Figure BDA0002444205230000098
wherein, fndFor the average error between the ultra-short term new energy power prediction and the actual output at each moment on the historical similar day, the method for calculating the frequency modulation capacity required by the new energy output fluctuation in the time period of the moment t is as follows:
Figure BDA0002444205230000099
wherein, Δ Pt energyRequired for new energy output fluctuationAnd calculating the frequency modulation capacity required by the predicted deviation of the new energy excess force according to the frequency modulation capacity requirement.
Step 6: the requirement of the unit plan deviation on the frequency modulation capacity is mainly the frequency modulation resource requirement caused by the output deviation of the SCHEO mode unit and the non-AGC untracked real-time plan, and the average deviation delta P between the real-time plan and the actual output of the SCHEO mode unit and the non-AGC unit in the past hour is countedt genAs the demand of the unit plan on the frequency modulation capacity, the calculation method comprises the following steps: the SCHEO mode machine set and the non-AGC controlled machine set are provided with m sets, the frequency modulation capacity requirement caused by the output deviation of the machine set is calculated according to the average deviation of the real-time plan and the actual output every 5 minutes in the past 1 hour, and the calculation method comprises the following steps:
Figure BDA0002444205230000101
in the formula: delta Pt genThe capacity requirements of m SCHEO mode machine sets and non-AGC machine sets at the time of t frequency modulation are met; Δ t is the deviation of the past time period from the current time period; PF (particle Filter)i,t0-ΔtReal-time planned output for the ith unit at the time t 0-delta t; pi,t0-ΔtThe real-time output of the unit at the time t 0-delta t is realized.
And 7: obtaining a preliminary AGC frequency modulation capacity requirement according to the steps 1 to 6, wherein the calculation mode is as follows:
Figure BDA0002444205230000102
wherein, Pt AGCFor the total regulation demand of AGC at the time t, the frequency modulation capacity demand changes along with the load demand and the like in one day, from the perspective of real-time market trading and scheduling control, if the frequency modulation capacity demand fluctuates at each time, the market trading is clear, the reserved capacity of a generator set is difficult to arrange in the regulation and control execution, therefore, in the real-time market, the frequency modulation capacity demand in the future 1 to 2 hours can be regarded as a value, the time change is not required to be considered, the maximum frequency modulation capacity demand of each 5-minute planning time in two hours after the current time can be taken as the frequency modulation capacity demand in the future 1 to 2 hours, and a calculation formula of the maximum frequency modulation capacity demand can beComprises the following steps:
Figure BDA0002444205230000103
wherein, PAGCFor a total demand of fm capacity of 1 to 2 hours in the future.
And 8: the capacity is corrected by using the assessment indexes, and the calculation result of the frequency modulation requirement can be corrected by referring to the meeting condition of the actual adjustment condition on the assessment standard according to the control and adjustment effect of the power grid in the actual operation. At present, the domestic power grid widely adopts the CPS standard at present, and taking this as an example, the mode of correcting the frequency modulation requirement is as follows: the CPS standard mainly comprises a CPS1 index and a CPS2 index, and the different objects are considered when the two indexes are examined and evaluated, so when the CPS standard is used for examination, the two indexes are respectively used for independent examination, the calculation result of the frequency modulation requirement is corrected according to different conditions, the condition of the number N of times of violating the examination indexes in the examination and evaluation results is counted, if N is less than or equal to 10, the correction of violating the indexes is not needed, and if N is less than or equal to 10>And 10, judging whether the CPS1 index or the CPS2 index is violated, and if the CPS1 index is violated, adopting a correction method as follows: p'AGC＝(1+λ)*PAGCWherein λ is a correction coefficient, λ is 0.1; if the CPS2 index is violated, the degree of violation of the CPS2 index is judged, the index correction is performed according to different violation degrees, and if L is violated, the index correction is performed10＜|CPS2|＜2*L10Then, the adopted correction method is as follows: p'AGC＝(1+λ)*PAGCTaking the correction coefficient lambda to be 0.05; if | CPS2| ≧ 2 × L10If the correction method is P'AGC＝PAGC+max{2*L10,PAGC*10％}。
And step 9: if in the statistical result, no condition violating the relevant examination indexes is found, which indicates that the frequency modulation requirement obtained by calculation may far exceed the frequency modulation capacity actually required by the system, that is, the calculation result has redundancy, in this case, in order to avoid resource waste during frequency modulation and improve the economy of frequency modulation service in the market environment, the AGC capacity should be reduced to a certain extent on the basis of the calculation result, and the calculation formula is:
P’AGC＝(1-λ)*PAGC(1-15)
after correction, in order to prevent the situation that the capacity is still redundant or insufficient, the corrected value of the frequency modulation capacity is reevaluated and cyclically corrected, and finally the completely corrected AGC frequency modulation capacity requirement is obtained, so that the problem of online quantitative evaluation and analysis of the frequency modulation resource requirement in the real-time market is solved.
The above-described embodiments are only preferred embodiments of the present invention, and are not intended to limit the present invention in any way, and other variations and modifications may be made without departing from the spirit of the invention as set forth in the claims.

Claims (9)

1. A power grid real-time market frequency modulation resource demand online evaluation method is characterized by comprising the following steps:
step S1: calculating the frequency modulation capacity requirement caused by the load change component of the power grid system; the Automatic Generation Control (AGC) mainly adjusts the power shortage between the system generation output and the actual power consumption, the historical load data is combined to preliminarily predict the frequency modulation capacity requirement of the AGC, and the frequency modulation capacity requirement caused by the system load change component is the frequency modulation capacity requirement caused by the system ultra-short-term load prediction deviation;
step S2: calculating the frequency modulation capacity requirement caused by the new energy output of the power grid; the output of the new energy has certain fluctuation, and the output change of the new energy is difficult to predict, so that the power grid needs more frequency modulation requirements;
step S3: calculating the capacity requirement of the planned component frequency modulation of the tie line; in an interconnected power grid, a power generation part of a provincial power grid meets the balance requirement of local loads, inter-provincial channel variation is also considered, inter-provincial market influence is also considered under the condition of developing the inter-provincial market, and the gateway deviation of a tie line is controlled within a certain range, so that the frequency modulation requirement of a system is also influenced by the change of a tie line exchange plan;
step S4: calculating the unit plan component frequency modulation capacity requirement;
step S5: obtaining a preliminary AGC frequency modulation capacity requirement according to the steps S1, S2, S3 and S4;
step S6, judging whether the preliminary AGC unit frequency modulation capacity requirement obtained in the step S5 meets the index, if not, performing index correction, repeating the step S6, otherwise, entering the step S7;
step S7: and obtaining the final AGC frequency modulation capacity requirement.
2. The online evaluation method for the frequency modulation resource demand of the real-time market of the power grid according to claim 1, wherein a historical day d is set, the past 4-hour system load of the current time of the historical day d is compared with the actual similarity of the system in the corresponding time period of the current day, the Euclidean distance is used as a judgment basis of the similarity degree, and the calculation formula is as follows:
Figure FDA0002444205220000011
wherein, the system load of the current day and the system load of the historical day d are normalized in the formula. Central european distance ΔdRepresenting the similarity between the system load of 4 hours before the current time of the historical day d and the system load of 4 hours after the current time of the current day of the course day, wherein the current time is t0, and the next 5-minute planning time of t0 is t; (t0-47, t0-46, … … t0-1, t0) is a time series every 5 minutes for the past 4 hours;
Figure FDA0002444205220000012
actual system load for the last 4 hours per 15 minutes;
Figure FDA0002444205220000013
Figure FDA0002444205220000014
the historical day d corresponds to the actual system load every 5 minutes for the past 4 hours at the current time. DeltadThe smaller the value is, the greater the similarity of the system load trend is, the given threshold value Z is, if deltadIf Z is less than or equal to Z, the day d is considered as the similar day of the current day, otherwiseRe-selecting the historical day d;
calculating the maximum error between the load forecast and the actual load of the ultra-short term system at each moment of the historical similar day:
Figure FDA0002444205220000021
wherein, (t, t +1, …, t +23) is every 5 minutes within 2 hours after the next 5 minutes at the current time;
Figure FDA0002444205220000022
Figure FDA0002444205220000023
the ultra-short-term system load prediction is carried out every 5 minutes within 2 hours after the next 5 minutes point of the current time of the historical similar day d;
Figure FDA0002444205220000024
is the actual system load of every 5 minutes within 2 hours after the next 5 minutes point of the current time of the historical similar day d;
calculating the frequency modulation requirement caused by system load fluctuation in the planning time t according to the error, wherein the calculation formula is as follows:
ΔPt load＝fed·LFt a(1-3)
in the formula: delta Pt loadThe capacity requirement of frequency modulation caused by system load change at the moment t; LF (Low frequency)t aAnd predicting the load of the ultra-short-term system at the time t.
3. The method for online evaluation of the demand of the frequency modulation resources on the real-time market of the power grid according to claim 2, wherein the demand of the capacity for frequency modulation caused by the new energy output is an adjustment demand caused by the amount deviation between the ultra-short term power prediction of the new energy and the actual output of the new energy at each time interval, and the calculation method of the Euclidean distance of the ultra-short term new energy prediction is as follows:
Figure FDA0002444205220000025
in the formula (I), the compound is shown in the specification,
Figure FDA0002444205220000026
is the installed capacity of new energy at the time t of the historical day,
Figure FDA0002444205220000027
is the installed capacity of the new energy at the moment t of the current day. Suppose (t, t +1, …, t +23) is every 5 minutes within 2 hours after the next 5 minutes of the current time,
Figure FDA0002444205220000028
for ultra-short term new energy power prediction every 5 minute period for two hours in the future of the current day,
Figure FDA0002444205220000029
the ultra-short term power for the corresponding period of historical day d is predicted as,
Figure FDA00024442052200000210
actual output of new energy, delta N, for a corresponding period of time of historical day ddThe similarity between the actual measurement of the new energy ultra-short term power two hours after the current time of d days and the prediction of the ultra-short term power two hours after the current time of the d days, namely delta NdThe smaller the value is, the larger the similarity of the output trend of the new energy is, the given threshold value ZN is, and if delta N isdIf the current date is less than or equal to ZN, the date d is considered to be a similar date of the new energy output on the current date, otherwise, the historical date d is selected again;
calculating the average error between the ultra-short term new energy power prediction and the actual output at each moment of the historical similar days of the new energy output:
Figure FDA00024442052200000211
wherein, fndBetween the prediction of the ultra-short term new energy power and the actual output for each moment of the historical similar dayAnd (3) averaging errors, namely, the method for calculating the frequency modulation capacity required by the new energy output fluctuation in the time period of the time t is as follows:
Figure FDA0002444205220000031
wherein, Δ Pt energyAnd the capacity requirement of frequency modulation required by the output fluctuation of the new energy is met.
4. A method according to claim 2 or 3, wherein the method for calculating the demand of the capacity for frequency modulation of the plan component of the tie line comprises:
Figure FDA0002444205220000032
wherein, Δ Pt linePlanning the frequency modulation capacity requirement of the component for the time interval tie line;
Figure FDA0002444205220000033
planning to output force for the connecting line at the current day t in real time,
Figure FDA0002444205220000034
planning the maximum error between the power and the actual power in real time for the tie line at the moment t;
the method for calculating the maximum error between the real-time planned power and the actual power of the tie line at each moment of the historical similar day comprises the following steps:
Figure FDA0002444205220000035
wherein, (t, t +1, …, t +23) is every 5 minutes within 2 hours after the next 5 minutes at the current time;
Figure FDA0002444205220000036
Figure FDA0002444205220000037
the real-time planning power of the connecting line is 2 hours per 5 minutes after the next 5 minutes point of the current time of the historical similar day d;
Figure FDA0002444205220000038
is the actual tie line power every 5 minutes within 2 hours after the next 5 minutes point of the current time on the historical similar day d.
5. The method for online evaluation of the demand of frequency modulation resources in the real-time market of the power grid according to claim 1, 2 or 3, wherein the calculation method of the unit plan component frequency modulation capacity is as follows: the SCHEO mode machine set and the non-AGC controlled machine set are provided with m sets, the frequency modulation capacity requirement caused by the output deviation of the machine set is calculated according to the average deviation of the real-time plan and the actual output every 5 minutes in the past 1 hour, and the calculation method comprises the following steps:
Figure FDA0002444205220000039
in the formula: delta Pt genThe capacity requirements of m SCHEO mode machine sets and non-AGC machine sets at the time of t frequency modulation are met; Δ t is the deviation of the past time period from the current time period; PF (particle Filter)i,t0-ΔtReal-time planned output for the ith unit at the time t 0-delta t; pi,t0-ΔtThe real-time output of the unit at the time t 0-delta t is realized.
6. The method for online evaluation of the demand for frequency modulation resources in the real-time market of the power grid according to claim 1, wherein the calculation formula of the obtained preliminary AGC frequency modulation capacity demand is as follows:
Pt AGC＝(ΔPt load+ΔPt line+ΔPt energy+ΔPt gen) (1-10)
wherein, Pt AGCThe total regulation requirement of AGC at the time t; delta Pt loadA total adjustment amount for system load fluctuations; delta Pt energyThe capacity requirement of frequency modulation caused by the output of new energy is met; delta Pt linePlanning the frequency modulation capacity requirement caused by output for the tie line; delta Pt genThe capacity requirements of m SCHEO mode machine sets and non-AGC machine sets at the time t are met.
7. The method for on-line assessment of frequency modulation resource demand of real-time market of power grid according to claim 6, wherein the frequency modulation capacity demand varies with load demand and the like in one day, from the perspective of real-time market trading and scheduling control, if the frequency modulation capacity demand fluctuates at each moment, it is difficult to arrange the reserved capacity of the generator set in the market trading and the regulation and control execution, so in the real-time market, the frequency modulation capacity demand in the future 1 to 2 hours can be regarded as a value, without considering the variation with time, the maximum frequency modulation capacity demand at each 5 minute planned time in two hours after the current time can be taken as the frequency modulation capacity demand in the future 1 to 2 hours, and the calculation formula is:
Figure FDA0002444205220000041
wherein, PAGCFor a total demand of fm capacity of 1 to 2 hours in the future.
8. The method as claimed in claim 1, wherein in step S6, a CPS standard is used to perform index determination on the preliminary AGC frequency modulation capacity, the CPS standard includes a CPS1 index and a CPS2 index, and the index correction method includes the following steps:
step S61: counting the number N of times of violating the assessment indexes in the assessment evaluation results, if N is less than or equal to 10, finishing the correction, and if N is greater than 10, entering the step S62;
step S62, judging whether the CPS1 index or the CPS2 index is violated, and if the CPS1 index is violated, entering step S63; if the CPS2 index is violated, the method goes to step S64;
step S63: when the CPS1 index is violated, the adopted correction method is：P′AGC＝(1+λ)*PAGCWherein λ is a correction coefficient, λ is 0.1;
step S64: the degree of violation of the CPS2 index is judged, and the index correction is performed according to different violation degrees.
9. The method as claimed in claim 8, wherein in step S64, if yes, the method further comprises
L10＜|CPS2|＜2*L10(1-12)
Wherein L is10Averaging a limit value of the control area ACE within 10 min; then P'AGC＝(1+λ)*PCGATaking the correction coefficient lambda to be 0.05;
if it is
|CPS2|≥2*L10(1-13)
The correction method is as follows:
P′AGC＝PAGC+max{2*L10,PAGC*10％} (1-14)
wherein, P'AGCIs the corrected total capacity requirement.
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