AU2019101413A4 - Method for evaluating and regulating consumption capacity of regional power grid for renewable energy sources - Google Patents

Method for evaluating and regulating consumption capacity of regional power grid for renewable energy sources Download PDF

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AU2019101413A4
AU2019101413A4 AU2019101413A AU2019101413A AU2019101413A4 AU 2019101413 A4 AU2019101413 A4 AU 2019101413A4 AU 2019101413 A AU2019101413 A AU 2019101413A AU 2019101413 A AU2019101413 A AU 2019101413A AU 2019101413 A4 AU2019101413 A4 AU 2019101413A4
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capacity
renewable energy
energy sources
power grid
consumption
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Zhi Cai
Changyou Feng
Rui GE
Jinshan HAN
Yiding Jin
Pengfei Li
Dunnan LIU
Jiangyan Liu
Da Song
Nan Wang
Xingkai Wang
Zhao ZHAO
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State Grid Corp of China SGCC
China Electric Power Research Institute Co Ltd CEPRI
State Grid Hebei Electric Power Co Ltd
North China Electric Power University
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State Grid Corp of China SGCC
China Electric Power Research Institute Co Ltd CEPRI
State Grid Hebei Electric Power Co Ltd
North China Electric Power University
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/04—Circuit arrangements for ac mains or ac distribution networks for connecting networks of the same frequency but supplied from different sources
    • H02J3/06—Controlling transfer of power between connected networks; Controlling sharing of load between connected networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
    • H02J7/34—Parallel operation in networks using both storage and other dc sources, e.g. providing buffering
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B70/00—Technologies for an efficient end-user side electric power management and consumption
    • Y02B70/30—Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
    • Y02B70/3225—Demand response systems, e.g. load shedding, peak shaving
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    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
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    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
    • Y04S20/20—End-user application control systems
    • Y04S20/222—Demand response systems, e.g. load shedding, peak shaving

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Abstract

The present invention relates to a method for evaluating and regulating consumption capacity of a regional power grid for renewable energy sources, which is technically characterized by comprising the following steps: step 1, constructing an index system for the consumption capacity for the renewable energy sources; step 2, collecting data reflecting load characteristics of a power grid and output characteristics of the renewable energy sources; step 3, processing the data collected in the step 2 to obtain corresponding index values in the step 1, and further evaluating the consumption capacity of the power grid for the renewable energy sources; step 4, if an evaluation result is greater than the consumption capacity, issuing a first regulation instruction to the power grid: stopping the power grid from accessing the renewable energy sources; and if the evaluation result is less than the consumption capacity, issuing a second regulation instruction to the power grid according to the calculated surplus consumption quantity of the renewable energy sources: appropriately increasing the renewable energy sources for grid connection. The present invention can realize visual quantitative evaluation and power optimization regulation on the consumption capacity of the regional power grid for the renewable energy sources, and further improve the consumption capacity for the renewable energy sources. stablish an index system for consumption capacity of Power grid for renewable energy source CoDlleet data Caleta a correspondine Determme a type of index according to the Av t consumption type oi~f consumption Substitute the index and the Set a specifi critically citical value into step I for* value I company ;Evaluation reult | UIMViu Regulation instruction 2

Description

METHOD FOR EVALUATING AND REGULATING CONSUMPTION CAPACITY OF REGIONAL POWER GRID FOR RENEWABLE ENERGY SOURCES
TECHNICAL FIELD [0001] The present invention belongs to the technical field of power grid regulation, and particularly relates to a method for evaluating and regulating consumption capacity of a regional power grid for renewable energy sources.
BACKGROUD OF THE PRESENT INVENTION [0002] At present, wind power, photovoltaic power generation and other renewable energy sources are connected and accessed to a grid on a large scale, which has a great impact on power demand side response, power dispatching and safe and stable operation of a power grid. More consumption of clean energy sources on the premise of ensuring safety of the power grid is a requirement for promoting large-scale optimal allocation of energy resources as well as a development direction of an energy strategy in China.
[0003] At present, China has become the largest clean energy source market and production place in the world. Photovoltaic power, wind power and other clean energy sources maintain a rapid development trend. However, due to the limitation of the regulation capacity and the load demand of the power grid, a serious phenomenon of “abandoning wind energy, photovoltaic energy and water energy” exists in some areas and has become a prominent problem restricting the development of the clean energy sources in China. On the whole, due to “dislocation” of load and energy endowment in space in China, the regional power grid generally has the problems that clean energy sources are unable to access the grid on one hand and the load demand cannot be met on the other hand. However, as shown in Fig. 7, a traditional method for regulating the consumption capacity of the power grid for the renewable energy sources only compares the calculated total amount of generated energy of the renewable energy sources, the number of utilization hours of the renewable energy sources, the amount of on-grid electricity of the renewable energy sources and the proportion of surplus electricity of the renewable energy sources with preset standard values, and then make corresponding adjustment. The evaluation index of the consumption capacity is too simple; the
2019101413 18 Nov 2019 evaluation system is not perfect enough; human factors have great influence on the evaluation result; it is difficult to quantitatively and objectively analyze the consumption capacity of the power grid for the renewable energy sources; and the corresponding regulation result is short of accuracy and scientificity.
[0004] Therefore, in order to fully exploit the consumption capacity of the power grid for the renewable energy sources, a method for evaluating the consumption capacity of a regional power grid for the renewable energy sources is urgently needed to optimize power regulation, deep peak load regulation and deep frequency modulation, effectively promote the grid connection and consumption of new energy sources, and provide support for the development planning of the renewable energy sources.
SUMMARY OF PRESENT INVENTION [0005] The purpose of the present invention is to overcome defects of the prior art and provide a method for evaluating and regulating consumption capacity of a regional power grid for renewable energy sources, which can realize visual quantitative evaluation and power optimization regulation on the consumption capacity of the regional power grid for the renewable energy sources and further improve the consumption capacity for the renewable energy sources.
[0006] The present invention solves practical problems by adopting the following technical solution.
[0007] The method for evaluating and regulating the consumption capacity of the regional power grid for the renewable energy sources comprises the following steps: [0008] step 1, considering influence factors of consumption of the power grid for the renewable energy sources, respectively determining evaluation indexes for short-term, medium-term and long-term consumption capacities for the renewable energy sources, and further constructing an index system for the consumption capacity for the renewable energy sources;
[0009] step 2, collecting data reflecting load characteristics of the power grid and output characteristics of the renewable energy sources;
[0010] step 3, processing the data collected in the step 2 to obtain corresponding index values in the step 1, and then evaluating the consumption capacity of the power grid for the renewable energy sources to obtain an evaluation result; and [0011] step 4, comparing the evaluation result with the consumption capacity of the power grid, and if the evaluation result is greater than the consumption capacity, issuing a first regulation instruction to the power grid: stopping the power grid from accessing the renewable energy sources; and if the evaluation result is less than the consumption capacity, issuing a second regulation instruction to the power grid according to the calculated surplus consumption quantity of the renewable energy sources: appropriately increasing the renewable energy sources for grid connection and increasing an access proportion.
[0012] Moreover, indexes for the short-term consumption capacity of the power grid for the renewable energy sources in the step 1 comprise: an adequacy of frequency modulation capacity of the system and an adequacy of peak load regulation capacity of the system, [0013] wherein the adequacy of the frequency modulation capacity of the system is calculated by a formula as follows:
100% [0014] [0015] In the above formula, refers to the adequacy of the frequency pcapacity modulation capacity of the system; z refers to the frequency modulation capacity of the system; the frequency modulation capacity refers to secondary pneed frequency modulation capacity of the power grid; and f refers to a frequency modulation need of the system, i.e., a minute level fluctuation value of a net load. [0016] The frequency modulation capacity of the system is calculated by a formula as follows:
[0017]
D [0018] In the above formula, AGC refers to an AGC reserve capacity of the system, which may need to be regulated up or down by an AGC unit due to uncertainty of a fluctuation direction of the net load. Therefore, an intermediate value of the AGC unit reserve capacity is taken to determine the frequency modulation capacity.
[0019] follows:
The frequency modulation need of the system is calculated by a formula as [0020]
Pfed = \nP( -NPt I
2019101413 18 Nov 2019 ά rry _ ryload ryenew [0021] r_ = + NP‘^ + + ^+1 + · · ·+ NPt+M) [0022] 2W
\.p plead [0023] In the above formula, 1 refers to a net load value at the moment; *
NP refers to the load value at the moment; ‘ refers to an output value of the
NP' renewable energy sources at the moment; 1 refers to the net load value smoothed by a rolling average method at the moment t; and an absolute value of a difference between ‘ and ‘ is the frequency modulation need of the system.
[0024] The adequacy of the peak load regulation capacity of the system is calculated by a formula as follows:
X 1OO% [0025] [0026] }neecl [0027] [0028]
In the above formula, refers to the adequacy of the peak load regulation pcapacity capacity of the system; p refers to the peak load regulation capacity of the pyneed p p>
system; p refers to peak load regulation need of the system; ie and Z min respectively refer to a rated output and a minimum technical output of a unit ';
refers to the number of schedulable generator units in the system; refers to a spinning reserve capacity during a peak load period; and NP™* and ΝΡαύη respectively refer to a maximum value and a minimum value of the net load. [0029] The indexes for the medium-term consumption capacity of the power grid for the renewable energy sources in the step 1 comprise: an average peak load regulation adequacy of the system, an average peak load regulation capacity of the system and average peak load regulation need of the system, [0030] wherein a formula for calculating the average peak load regulation adequacy of the system is as follows:
pcapacity β = p_ , x!00% • P Tyneea [0031] p
2019101413 18 Nov 2019 [0032] In the above formula, dp refers to the average peak load regulation pcapacily adequacy of the system; p refers to the average peak load regulation capacity pneed of the system; and p refers to the average peak load regulation need of the system.
[0033] The average peak load regulation capacity of the system is calculated by a formula as follows:
[0034] ) capacity
P Mc, =Σ(Υ-ρ^) /=1
P P [0035] In the above formula, “ and “nin respectively refer to the rated output and the minimum technical output of the unit 1; the minimum technical output of hydropower is constantly changing with the incoming water and can be averaged herein; and Mg refers to the number of runnable generator units in the system. [0036] The average peak load regulation need of the system comprises a peak load regulation need caused by a load peak-valley difference, a load reserve capacity of a spinning reserve part during a peak load period and a new peak load regulation need considering wind power and photovoltaic power generation output aggregation, and is calculated by a formula as follows:
[0037] [0038] In the above formula, jdoad refers to the load peak-valley difference of the system on a jth day; N refers to a statistical time scale; reserve refers to a spinning reserve part during the peak load period; renew refers to the new peak load regulation need considering wind power and photovoltaic power generation pall output aggregation; renew refers to an installed capacity of the renewable energy sources; and Λ refers to a correction coefficient. The installed capacity of the renewable energy sources is generally considered as the new peak load regulation need. However, the new peak load regulation need may be smaller than the installed capacity of renewable energy sources due to an aggregation effect of the renewable energy sources; and if the installed capacity of renewable energy sources is larger, the correction coefficient is smaller.
2019101413 18 Nov 2019 [0039] The load reserve capacity of the spinning reserve part during the peak load period is calculated by the following steps:
[0040] (1) calculating an LOLP (Loss of Load Probability) LOLP caused by load fluctuation according to a probability density function of load fluctuation near a predicted value by a formula as follows:
[0041]
--exp([0042] in the formula, load refers to a standard deviation of load fluctuation near the predicted value, which is generally l%-2%, 1% for systems with relatively large load and 2% for systems with relatively small load; and 1 refers to a load reserve capacity of the system.
[0043] (2) since the prediction accuracy of wind power and photovoltaic power generation is also approximately subjected to normal distribution after the renewable energy sources are connected into the power grid, setting as a predicted standard deviation of generated energy of a wind power and photovoltaic power generation aggregate, then:
[0044] [0045] . ->2
Λ 4- load renew
LOLP for a new load prediction standard deviation, in order to maintain the given fioip, calculating the corresponding load reserve capacity through a normal distribution table:
[0046]
- — <—z new [0047] [0048] in the formula, Φ 1 refers to an inverse function of standard normal distribution; and 2 refers to the spinning reserve part during the peak load period. [0049] The indexes for the long-term consumption capacity of the power grid for the renewable energy sources in the step 1 comprise: flexibility resources for controlling stability and operation economy of a power system and flexibility needs caused by various uncertain factors in the power system.
[0050] The flexibility resources for controlling stability and operation economy of the power system are calculated as follows:
[0051] for the statistic of the quantity of the flexibility resources, a time scale T is set; data and probability distribution of available flexibility resources of the system
2019101413 18 Nov 2019 are counted; and the probability distribution of a flexibility resource i is recorded as D> wherein X represents a regulation capacity which can be provided by the flexibility resource i.
[0052] A flexibility need index caused by various uncertain factors in the power system is calculated by a method as follows:
[0053] Y = Δ/^θί \P' [0054] in the above formula, Y refers to a flexibility need quantity; net refers to a variation of the net load within the time scale of T, so the probability that the
D (APT ) flexibility need of the system can be met is ' ·τ net ; and from a critical point, the probability that the flexibility need of the system cannot be met can be expressed as:
[0055] = Di,A^pLt > O ζ
[0056] wherein ’ is a positive value which is small in absolute value.
[0057] Moreover, the reaction load characteristics collected in the step 2 comprise: [0058] (1) annual load characteristic indexes, which are mainly classified into an annual maximum load, an annual maximum peak-valley difference, a seasonal load rate and a seasonal unbalance coefficient; and [0059] (2) monthly load characteristic indexes, which are mainly classified into a monthly maximum load, a monthly average daily load rate, a monthly maximum peak-valley difference rate and a monthly load rate.
[0060] Data collected to reflect the output characteristics of the renewable energy sources comprise photovoltaic power generation output and wind power output. [0061] Moreover, the step 3 specifically comprises:
[0062] (1) dividing the evaluation of consumption capacity into three time dimensions of short-term consumption, medium-term consumption and long-term consumption according to consumption requirements of the power grid for the renewable energy sources;
[0063] (2) in the case of short-term consumption, calculating the adequacy of frequency modulation capacity and the adequacy of peak load regulation capacity of the power grid according to the data collected in the step 2 and the formulae
2019101413 18 Nov 2019 jpcapacity
-F---— X 1 oo% βρ = r^neect f and , capacity
---— X 100% y-^neea p about the adequacy of frequency modulation capacity and the adequacy of peak load regulation capacity of the system in the step 1;
[0064] in the case of medium-term consumption, calculating the average peak load regulation adequacy of the system according to the formula of calculating the average peak load regulation adequacy of the system in the step 1:
pcapacity β= !L „ xl00% • P pneea p ; and [0065] in the case of long-term consumption, calculating an insufficiently ramping resource expectation value of the system according to the following formula of the insufficiently ramping resource expectation value of the system in the step 1: FLnj = > □.
[0066] (3) setting a specific critical value (which is greater than or equal to 100% at least) according to an actual load situation of the power grid, a proportion of the renewable energy sources and the index of the stability of the power grid, and substituting the required index and the specific critical value into the step 1 to evaluate the consumption capacity of the power grid for the renewable energy sources.
[0067] Moreover, the method in the step 4 specifically comprises the following steps:
[0068] comparing the evaluation result with the consumption capacity, and issuing regulation instructions for the consumption of the power grid for the renewable energy sources;
[0069] if the index value is smaller than the critical value, issuing a first regulation instruction to the power grid: stopping the power grid from accessing new renewable energy sources, if necessary, abandoning wind energy and photovoltaic energy, and performing deep peak load regulation and start-stop peak load regulation;
[0070] if the index value is greater than the critical value, calculating the surplus consumption quantity of the renewable energy sources by a formula as follows:
[0071]
Surplus consumption quantity of renewable energy sources [0072] issuing a second regulation instruction to the power grid according to the
2019101413 18 Nov 2019 calculated surplus consumption quantity of the renewable energy sources:
appropriately increasing purchase for the renewable energy sources, increasing the access proportion, and taking an upper limit of the purchase amount as the calculated surplus consumption quantity of the renewable energy sources.
[0073] The present invention has the advantages and beneficial effects that: [0074] the method provided by the present invention makes more objective, accurate and quantitative description and evaluation on the consumption capacity of the power grid for the renewable energy sources by rich consumption capacity evaluation indexes, reduces the influence of human factors on the evaluation result, enriches scientific connotation of evaluation of the consumption capacity, and can more accurately and scientifically regulate the consumption capacity of the regional power grid for the renewable energy sources.
DESCRIPTION OF THE DRAWINGS [0075] Fig. 1 is a flow chart of a method for evaluating and controlling consumption capacity of a power grid for renewable energy sources according to the present invention;
[0076] Fig. 2 is a diagram of an index system for consumption of a power grid for renewable energy sources according to the present invention;
[0077] Fig. 3 is a diagram of a relationship between a frequency modulation need and a frequency modulation capacity in a typical day in summer according to a specific embodiment of the present invention;
[0078] Fig. 4 is a schematic diagram of an adequacy of frequency modulation capacity in a typical day in summer according to a specific embodiment of the present invention;
[0079] Fig. 5 is a diagram of a relationship between a frequency modulation need and a frequency modulation capacity after increase of installed capacity of wind power and photovoltaic power generation in a typical day in summer according to a specific embodiment of the present invention;
[0080] Fig. 6 is a schematic diagram of an adequacy of a frequency modulation capacity after increase of installed capacity of wind power and photovoltaic power generation in a typical day in summer according to a specific embodiment of the present invention; and [0081] Fig. 7 is a flow chart of traditional evaluation of a consumption capacity of
2019101413 18 Nov 2019 renewable energy sources according to the present invention.
DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS [0082] Embodiments of the present invention are further described in detail below with reference to the accompanying drawings.
[0083] A method for evaluating and regulating consumption capacity of a regional power grid for renewable energy sources, as shown in Fig. 1 and Fig. 2, comprises the following steps:
[0084] step 1, considering influence factors of consumption of the power grid for the renewable energy sources, respectively determining evaluation indexes for short-term, medium-term and long-term consumption capacities for the renewable energy sources, and further constructing an index system for the consumption capacity for the renewable energy sources;
[0085] indexes for the short-term consumption capacity of the power grid for the renewable energy sources in the step 1 comprise: an adequacy of frequency modulation capacity of the system and an adequacy of peak load regulation capacity of the system, [0086] wherein the adequacy of the frequency modulation capacity of the system is calculated by a formula as follows:
^capacity β f = -I-- x 1 OO% [0087] .
[0088] In the above formula, refers to the adequacy of the frequency pcapacity modulation capacity of the system; f refers to the frequency modulation capacity of the system, wherein the frequency modulation capacity refers to pneed secondary frequency modulation capacity of the power grid; and f refers to frequency modulation need of the system, i.e., a minute level fluctuation value of a net load.
[0089] The frequency modulation capacity of the system is calculated by a formula as follows:
n pyccipacity__ ACC [0090] 7 2 .
D [0091] In the above formula, AGC refers to an AGC reserve capacity of the system, which may need to be regulated up or down by an AGC unit due to
2019101413 18 Nov 2019 [0092] follows [0093] [0094] [0095] [0096] uncertainty of a fluctuation direction of the net load. Therefore, an intermediate value of the AGC unit reserve capacity is taken to determine the frequency modulation capacity.
The frequency modulation need of the system is calculated by a formula as pn f eed = |a^' -np, \TT) _ reload ryenew NP‘ = +NP'-^+np< + np^ +-+np<^ ]\rp pload
In the above formula, /v 1 refers to a net load value at the moment; ‘
NP refers to the load value at the moment; ‘ refers to an output value of the
NP' renewable energy sources at the moment; 1 refers to the net load value smoothed by a rolling average method at the moment t; and an absolute value of a difference JVP NP* between 1 and j is the frequency modulation need of the system.
[0097] Np‘ is calculated by a formula, wherein refers to the number of rolling and calculating averaged net load values; the value of is related to sampling and storage periods of a renewable energy source generated power value and the time length of rolling and calculating the average; when the frequency modulation capacity of the system can meet the frequency modulation need, the value of @ f is greater than the set critical value, which indicates that the system still has surplus capacity to consume the renewable energy sources; and when the value of f is less than the set critical value, the frequency modulation capacity of the system cannot fully meet the frequency modulation need, which indicates that the amount of consumed renewable energy sources is too large and the system is supersaturated.
[0098] The adequacy of the peak load regulation capacity of the system is calculated by a formula as follows:
[0099]
x 1OO%
[00100]
2019101413 18 Nov 2019 [00101] [00102] In the above formula, ^p refers to the adequacy of the peak load regulation pcapacily capacity of the system; p refers to the peak load regulation capacity of the pneed p p} system; p refers to peak load regulation need of the system; >e and ' -min respectively refer to a rated output and a minimum technical output of a unit 1; Ng refers to the number of schedulable generator units in the system; refers to a spinning reserve capacity during a peak load period; and and NP™n respectively refer to a maximum value and a minimum value of the net load. [00103] When the value of ^p is greater than the set critical value, it indicates that the peak load regulation capacity of the system can meet the peak load regulation need, and the system has relatively strong consumption capacity for the renewable energy sources; and when the value of ^p is less than the set critical value, it indicates that the system has a peak load regulation pressure, and the power grid may face abandonment of wind energy and photovoltaic energy or deep peak load regulation and start-stop peak load regulation, which reflects that the amount of the renewable energy sources consumed by the system is supersaturated.
[00104] The indexes for the medium-term consumption capacity of the power grid for the renewable energy sources in the step 1 comprise: an average peak load regulation adequacy of the system, an average peak load regulation capacity of the system and an average peak load regulation need of the system, [00105] wherein a formula for calculating the average peak load regulation adequacy of the system is as follows:
pcapacily β=Ρ-< xl00% [00106] % .
[00107] In the above formula, ^p refers to the average peak load regulation pcapacily adequacy of the system; p refers to the average peak load regulation capacity pneed of the system; and p refers to the average peak load regulation need of the system.
[00108] When the value of is greater than the set critical value, the system has relatively strong consumption capacity for the renewable energy sources in a medium-term time scale; and when the value of p is less than the set critical value, it reflects that the amount of the renewable energy sources consumed by the power grid is supersaturated.
[00109] The average peak load regulation capacity of the system is calculated by a formula as follows:
2019101413 18 Nov 2019 [00110] [00111] In the above formula, “ and “nin respectively refer to the rated output and the minimum technical output of the unit 1; the minimum technical output of hydropower is constantly changing with the incoming water and can be averaged herein; and Mg refers to the number of runnable generator units in the system; and the runnable generator units are obtained by subtracting maintenance scheduling units from all the installed power supplies.
[00112] The average peak load regulation need of the system comprises a peak load regulation need caused by a load peak-valley difference, a load reserve capacity of a spinning reserve part during a peak load period and a new peak load regulation need considering wind power and photovoltaic power generation output aggregation, and is calculated by a formula as follows:
[00113] [00114] In the above formula, j-load refers to the load peak-valley difference of the system on a jth day; N refers to a statistical time scale; reserve refers to a ^pall spinning reserve part during the peak load period; renew refers to the new peak load regulation need considering wind power and photovoltaic power generation j^all output aggregation; renew refers to an installed capacity of the renewable energy sources; and Λ refers to a correction coefficient. The installed capacity of the renewable energy sources is generally considered as the new peak load regulation need. However, the new peak load regulation need may be smaller than the installed capacity of renewable energy sources due to an aggregation effect of the renewable energy sources; and if the installed capacity of renewable energy sources is larger, the correction coefficient is smaller.
[00115] The load reserve capacity of the spinning reserve part during the peak load period is calculated by the following steps:
[00116] (1) since the statistics show that the probability that the load varies randomly near a predicted value belongs to normal distribution, and the magnitude of a variance is related to the prediction accuracy of the load, calculating an LOLP p
(Loss of Load Probability) L0LP caused by load fluctuation according to a probability density function of load fluctuation near the predicted value by a formula as follows:
1 />'
Pon- = J FrvE---exP(~5—2—
Γ001171 r ^-σload [00118] in the formula, σΐοαά refers to a standard deviation of load fluctuation near the predicted value, which is generally 1 %-2%, 1% for systems with relatively large load and 2% for systems with relatively small load; and P' refers to a load reserve capacity of the system.
[00119] (2) since the prediction accuracy of wind power and photovoltaic power generation is also approximately subjected to normal distribution after the renewable energy sources are connected into the power grid, setting as a predicted standard deviation of generated energy of a wind power and photovoltaic power generation aggregate, which is generally valued between 10% and 20%, then, [00120] 8 new load ' renew .
[00121] for a new load prediction standard deviation, in order to maintain the given
LOLP fioip, calculating the corresponding load reserve capacity through a normal distribution table:
[00122] O-Plolp) [00123] ^reserve [00124] in the formula, Φ ' refers to an inverse function of standard normal distribution; and refers to the spinning reserve part during the peak load period.
[00125] The indexes for the long-term consumption capacity of the power grid for the renewable energy sources in the step 1 comprise: flexibility resources for controlling stability and operation economy of a power system and flexibility needs caused by various uncertain factors in the power system.
2019101413 18 Nov 2019 [00126] <1> The flexibility resources for controlling stability and operation economy of the power system are calculated as follows:
[00127] For example, the conventional planned installed capacity of the power supply is an important flexibility resource, which uses regulatory hydropower, thermal power and gas power generation as important resources to balance the fluctuation of the renewable energy sources; for the statistic of the quantity of the flexibility resources, a time scale T is set; data and probability distribution of available flexibility resources of the system are counted; and the probability distribution of a flexibility resource i is recorded as 4 , wherein X represents a regulation capacity which can be provided by the flexibility resource i.
[00128] <2> A flexibility need index caused by various uncertain factors in the power system is calculated by a method as follows:
Y = APT [00129] net ;
APT [00130] in the above formula, Y refers to a flexibility need quantity; net refers to a variation of the net load within the time scale of T, so the probability that the
D A APT } flexibility need of the system can be met is ' n net ; and from a critical point, the probability that the flexibility need of the system cannot be met can be expressed as:
[00131] Ρ“η>' > Ο ζ
[00132] wherein ’ is a positive value which is small in absolute value, between 1 MW and 2 MW.
[00133] Step 2, collecting data reflecting load characteristics of the power grid and output characteristics of the renewable energy sources.
[00134] In the preset embodiment, the collected reaction load characteristics comprise:
[00135] (1) annual load characteristic indexes, which are mainly classified into an annual maximum load, an annual maximum peak-valley difference, a seasonal load rate and a seasonal unbalance coefficient; and [00136] (2) monthly load characteristic indexes, which are mainly classified into a monthly maximum load, a monthly average daily load rate, a monthly maximum peak-valley difference rate and a monthly load rate.
2019101413 18 Nov 2019 [00137] In the preset embodiment, the data collected to reflect the output characteristics of the renewable energy sources comprise photovoltaic power generation output and wind power output.
[00138] Step 3, processing the data collected in the step 2 to obtain corresponding index values in the step 1, and then evaluating the consumption capacity of the power grid for the renewable energy sources to obtain an evaluation result.
[00139] The step 3 specifically comprises:
[00140] (1) dividing the evaluation of consumption capacity into three time dimensions of short-term consumption, medium-term consumption and long-term consumption according to consumption requirements of the power grid for the renewable energy sources.
[00141] In the present embodiment, the time dimensions that need to be determined in the evaluation should be comprehensively considered when evaluating the actual consumption capacity: the short-term consumption is the evaluation of the consumption capacity within 1-15 min, which provides a data basis for real-time power dispatching; the medium-term consumption is daily or monthly consumption, which provides a basis for the formulation of the power dispatching daily plan and monthly power generation plan; and the long-term consumption is annual consumption, which provides a basis for regional power planning and new energy construction.
[00142] (2) In the case of short-term consumption, calculating the adequacy of frequency modulation capacity and the adequacy of peak load regulation capacity of the power grid according to the data collected in the step 2 and the formulae
pcapacity = ——— xlOO% need p about the adequacy of frequency modulation capacity and the adequacy of peak load regulation capacity of the system in the step 1;
[00143] in the case of medium-term consumption, calculating the average peak load regulation adequacy of the system according to the formula of calculating the average peak load regulation adequacy of the system in the step 1:
pcapacity β = L , xl00% ' P pneed p ; and [00144] in the case of long-term consumption, calculating insufficiently ramping resource expectation of the system according to the following formula of the insufficiently ramping resource expectation of the system in the step 1:
2019101413 18 Nov 2019 unj net [00145] (3) setting a specific critical value (which is greater than or equal to 100% at least) according to an actual load situation of the power grid, a proportion of the renewable energy sources and the index of the stability of the power grid, and substituting the required index and the specific critical value into the step 1 to evaluate the consumption capacity of the power grid for the renewable energy sources.
[00146] Step 4, comparing the evaluation result with the consumption capacity of the power grid, and if the evaluation result is greater than the consumption capacity, issuing a first regulation instruction to the power grid: stopping the power grid from accessing the renewable energy sources; and if the evaluation result is less than the consumption capacity, issuing a second regulation instruction to the power grid according to the calculated surplus consumption quantity of the renewable energy sources: appropriately increasing the renewable energy sources for grid connection and increasing an access proportion.
[00147] In the present embodiment, the method in the step 4 specifically comprises the following steps:
[00148] comparing the evaluation result with the consumption capacity, and issuing regulation instructions for the consumption of the power grid for the renewable energy sources;
[00149] if the index value is smaller than the critical value, issuing a first regulation instruction to the power grid: stopping the power grid from accessing new renewable energy sources, if necessary, abandoning wind energy and photovoltaic energy, and performing deep peak load regulation and start-stop peak load regulation;
[00150] if the index value is greater than the critical value, calculating the surplus consumption quantity of the renewable energy sources by a formula as follows:
Surplus consumption quantity of _ rf__rf_____ [00151] renewable ener£V sources β [00152] issuing a second regulation instruction to the power grid according to the calculated surplus consumption quantity of the renewable energy sources:
2019101413 18 Nov 2019 appropriately increasing purchase for the renewable energy sources, increasing the access proportion, and taking an upper limit of the purchase amount as the calculated surplus consumption quantity of the renewable energy sources.
[00153] In the present embodiment, taking evaluation and regulation of the consumption capacity of the power grid in a province for the renewable energy sources in summer as an example, firstly, two indexes of the adequacy of the frequency modulation capacity and the adequacy of the peak load regulation capacity of the power grid in the province at a present output level of the renewable energy sources are calculated based on load data and power supply output data of the power grid in the province in summer; and on this basis, change of the two indexes is predicted when the output of the renewable energy sources is increased several times, to reflect the change of the consumption capacity of the power grid in the province for the renewable energy sources.
[00154] The short-term consumption on a typical day in summer is specifically analyzed as follows:
[00155] 1) Adequacy of frequency modulation capacity [00156] A distribution diagram of the frequency modulation need and the frequency modulation capacity on May 15, the typical day in summer, is selected as shown in Fig. 3 and Fig. 4. Fig. 3 is a diagram of a relationship between the frequency modulation need and the frequency modulation capacity on the typical day in summer; Fig. 4 is a schematic diagram of the adequacy of frequency modulation capacity on the typical day in summer; it can be seen with reference to the adequacy of frequency modulation capacity that the index of the adequacy of frequency modulation capacity of the system at 0: 00 is lower than 1, the frequency modulation capacity cannot meet the frequency modulation need, and the adequacy of frequency modulation capacity is insufficient once throughout the day; and it can be seen from Fig. 5 and Fig. 6 that the adequacy of frequency modulation capacity is insufficient twice, at 0: 00 and 6: 45 respectively, after the installed capacity of wind power and photovoltaic power generation is increased ten times.
[00157] 2) Adequacy of peak load regulation capacity [00158] The peak-valley difference of the net load on the typical day in summer is 4641 MW, and a regulation range of the units is 7068 MW, from which the adequacy of peak load regulation capacity can be calculated to be 152.29%.
[00159] The peak-valley difference of net load is 6386 MW after the installed
2019101413 18 Nov 2019 capacity of wind power and photovoltaic power generation is increased ten times, and it is assumed that the regulation range of the units is invariable, and then the adequacy of peak load regulation capacity is 110.67%.
[00160] 3) Evaluation of the consumption capacity of the power grid in the province for the renewable energy sources in summer [00161] Firstly, a critical value of 100% is set according to the actual situation of the power grid in the province in summer; and secondly, the calculated adequacy of frequency modulation capacity and the adequacy of peak load regulation capacity are substituted into the index system for consumption for the renewable energy sources established in the step 1 to evaluate the consumption capacity of the power grid in the province for the renewable energy sources in summer and obtain the evaluation result, i.e., if the power grid in the province in summer has adequate consumption capacity for the renewable energy sources and relatively high adequacy of the peak load regulation capacity, the power grid in the province in summer has relatively strong consumption capacity for the renewable energy sources and still has surplus consumption capacity.
[00162] 4) Regulation of access of the renewable energy sources to the power grid in the province in summer [00163] The index value is greater than the set critical value (which is set as 100% in the present embodiment); the surplus consumption quantity of the power grid in the province for the renewable energy sources in summer can be calculated to be 1327 MW according to the formula for calculating the surplus access amount of the renewable energy sources in the step 4, so the second regulation instruction 2 is issued: more renewable energy sources can purchased appropriately, and the access proportion of the renewable energy sources can be increased, but the purchase amount cannot exceed 1327 MW.
[00164] It should be emphasized that embodiments of the present invention are illustrative rather than restrictive. Therefore, the present invention includes, but not limited to embodiments described in the Detailed Description; and all other embodiments obtained by those skilled in the art according to the technical solution of the present invention also belong to the protection scope of the present invention. [00165] It will be understood that the term “comprise” and any of its derivatives (eg comprises, comprising) as used in this specification is to be taken to be inclusive of features to which it refers, and is not meant to exclude the presence of any
2019101413 18 Nov 2019 additional features unless otherwise stated or implied.
[00166] The reference to any prior art in this specification is not, and should not be taken as, an acknowledgement or any form of suggestion that such prior art forms part of the common general knowledge.
[00167] It will be appreciated by those skilled in the art that the invention is not restricted in its use to the particular application described. Neither is the present invention restricted in its preferred embodiment with regard to the particular elements and/or features described or depicted herein. It will be appreciated that various modifications can be made without departing from the principles of the invention. Therefore, the invention should be understood to include all such modifications in its scope.

Claims (5)

  1. (1) dividing the evaluation of consumption capacity into three time dimensions of short-term consumption, medium-term consumption and long-term consumption according to consumption requirements of the power grid for the renewable energy sources;
    (1) annual load characteristic indexes, which are mainly classified into an annual maximum load, an annual maximum peak-valley difference, a seasonal load rate and a seasonal unbalance coefficient; and (2) monthly load characteristic indexes, which are mainly classified into a monthly maximum load, a monthly average daily load rate, a monthly maximum peak-valley difference rate and a monthly load rate;
    data collected to reflect the output characteristics of the renewable energy sources comprise photovoltaic power generation output and wind power output.
    1. A method for evaluating and regulating consumption capacity of a regional power grid for renewable energy sources, comprising the following steps:
    step 1, considering influence factors of consumption of the power grid for the renewable energy sources, respectively determining evaluation indexes for short-term, medium-term and long-term consumption capacities for the renewable energy sources, and further constructing an index system for the consumption capacity for the renewable energy sources;
    step 2, collecting data reflecting load characteristics of the power grid and output characteristics of the renewable energy sources;
    step 3, processing the data collected in the step 2 to obtain corresponding index values in the step 1, and then evaluating the consumption capacity of the power grid for the renewable energy sources to obtain an evaluation result; and step 4, comparing the evaluation result with the consumption capacity of the power grid, and if the evaluation result is greater than the consumption capacity, issuing a first regulation instruction to the power grid: stopping the power grid from accessing the renewable energy sources; and if the evaluation result is less than the consumption capacity, issuing a second regulation instruction to the power grid according to the calculated surplus consumption quantity of the renewable energy sources: appropriately increasing the renewable energy sources for grid connection and increasing an access proportion.
  2. (2) in the case of short-term consumption, calculating the adequacy of frequency modulation capacity and the adequacy of peak load regulation capacity of the power grid according to the data collected in the step 2 and the formulae
    1ΟΟ% βρand x 100% about the adequacy of frequency modulation capacity and the adequacy of peak load regulation capacity of the system in the step 1;
    in the case of medium-term consumption, calculating the average peak load regulation adequacy of the system according to the formula of calculating the average pcapacitv β= p_ , xl00% ~ P fenced
    P · peak load regulation adequacy of the system in the step 1:
    in the case of long-term consumption, calculating an insufficiently ramping resource expectation value of the system according to the following formula of the insufficiently ramping resource expectation value of the system in the step 1:
    ^unj = > O.
    ^2=^^(1-7^) ^rc'.sc'rve · in the formula, Φ 1 refers to an inverse function of standard normal distribution; and refers to the spinning reserve part during the peak load period;
    the indexes for the long-term consumption capacity of the power grid for the renewable energy sources in the step 1 comprise: flexibility resources for controlling stability and operation economy of a power system and flexibility needs caused by various uncertain factors in the power system, wherein the flexibility resources for controlling stability and operation economy of the power system are calculated as follows:
    for the statistic of the quantity of the flexibility resources, a time scale T is set; data and probability distribution of available flexibility resources of the system are counted; and the probability distribution of a flexibility resource i is recorded as D> wherein X represents a regulation capacity which can be provided by the flexibility resource i, wherein a flexibility need index caused by various uncertain factors in the power system is calculated by a method as follows:
    Y = APT t net \p' in the above formula, Y refers to a flexibility need quantity; net refers to a variation of the net load within the time scale of T, so the probability that the flexibility need of the system can be met is APnet). anj from a critical point, the probability that the flexibility need of the system cannot be met can be expressed as:
    Pun,t = P.A^net ~ <ξ),<ξ > O ζ
    wherein ’ is a positive value which is small in absolute value.
    (2) since the prediction accuracy of wind power and photovoltaic power generation is also approximately subjected to normal distribution after the renewable energy sources are connected into the power grid, setting as a predicted standard deviation of generated energy of a wind power and photovoltaic power generation aggregate, then:
    2019101413 18 Nov 2019 for a new load prediction standard deviation, in order to maintain the given
    LOLP calculating the corresponding load reserve capacity through a normal distribution table:
    2. The method for evaluating and regulating consumption capacity of a regional power grid for renewable energy sources according to claim 1, wherein indexes for the short-term consumption capacity of the power grid for the renewable energy sources in the step 1 comprise: an adequacy of frequency modulation capacity of the system and an adequacy of peak load regulation capacity of the system, wherein the adequacy of the frequency modulation capacity of the system is calculated by a formula as follows:
    pcapacily in the above formula, refers to the adequacy of the frequency modulation
    2019101413 18 Nov 2019 pcapacity capacity of the system; 7 refers to the frequency modulation capacity of the system; the frequency modulation capacity refers to secondary frequency modulation pneed capacity of the power grid; and f refers to a frequency modulation need of the system, i.e., a minute level fluctuation value of a net load, wherein the frequency modulation capacity of the system is calculated by a formula as follows:
    D in the above formula, AGC refers to an AGC reserve capacity of the system, which may need to be regulated up or down by an AGC unit due to uncertainty of a fluctuation direction of the net load; therefore, an intermediate value of the AGC unit reserve capacity is taken to determine the frequency modulation capacity, wherein the frequency modulation need of the system is calculated by a formula as follows:
    Pnfeed = \np( -NPt | ]\Jp _ pload prenew NP‘ = Ίμ(ΝΡι^ + NP‘-w-v + NP< + NP,n +-+NPt+M) _ pload in the above formula, ' refers to a net load value at the moment; ‘
    NP refers to the load value at the moment; ' refers to an output value of the
    NP' renewable energy sources at the moment; 1 refers to the net load value smoothed by a rolling average method at the moment t; and an absolute value of a difference τιτρ ΆΓΡ' between 1 and 1 is the frequency modulation need of the system, herein the adequacy of the peak load regulation capacity of the system is calculated by a formula as follows:
    X 1OO%
    (P P )
    V ιβ i,min <
    2019101413 18 Nov 2019 in the above formula, p p refers to the adequacy of the peak load regulation pycapacity capacity of the system; p refers to the peak load regulation capacity of the priced p p} system; p refers to peak load regulation need of the system; >e and ' min respectively refer to a rated output and a minimum technical output of a unit 1; refers to the number of schedulable generator units in the system; refers to a spinning reserve capacity during a peak load period; and and ^nin respectively refer to a maximum value and a minimum value of the net load;
    the indexes for the medium-term consumption capacity of the power grid for the renewable energy sources in the step 1 comprise: an average peak load regulation adequacy of the system, an average peak load regulation capacity of the system and average peak load regulation need of the system, wherein a formula for calculating the average peak load regulation adequacy of the system is as follows:
    pcapacity β = p_ , xl00% • P priced 1 P · in the above formula, d p refers to the average peak load regulation adequacy of pcapacity the system; p refers to the average peak load regulation capacity of the system; pneed and p refers to the average peak load regulation need of the system, wherein the average peak load regulation capacity of the system is calculated by a formula as follows:
    Mc, pcapacity _ zp _ p \ p / > ie 1 i, min / /=1 ·
    P P in the above formula, ie and “nin respectively refer to the rated output and the minimum technical output of the unit 1; the minimum technical output of hydropower is constantly changing with the incoming water and can be averaged herein; and Mg refers to the number of runnable generator units in the system, wherein the average peak load regulation need of the system comprises a peak load regulation need caused by a load peak-valley difference, a load reserve capacity of a spinning reserve part during a peak load period and a new peak load regulation need considering wind power and photovoltaic power generation output aggregation, and is calculated by a formula as follows:
    2019101413 18 Nov 2019
    in the above formula, j’load refers to the load peak-valley difference of the p
    system on a jth day; N refers to a statistical time scale; reserve refers to a spinning reserve part during the peak load period; renew refers to the new peak load regulation need considering wind power and photovoltaic power generation output pall aggregation; renew refers to an installed capacity of the renewable energy sources;
    τ and Λ refers to a correction coefficient. The installed capacity of the renewable energy sources is generally considered as the new peak load regulation need; however, the new peak load regulation need may be smaller than the installed capacity of renewable energy sources due to an aggregation effect of the renewable energy sources; and if the installed capacity of renewable energy sources is larger, the correction coefficient is smaller, wherein the load reserve capacity of the spinning reserve part during the peak load period is calculated by the following steps:
    p (1) calculating an LOLP (Loss of Load Probability) LOLP caused by load fluctuation according to a probability density function of load fluctuation near a predicted value by a formula as follows:
    7 1 Τ’2
    Blolp J ----exP(— X.--j--y/p
    P 21 LCT/f>ad load · in the formula, σ/' refers to a standard deviation of load fluctuation near the predicted value, which is generally l%-2%, 1% for systems with relatively large load and 2% for systems with relatively small load; and P' refers to a load reserve capacity of the system;
  3. (3) setting a specific critical value (which is greater than or equal to 100% at
    2019101413 18 Nov 2019 least) according to an actual load situation of the power grid, a proportion of the renewable energy sources and the index of the stability of the power grid, and substituting the required index and the specific critical value into the step 1 to evaluate the consumption capacity of the power grid for the renewable energy sources.
    3. The method for evaluating and regulating consumption capacity of a regional power grid for renewable energy sources according to claim 1, wherein the reaction
    2019101413 18 Nov 2019 load characteristics collected in the step 2 comprise:
  4. 4. The method for evaluating and regulating consumption capacity of a regional power grid for renewable energy sources according to claim 1, wherein the step 3 specifically comprises:
  5. 5. The method for evaluating and regulating consumption capacity of a regional power grid for renewable energy sources according to claim 1, wherein the method in the step 4 specifically comprises the following steps:
    comparing the evaluation result with the consumption capacity, and issuing regulation instructions for the consumption of the power grid for the renewable energy sources;
    if the index value is smaller than the critical value, issuing a first regulation instruction to the power grid: stopping the power grid from accessing new renewable energy sources, if necessary, abandoning wind energy and photovoltaic energy, and performing deep peak load regulation and start-stop peak load regulation;
    if the index value is greater than the critical value, calculating the surplus consumption quantity of the renewable energy sources by a formula as follows:
    Surplus consumption quantity of = _______ renewable energy sources β ^-critical issuing a second regulation instruction to the power grid according to the calculated surplus consumption quantity of the renewable energy sources: appropriately increasing purchase for the renewable energy sources, increasing the access proportion, and taking an upper limit of the purchase amount as the calculated surplus consumption quantity of the renewable energy sources.
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CN111415061A (en) * 2020-02-10 2020-07-14 南方电网科学研究院有限责任公司 Comprehensive evaluation method and device for renewable energy power system
CN111695235A (en) * 2020-04-24 2020-09-22 广东电网有限责任公司 Regional decomposition method, device and system for renewable energy consumption responsibility weight
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CN111415061A (en) * 2020-02-10 2020-07-14 南方电网科学研究院有限责任公司 Comprehensive evaluation method and device for renewable energy power system
CN111415061B (en) * 2020-02-10 2022-07-22 南方电网科学研究院有限责任公司 Comprehensive evaluation method and device for renewable energy power system
CN111340335A (en) * 2020-02-13 2020-06-26 国网青海省电力公司经济技术研究院 Method and system for evaluating flexibility supply capacity of thermal power generating unit
CN111695235A (en) * 2020-04-24 2020-09-22 广东电网有限责任公司 Regional decomposition method, device and system for renewable energy consumption responsibility weight
CN111695235B (en) * 2020-04-24 2023-05-26 广东电网有限责任公司 Regional decomposition method, device and system for renewable energy source absorption responsibility weight
CN111861023A (en) * 2020-07-28 2020-10-30 南方电网科学研究院有限责任公司 Statistical-based hybrid wind power prediction method and device
CN111861023B (en) * 2020-07-28 2024-02-09 南方电网科学研究院有限责任公司 Mixed wind power prediction method and device based on statistics
CN112132453A (en) * 2020-09-22 2020-12-25 国网能源研究院有限公司 Method, system and device for evaluating optimal admission scale of renewable energy sources of regional power grid
CN116632946A (en) * 2023-07-21 2023-08-22 湖南大学 Openable capacity assessment method for power distribution area of industrial park
CN116632946B (en) * 2023-07-21 2023-09-19 湖南大学 Openable capacity assessment method for power distribution area of industrial park

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