Note: Descriptions are shown in the official language in which they were submitted.
1
INTEGRATED REGIONAL RENEWABLE ENERGY CONTROL SYSTEM
TECHNICAL FIELD
[1] The present i nventi on rel at es to an i ntegrated regi onal
renewable energy control system, and more particularly, to an
i ntegrated regi onal renewabl e energy control system capabl e of
determi ni ng the i mpact of renewabl e energy generati on on the
power gri d of a regi on and control I i ng the amount of renewabl e
energy generation of each renewable energy generation zone
wi thi n the regi on accordi ng to the determi nati on resul ts.
BACKGROUND ART
[2] I n the 2011 Durban General Assembly, it was agreed to form a
new climate regi me after 2020 i n which both devel oped and
developing countries part i ci pate as a follow-up to the Kyoto
Protocol . I n addi ti on, with the si gni ng of the Par i s Agreement
i n December 2015, the new cl i mate regi me bei ng applied to all
countries was i ntroduced i n 2020, and in Ii ne with thi s new
cl i mate regi me, new energy i ndust ri es centered on renewabl e
energy are bei ng promoted.
[3] Renewabl e energi es refer to energi es whi ch are renewabl e
i ncl udi ng sunl i ght, water, preci pi tat i on,
bi ol ogi cal organi sms
and the I i ke, and power generati on usi ng them i ncl udes sol ar
energy, wi nd energy, hydroelectric energy and the I i ke.
[4] These renewabl e energi es have advantages of bei ng cl ean and
no fear of depl et i on, as well
as bei ng renewable without
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poll uti on, but have di sadvantages that the amount of generati on
is less and that the amount of generati on is al so affected by
weather conditions compared to basel oad generation such as
petrol eum, coal , nucl ear power and the I i ke.
[5] Specifically, the renewable energy has charact er i st i cs of
the
uncert ai nty that is di f f i cult to predict an out put for
generati on and high van i ability i n the amount of output because
the renewable energy is dependent on natural condi ti ons (solar
i rradi ance, temperature, wi nd and the I i ke).
[6] As an example, in the case of solar generation, the amount
of generati on is intermittent because it is greatly affected by
weather condi ti ons such as the amount of sunlight, and the
uncertainty of the amount of generati on is al so high because it
is difficult to accurately predict the weather condi ti ons, .
[7] Fl G. 1 is a di agram for expl ai ni ng the necessi ty of managi ng
reserve power due to the intermittence of the amount of
renewable energy generation.
[8] As shown i n Fl G. 1, a renewabl e energy generated i n each
generati on zone 1 i s suppl i ed to an power network 3. I n addi ti on,
an energy generated from the basel oad generati on means 2 such as
petrol eum, coal
and nucl ear i s al so suppl i ed to the power
network 3, and thus the renewable energy and the basel oad
generati on energy are combined in the power network 3.
[9] As an example, in case that the renewable energy is an
energy created by solar generation, as shown in a grape of FIG.
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1, the amount of basel oad generati on requi red dun i ng the dayti me
i s reduced, and the power reserve capacity requi red after the
sunset becomes hi gher than dun i ng the dayti me.
[10] On the other hand, i n case that the amount of renewabl e
energy generati on dun i ng the dayti me is reduced due to weather
condi ti ons, the amount of the basel oad generati on is further
requi red as much as the reduced amount of renewable energy
generati on and for the above reasons, if the predi cti on error
for the generati on of renewable energy i ncreases, a difference
occurs between the amount of the pl anned generati on and the
actual amount of power supply and therefore, it is necessary to
i ncrease the reserve power to cope with the predi cti on error.
This has a probl em I eadi ng to an i ncrease i n generati on costs.
[11] Therefore, there needs to be an i ntegrated renewabl e energy
control system that can respond to renewabl e energy vol at i I i ty
and generati on acceptance issues by predicting and controlling
the amount of renewabl e energy generati on and stably operate
(moni t or i ng, predi cti ng, control I i ng and the I i ke) the regi onal
power grid.
DETAILED DESCRIPTION OF THE INVENTION
TECHNICAL TASKS
[12] A techni cal task to be achi eyed by the present i nventi on i s
to pr ovi de an i nt egr at ed regi onal
renewabl e energy control
system capabl e of determi ni ng the i mpact of renewabl e energy
generati on on the regi onal
power grid and control I i ng the
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renewable energy production through the processes of the output
predi cti on, stability/acceptance I i mi t
eval uati on and output
control of each renewable energy generation zone in the region
according to the determi nati on result.
[13] Other technical task to be achieved by the present
invention is to provi de an i n- memory application database unit
optimized for control of a regional power grid.
[14] Another techni cal task to be achieved by the present
i nventi on i s to provi de an appl i cat i on i nf rastructure that
provi des an envi ronment for renewabl e energy output predi cti on,
stability/acceptance I i mi t eval uati on and
output control
performance i n conj uncti on with a renewabl e energy generati on
source and a grid system in the region.
[15] Still another techni cal task to be achieved by the present
invention is to provi de an analysis infrastructure capable of
warehousing by distributed-parallel processing grid data,
weather data and a pl ural i ty of i nf ormati on generated i n the
course of eval uati ng the stability/acceptance I i mi t of the power
gri d, and anal yzi ng the warehoused bi g data i n real ti me.
[16] The techni cal tasks to be achi eyed by the present i nventi on
are not limited to the above-mentioned techni cal tasks, and
other techni cal tasks not menti oned above will
be cl early
understood by those ski I I ed i n the art from the descri pti on
bel ow.
TECHNI CAL SOLUTI ON
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[17] I n or der to achi eve the above t echni cal
tasks, one
embodi ment of the present i nventi on provi des an i ntegrated
regional renewable energy control system comprising:
a renewable energy control i nf rastructure bei ng connected to
a gri d system that aggregates renewabl e energy generati on
information from a renewable energy generati on source in each
renewable energy generati on zone i n a region for col I ecti ng gri d
data i ncl udi ng the renewable energy generati on i nf ormati on; and
an
appl i cat i on unit for communi cat i ng with the renewabl e
energy control i nf rast r uct ur e to receive
the grid data,
determining power grid stability of the region based on the grid
data, and generati ng output control i nf ormati on accordi ng to the
power gri d stability,
wherei n the gri d system col I ects the
renewable energy generati on i nf ormati on of the renewable energy
generation source by means of a plurality of information
col I ecti on termi nal s, and wherei n the renewable energy control
i nf r ast r uct ur e control s the amount of
renewable energy
generation of each renewable energy generation zone in the
regi on accordi ng to the output control i nf ormati on.
[18] The grid system comprises a Supervisory Control And Data
Acqui si ti on( SCADA) module for communicating with each of the
pl ural i ty of i nf ormati on col I ecti on termi nal s i n real ti me to
col I ect the renewabl e energy generati on i nf or mat i on.
[19] The grid system includes an energy management system ( EMS)
for col I ecti ng basel oad generati on data,
power facility
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information and power facility characteristic information in the
regi on, the gri d data further i ncl udi ng data collected by the
EMS
when the renewabl e energy generati on i nf or mat i on i s
col I ected,
wherei n the power facility i nf ormati on is i nf ormati on on
power facilities connected to the power gri d of the regi on, and
wherei n the power facility char act er i sti c i nf or mat i on i s
i nf or mat i on i ndi cat i ng characteri sti cs of each
of power
facilities connected to the power gri d of the regi on.
[20] The integrated regional renewable energy control system
further compri ses a di st r i but ed parallel
processi ng unit for
provi di ng weather data of the regi on to the renewable energy
control infrastructure,
wherei n the appl i cat i on unit compri ses:
a weather prediction modul e for generati ng weather predi cti on
information of the renewable energy generati on zone based on the
weather data;
a renewabl e energy output predi cti on modul e for generati ng
output predi cti on i nf ormati on of the renewable energy generati on
zone based on the weather predi cti on i nf ormat i on and the gri d
data;
other grid analysis module being configured to operate based
on power facility char act eri sti c i nf or mat i on and power facility
i nf or mat i on, wherei n the other gri d anal ysi s module cal cul at es
the exact magni tude and phase angl e of a bus vol tage based on
the
dynamic and stat i c i nf ormati on of the power facility and
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generates state est i mat i on result i nf ormat i on by detect i ng an
over I oad of I i ne and transformer, a viol at i on of bus voltage
const rai nt and a vi ol at i on of reactive power const rai nt of
generator and synchronous condenser based on the cal cul at ed
val ue;
a stability eval uat i on modul e for generat i ng a stability
eval uat i on i nf or mat i on of the regi onal power grid based on at
least two or more of the out put predi ct i on i nf ormat i on, state
estimation result information, power facility characteristic
i nf or mat i on and power facility i nf ormat i on;
an acceptance limit evaluation module for generating
acceptance limit eval uat i on i nf ormat i on based on the voltage
standard viol at i on,
facility and t ransmi ssi on I i ne overl oad,
t ransi ent
stability and renewabl e energy I ow vol t age ri de
through ( LVRT) and the magnitude of the fault current of the
regi onal power gri d; and
a renewable energy output control module for generat i ng the
out put control
i nf or mat i on based on the stability eval uat i on
i nf or mat i on and the acceptance limit eval uat i on i nf or mat i on,
wherei n the application unit returns the weather predi ct i on
i nf or mat i on, the out put predi ct
i on i nf or mat i on, the state
est i mat i on result i nf or mat i on, the
stability eval uat i on
i nf or mat i on, the acceptance limit eval uat i on i nf or mat i on and the
out put control
i nf or mat i on to the renewable energy control
i nf rastructure.
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[21] The renewable energy generati on source is a wi nd generati on
source, and the renewable energy output prediction module can
col I ect wi nd speed and wi nd generati on data of a predetermi ned
period of the renewable energy generati on source; set an ARI MAX
model based on at I east some of the wi nd speed and wi nd
generati on data of the predetermi ned period of ti me to esti mate
a f i rst amount of generati on; set a polynomial regressi on model
based on at I east some of the wi nd speed and wi nd generati on
data of the predetermi ned period of ti me to esti mate a second
amount of generati on; esti mate a t hi rd amount of
generati on
based on wi nd speed data at a poi nt near the renewable energy
generati on source; and generate out put predi cti on i nf or mat i on
based on an anal og ensembl e by usi ng the f i rst amount of
generati on, the second amount of generati on, the t hi rd amount of
generati on and the past wi nd speed and wi nd generati on data of
the renewabl e energy generati on source.
[22] The renewabl e energy output predi cti on modul e can col I ect
wi nd speed predi cti on data at a poi nt near the renewabl e energy
generati on source and spatial data near the renewable energy
generati on source; predict the wi nd speed at the I ocati on of the
renewable energy generati on source based on a Kr i gi ng technique;
correct the wi nd speed accor di ng to the altitude of the
renewabl e energy generati on source based on the Deacon equati on;
and esti mate the t hi rd amount of
generati on based on the
corrected wi nd speed.
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[23] The renewable energy management i nf rastructure i ncl udes an
infrastructure management unit,
wherei n the i nf rastruct ure management unit i ncl udes an i memory database
management modul e, an i nt egr at ed process
management modul e, an al arm/event management modul e, and a I og
management modul e,
wherei n the i n- memory database management module controls the
executi on, control, state management of the i n- memory database
unit and the process of modul es bel ongi ng to the appl i cat i on
unit,
wherei n the i ntegrated process management module controls the
process of each component i n the renewabl e energy control
i nf r ast r uct ur e based on process management i nf or mat
i on, a
predetermi ned pri or i ty and a current state of each component i n
the renewable energy control
i nf r ast r uct ur e, and stores and
handl es the al arm and event generated i n the control process,
wherei n the alarm/event management module stores and handl es
al arm and event information generated in the grid system and
transmits the al arm and event i nf or mat i on to the i nt egr at ed
control unit and di stri buted parallel processi ng unit, and
wherei n the log management module generates a log file by
ref erri ng to the process log i nf ormati on stored i n the i n- memory
database unit, records log i nf ormati on i n the I og file accordi ng
to a log level and del et es the log i nf ormati on of the log file
accordi ng to a predetermi ned cycl e.
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[24] The renewable energy management i nf rast ruct ure further
i ncl udes an i nf rastructure management i nf or mat i on memory whi ch
is connected to the infrastructure management unit and which
stores the process management i nf ormati on, the meta i nf ormati on
of the in-memory database, the power facility modeling
i nf or mat i on, the power facility char act er i sti c i nf or mat i on and
module information of the SCADA module.
[25] The di stri but ed paral I el
processi ng uni t recei ves the
weather data of the region from an external weather database or
weather server and provide the weather data to the renewable
energy control infrastructure.
[26] The renewable energy control
i nf rast r uct ure i ncl udes a
real-time database and wherein the real-time database stores the
grid data and i nf ormati on returned from the application unit.
[27] The di stri but ed parallel processi ng unit compr i ses:
a
data collection unit that collects data and i nf or mat i on
stored in the real -ti me database;
a data I oadi ng unit for di stri but i ng the data or i nf ormati on
collected by the data collection unit and I oadi ng the data or
i nf ormati on i nto a non- rel at i onal database or a di stri but ed file
system;
a data processi ng search unit for queryi ng the data or
i nf or mat i on I oaded i nto the data I oadi ng unit, convert i ng the
data or i nf ormati on i nto a predetermi ned format and warehousi ng
the converted data; and
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a data analysis application unit for secondly analyzing the
stability of the regional power grid.
[28] The data collection unit i ncl udes a f i rst di st ri but ed queue
module, a grid data col I ecti on/ I oadi ng
module, a second
distributed queue module, and a weather data col I ect i on/ I oadi ng
modul e,
wherein the first distributed queue module is connected to
the real-time database to receive data stored in the real-time
database,
and pushes the recei ved data to the grid data
collection/loading module, and
wherei n the second di stri buted queue modul e i s connected to
at
I east one of an power di st r i but i on aut omat i on system, a
meteri ng data management system and a weather database or a
weather server, and receives data from the connected
configuration and pushes the received data to the weather data
col I ect i on/ I oadi ng modul e.
[29] The data processi ng search uni t i ncl udes a SQL processi ng
engi ne, an aggregate i nf ormati on generati on module, an aggregate
i nf or mat i on generati on hi story management modul e and a data
warehousi ng modul e,
wherei n the SQL processi ng engi ne queri es data I oaded i n the
non- rel at i onal database or the di st ri but ed file system and loads
the data i n the data warehousi ng modul e,
wherei n the aggregate i nf ormati on generati on module generates
aggregate i nf or mat i on of data loaded i n the non- r el at i onal
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database or di stri buted file system and loads the aggregate
i nformati on i n the data warehousi ng modul e, and
wherei n the aggregate i nformati on gener at i on
hi story
management module generates generat i on hi story i nformati on of
the aggregate i nformati on, key aggregates and stati sti cal meta
i nformati on and I oads them i nto the data warehousi ng modul e.
[30] The data analysis appl i cat i on unit i ncl udes an art i f i ci al
neural network model whi ch I earns based on the data stored i n
the data warehousi ng module, receives at least some of the data
I oaded i n the non- rel at i onal database or the di stri but ed file
system as an i nput val ue and esti mates the amount of renewabl e
energy generati on i n the regi on.
[31] The integrated regional renewable energy control system
further compri ses an i ntegrated control unit which recei ves the
data warehoused into the data loading unit and visualizes the
data to provi de to a user,
wherei n the i nt egrat ed control
unit generates a control
message i n response to a user' s i nput, and
wherei n the control message control s the amount of renewabl e
energy generation in each renewable energy generation zone in
the regi on i n preference to the output control i nformati on.
[32] The renewable energy generation source is connected to an
i nverter, the output control i nformati on i s transmitted to the
i nformati on col I ecti on termi nal , and the i nformati on col I ecti on
termi nal control s the amount of generat i on of the renewabl e
energy generation source by controlling the inverter.
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[33] The i nf or mat i on coil ect i on t ermi nal is a remote t ermi nal
unit and wherei n the i nterval that the i nf or mat i on coil ecti on
t ermi nal coil ect s the renewable energy generati on i nf or mat i on i s
1 second or I ess.
EFFECTS OF THE INVENTION
[34] Accordi ng to an embodi ment of the present i nventi on, based
on the grid data collected through the SCADA module of the grid
system, the power usage envi ronment data of the regi on and the
weather data,
the out put amount of the renewabl e energy
generati on source i n the regi on is predi cted to generate output
control information, and the amount of generation of each
renewable generati on zone in the regi on or the renewable energy
generati on source belonging to the each renewable generati on
zone i s control led usi ng the output control i nf ormati on, thereby
reduci ng the I oad of the power gri d due to the i ntermi ttency of
the renewabl e energy.
[35] In addition, according to an embodiment of the present
i nventi on, it is possi bl e to predict the output of a renewabl e
energy generati on source, thereby establ i shi ng an a generati on
pl an i n advance based on the predi cted val ue and reduci ng power
grid operati ng costs through automati c control .
[36] In addition, according to an embodiment of the present
i nventi on, a vast amount of gri d data and i nf ormati on generated
by the oper at i on of the appl i cat i on unit
are
stored/ managed/anal yzed by the di st ri but ed parallel processi ng
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unit, thereby i mprovi ng the overall data processi ng capability
of the i ntegrated regi onal renewabl e energy control system.
[37] It shoul d be understood that effects of the present
i nventi on are not limited to the effects descri bed above and
encompass all effects that can be i nf er red
from the
conf i gur at i on descri bed i n the descri pt i on of the present
i nventi on or the cl ai ms.
BRI EF DESCRI PTI ON OF THE DRAW! NGS
[38] Fi gure 1 i s a di agram for expl ai ni ng a process i n whi ch a
load is generated in the power network due to the intermittency
of renewable energy generation.
Fi gure 2 i s a bl ock di agram schemati call y showi ng an i ntegrated
r egi onal renewabl e energy control
system accor di ng to an
embodiment of the present invention.
Fi gure 3 i s a di agram for
expl ai ni ng a renewabl e power
gener at i on i nf ormati on col I ecti on unit and
a gri d system
associated therewith
according to an embodiment of the present
i nventi on.
Fi gure 4 i s a bl ock di agram for expl ai ni ng a renewabl e energy
control i nf rastruct ure and an appl i cat i on unit accordi ng to an
embodiment of the present invention.
Fi gure 5 i s a di agram for expl ai ni ng a process i n whi ch a
renewable energy control i nf rastructure is associ ated with an
i ntegrated control unit and a di stri but ed parallel processi ng
unit according to an embodiment of the present invention.
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Fi gure 6 i s an exempl ary di agram for expl ai ni ng a process i n
which a renewable energy out put prediction module generates
output predi cti on i nf ormati on of a generati on zone i n a regi on
accordi ng to an embodi ment of the present i nventi on
BEST MODE FOR CARRY! NG OUT THE I NVENTI ON
[44] Her ei naf ter, the present i nventi on will be descri bed with
reference to the accompanyi ng drawi ngs. However, the present
invention may be embodied in many different forms and thus is
not I i mi ted to the embodi ments descri bed herei n. I n addi ti on, i n
order to cl early expl ai n the present i nventi on i n the drawi ngs,
parts i r rel evant to the descri pt i on are omitted, and si mi I ar
reference numeral s denotes si mi I ar parts
throughout the
speci f i cat i on.
[45] In the entire specification, when a part is said to be
"connected (coup! ed, contacted, combi ned) " with another part,
t hi s i ncl udes not only the case of bei ng "di rect I y connected"
but al so the case of being "i ndi rect I y connected" with another
member i n between.
I n addi ti on, when a part " compr i ses" a
certai n component, it means that it may further i ncl ude other
components not excl udi ng other components unl ess ot herwi se
stated.
[46] Terms used in this specification are only used to descri be
specific embodi ments, and are not i ntended to limit the present
i nventi on. Si ngul ar expressi on i ncl udes pl ural expressi on unl ess
the context clearly dictates otherwise.
It should be understood
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that the terms "compri se" or "have" in this specification are
i ntended to desi gnate that a feature, number, step, operati on,
component, part or combi nati on thereof descri bed
i n the
speci f i cat i on exi sts and that the possi bi I i ty of the presence or
addition of one or more other features or numbers, steps,
oper at i ons, components, parts, or combi nati ons thereof i s not
excl uded i n advance.
[47]
[48] Her ei naf t er, embodi ment s of the present i nvent i on will be
descri bed i n detail with reference to the accompanyi ng drawi ngs.
[49] Figure 2 is a block di agram schematically showi ng an
i ntegrated regi onal renewable energy control system accordi ng to
an embodi ment of the present i nventi on.
[50] As shown i n FIG. 2, the i ntegrated r egi onal
r en ewa b I e
energy control
system may i ncl ude a renewable generati on
i nf ormati on collection unit 100, a grid system 200, a renewabl e
energy control i nf rastructure 300, an appl i cat i on unit 400, an
i ntegrated control unit 50 and a di stri but ed parallel processi ng
uni t 600.
[51] The renewabl e gener at i on i nf or mat i on col I ect i on unit 100
may
col I ect gener at i on i nf or mat i on of each renewabl e energy
generation zone in the region. As an example, the renewable
gener at i on i nf or mat i on col I ecti on unit
100 may i ncl ude a
pl ur al i ty of i nf or mat i on collection t ermi nal s 110, 120 and 130
and may be respectively i nst al led i n a pl ural i ty of renewabl e
energy generat i on zones. Speci f i cal I y, the
pl ur al i ty of
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i nf or mat i on coil ecti on termi nal s 110, 120 and 130 may be f i el d
termi nal devi ces i nst al I ed to monitor, measure and control the
renewable energy generati on source within each renewable energy
generati on zone.
[52]
As an example, the renewable generati on information
col I ecti on unit 100 can perform the f unct i ons of
digital
conversi on of meter CT and PT measurement val ues and data
transmission to the renewable generati on data linkage module,
conversi on and rel ay transmi ssi on i n an
i nf ormati on
communication manner requested by the renewable generati on data
I i nkage modul e, and col I ecti on and transmi ssi on of basic power
qual i ty i nf ormati on.
[53] As an example, the generati on i nf ormati on collected by each
of the i nf or mat i on col I ecti on termi nal s 110,
120 and 130
bel ongi ng to the renewabl e generati on i nf or mat i on col I ecti on
uni t 100 may be converted i nto a predetermi ned protocol and
transmitted to the grid system 200.
[54] The gri d system 200 may monitor the state of the power gri d
i n the regi on based on the col I ected generati on i nf ormati on. I n
addi ti on,
the grid system 200 may col I ect and manage the
generati on i nf ormati on from not only renewable energy generati on
zones in the region but al so basel oad generati on zones P.
[55] In addition, the grid system 20 may transmit the collected
i nf ormati on to the renewable energy control i nf rastructure 300.
Herei naf ter, i nf ormati on transmitted by the gri d system 200 to
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the renewabl e energy control i nf rastructure 300 will be ref erred
to as system data.
[56] On the other hand, the renewable energy control
i nf rastructure 300 provi des an executi on envi ronment necessary
for
perf ormi ng the overall f uncti ons of the i ntegrated regi onal
renewable energy control system.
[57] As an example, the renewable energy control infrastructure
300 may be associ ated with the appl i cat i on unit 400, and based
on gri d data, provi de weather predi cti on,
renewabl e energy
out put pr edi ct i on, power gri d stability eval uat i on,
renewabl e
energy out put control ,
i nput/ out put data management and an
executi on envi ronment for perf ormi ng al arm processi ng.
[58] Al so, the renewable energy control i nf rastructure 300 i s
connected to each component i ncl udi ng the gri d system 200, the
appl i cat i on unit 400, the i ntegrated control unit 500 and the
di st r i but ed parallel processi ng unit 600 which bel ong to the
integrated regi onal renewable energy control system, and can
control the overall process of the i ntegrated regi onal renewabl e
energy control system.
[59] The components and f uncti ons thereof f ormi ng the renewabl e
energy control i nf rastructure 300 will be descri bed i n detail
with reference to FIG. 4.
[60]
In addi ti on, the application unit 400 may predict or
cal cul ate van i ous i nf or mat i on requi red for st abl e oper at i on of
the power grid in association with the renewable energy control
i nf rast ruct ure 300.
CA 03212040 2023- 9- 13
19
[061] As an example, the application unit 400 may receive gri d
data from the renewable energy control i nf rastructure 300. I n
addi ti on, based on the grid data, the appl i cation unit 400 may
perform weather predi cti on, renewabl e energy output predi cti on,
gri d safety eval uat i on, acceptance I i mi t eval uat i on, and other
grid analysis, and generate renewable energy out put control
i nformati on based on the performed results. I n addi ti on, the
appl i cat i on unit 400 may return the i nformati on generated i n
each preformed process to the renewable energy control
i nf rast ruct ure 300.
[062] I n addi ti on,
the out put control i nformati on may be
transmitted to each i nformati on col I ecti on termi nal s 110 and 120
via the grid system 200, and the amount of generation of each
renewable energy generation source may be controlled based on
the output control i nformati on. Of course, a series of these
control processes may be performed automat i cal I y.
[063] In addi ti on,
the i ntegrated control unit 500 may be
connected to the renewable energy control i nf rastructure 300.
The i ntegrated control unit 500 may receive i nformati on from the
renewable energy control
infrastructure 300 ( or information
start i ng from the renewabl e energy control i nf rastructure 300
and passi ng through the di stri but ed parallel processi ng unit 600)
and vi sual i ze the recei ved i nformati on to provi de to the user.
As an example, the integrated control unit 500 may provide the
information to the user using a web-based user interface.
CA 03212040 2023- 9- 13
20
[064] In addi ti on, the di st ri but ed parallel processi ng unit 600
may receive grid data from the renewable energy control
i nf rastructure 300, and may process the recei ved gri d data i n a
di st r i but ed way or i n parallel . As an exampl e, the di st r i but ed
paral I el processi ng unit 600 may perform f uncti ons of col I ecti ng,
I oadi ng, processi ng, searchi ng, anal yzi ng and appl yi ng the gri d
data. The detail ed conf i gur at i on and
functions of t hi s
di st ri but ed parallel processi ng unit 600 will be descri bed i n
detail with reference to FIG. 5.
[065] Figure 3 is a diagram for explaining the renewable
generati on i nf ormat i on collection unit 100 and the grid system
200 associated with it according to an embodiment of the present
i nvent i on.
[066] As shown i n FIG. 3, the renewabl e generati on i nf ormati on
col I ecti on unit 100 may i ncl ude a pl ural i ty of
i nf or mat i on
col I ecti on termi nal s. I n FIG. 3, for conveni ence of descri pti on,
it is assumed that the renewable generation information
col I ecti on unit 100 i ncl udes a f i rst
i nf or mat i on col I ecti on
termi nal 110 and a second i nf ormati on collection termi nal 120.
[67] The f i rst
i nf ormat i on collection termi nal 110 may be
connected to a fi rst renewable energy generati on source R11
wi t hi n a f i rst generati on zone. As an exampl e,
the f i rst
renewable energy generati on source R11 may be wind generation.
[68] As an example, the f i rst renewable energy generati on source
R11 may be sequenti ally connected to a transformer R12, a
CA 03212040 2023- 9- 13
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ci rcui t breaker R13 and the f i rst i nf ormati on
coil ecti on
termi nal 110 through a di stri but i on I i ne.
[69] The transformer may boost the electrical energy produced by
the f i rst renewabl e energy generati on source R11
to a
distribution level voltage.
[70] The ci rcui t breaker may be i nstal I ed on the di stri but i on
I i ne and can detect whether abnormal currents occur due to
overcurrent short circuits and earth faults. In addition, the
circuit breaker can block the flow of current when an abnormal
current occurs. As an exampl e, the ci rcui t breaker may be a
vacuum circuit breaker (VCB).
[71] I n addi ti on, the f i rst i nf ormati on collection termi nal 110
may
be connected to a meter i ng poi nt of a di stri but i on I i ne
through which renewable energy is transmitted or the circuit
breaker R13. Al so, the f i rst i nf ormati on collection termi nal 110
may measure power i nformati on of a meter i ng poi nt or a ci rcui t
breaker. As an example, the f i rst
i nf ormati on col I ecti on
termi nal may be connected to the met en i ng poi nt through a PT, CT
cabl e for met eri ng.
[72] I n addi ti on, the f i rst i nf ormati on collection termi nal 110
may transmit the measured power i nf ormati on to the grid system
200.
Herei n, the f i rst i nf or mat i on collection termi nal
110 may
convert the power i nf or mat i on accor di ng to a predet ermi ned
protocol . As an exampl e, the predetermi ned protocol may be any
one of Modbus, DNP and K- DNP.
CA 03212040 2023- 9- 13
22
[73] On the other
hand, the f i rst i nf ormat i on coil ecti on
termi nal 110 may be connected to the first renewable energy
generati on source R11 with a fi rst control devi ce R14 i nterposed
t her ebet ween.
As an exampl e, the f i rst i nf ormat i on col I ecti on termi nal 110 may
receive output control i nf ormati on from the grid system 200. I n
addi ti on, the f i rst i nf or mat i on col I ecti on termi nal
110 may
control the output of the f i rst renewable energy generati on
source R11 using the first control devi ce R14 based on the
recei ved output control i nf ormat i on.
[74] Speci f i cal I y, the f i rst i nf or mat i on collection termi nal 110
may recei ve, through a modem,
the moni t or i ng and met er i ng
control i nf ormati on recei ved from the renewabl e generati on data
I i nkage modul e connected to the grid system 200. I n addi ti on,
the f i rst i nf ormat i on col I ecti on termi
nal 110 sends a
cor r espondi ng request to a sub- dest i nat i on (for example, the
f i rst control devi ce R14) accordi ng to a predetermi ned protocol
(for example, Modbus) address wherein the sub- desti nati on may be
an i nverter ( not shown) connected to the f i rst renewabl e energy
generati on source R11.
[75] I n short, the f i rst i nformati on col I ecti on termi nal 110 can
control the f i rst renewable energy generati on source R11 based
on the output control i nf ormati on recei ved from the grid system
so that the amount of generati on of the f i rst renewabl e energy
generati on source R11 increases or decreases.
CA 03212040 2023- 9- 13
23
[76] On the other hand,
the second i nf or mat i on coil ect i on
termi nal 120 may be connected to a second renewabl e energy
generation source R21 in the second generation zone. As an
example, the second renewable energy generation source R21 may
be a solar gener at i on. Herei naf ter, a
descri pt i on of a
conf i gurat i on over appi ng with the f i rst i nf or mat i on col I ect i
on
termi nal 110 among the conf i gurati ons of the second i nf ormati on
col I ect i on termi nal 120 will be omitted.
[77] As an example, the second renewable energy generation
source R21 may be sequentially connected to an inverter R22, a
transformer R23, a ci rcui t breaker R24,
and the second
i nf or mat i on collection termi nal 120 through a di st r i but i on I i ne.
[78] The i nverter R22 may convert DC energy stored i n the
collector plate of the second renewable energy generation source
R21, that i s, the sol ar col I ect or pl ate, i nto AC energy.
[79] I n addi ti on, the second i nf or mat i on collection termi nal 120
may
be connected to a met er i ng poi nt of a di stri but i on I i ne
through which renewable energy is transmitted or a ci rcui t
breaker R24. Al so, the second i nf or mat i on col I ect i on termi nal
120
may measure power i nf ormat i on of the met er i ng poi nt or
ci rcui t breaker R24.
[80] On the other hand,
the second i nf ormat i on col I ect i on
termi nal 120 may be di rect I y connected to the i nverter R22.
Al so, the second i nf or mat i on col I ect i on termi nal 120 may receive
out put control i nf ormat i on transmitted through the grid system
200.
CA 03212040 2023- 9- 13
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Al so, the second i nf ormati on coil ect i on termi nal 120 may control
the amount of generation of the second renewable energy
generat i on source R21 by control I i ng the i nverter R22 based on
the recei ved output control i nf ormati on.
[81] I n the above,
it has been descri bed that the f i rst
i nf or mat i on coil ect i on termi nal 110 and the second i nf or mat i on
coil ect i on termi nal 120 control the amount of generat i on of the
renewable energy generation source using the first control
device R14 and the inverter R22, respectively. However, a
conf i gurat i on i n whi ch each i nf or mat i on coil ect i on termi nal s
110
and 120 control other conf i gurati ons wi t hi n each generation zone
to control the amount of generati on i s al so i ncl uded i n the
techni cal i dea of the present i nventi on.
[82] On the other hand, each of the i nf or mat i on coil ect i on
termi nal s 110 and 120 bel ongi ng to the r enewabl e gener at i on
i nf ormati on coil ecti on unit 100 may be a remote termi nal uni t
( RTU) .
I n addi ti on, the i nf or mat i on coil ect i on termi nal may measure the
power i nf ormat i on of the met eri ng poi nts at i nterval s of up to 1
second. Thi s i s because it is necessary to moni tor and anal yze
the i nst ant aneous gri d effects i n vi ew of the characteri sti cs of
renewable energy generation source with very fast
out put
f I uct uat i ons and I arge fl uctuat i ons.
[83] I n other words, each i nf ormat i on coil ect i on termi nal s 110
and 120 measure the power i nf ormat i on at i nterval s of up to 1
CA 03212040 2023- 9- 13
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second, so that the current state of renewable generation can be
accurately t i me- synchroni zed.
[84] I n addi ti on, the generati on i nf ormat i on, that is, the power
i nf or mat i on col I ected by the renewabl e generati on i nf or mat i on
collection unit 100 may be transmitted to the grid system 200
via the renewable generation data linkage module.
[85] Renewabl e generati on data I i nkage modul e Ti may col I ect
generati on i nf ormati on transmitted from the renewable generati on
i nf or mat i on collection unit 100 and transmit it to the gri d
system 200. In addition, the renewable generation data linkage
module Ti may transmit the control signal received from the grid
system 200 to each i nf ormati on collection termi nal s 110 and 120
bel ongi ng to the renewabl e generati on i nf or mat i on col I ect i on
uni t 100.
[86] On the other hand, the gri d system 200 may monitor the
state of the power grid in the regi on based on the collected
generati on i nf or mat i on.
[87] To t hi s end, the gri d system 200 may i ncl ude a Supervi sory
Control and Data Acqui si t i on( SCADA) module 210 for controlling
generation sources in each renewable generation zone and
collecting the information. The SCADA module 210 may collect
generati on i nf ormat i on generated i n each generati on zone i n the
regi on i n real ti me.
[88] In addition, the grid system 200 may include an energy
management system ( EMS) 220 that col I ect s basel oad generati on
data of basel oad generation sources in the regi on and power
CA 03212040 2023- 9- 13
26
usage envi ronment data on the amount of consumption i n the
regi on. I n thi s case, the basel oad generati on source refers to
other generati on source that is not
a renewable energy
generati on source i n the regi on. However, it goes without sayi ng
that the EMS 220 may col I ect generati on data of all generati on
sources i n the regi on. Al so,
the EMS 220 may f uncti onal I y
i ncl ude the SCADA modul e 210.
[89] Figure 4 is a block diagram for explaining the renewable
energy control i nf rastructure 300 and the application unit 400
accordi ng to an embodi ment of the present i nventi on.
[90] As shown in FIG. 4, the renewable energy control
i nf rastructure 300 may i ncl ude a grid system I i nkage unit 310,
an
i n- memory database unit 320, an i nf rastructure management
unit 330, an i nf rastructure management i nf ormati on memory 340
and a real -ti me I i nkage unit 350.
[91] The grid system I i nkage unit 310 may i ncl ude a grid data
receiving module 311 and a control message transmission module
312.
[92] The grid data recei vi ng modul e 311 may receive facility
i nf or mat i on and real-ti me generation i nf or mat i on necessary for
the operati on of the appl i cat i on unit 400 from the grid system
200.
[93] As an example, the grid data receiving module 311 may
receive real -ti me generati on information of each generati on zone
i n the regi on from the SCADA modul e 210. I n addi ti on, the gri d
CA 03212040 2023- 9- 13
27
data receiving module 311 may receive facility information of
each generati on zone in the region from the EMS system 220.
[94] In addition, the grid data receiving module 311 may receive
weather data from the di stri buted parallel processi ng unit 600.
As an example, the weather data may be data collected by the
di stri buted paral I el processi ng unit 600 from an external server.
As an example, the weather data may i ncl ude at least one of
numer i cal weather predi ct i on data, met eorol ogi cal admi ni strati on
observati on data, generati on compl ex data, and sol ar i rradi ance
measurement data. It goes without sayi ng that the weather data
may be collected by other components bel ongi ng to a renewabl e
energy control system such as the grid system 200 i n addi ti on to
the di stri buted parallel processi ng unit 600.
[95] In addition, the control message transmission module 312
may transmit the output control i nf ormati on generated through
the I i nkage between the renewable energy control i nf rastructure
300 and the application unit 400 to the grid system 200. As an
exampl e, the output control i nf ormati on may be delivered to each
i nf ormati on col I ecti on termi nal s 110 and 120 of each generati on
zone in the region via the SCADA module 210.
[96] The i n- memory database unit 320 may i ncl ude a real -ti me
database 321 and an appl i cat i on database 322.
[97] The real -ti me database 321 may store facility information,
real -ti me generation i nf or mat i on,
weather data and the I i ke
received by the grid data receiving modul e311 from the grid
system 200. I n addi ti on, the real -ti me database 321 may provi de
CA 03212040 2023- 9- 13
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the appl i cat i on unit 400 with stored facility i nf or mat i on, real -
time generati on i nf ormati on, weather data and the I i ke.
[98] I n addi ti on, the appl i cat i on unit 400 may i ncl ude a weather
i nf or mat i on predi cti on modul e 410, a renewabl e energy out put
predi cti on module 420, a stability eval uati on modul e 430, an
acceptance limit evaluation module 440, a renewable energy
output control module 450 and other grid analysis modul es 460.
[99] The weather information prediction module 410 may generate
weather predi cti on i nf ormati on of a renewabl e energy generati on
zone i n the regi on based on weather data. As an exampl e, weather
predi cti on i nf ormat i on may be predi
cted val ues of sol ar
i rradi ance, wi nd speed and temperature and the I i ke.
[100] I n addi ti on, the renewable energy out put predi cti on modul e
420 may generate out put prediction i nf ormati on of each renewabl e
energy generati on zone i n the regi on based on the weather
predi cti on i nf ormati on and grid data. The out put predi cti on
i nf ormati on may be the amount of generati on predi cted to be
generated i n each renewabl e energy generati on zone.
[101] I n addi ti on,
the stability eval uati on module 430 may
generate stability eval uati on i nf or mat i on of the power gr i d
based on at least two or more of output predi cti on i nf ormati on,
state est i mat i on result i nf ormat i on, power
facility
characteristic i nf ormat i on, and power facility i nf ormat i on.
[102] Her ei n, the power facility character i st i c i nf or mat i on i s
i nf ormati on for managi ng the characteri st i cs of each power
facility and may be i nf ormat i on such as facility name, capaci ty,
CA 03212040 2023- 9- 13
29
facility type, dynamic i nf ormati on, the amount of generati on,
frequency, and power factor. Also, the power facility
i nf or mat i on may be power facility i nf or mat i on I i nked to the
power gri d. As an exampl e, a power facility I i nked to a power
grid may be a generator, a transformer, or a switch. I n addi ti on,
the
state est i mat i on result i nf or mat i on is based on power
facility characteristic information and power
facility
i nf ormati on, cal cul ates the magnitude and phase angl e of the
correct bus voltage based on the dynami c and static i nf ormati on
of the power facility and based on the calculated value. It may
be i nf ormati on that detects overl oad of I i nes and transformers,
viol at i on of bus voltage const rai nts, and viol at i on of reactive
power constrai nts of generators and synchronous ancestors. Such
power facility characteri sti c i nf ormati on, power
facility
i nf or mat i on,
and state esti mat i on result i nf ormati on may be
generated by other system anal ysi s modul es 460. Meanwhile, the
power facility char act eri sti c i nf or mat i on and the power facility
i nf or mat i on may be i nf or mat i on recei ved from the gr i d system.
[ 103] I n addi ti on, the stability eval uati on i nf or mat i on may be
i nf or mat i on that eval uat es transi ent
stability, voltage
stability and the I i ke dun i ng a normal state or a transi ent
state before and after a di sturbance by ref I ecti ng the dynami c
characteristics of the power facility based on the static state
of the power grid.
[ 104] Next, the acceptance limit eval uati on module 440 is a
modul e for anal yzi ng the acceptance I i mi t to respond to the
CA 03212040 2023- 9- 13
30
output van i ability of renewabl e energy, and can pen i odi call y
eval uat e the acceptance I i mi t for the processed gr i d data
i ncl udi ng output predi cti on i nf or mat i on.
[ 105] As an example, the acceptance limit eval uati on module 440
may
generate acceptance I i mi t eval uati on i nf or mat i on based on
voltage standard violation degree, facility and transmission
I i ne over! oad degree, t ransi ent stability, r enewabl e energy LVRT
( Low Vol tage Ride Through), and fault current size.
[ 106] Here, the degree of vol tage standard vi ol at i on can be
determi ned based on the vol tage change anal ysi s resul t or the
voltage mai ntenance standard and vol tage regul at i on target
violation analysis result according to the regional power grid
due to the conti ngency. Here, the conti ngency means
a
hypot het i cal si ngl e or multi pl e power facility fail ure that may
occur in the power grid.
[ 107] I n addi ti on, the facility and the degree of transmi ssi on
I i ne overl oad can be determi ned based on the result of anal yzi ng
the transformer and transmi ssi on I i ne overl oad i n the gri d due
to the conti ngency or on the result of anal yzi ng the change of
power flow due to the change in renewable energy output.
[ 108] In addition, the transient stability can be determi ned
based on the phase angl e i nstabi I i ty anal ysi s resul t after the
conti ngency. Herei n, a screeni ng through I i near i zati on can be
performed pri or to determi ni ng the t ransi ent stability.
[ 109] I n addi ti on, the renewabl e energy LVRT can be determi ned
based on the LVRT standard violation analysis result of the
CA 03212040 2023- 9- 13
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renewable energy t ransi ent voltage waveform dun i
ng the
cont i ngency.
[ 110] I n addi ti on, the fault current magnitude may be a fault
current magnitude cal cul at ed based on the power contri but i on of
renewabl e energy.
[ 111] Next, the renewable energy output control module 450 can
generate out put control
i nf or mat i on based on the stability
eval uat i on i nf ormat i on and the acceptance I i mi t
eval uat i on
i nf or mat i on. As an exampl e, the out put control i nf or mat i on may
be generat i on control
i nf or mat i on of each renewabl e energy
generation zone in the region.
[ 112] I n addi ti on, the application unit 400 can return the
output control i nf ormati on and each i nf ormati on generated i n the
process of generati ng the output control i nf ormat i on to the
renewable energy control i nf rastruct ure 300. As an example, the
appl i cation unit 400 can return power facility charact eri st i c
i nf or mat i on, power facility i nf or mat
i on, out put control
i nf or mat i on, out put predi ct i on i nf or mat i on, weather pr edi ct i
on
i nf ormat i on, state est i mat i on
result i nf ormat i on, stability
eval uat i on i nf or mat i on, and acceptance limit
eval uat i on
information to the renewable energy control infrastructure 300.
[ 113] Speci f i cal I y,
i nf or mat i on returned from the appl i cat i on
unit 400 can be returned to the application database 322 in the
in-memory database unit 320. Al so, the returned i nf ormati on may
be recorded in the real-time database 321.
CA 03212040 2023- 9- 13
32
[114] That
is, the real -ti me database 321 can store all
i nf or mat i on
i ncl udi ng gri d data obtai ned from the gri d system 200, weather
data obtained from the distributed parallel processing unit 600
and each i nf ormat i on returned from the appl i cation unit 400.
[115] I n addi ti on, the i nf or mat i on stored i n the real-ti me
database 321 can be provided to the integrated control unit 500
or the di st r i but ed parallel processi ng unit 600.
Further,
generation sources belonging to each renewable generation zone
i n
the r egi on may be control I ed based on the returned
i nf or mat i on.
[116] On the other hand, the i nf rastructure management unit 330
can i ncl ude an in-memory database management module 331, an
i ntegrated process management modul e 332,
an al arm/event
management modul e 333 and a I og management modul e 334.
[117] The in-memory database management module 331 can perform
execut i on, control and state management
of the i n- memory
database unit 320. Further, the in-memory database management
module 331 can execute and manage a management process ( node
management, i ntegrated management process, etc. ) for control I i ng
applications operating based on the in-memory database unit 320.
[118] The i ntegrated process management modul e 332 can refer to
the i nf or mat i on (process management i nf or mat i on,
pr i or i t y,
current state, etc.) stored in the i n- memory database unit 320
and perform process executi on, control , schedul i ng management,
CA 03212040 2023- 9- 13
33
state management, process al arm and event handl i ng of each
component i n the renewabl e energy control i nf rastructure.
[ 119] The al arm/event management modul e 333 can store and handl e
al arm and event i nf ormati on generated i n the gri d system 200 and
transmit the al arm and event i nf ormat i on to the i nt egrat ed
control unit 500 and the di stri but ed parallel processi ng unit
600.
[ 120] The log management module 334 can refer to the process log
information stored i n the in-memory database unit 320 to create
a log file, record the log i nf ormati on i n the I og file accordi ng
to the I og I evel , and del ete the I og i nf ormati on of the I og file
accordi ng to a predetermi ned cycl e.
[ 121]
I n addi ti on the i nf rastructure management i nf or mat i on
memory 340 can store process management i nf ormati on requi red for
dri vi ng each module in the i nf rastructure management unit 330,
in-memory database meta i nf ormati on,
power facility model i ng
i nf ormati on, power facility character i sti c i nf ormati
on, and
SCADA modul e 210 i nf ormati on. That i s,
the i nf rastructure
management unit 330 may access the i nf rastructure management
i nf ormati on memory 340 when dri vi ng a modul e bel ongi ng thereto
to load and use data necessary for driving.
[ 122] In addition, the real-time linkage unit 350 can include a
real-time transmission module 351, a real-time control module
352 and a real-time receiving module 353 and perform
communi cat i ons among the renewabl e energy control i nf rastructure
CA 03212040 2023- 9- 13
34
300, the i nt egr at ed control unit
500 and the di stri but ed
paral I el processi ng uni t 600.
[ 123] Fig. 5 i s a diagram for expl ai ni ng a process i n which the
renewable energy control
i nf rast r uct ur e 300 accor di ng to an
embodi ment of the present i nvent i on is associ at ed with the
i nt egr at ed control unit 500 and the
di stri but ed par al I el
processi ng uni t 600.
[ 124] As shown in FIG. 5, the real-time transmission module 351
may transmit information stored in the in-memory database unit
320 to the di stri buted paral I el processi ng unit 600.
[ 125] Di stri but ed paral I el processi ng unit 600 can provide a
di stri but ed par al I el processi ng envi ronment for di stri but i ng and
quickly processi ng data obtained in large quantities from the
grid system 200 associated with the renewable energy control
i nf rast ruct ure 300.
[ 126] Thi s i s because the amount of gri d data acqui red i n
r el at i on to renewabl e energy gener at i on i nf or mat i on and data
created as a result of the operati on of the appl i cat i on unit 400
i s enormous. These data have hi gh uti I i zati on as basi c data for
analysis and prediction of the degree of grid risk due to the
i ncr ease i n renewabl e energy, but when i nf ormati on is collected
i n multi pl e regi ons, it is not easy to store and analyze the
i nf ormati on due to the I arge amount of data.
[ 127] That i s,
the di stri but ed parallel processi ng unit 600
accordi ng to an embodi ment of the present i nventi on can store
and analyze large-capacity data, and functions to more
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accurately cal cul ate regi onal gri d stability and rel i ability for
renewable energy through tools such as the visualization
analysis module 641 and the like.
[ 128] Speci f i cal 1 y, the di stri buted parallel processi ng unit 600
can i ncl ude a data col I ecti on unit 610, a data 1 oadi ng unit 620,
a data processing search unit 630, and a data analysis
application unit 640.
[ 129] Data col I ecti on unit 610 can collect data from the
renewable energy control infrastructure 300 or an external
network or server.
[ 130] To this end, the data collection unit 610 can include a
f i rst di stri buted queue module 611, a grid
data
collection/loading module 612, a second distributed queue module
613, and a weather data col I ecti on/1 oadi ng module 614.
[ 131] The first distributed queue module 611 may receive data
stored in the real -ti me database 321 in the in-memory database
unit 320 from the real-time transmission module 351. As an
example, the first distributed queue module 611 may be grid data
or a pl ural i ty of i nformati on data generated by the appl i cat i on
unit 400. I n addi ti on, the f i rst di stri buted queue modul e 611
can push the received data.
[ 132] I n addi ti on, the grid data col I ecti on/ I oadi ng module 612
can pull the data loaded i nto the f i rst di stri buted queue modul e
611 to receive the data and load the received data into the non-
rel at i onal database 621 or the di stri buted fi I e system 622 i n
the data I oadi ng unit 620. As an exampl e, the non- r el at i
onal
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database 621 may be No-SQL. As an example, the distributed file
system 622 may be a Hadoop Di st ri but ed File System ( HDFS) .
[ 133] On the other hand, the second di st ri but ed queue modul e 613
can be connected to an external network, system or server.
[ 134] As an example, the second distributed queue module 613 can
be connected to a distribution aut omat i on system ( DAS) (11) and
receive i nf or mat i on about the state
i nf or mat i on, current,
voltage or fail ure presence or absence of
di st ri but i on
facilities from a di st ri but i on I i ne aut omat i on termi nal devi ce.
[ 135] As another example, the second distributed queue module
613 can be connected to a Meter Data Management System ( MDMS)
( 12) to receive metering data.
[ 136] As another example, the second distributed queue module
613 can be connected to the weather database 13 or weather
server 13 to recei ve regi onal weather data.
[ 137] I n addi ti on, the second di stri but ed queue module 613 can
transmit
information to the weather data col I ect i on/ I oadi ng
module 614. I n addi ti on, the grid data col I ect i on/ I oadi ng module
612 can pul 1 data I oaded i n the second di st ri but ed queue modul e
613 to receive the data and load the received data into the non-
rel at i onal database 621 i n the data I oadi ng unit 620 or the
di st r i but ed file system 622.
[ 138] On the other hand, the loaded weather data may be
transmitted to the real-ti me recei vi ng modul e 353 vi a the f i rst
distributed queue module or the second distributed queue module.
In addition, the weather data received by the real-time
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receiving module 353 can be transferred to the real-time
database 321 and used as a basis for creating the weather
predi cti on i nf ormati on.
[139] The memory cache 623 can function as a cache memory in the
process of stori ng data recei ved by the f i rst di stri buted queue
modul e 611 i n the non- rel at i onal database 621 or the di stri buted
file system 622.
[140] The data processi ng search uni t 630 can i ncl ude a SQL
processi ng engi ne 631, a data warehousi ng modul e 632,
an
aggregate information creati on module 633 and an aggregate
information creati on hi story management module 634.
[141] The SQL processi ng engi ne 631 can query, manage, or
process the loaded data in association with the non-relational
database 621 or the di stri buted file system 622.
[142] After creati ng the aggregate information based on the load
data of the non- rel at i onal database 621 or the di stri buted fi I e
system 622, the aggregate i nf ormati on creati ng modul e 633 can
load the created aggregate i nformati on i nto the data warehousi ng
modul e 632.
[143] I n addi ti on, the aggregate i nf ormati on creati on hi story
management module 634 can create hi story information, creati on
hi story i nf or mat i on or mai n aggregate/statistic meta i nf or mat i on
when creati ng aggregate i nf ormati on, and load them i nto the data
warehousi ng modul e 632.
[ 144] In addition, the data warehousing module 632 may be
connected to the SQL processi ng engi ne 631. As an exampl e, the
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data warehousi ng modul e 632 may be a database that converts data
loaded by the SQL processi ng engine from the non-relational
database 621 or the di stri but ed file system 622 i nto a
predetermi ned format and that manages the converted data. I n
addition, the data warehousi ng module 632 may transmit the data
converted i nto a predetermi ned format to the i ntegrated control
unit 500 upon request from the integrated control unit 500.
[ 145] I n addi ti on, the data anal ysi s appl i cat i on unit 640 may
perform a secondary analysis on the stability of the regi onal
power gri d for renewabl e energy usi ng the vi sual i zati on anal ysi s
modul e 641. As an example, the visualization analysis module may
vi sual i ze data stored i n the data warehousi ng modul e 632 by
analyzing power data. In addition, the data analysis application
unit 640 may include an artificial neural network model ( not
shown) that I earns based on the data stored i n the data
warehousi ng module 632 and that receives at least some grid data
as an i nput val ue to predi ct the amount of renewabl e energy
generati on i n the regi on.
[ 146] Meanwhi I e, the i ntegrated control unit 500 can receive
data from the di stri buted paral I el processi ng unit 600, that i s,
the data warehousi ng modul e 632, vi sual i ze the recei ved data and
provi de it to the user. Herei n, the i ntegrated control unit 500
may
receive data that did not go through the di stri but ed
paral I el processi ng uni t 600. That i s, the i ntegrated control
unit 500 may receive data di rect I y from the renewable energy
control infrastructure 300.
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[147] As an example, the integrated control unit 500 can include
a
real-ti me moni t or i ng module 510, an infrastructure resource
moni t or i ng modul e 520, a di stri but ed paral
I el resource
moni t or i ng modul e 530, a power facility i nf ormati on management
modul e 540, a renewable energy moni t or i ng/control
process
management modul e 550, an i nf rast r uct ur e process management
modul e 560, and statistics and aggregate i nf ormati on management
modul e 570.
[148] The real-time monitoring module 510 can provide various
information requi red for power grid management.
[149] As an example, based on the data received from the
di st ri but ed parallel processi ng unit 600, the
real-ti me
moni t or i ng module 510 can provide real-ti me renewable gener at i on
comprehensive state i nf or mat i on, real-ti me weather i nf or mat i on
comprehensive state information and real-ti me
line-specific
I i nkage state i nf ormati on to the user i n real ti me.
[ 150] I n addi ti on, the real-ti me moni t or i ng module 510 monitors
the states of the SCADA module 210, the EMS system 220, the
di st ri but i on automation system 11, the meteri ng data management
system 12, or the weather database I 3 and sets a threshold val ue
(collection cycle, speed, I/O and the like) to thereby provide
an al arm f uncti on when the threshol d i s exceeded.
[ 151] 1 n addi ti on,
the real-ti me moni t or i ng module 510 can
provide a function capable of monitoring Mvar information,
control lab! e capacity i nf ormati on, facility capacity and weather
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i nf ormati on sui tabl e for the type of generator of each renewabl e
energy generation source in the region.
[ 152] I n addi ti on,
the real-ti me moni t or i ng module 510 can
provi de a function capabl e of
moni t or i ng voltage, supply
capacity, current I oad, output and the I i ke for each DL uni t
generator.
[ 153] I n addi ti on,
the real-ti me moni t or i ng module 510 can
provi de a function capable of moni tor i ng i nf or
mat i on on
renewabl e energy generati on sources, measured val ues for current
outputs, predi cted output val ues, weather predi cti on i nf ormati on
and the like.
[ 154] 1 n addi ti on,
the real-ti me moni t or i ng module 510 may
provi de real-ti me event i nf ormat i on of
renewable energy
generation sources and provi de an al arm function when exceeding
or fall i ng short of a predetermi ned threshol d val ue.
[ 155] I n addi ti on, the i nf rast ructure resource moni tor i ng modul e
520 may provi de a f uncti on capabl e of moni tori ng the CPU usage
rate, memory usage rate, di sk usage rate, RTDB state i nf ormati on
and the 1 i ke of the configuration i n the renewable energy
control infrastructure 30.
[ 156] In addi ti on, the di stri but ed parallel resource moni t or i ng
module 530 provi des a function capable of monitoring a real-time
DI SK I 0, cl uster CPU usage rate, network I 0, di stri but ed fi I e
system ( 622) 10 and the 1 i ke.
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[157] I n addi ti on, the power facility i nf or mat i on management
modul e 540 may manage power facility characteristic i nf or mat i on,
modeling ( I ayer/ I i nk) information, and information acqui red by
the SCADA module 210.
[ 158] In addition, the renewable energy monitoring/control
process management module 550 can monitor the state of each
renewable energy generating source belonging to each renewable
generation zone in the region. As an example, the renewable
energy monitoring/control process management module 550 can
monitor real-time generation i nf ormat i on of
each renewable
energy generation source collected by the SCADA module 210. As
an example, the renewable energy monitoring/control process
management module 550 can obtain real-time generation
information of each renewable energy generation source from the
grid data.
[ 159] In addition, the renewable energy monitoring/control
process management modul e 550 may moni tor al arm and event
i nf ormat i on. Herei n, the monitored i nf ormat i on may be vi sual i zed
and provided to the user.
[ 160] I n addi ti on, the i nf rast ruct ure process management modul e
560 provides a function capable of managing schedule inquiry,
regi strati on, modi f i cat i on, del et i on,
and execut i on of each
component i n the renewabl e energy control i nf rast ructure 300.
[ 161] I n addi ti on, the st at i st i cs and aggregated i nf or mat i on
management module 570 provides the statistics management
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function for data collection state, al arm data, event data and
grid data.
[ 162] On the other hand, the renewable energy detection/control
process management module 550 may transmit a control message to
the real -ti me control module 352 i n the real -ti me I i nkage unit
350 of the renewabl e energy control i nf rast r uct ure
300. I n
addition, the real-time control module 352 may transmit the
control message to a termi nal device of each generati on zone i n
the regi on through the control message transmission module 312
i n the gri d system I i nki ng unit 310. Herei n, the control message
generated by the renewable energy detection/control process
management module can be appl i ed with priority over the control
command transmitted to each renewabl e energy generati on zone i n
the regi on, that is, each i nf ormati on collection termi nal . As an
example, the control message may be applied with priority over
out put control i nf or mat i on.
[ 163] Fl G. 6 i s an exempl ary di agram for expl ai ni ng a process i n
which the renewable energy output prediction module 420
accordi ng to an embodi ment of the present i nventi on generates
output predi cti on i nf ormati on of a generati on zone i n a regi on.
[ 164] In FIG. 6, for convenience of explanation, the renewable
energy generati on source located i n the generati on zone will be
descri bed as an exampl e of a wi nd generati on source.
[ 165] First, as shown in FIG. 6(a), a step of collecting wind
speed and wind generati on data of a renewable energy generati on
source for a certain period of ti me may be perf ormed( S510) .
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[166] Herei n, when the renewable energy generati on source is not
a wi nd generati on source, wi nd speed and wi nd generati on data
can be repl aced with weather data, of course.
[167] I n addi ti on, a step of esti mat i ng the f i rst amount of
generati on by sett i ng an ARI MAX model based on at least some of
the wi nd speed and wi nd generati on data among the data col I ected
in step S510 may be perf ormed( S520).
[168] I n addi ti on, a step of esti mat i ng the second amount of
generati on by sett i ng a polynomial regressi on model based on at
least some wi nd speed and wi nd generati on data among the data
collected i n step S510 may be perf ormed( S530) .
[169] I n addi ti on, a step of esti mat i ng the t hi rd amount of
generati on based on wi nd speed data at a poi nt near the
renewable energy generati on source may be perf ormed( S540) .
[170] I n addi ti on, a step of cal cul at i ng the predi cted val ue of
the amount of generati on based on the analog ensemble using the
f i rst amount of generati on, the second amount of generati on, the
t hi rd amount of generati on,
and past data among the data
col I ected i n the step S510 may be performed( S550).
[171] FIG. 6( b) is a flowchart for explaining the process of
esti mat i ng the t hi rd amount of generati on i n step S540.
[172] As shown in FIG. 6( b), a step of collecting wi nd speed
predi cti on data of a poi nt near a renewabl e energy generati on
source and spatial data near the renewable energy generati on
source may be perf ormed( S541).
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[173] I n addi ti on, a step of predi ct i ng the wi nd speed i n the
point of the renewable energy generation source based on the
Kri gi ng technique may be perf ormed( S542) .
[174] Herei n, the wi nd speed predi cti on may have been performed
i n advance by the weather i nf or mat i on predi ct i on module 410.
[175] I n addi ti on, based on the Deacon equat i on, a step of
cor r ect i ng the wi nd speed based on the altitude may be
perf or med( S543) .
[176] I n addi ti on, a step of est i mat i ng the t hi rd amount of
gener at i on based on the corrected wi nd speed
may be
perf or med( S544) .
[177] The above descri pt i on of the present i nventi on i s for
ill ust rat i ve purposes, and it will
be appreci at ed that those
ski I I ed in the art to which the present invention pertains can
easily be modi f i ed i nto other speci fic forms without changi ng
the t echni cal spi r i t or essent i al
features of the present
i nvent i on. Therefore, the embodi ment s descri bed above shoul d be
understood as ill ust rat i ve i n all respects and not I i mi ti ng. For
exampl e, each component descri bed as a si ngl e type may be
i mpl emented i n a di st ri but ed manner, and si mi I ar I y, components
descri bed as di st ri but ed may be implemented i n a combi ned form.
[178] The scope of the present invention is indicated by the
cl ai ms to be descri bed I at er, and all changes or modi f i cat i ons
derived from the meaning and scope of the claims and equivalent
concepts should be construed as bei ng i nd l uded i n the scope of
the present invention.
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MODES FOR CARRYING OUT THE INVENTION
[179] Modes for carryi ng out the i nventi on have been descri bed
together i n the best modes for carryi ng out the i nventi on.
I NDUSTRI AL APPLI CABI LI TY
[180] The present i nventi on rel ates to an i ntegrated regi onal
renewable energy control system, and more particularly, to an
i ntegrated regi onal renewabl e energy control system capabl e of
determi ni ng the i mpact of renewabl e energy generati on on the
regi onal power grid of a regi on and controlling the amount of
the renewable energy production of each renewable energy
generation zone i n the regi on accordi ng to the determi nati on
resul ts. The system can be used i n van i ous f aci I iti es that
manage renewabl e energi es and thus has the
i ndustri al
appl i cabi I i ty.
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