air-conditioning control of hems and bems toward cems€¦ · air-conditioning control of hems and...

Keio Univ. West Lab 0

Kyosuke Funami

Nishi Laboratory

Graduate School of Science and Technology, Keio University, Japan

Air-Conditioning Control of HEMS and BEMS

toward CEMS

Table of Contents

1. Introduction

2. KNIVES

3. Comfort Index PMV

4. Air-conditioning Control in HEMS/BEMS

5. Demonstration of BEMS/CEMS

6. Conclusion and Future Works


Introduction


Saving energy is indispensable to solve environmental problems.

Global warming by greenhouse gas

Exhaustion of fossil fuels

Fig.2 Power consumption rate of home appliance

Air conditioners account for one quarter of power consumption.

Energy consumption in a household and an office sector

increases rapidly.

Fig.1 Change of energy consumption in Japan

EMS(Energy Management System)


BEMS(Building Energy Management System)

Monitors total power consumption

Controls mainly air conditioners and lighting

Problem • Maintain comfort of people in the building

HEMS(Home Energy Management System)

Observes electric appliances in a house

Controls them by using a demand control terminal or HA

• Perform fine-grained control of the air conditioners Problem

※ HA : Home Automation terminal

Purpose


Consideration of air-conditioning controls other than ON/OFF control

Comparison with power consumption and comfort of three control methods

Experiment in real environment

• Environmental measurement and control ・・・ KNIVES (Keio university Network oriented Intelligent and Versatile Energy saving System)

• Comfort Index ・・・ PMV(Predicted Mean Vote)

Implement BEMS into the existing building

Maintain comfort while energy saving

Experiment in real environment

HEMS

BEMS

KNIVES


Server System

Fig.4 KNIVES terminal

Analyzes and saves the information

(environment and power)

Determines a control command

Client System

Measures the information

(environment and power)

Controls electric devices

→ photo MOS relay

Sends and receives digital signal

→ I/O port

Environmental information : Temperature, Humidity, Illuminance and Carbon dioxide concentration

Fig.3 Structure of KNIVES

Comfort Index PMV


Evaluated by measuring environment including physical and human elements

Takes values from -3(cold) to +3(hot)

ex. PMV = 0 comfortable

PMV（Predicted Mean Vote）

PMV value Sensation

+3 Hot

+2 Warm

+1 Slightly warm

0 Comfort

-1 Slightly cool

-2 Cool

-3 Cold

)028.0303.0(036.0 pM

ePMV

)( CRCreEreEsEdWM

M: metabolic rate（W/㎡） W：external work（W/㎡） Ed：heat loss by water vapor diffusion through skin（W/㎡） Es：rate of evaporative heat loss from skin（W/㎡） Ere：rate of evaporative heat loss from respiration（W/㎡） Cre： rate of convective heat loss from respiration （W/㎡） R：radiative heat loss from the surface of the clothed body（W/㎡） C: convective heat loss from the surface of the clothed body （W/㎡）

(1)

Physical elements : temperature, humidity, wind speed and MRT(Mean Radiant Temperature)

Human elements : amount of clothing and metabolic rate

Air-Conditioning control in HEMS/BEMS


Air-conditioning Control(1/2)


ON/OFF Control

Photo MOS relay turns on and off a compressor of the air

conditioner. Fig.4 KNIVES terminal

Fig.5 Infrared transmitter module

Fig.6 Demand control unit

Control Method

Infrared Control

The setting temperature is changed by sending infrared

signals from infrared transmitter module.

The module is connected with digital I/O port of KNIVES

terminal.

Inverter Control

The demand power is regulated through the special

demand control unit.

The power can be regulated into 0, 40, 80% and no control.

Air-conditioning Control(2/2)


Control Algorithm

control method

PMV value ON/OFF Infrared Inverter

PMV < -0.25 Power ON / 27 ˚C / No limitation

-0.25 < PMV < 0.00 Keeping previous

state

(temperature + 1)

˚C 80%

0.00 < PMV < 0.25 Keeping previous

state

(temperature)

˚C 40%

0.25 < PMV Power OFF (temperature - 1)

˚C 0%

Fig.7 Control Algorithm

TABLE I

Operation by each control method according to PMV value

ex. Infrared control

Temperature : 20 ˚C, PMV : -0.5

Temperature : 23 ˚C, PMV : -0.1

27 ˚C

24 ˚C

i : time count

Experiments


area 30m2

height 3.4m

volume 102m3

Experimental Environment

Measurement Sensor

temperature-humidity sensor

(temperature, humidity)

amenity meter

(wind speed)

Air conditioner rated heating capacity(kW)・・・ 5.6 (2.5~7.1)

rated power consumption(kW)・・・ 1.36

manufacturer・・・Daikin Industries, Ltd

Experimental Device

Fig.8 Setting place of the sensors •Top(2.9m)

•Middle(1.5m)

•Bottom(0.1m)

* PMV was calculated by using the central measured value.

Air Conditioner

Ceiling Light

Ventilating Fan

Temperature-Humidity Sensor Wind Speed Sensor

0

2

4

6

8

10

12

14

16

18

20

0 30 60 90 120 150 180 210

ON/OFF

Infrared

Inverter

-1.2

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0 30 60 90 120 150 180 210

ON/OFF

Infrared

Inverter

21

22

23

24

25

26

27

28

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

0 30 60 90 120 150 180 210

ON/OFF

Inverter

Infrared

Results of Experiments (ON/OFF Control)


• PMV is oscillated extremely.

• The oscillation can be reduced by narrowing

the difference of control thresholds of PMV.

• Total preparation time for operation is extended.

• Power consumption of the time is wasted.

Fig.10 Comparison of PMV

Fig.11 Power consumption

ON/OFF control

Time(min) Time(min)

Time(min)

Time(min) Time(min)

Time(min)

PM

V

Time(min)

Time(min)

ON

/OF

F(0

-1)

/ O

utp

ut

rate

Set

tin

g T

emp

eratu

re (℃

)

Pow

er C

on

sum

pti

on

(W

h)

Fig.9 Operation history

0

2

4

6

8

10

12

14

16

18

20

0 30 60 90 120 150 180 210

ON/OFF

Infrared

Inverter

-1.2

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0 30 60 90 120 150 180 210

ON/OFF

Infrared

Inverter

21

22

23

24

25

26

27

28

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

0 30 60 90 120 150 180 210

ON/OFF

Inverter

Infrared

Results of Experiments (Infrared Control)


• The maximum value of accumulated power

consumption has decreased as PMV is close to

zero.

• The switching frequency of setting temperature

has decreased gradually.



Infrared control

Time(min) Time(min)

Time(min)

Time(min) Time(min)

Time(min)

PM

V

Time(min) Time(min) Time(min)

Time(min)

Time(min)

ON

/OF

F(0

-1)

/ O

utp

ut

rate

Set

tin

g T

emp

eratu

re (℃

)

PM

V

Pow

er C

on

sum

pti

on

(W

h)


0

2

4

6

8

10

12

14

16

18

20

0 30 60 90 120 150 180 210

ON/OFF

Infrared

Inverter

-1.2

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0 30 60 90 120 150 180 210

ON/OFF

Infrared

Inverter

21

22

23

24

25

26

27

28

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

0 30 60 90 120 150 180 210

ON/OFF

Inverter

Infrared

Results of Experiments (Inverter Control)


• The air conditioner is operated in 0.4(40% )

or 0.8(80%).

• Power consumption is also shifted between

4Wh and 8Wh.



Inverter control

Time(min)

Time(min)

Time(min)

PM

V

Time(min) Time(min)

ON

/OF

F(0

-1)

/ O

utp

ut

rate

Set

tin

g T

emp

eratu

re (℃

)

PM

V

Pow

er C

on

sum

pti

on

(W

h)


0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

0

2

4

6

8

10

12

14

16

18

0 30 60 90 120 150 180 210

ac_power

Output Rate

Relation between Controls and Power Consumption

ON/OFF, inverter Control

⇒ Power consumption is controllable.

Infrared Control

⇒ Power consumption is uncontrollable.


Fig.12 Results of controls and power consumption

(a) ON/OFF control (b) Infrared control

(c) Inverter control

It is easy to forecast power consumption in

inverter control.

0

0.2

0.4

0.6

0.8

1

1.2

0

2

4

6

8

10

12

14

16

18

0 30 60 90 120 150 180 210

ac_powerON-OFF

Pow

er C

on

sum

pti

on

(W

h)

ON

/OF

F(0

-1)

Time(min)

21

22

23

24

25

26

27

28

0

2

4

6

8

10

12

14

16

18

0 30 60 90 120 150 180 210

ac_powerSetting Temp

Pow

er C

on

sum

pti

on

(W

h)

Time(min)

Set

tin

g T

emp

eratu

re (℃

)

Time(min)

Ou

tpu

t ra

te

Evaluation of Power Consumption


Constant 24˚C ON/OFF Infrared Inverter

Accumulated power

consumption(Wh) 2100.5 1931.0 1562.0 1337.5

Power saving rate

from constant (%) ― 8.1 25.6 36.3

Power saving rate

from ON/OFF (%) ― ― 19.1 30.7

TABLE II

Accumulated power consumption and energy saving rate*

* The power consumption was calculated by using measured instant power

consumptions for 210 minutes except first 30 minutes when PMV is under

-0.25.

18

19

20

21

22

23

24

25

26

27

0 30 60 90 120 150 180 210

topmiddlebottom

18

19

20

21

22

23

24

25

26

27

0 30 60 90 120 150 180 210

topmiddlebottom

18

19

20

21

22

23

24

25

26

27

0 30 60 90 120 150 180 210

topmiddlebottom

Evaluation of Comfort


Fig.13 Environmental measurement result

Time(min) Time(min)

Controls ON/OFF Infrared Inverter

Temperature difference (t = 30 min) 2.9℃ 2.8℃ 3.0℃



Temperature of bottom(t = 150min) 21.1℃ 21.9℃ 22.3℃

TABLE III Evaluation of indoor temperature

(a) ON/OFF control (b) Infrared control (c) Inverter control

Tem

per

atu

re (℃

)

Tem

per

atu

re (℃

) Time(min)

Tem

per

atu

re (℃

)

Comprehensive Evaluation

Controls ON/OFF Infrared Inverter

Saving power

consumption ○ ◎ ◎

Power consumption

controllability ○ - ○

Maintaining comfort - ○ ○

Comfort of whole room* - ○ ○


TABLE IV Comprehensive Evaluation

* The temperature change of four corners in the room resembles that of the center.

Inverter control is the most effective method.


Wrap-up of Air-conditioning Control in HEMS/BEMS

Future works of this section

• The infrared control and inverter control can save 19.1% and 30.7% of

power consumption compared with ON/OFF control, respectively.

• The inverter control is effective in maintaining indoor thermal environment.

Moreover, power consumption can be controlled and saved.

• Cooperative control of multi air conditioners should be conducted under the

condition of total-power-demand limitation.

• It is required to improve the control algorithm into fluidly operation.

Wrap-up


Demonstration of BEMS/CEMS

Construction Environment

Site

Kurihara City Hall, Kurihara City, Miyagi Prefecture, Japan

Summer : max average temperature 29.3˚C , min average temperature 19.9 ˚C

Winter : max average temperature 1.9˚C , min average temperature -7.8 ˚C

3 Floors (1F: 540m2, 2F: 280m2, 3F: 501m2) , Entrance : 370m2

Measurement Item

Environmental information

Temperature, Humidity, Illuminance, CO2

Power information

Demand Power

Air Handling Unit (AHU) Power

Control Device (Air-Conditioner)

Hot and Chilled Water Generator - Oil type

AHU ×4


Installed KNIVES Terminal


KNIVES terminals were installed in the building.

Measure power information Measured point : network connection point (demand)

: machine room (AHU)

Measure environmental information

Control air-conditioner devices Control method : ON/OFF control

Wireless environmental sensor Temperature-Humidity-Illminunce Sensor

1floor : 5 – 7 sensors

Carbon dioxide Sensor

1floor : 1 sensor

Environmental sensors

Measure environment

Control the devices

Measure demand power

Control Experiment of Air Conditioner in Winter

Purpose

To observe the influence of the air-conditioner control on

indoor environment

Premise

In the usual case, the control devices are turned off at 17:30.

Peak Demand Time : 16:30 – 17:30

Air-Conditioner Power : 45 kWh

Condition

The control devices were turned off

at 16:30.

The indoor temperature was kept

over 20˚C.


0

10

20

30

40

50

60

70

80

90

30

min

_d

ema

nd

_p

ow

er（ｋ

Wh）

Time

City_Hall

Fig.14 Current change of demand power

Result of Experiment

Air-Conditioner

OFF time (min)

Demand power of

control time (kWh)

Oil consumption of

Control time (L)

Reduced Rate of

Power (%)

No Control 0 144.4 20.9 -

Control 60 110.8 1.1 23.2%


30

40

50

60

70

80

90

30

min

_d

ema

nd

_p

ow

er（ｋ

Wh）

Time

No_Control

Control

Control time

Fig.15 A change of demand power

TABLE V The evaluation of the control result

Influence on Environment

The temperature at the time of control dropped slower than the

temperature at the time of no control.

A change of the temperature became rapid after sunset while the

control devices were turned off.


21.5

22

22.5

23

23.5

24

24.5

25

Tem

per

atu

re(℃

)

Time

No_Control

Control

Control time

sunset

1 hour

30 min

Fig.16 Influence of control on temperature

Wrap-up of Demonstration of BEMS


Enhancing from BEMS to HEMS

• Existing building was used for installing BEMS.

• KNIVES was installed into Kurihara City Hall and other concerning facilities

for measuring power and environmental information and controlling the

devices.

• As the result of this experiment, 23% of the demand power can be reduced by

stopping the air-conditioner for one hour.

• We build CEMS by connecting two or more BEMS and to carry out electric

power trading between BEMS, and it will be shown in the next section.

• In CEMS, it is indispensable to consider cluster configuration of each

building.

Wrap-up

From BEMS to CEMS

To build CEMS with 8 buildings.

To measure information and control devices in City Hall, Annex,

Library and Memorial House.


Library

Community

Center

Memorial

House

Shed

Apartment

House

Divided

point

Divided

point

Profile of Buildings

City Hall

The top of demand power in the buildings is used regularly.

The number of people is larger than other city buildings.

Fixed Holidays : Saturday, Sunday

Junior School

There is a proper demand tendency.

The building has a little cooling equipment.

Fixed Holidays : Saturday, Sunday

Library

The demand power on weekends is larger than one on weekdays.

Fixed Holiday : Monday


Structure of CEMS


City Hall area

Connection point of some areas

Library area

Environmental Sensor

Pulse Sensor

・Power Consumption ・Oil Consumption

Demand Information

Sensor Information

Network(Wired/Wireless）

Resource Management Server

Internet

Air-Conditioner

制御端末

Network Device

PC

Sensor Information

Keio University

A variety of Information Laptop etc.

Air-Conditioner

Ventilation fan

Environmental Sensor

Terminal

Terminal

Terminal

Control

Command

Control

Command

Control Command

Pulse Sensor

Power Demand Control in CEMS


Long-term forecasting

Short-term forecasting

Demand forecasting of next period

Dynamic clustering

Demand control plan

Demand control

CEMS

t = 0 [min] 60

Pow

er

Consu

mptin

START

1cycle

supply forecasting cycle

START

120 180 240

Supply forecasting Cycle (24h)

Demand Control Cycle (30min)

Dynamic Clustering


Building １

Group 2 Group 1

Building 2 Building 3

GMD : Total demand of a group

MD : Total demand of a building

X : Grouping Matrix

M : Demand Group

N : Number of buildings

Grouping Matrix X is used to express which building (demand) group belongs to

which supply group.

𝐺𝑀𝐷1(𝑡) ⋯ 𝐺𝑀𝐷𝑀(𝑡) = 𝑀𝐷1(𝑡) ⋯ 𝑀𝐷𝑁(𝑡)

𝑋1,1(𝑡) ⋯ 𝑋𝑀,1(𝑡)

⋮ ⋱ ⋮𝑋1,𝑁(𝑡) ⋯ 𝑋𝑀,𝑁(𝑡)

𝐺𝑀𝐷1(𝑡) 𝐺𝑀𝐷2(𝑡) = 𝑀𝐷1(𝑡) 𝑀𝐷2(𝑡) 𝑀𝐷3(𝑡)1 00 10 1

Simulation Conditions


Used data

Real 60 buildings or houses

SFC of Keio University (18th Nov 2011 to 28th)

This campus has 9 buildings.

Fukue port terminal(18th Nov 2011 to 28th) (This BEMS will be explained in the next presentation.)

50 general Households (18th Nov 2007 to 28th)

Simulation Parameters

Supply forecasting cycle: 24hours

Demand control cycle: 30 minutes

Demand groups

2 groups

Supply rate was fixed; Group 1 ： Group2 = 3 ： 2

Demand control

5,10,15,20% control was virtually achieved according to the AC power consumption.

Simulation Result


Demand

ID 12:30 13:00 13:30

No. 1 1 2 1 No. 2 1 1 1 No. 3 1 1 1 No. 4 2 2 2

⋮ ⋮ ⋮ 1 No.12 2 2 2 No.13 1 1 1 No.14 1 1 1 No.15 2 1 2

Difference between prediction

and real demand

One day One hour

Prediction

Difference [%]

3.28 2.30

Dispersion 2.49 1.98

Group shifting

Demand Control

Grp1 Demand

Grp2 Demand

Grp1 Supply

Grp2 Supply

Grp1 Supply

Grp2 Supply

Grp1 DC

Grp2 DC Pow

er

Consu

mption [

kW

h]

Pow

er

Consu

mption [

kW

h]

Improvement of Demand Supply Balancing of AC control


18th 19th 20th 21th 25th 26th 27th 28th

Average of difference [%] 2.35 1.68 2.74 2.90 6.24 3.53 2.27 3.77

Dispersion 1.67 1.34 1.96 2.09 3.13 2.60 1.86 2.41

25th is the worst day

This is because the supply exceeded to the

demand and AC control can not be

achieved to the balancing as shown in the

graph.

19th is the best day

Balancing rate of days

Grp1 Supply

Grp2 Supply

Grp1 Forecast Demand

Grp2 Forecast Demand

Grp1 Real Demand

Grp2 Real Demand

Pow

er

Consu

mption [

kW

h]

Conclusion and Future works


Future works

• The inverter control is effective in maintaining indoor thermal environment.

Moreover, power consumption can be controlled and saved.

• KNIVES was installed into Kurihara City Hall and other concerning

facilities for measuring power and environmental information and

controlling the devices.

• Dynamic clustering method is proposed that updates cluster configuration of

a building in which the difference between the amount of supply forecasting

and demand forecasting becomes the minimum.

• We would like to calculate grouping matrix considering the environmental

amenity of each room or facility.

Conclusion

Thank you for your kind attention.


CEMS (Cluster Energy Management System)


Building

Distributed generators

HEMS BEMS

Power-grid

Control Command Energy

Information

CEMS

Home

CEMS Cooperates with various distributed

generators and electric devices

Coordinates among energy

management systems of BEMS or HEMS

Optimizes electric and thermal

energy usage in CEMS

Cluster of buildings is formed by Controllable buildings and power

generators where EMS installed

Uncontrollable buildings and power

generators

Conclusion and Future works


Future works

• The system is introduced into Kurihara City Hall that measures power and

environmental information and controls the devices. Then, the existing

building is made into BEMS.

• As the result of this experiment, 23% of the demand power can be reduced

by stopping the air-conditioner for 1 hour.

• Measure exact air-conditioners power.

• We will evaluate a weighted value of each floor. If the value is determined,

we find a relationship between comfort and power consumption. As the

result, the schedule of air-conditioners can be determined.

• It is necessary to build CEMS from two or more buildings, and to carry out

power conditioning between buildings.

Conclusion

Real environmental CEMS


Structure of CEMS

Outside temperature


0

1

2

3

4

5

6

7

8

9

10

1

16

31

46

61

76

91

106

121

136

151

166

181

196

211

226

onoff

Infrare

Inverter

d

KNIVES


Structure of experimental system


Infrared transmitter module


amount of clothing and metabolic rate


clothing Clo value

half-pants, T-shirt, sandal 0.300

summer-pants , short-sleeved shirt, tie 0.560

Winter-pants, long-sleeved shirt, high socks, sweater, coat 1.09

activity Met value

Sleeping 0.70

Reading 1.00

Working(light) 2.10

Amount of clothing

metabolic rate

KNIVESML(sample)


<knives> <head> <version name="major">0</version> <version name="minor">1</version> <id>ir_data</id> <device name="ip">10.24.128.242</device> <device name="mac">00:11:0C:04:09:23</device> <owner name="company">KEIO_University</owner> </head> <body> <message name="sensor_data"> <timestamp name="date">2010-12-26 2:37:52</timestamp> <timestamp name="timezone">JST</timestamp> <response name="temperature" type="temp" id="">20.8</response> <response name="humidity" type="hum" id="hum_01">14</response> <response name="illuminance" tyape="illmina" id="illmina_01">447</response> <response name="co2" type="co2" id="co2_01">638</response> <response name="temperature" type="temp_hum" id="82">20.0</response> <response name="humidity" type="temp_hum" id="82">18.1</response> <response name="temperature" type="temp_hum" id="83">19.9</response> <response name="humidity" type="temp_hum" id="83">17.4</response> <response name="temperature" type="temp_hum" id="84">20.8</response> <response name="humidity" type="temp_hum" id="84">19.2</response> <response name="ac_power" type="kw-pulse" id="kw-pulse_01">0.0000</response> <state name="relay" type="relay" id="relay_stt_01">0</state> </message> </body> </knives>

Infrared Signal Code


lighting Air-conditioner

制御の有無での消費電力の違い（冬期）

制御なし弱制御全体制御

1時間の合計電力（kWh） 144.4 145.1 110.8

空調停止時間（分） 0

15

(16:30~

16:45)

55

(16:30~

17:25)

30

40

50

60

70

80

90

30分間需要電力（ｋ

Wh）

制御なし

弱制御

全体制御

制御時間制御条件 • ピークとなる１６時半から空調が停止される１７時半までの１時間を制御時間とする

• 弱制御は人事課の温度低下を考慮し、最小時間での制御

• 全体制御は人事課は考慮せずに、フロアの温度が20℃以上に保たれるよう制御

結果・弱制御空調停止の効果以上に、運転開始時の

電力増加が大きかったため、制御なしの場合と需要電力の差は出ない

・全体制御気温による消費電力の誤差を考慮しても、１５％以上の需要電力削減が可能

制御方法による受電電力の変化（１/１７～１９）

46


19

20

21

22

23

24

25

26

27

15:00:00 15:30:00 16:00:00 16:30:00 17:00:00 17:30:00 18:00:00 18:30:00

2F_no

2F_con

3F_no

3F_con

air-conditioning control of hems and bems toward cems€¦ · air-conditioning control of hems and...

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