SHARP HEMS
with
ith demand
d
d response system
t
in Smart House demonstration
(Final Report)
June, 19th, 2014
Smart Community JAPAN 2014
SHARP CORPORATION
Yoshihiro Kitaura
1
Contents





Smart House Overview
Smart Appliances
HEMS (Home Energy Management System)
Demonstration Result
 Power Consumption in Smart House
 Prediction of Power Consumption
 Optimal Battery Control
 Battery Control with Demand Response
 Smart Appliances Control with Demand Response
Summary
2
Smart House Overview
Strong points of SHARP HEMS in NEDO Smart House Demonstration
 Visualization and Smart Appliance Control
 Energy device control with demand response
Energy Creation
PV
Demand Response
ＨＥＭＳ
Utility
Smart Meter
B tt
C t l
Battery
Control
Auto Control
Energy Load
Energy Storage
Battery
discharging
Smart Appliances
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Smart Appliances
 Japanese market model (TV: US model) with developed communication adaptor
 Visualization and Appliance Control from anywhere in the house using HEMS
Wireless Communication
(IEEE802
(IEEE802.15.4)
15 4)
Air Conditioner
TV
HEMS(t bl t PC)
HEMS(tablet
LED Lighting
Refrigerator
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HEMS
Main feature of SHARP HEMS
 Emulation of power consumption in smart house
 Prediction of power consumption in smart house
 Optimal battery control
 Battery
y control with demand response
 Smart appliances control with demand response
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Power Consumption in Smart House
 Developed virtual life pattern based on emulated ordinary American family
 Automatic control by HEMS based on virtual life pattern in uninhabited house
Power consumption each time division and appliances
(February 3rd, 2014)
18,000 14,000 2014/2/3
other
7_その他
12,000 hot water heater
6_給湯器
10,000 dummy load
5_模擬負荷
8,000 refrigerator
4_冷蔵庫
6,000 3_テレビ
TV
4,000 2_照明器具
照明器具
LED lighting
2,000 1_エアコン
Air conditioner
0 0:00
1:00
2:00
3:00
4:00
5:00
6:00
7:00
8:00
9:00
10:00
11:00
12:00
13:00
14:00
15:00
16:00
17:00
18:00
19:00
20:00
21:00
22:00
23:00
消費電力
消
量(Wh@
1h)
Po
ower
Consu
umption
(W
Wh@1h)
16,000  Hot Water Heating in night time
 Almost
Al
t off power consumption
ti
are air
i conditioner
diti
and
d
LED Lighting
 Dummy Load emulates power consumption of other
appliances (such as IH cooking heater, wash machine,
g machine))
and cleaning
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Prediction of Power Consumption
 Prediction of tomorrow’s
tomorrow s power consumption using past actual power
consumption and weather information
 High accuracy prediction(approximately +2%)
Actual value
Estimated value
Predict distortion
(winter season)
予測誤差(冬季)
 Mode: +2%
F
Frequency
Pow
wer Consumption (kW
Wh)
Comparison with actual and estimated value
(February, 2nd, 2014)
(actual - estimated) / estimated[%]
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Optimal Battery Control
 Planning schedule of battery charging/discharging for minimize daily electricity bill
using price information from smart meter and predict information
 Cost reduction compared with simple model
Daily e
electricity b
bill (cent)
Comparison of cost (winter season)
Optimal
Simple
Date
Simple model:
Maximum charging in low buy power price period
Maximum discharging in high sell power price period
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Battery Control with demand response
Power Consumption (KW
Wh)
Buyy Power (KW)
(i)
(ii)
(kW)
Battery disccharging (KW)
蓄電池充放電電力
House residents do not notice DR order
((It means HEMS keeps
p house
comfortable environment)
(k
kW)
Approx.100% response rate of DR
order （request for power reduction）
系統電力
HEMS responds DR order without
reducing house power consumption
((kW)
(ii)Compensation for buy power
reduction by discharging
総負荷電力
(i) Buy power reduction according to
DR order（request for power reduction）
DR
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Smart Appliance Control with DR
 R
Reduction
d ti off power consumption
ti b
by controlling
t lli smartt appliance
li
(No battery remaining in DR)
 No frustration of smart appliance control（keeping house comfortable）
Air Conditioner: control of setting temperature decrease(winter season)
LED Lighting: control of luminance level

Keeping less than 2 Celsius of
decrease of room temperature
Fall in room temperature[Celsius]
Fall in room luminance
by air conditioner control
The number o
of controls
The number of c
controls
Fall in room temperature
by air conditioner control

Keeping more than 90lx of
room luminance
room luminance [lx]
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Summary

Visualization and Smart Appliance Control
We achieved our goal by using HEMS(tablet PC).

Prediction technology
W achieved
We
hi
d our goall th
thatt we make
k hi
high
h accuracy prediction
di ti off power
consumption (predict distortion approx. +2%).

Battery Control technology
We achieved our goal that our HEMS minimized cost (daily electricity bill).
（Cost reduction 2-10% compared with simple model）

Demand Response technology
We achieved our goal that our HEMS made it possible to response high
rate of DR(approx.100%) and keep house comfortable environment.
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Thank you for your attention
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