1) The document discusses using demand response to reduce peak electricity demand in Japan's urban residential sector. Demand response aims to incentivize consumers to reduce consumption during peak periods. 2) The study aims to simulate demand response for condominium households to understand potential demand reduction. It analyzes electricity data from 184 households to define response scenarios and reduction levels. 3) Preliminary results suggest that with complete participation, demand could be reduced by 612 watt-hours per household during a 2-hour peak period, totaling over 1 megawatt for a large condominium development. The study plans to verify the simulation and record results.

1. Demand Response Estimation in
Urban Residential Sector of Japan
Reina Kohtake
The University of Tokyo

2. Overview
• Introduction & Problem Statement
• Research Objective
• Data and Methodology
• Preliminary Results
• Discussion
2

3. Peak Demand
Source: Federation of Electric Power Companies of Japan, Graphical Flip-Chart of Nuclear 3
and Energy Related Topics 2011

4. Peak Demand
• Reaches maximum
on hot/cold days in
Kanto area.
• Challenges suppliers
of electricity
– Minimizing Demand/
Supply gap
4

5. Supply Capacity Meets Peak
Total Supply Capacity
Electricity
supplied
Energy Unit
Demand
Time
5

6. Demand adjusts to supply
Excess Demand
Electricity
supplied
Energy Unit
Demand
Time
6

7. Failure of electricity grid
MUST PREVENT at all times
7

8. Demand Response?
Demand response reduces peak electricity
demand by
1. Requesting electricity consumers to
reduce consumption through incentives
2. Raising real-time electricity prices
Source: METI. Demand Response About (in Japanese). N.p., 24 Mar. 2012. Web.
<http://www.meti.go.jp/committee/sougouenergy/sougou/denryoku_system_kaikaku/00
2_s01_01_05.pdf 8

9. Demand Response
Demand
Response
Electricity
supplied
Energy Unit
Demand
Time
9

10. Problem
• Demand response may deploy more as fluctuant
renewable energy replace stable non-renewable
energy
• Further investigation to understand its potential
will lead to more reliable operation of the
electrical grid.
10

11. Research Objectives
• Simulate demand response for urban
condominium type households to
understand the range of demand reduction
possible.
11

12. Target sector: Residences
• Fastest growing sector of energy where
consumption 30% in last 20 years
• Estimated consumption at 27% of total
electricity demanded during peak
• Market formation of HEMS
Source: energy demand/supply records (Energy &
Environment, Ministry of Economy Trade and Industry, 2011)
12

13. Data: household electricity data
Data type
• 10 minute individual
household electricity data
• 184 samples
• April 2011~Present
Characteristics
• Uniform building
characteristics
• Minimum Hawthorne
effect 13

14. Data Collection
Data
Housing Unit(s)
Research
Database
institutes
Server
Visualization
14

15. Methodology
Simulate Demand Response…
1. Determine potential demand response
setting
2. Identify target households
3. Define level of demand reduction
4. Run scenarios and compare results with
status quo
15

16. 1. Determine potential demand
response setting
• Select dates with
highest and lowest
annual average
temperature
• Select appropriate
time range for
demand response
 2PM to 4PM (2 hours)
16

17. 2. Identify target households
• Select electricity House B
consuming households
W
000204060810121416182022
• Verify algorithm’s
validity House C
W
000204060810121416182022
17

18. 3. Define level of demand
reduction
• Define action
• Exit house
• Minimal reduction
• No action
• Verify algorithm’s
validity
18

19. 4. Run scenarios
Minimal Reduction
Scenarios Exit House No action
reduction per unit
Complete
Participation 100 % 0% 0% -612wh
Effortful
Participation 40% 30% 30% -300wh
Easy
Participation 20% 40% 40% -197wh
Figures taken from 8/18/2011 19

20. Preliminary/Expected Results
• Complete (maximum) participation
contributes 612wh electricity demand
reduction per household in 2 hours
• 612Wh X 2000 households of Kashiwa-no-ha
condominium units ～1200KWh in 2 hours
20

21. Expected Results
1000KW level Mega-solar
Plant
X 1hr
21

22. Research Plan
• Verify algorithm’s validity
• Run simulation and record all results
• Finish editing thesis
22

23. Thank you for your
attention

24. Electricity Wholesale Market
Electricity Supplier A Electricity Consumer D
Supply Demand
Can generate bid bid Can reduce
Bidding 1MW X 2 hours 1MW X 2 hours
for $... tomorrow for $... tomorrow
Electricity
Whole-
sale
Market
Demand
Supply Reduction
Actual Supply 0.5 MW Reduce 0.5 MW
results for 2 hours for 2 hours
$ $
Source: Proposal for overcoming summer peak in Kansai, Murakami 4.2012 24

25. Limitations
• Disaggregation of electricity use based on
careful assumptions
25