smart grid concepts consumer level selected...

Villamos Művek és Környezet Csoport Villamos Energetika Tanszék

Smart Grid Concepts – Consumer Level Selected Topics

2nd Green Waves - Autumn Academy - Renewables & Smart Cities

A Magyar Tudomány Ünnepe

Dávid Raisz, PhD, associate professor

Contents

How can customer interaction be fostered?

• Demand-Side Management (DSM)

• Consumption feedback

• Tariff incentives

• Direct Load Control (DLC)

• Customer reactions to feedback and tariffs

• Possible benefits from DLC

22.11.2013 2

• Smart Grid @ Consumer Level

2nd Green Waves - Autumn Academy - Renewables & Smart Cities, A Magyar Tudomány Ünnepe

Customer reactions to feedback and tariffs

22.11.2013 2nd Green Waves - Autumn Academy - Renewables & Smart Cities, A Magyar Tudomány Ünnepe 3


Hungarian survey


• Smart Grid @ Consumer Level – Feedback and Tariff Incentives

58,5 26,2

9,0 4,0 2,4

Would you re-schedule your activities (eg. cloth

washing) if you knew the actual price of

electricity? (%)

Igen, mindenképp Inkább igen Inkább nem Egyáltalán nem Nem tudja/Nem válaszol

Yes, definitely Rather yes Rather no Definitely no NA

Hackl Mónika: Lakossági OKOS MÉRÉS kommunikáció, 2013

Austria - pilot

• Linz, December 2009– November 2010

• 1525 households: pilot group (775) – control group

• Hourly recordings

• Feedback: web-portal OR post (no in-house display)

• Hourly, daily and monthly reports + saving suggestions

+ statistical evaluations (dependencies on income, floorsize, age, education, appliance stock, computer usage time etc.)



Austria - pilot

RESULTS:

• 4,5% annual saving on average (30 € / a)

• No savings at low consumption households

(already exhausted potentials…)

• No reduction at high consumption households

(no motivation)

• No evidence for differences in the effectiveness of feedback types (web – post)

Interesting: 95% SM penetration considered as cost-effective (PriceWaterhouseCoopers CBA in 2010)

J. Schleich, M. Klobasa and S. Gölz: Does smart metering reduce residential electricity demand? (EEM12)



Germany

A large assessment of running pilot projects from 2009 resulted in average electricity savings of around 6%.

However, during this study it was entirely clear whether these were actual savings or rather savings expected, as parts of the pilot projects were still at very early stages.

KEMA: Development of Best Practice Recommendations for Smart Meters Rollout in the Energy Community, Bonn, February 24, 2012



USA

• 14 pilots between 1996 and 2007 • Several ten thousands of customers • How do different tariff systems influence PEAK LOAD?

• TOU (Time of Use): different prices in peak and valley load times • CPP (Critical Peak Pricing): application of very high price during peak

hours of (a limited number of) most critical days of the year (the exact dates are known only 1-2 days in advance)

• PTR (Peak Time Rebate): rebates per kWh consumption decrease (during critical days, compared to usual consumption during peak hours)

• RTP (Real-Time Pricing)

• Explanations to the results on the next slide: • „w/Tech”: application of some intervention technology (remote control,

automatic/programmable intervention etc.) • „CAC” = central air conditioning – in large blocks



USA



Pricing systems

• Flatrate: no customer risk – no motivation

• RTP: minimal utility risk



France

• 3 types of energy tariff systems:

Low consumption households, weekend houses – fix tariff

Higher consumption households without electric heating– ToU

High-consumption households with electric heating – Tempo:

3 day colors, different ToU rates

Blue - cheap, white – medium tariff, red - expensive

300 blue, 43 white, 22 red days a year

Control by the customer, optional DLC for water heating and space heating

Result: 15% reduction on white days, 45% on red days, 90%-customer satisfaction, 10% saving on consumer bills

22.11.2013 11



Austria - simulation

Annual consumption reduction: FEEDBACK; peak load reduction: pricing (CPP & automation)

Luis Olmos, Sophia Ruester, Siok Jen Liong, Jean-Michel Glachant: ENERGY EFFICIENCY ACTIONS RELATED TO THE ROLLOUT OF SMART METERS FOR SMALL CONSUMERS



Finland - survey

• In 2011, 2103 people were asked

• 2000..5000 kWh/a for most respeondents

• 1.: web, 2.: email responses

• Motivation for allowing remote load control?

• Expected cost reduction? 103 €/a on avg. (out of 355 € !)



Lower electricity

costs

Reduce emissions

Opportunity to remotely control own appliances

NO

74% 29 % 32 % 14 %

What types of appliances?



Summary

• Direct Load Control is considered in many countries as an important asset for tariff switching, lighting control and different load control purposes.

• DLC offers huge saving potentials. An example from Finland: “transferring the storage heating of controlled night-time sites in the Helsinki region to the cheapest hours (according to day-ahead prices!) of the 24-hour period would bring a benefit of EUR 100 000 per year compared with the current control when only the price of electric energy according to the spot price is examined”

• Various levels of price-driven demand response have been reported. However in some countries ToU tariffs proved to be an efficient way of load shifting, DLC seems to be even more efficient. An optimal way for DSM is the combined usage of dynamic pricing and DLC.

• Customers can adjust to sophisticated and dynamic tariff. DLC schedules can also be varied on medium term notice (in the Czech Republic it is common to change schedules monthly.)



Summary

• A promising way of making DLC even more widely accepted among customers is by allowing them to interact: “EDF can switch the conditioning/heating off or diminish its performance for 20 minutes but the customer can switch it back any time with a button if it causes any inconvenience.”

• New ways are being explored of using DLC infrastructure dynamically. This means, that using DLC for increasing ancillary reserve capacity is considered as a viable option, especially considering the increasing penetration of EVs (or heat pumps). This is especially true for power generation portfolios with small hydro proportion.

• The transition from ripple- (or radio-) controlled infrastructure to a smart metering based DLC infrastructure is a long-lasting process, spanning several years. Some countries have recently invested into renewing ripple-control infrastructure.



Direct Load Control Technology, Modeling methodology, benefits, CBA



DLC Technology – Ripple Control

Main parameters:

• Since: 1975 in HU

• One-way communication

• Non ideal signal transmission (DG’s, capacitor banks)

• Telegram based, task: switching on an off

• Modulation frequency: HV: 216.7, LV: 183.3 Hz


• Smart Grid @ Consumer Level – DLC

DLC Technology – LW Radio Control

• 3 antennas: Mainflingen, Burg (Germany), Lakihegy (Hungary)

• Wider addressing interval

• DG’s do not influence the signal transmission

• Since: 1995

• Coverage:



Electric Storage Water Heaters

q’(t): hot water rate of extraction (m3/s) at time t (also called intensity) b(t): the state of the thermostat (on or off - binary) v(t): the control signal applied by the utility (1 for on and 0 for off)

Tr(t): the average room temperature at time ‘t’



𝐶 ∗𝑑𝑇 𝑡

𝑑𝑡= −𝑎 ∗ 𝑇 𝑡 − 𝑇𝑟 𝑡 − 𝑐𝑣 ∗ 𝑞

′ 𝑡 ∗ 𝑇 𝑡 − 𝑇𝑖𝑛 + 𝑃 ∗ 𝑣 𝑡 ∗ 𝑏(𝑡)

Abbr. Description Value C Water storage capacity 162.16 l a Thermal conductance ratio of the ESWH wall 1.13 W/oC cv Specific heat constant of the water 1.16 Wh/m3oC Tin Inlet water temperature 13.5 oC P Rated power of ESWHs 1755 W

ESWH model

• Water consumption – stochastic model



Nr.

of

ho

use

ho

lds

litres/da

y

Other controllable loads

• Freezers

• Air-conditioners and space heating

• EVs



88%

90%

92%

94%

96%

98%

100%

102%

1 2 3 4 5 6 7 8 9 101112131415161718192021222324

Ava

ila

ble

ve

hic

les [

%]

Time of the day [hour]

Availability of the electric vehicles

0%

10%

20%

30%

40%

Ava

ila

ble

ve

hic

les [

%]

The distance covered [km]

Density function of the driving distance

ESWHs



0 4 8 12 16 20 240

50

100

150

200

250

Hour

Pow

er

[MW

]Power consumption (755MW ESWHs)

mustrun

controlled

self

setpoint

original

Freezers



0 4 8 12 16 20 240

5

10

15

20

25

30

35

Hour

Pow

er

[MW

]

Power consumption (224MW freezers)

mustrun

controlled

self

setpoint

original

EVs



0 4 8 12 16 20 240

50

100

150

200

250

300

Hour

Pow

er

[MW

]Power consumption (2030MW EVs)

mustrun

controlled

self

setpoint

original

SO WHAT?


We now have a model that is able to predict the load curve shape for any switching schedule of the remote-controlled load groups.

DLC – Loss Reduction



[Ws]

T

loss dttiRW0

2 )(

[Ws] 2

)( meaniRTW idealloss [A]dt

T

mean tiT

i0

)(1 2

0

0

2

)(1

)(

dt

T

T

tiT

dtti

LCF

| K | Sze | Cs | P | 0

5

10

15

20

25

I 1 eff ,

A

"A" utca, Kcs = 1.14

"B" utca, Kcs = 1.11

Network „A”, LCF = 1.14 Network „B”, LCF = 1.11

TUE

WED

THU

FRI

Cu

rren

t R

MS,

on

e p

has

e, A

mp

s

DLC – Decrease Transformer Peak Load

• Postpone investments (e.g. into larger transformers)



DLC – PEAK/BASE ratio, procuring cost reduction

Version ΔTavg of ESWHs: Average ON time: Average price current –2.30°C 12.09 h 15.079 HUF/kWh ideal –9.15 C 8.08 h 14.796 HUF/kWh Still possible –5.87°C 13.55 h 14.970 HUF/kWh



MW

DLC for Reducing Balancing Energy

Algorithm

Result:



-200 -150 -100 -50 0 50 100 150 200-200

-150

-100

-50

0

50

100

150

200

Original deviation (MW)

Resultin

g d

evia

tion (

MW

)

DLC for Secondary Reserve

• Has the greatest financial potential

• Allows further integration of renewable sources

• If using DLC dynamically (i.e. by modifying the schedule online) the day-ahead scheduling methods have to be

reiterated!

• A pilot measurement campaign is being prepared for dynamic DLC!



Thank you for your attention! Dr. Dávid Raisz

[email protected]



Modelling of ToU tariff effects

• Reschedulable Appliances

22.11.2013 33

Appliance Power Daily switching rate Op. time Incidence

washing machine 1000 W 0.35 64 min 72 %

dish-washer 900 W 0.60 61 min 14 %

vacuum-cleaner 1200 W 0.20 21 min 95 %

water heater 2000 W 0.16 20 min 50 %

el. oven 3500 W 0.15 80 min 10 %


Smart Grid @ Consumer Level – Backup

Washing machine Expected value Std. Dev. Minimum Maximum

Power (W) 1000 150 450 1800

Nr. of usages a day 0,35 0,38 0,0 4,0

Working time (min.) 64 38 10 380



400 600 800 1000 1200 1400 1600 18000

50

100

150

200

250

300

350

Teljesítmény [MW]

Dara

bszám

0 1 20

1000

2000

3000

4000

5000

6000

7000

Napi bekapcsolás [db]

Dara

bszám

0 50 100 150 200 2500

50

100

150

200

250

Dara

bszám

Üzemidő [perc]

Washing machine

Measured aggregated load curve

Switch-on time probability density function


Residential Monitoring to Decrease Energy Use and Carbon Emission in Europe, Publishable Report, 2008


Results

22.11.2013 36 2nd Green Waves - Autumn Academy - Renewables & Smart Cities, A Magyar Tudomány Ünnepe


smart grid concepts consumer level selected...

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