Local optimization of consumption against

local generation as a smart solution

František Müller, CEZ, Czech Republic

Filip Prochazka, Masaryk University, Czech Republic

September 21th 2015

Klagenfurt

LODIS project

Base conditions

CZECH GRID USING RIPPLE CONTROL (HDO)• Years operation proved (60’s), it is the DSO’s tool• un/blocking customers devices (boilers, electric heating)

• IT IS A CUSTOMER SERVICE IN HIGH-PEAK TIME• 1 from 3,5 million CEZ customers using it

• One direction communication

Small generations connected into LV part of distribution grid• Most of them are renewables – intermittent character, 10 000+ installed• Challenge for grid operation – grid management

Smart-metering is one possible way how to locally monitor and manage LV• Load management based by local conditions and features (SG cells)• Optimization of demand against to generation – automated peak shaving

1

Smart grid – real example for hypothesis

Sec. Subs.

22kV/0,4kV

250kVA

Common MV supply line (22kV )

Demand Renewable gen. 6 PV LV grid

Pinst = 140 kWp

100 MP

280 inhabit.

Common

house

demand

Hot water

accumulat.

(50 OM)

Controlled

demand

Uncontrolled

demand

Field example showing situation where 80% of PV production flow to MV thru substation.

Can it be managed by on demand switching of boilers by PV production prediction ? Optimization of energy flow?

Neighborhood

Sec. Subs.

Su

pp

ly f

rom

gri

dS

up

lyto

gri

d

Neighborhood

Sec. Subs.

Substation energy flow

-100

-80

-60

-40

-20

0

20

40

60

80

1 3 5 7 9 11 13 15 17 19 21 23

Smart Grid Cell

HYPOTHESIS TARGET – optimization of consumption

What does it mean optimization in dynamic Grid world?Flexible adaptation to actual grid situation!

Common grid situations:1. Stable grid = target is optimize to lowest losses in a SG cell2. Overloaded grid = lowest total consumption/manage supply sources3. Oversupplied grid = maximize consumption/manage supply sources4. Under/over voltage

Each SG cell getting own targets for a time period and optimize own behavior in defined limits (tariffs, fuses, transformers, etc.)

Lot of flexible SG cels = Flexibility of whole grid

3

PROJECT LODIS - introduction

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• 3 LV grids with Smart meters• Secondary substations with smart data concentrator units (SCU)• Local load management SW support in data center

• Project goals – prove hypothesis• check possible effects of local load control

in different locations• check maturity of technology for advancedsmart metering applications• check synergy between global load management (ripple system) and local load management• utilize measurements from CEZ large AMM pilot project

Possible architecture of SG Cell

5

SGU_A

SIEM

A

Síť NN

A

SGU_B

SIEM

A

Síť NN

A

FVE/VE

SGU_C

SIEM

A

Síť NN

A

SGU_D_master

SIEM

Síť NN

A

FVE/VE

Síť VN Síť VN Síť VNSíť VN

DTSDTSDTSDTS

MV grid MV grid MV grid MV grid

LV grid LV grid LV grid LV grid

Behaviour model:

Detection and prediction of controlled load - boilers

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response rate calculation for each consumption point

Manageable consumption at individual consumption

points

7

10,5

9

8,93 9,15

6,92 9,

25

4,81 6,

04

5,37

12,2

6

9,92

8,94

6,83

19,3

9

12,6

8

9,09

7,96 9,

24

6,77 7,28 9,

32

2,78

2,04

9,97

7,60 8,

79

6,66 7,44

6,08

3,26

2,89

14,2

3

11,9

1

2,04

2,99

2,08

2,24

2,44

2,86 2,

67

2,74

4,27

6,52

4,40

4,55

6,83

9,72

6,00

6,80

3,37

3,54 4,

90

9,59

2,81

4,47

4,88

5,68 5,

37

5,66 5,

13

3,94

2,79

2,44

13,2

2

15,6

9

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30

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i1395 i1407 i1413 i1414 i1416 i1433 i2659 i2662 i2664 i6249 i6252 i6287 i6291 i6293 i938 i999

kWh

vzorek a typ dne

Poměr odhadnuté base a bojlerové spotřeby Součet odhadu denní bojlerové spotřeby

Součet odhadu denní base spotřeby

Production prediction

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The algorithm

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• Algorithm goal:

find optimal tariff switching plans for tomorrow for each consumption point

according to expected production and consumption at given location

• Optimality:

• To consume production from renewables as close to production points as possible

• To minimize technical losses

Weather prediction

Production prediction

Consumption prediction

Prediction of customer

reaction to tariff switch

Compute optimal

switching plans in a form of TOU tables

Distribute TOU tables to

consumption points

Local demand management effect example(DTS HK 25.1., Expected cut of t. losses against as-is (HDO) is 7,04%)

10

24h DTS load (green – local load mgmt., black - as-is (HDO))

24 production

Calculated optimal unblocked boilers (low tariff) time periods (green)

Project LODIS – status

Technology• 3 substations are installed - cca 300 meters + substation meter + SCU

• IEM <> SCU communication via BPL, SCU <> DC comm. via 3G GPRS)

• Data collections is running into SCU and then to Data center

• Data collection end evaluations every 15 minutes (SCU)

• Prediction and optimization calculations 2 times per day (DC)

• TOU management using static TOU in the first project phase (data collections)

Preconditions / limits• Regulator rules – 2h of low tariff in night, 6h in day, min 2h lasting blocks

• System has to work also in bad communication conditions

• Different reachibility of MP in different time in a different speed

• System integration to utility processes (ERP, Workforce, Ripple control, etc.)

11

0,00%

20,00%

40,00%

60,00%

80,00%

100,00%

120,00%

GOOD CONDITIONS (METER REACHABILITY 2-WEEK PERIOD)

0,00%

10,00%

20,00%

30,00%

40,00%

50,00%

60,00%

70,00%

Load management in bad communication conditionsStandard AMM solution challenge in data demanding apps

BAD CONDITIONS (METER REACHABILITY 2-WEEK PERIOD)

Note: In both conditions meter data collection within 24 hours was close to 100 %

Achieved preliminary results from the lodis project

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• National weather forecast service can be used for PV production as well as

consumption

• smart-metering data can be used for quite precise consumption behaviour of

consumption points clusters (e.g. 20 -100+ consumption points)

• There is consumption which can be shifted using fully automated algorithms

• It is possible to optimize energy flows (e.g. Peak shaving) using dynamically

computed tariff switching schemes

• For each grid cell

• For selected time period (e.g. 15m – 72h)

• It brings benefits even if no PV production is present

Conclusions

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• We are using flexibility in consumption to achieve flexible grid

• We are now using accumulation in hot water boilers, the solution is open for

other types of flexible consumption (heating, air conditioning,

charging stations, …)

• It is automated service for customers

• It is example of application based on smart meter data and infrastructure which shows

• 15m consumption profiles are useful and they can be base for prediction models

• Targeted ad-hoc readings during day are useful for intra day optimization

LODIS project testing hypothesis, that we can utilize data from

Smart metering for Smart grid world with real actions and real

benefits.

Thank you for your attention

Frantisek Muller

Frantisek.muller@c

ez.cz

15

Mgr. Filip Procházka, PhD

Filip Procházka has more than 15 years experience in the real-

time processing of large data volumes, event detection and big-

data analysis for industry and utilities.

He leads the development team of the Smart Grid Simulator for

the modelling and simulation of large smart metering and smart

grid systems. Models contain millions of interconnected elements

and are capable of precisely modelling and simulating various

aspects of both today's and tomorrow's grids.

He is SW architect of the LODIS project – CEZ local load control

pilot project.

He works as senior researcher at Institute of Computer Science at

Masaryk University, Brno, Czech Republic and is also co-founder

and CEO of the company Mycroft Mind.

František Müller

František Műller has been working in the sector of information

technologies for the power industry since 1994.

Until 2005 he was with Východočeská energetika, a.s. (East-Bohemian

Power Distribution Co.) in the position of the Senior Manager of GIS.

At present he works as a specialist in ČEZ Distribuční služby, s.r.o. (ČEZ

Distribution Services Ltd.).

He was, among others, responsible for the following projects:

Geographic information system of Východočeská energetika

(VČE),

Data acquisition from the distribution system of VČE,

Automatic system of technical and economic management of

power distribution,

Project: Migration of customer service system of VČE into SAP/R3

Project: Management of output at ČEZ Měření

Pilot project: Automated Meter Management in the Group ČEZ

Wide pilot project WPP AMM

LODIS project – smart local optimization in distribution grid

He also participated in the formation of the Association of East-Bohemian

Network Administrators, NEMOFORUM.

He is a representative of CEZ Dist. slu. in the PRIME Alliance