GROWDERS & PLATOS, session 1

Optimal use of storage systemsPetra de Boer Project coordinator GROWDERS

Roger Cremers Developer PLATOS tool

Gabriël Bloemhof Consultant decision making models

14 February 2011 16:00 – 17:30

17 February 2011 16:00 – 17:30

2

Introduction Workshop Leaders

Petra de Boer Roger Cremers Gabriël Bloemhof

Energy storage

Electric vehicles

Smart Grids

Power Factory

Developer PLATOS

Energy systems

Grid integration

Optimization

3

Agenda

14 February 2011, 16:00 – 17:30– Introduction of GROWDERS project

– Benefits of grid connected storage

– 4 field tests in Europe using storage

– Decision making model in PLATOS

17 February 2011, 16:00 – 17:30 – Main features of PLATOS

– Demonstration with the PLATOS tool

– Examples, results

4

Introduction of GROWDERS

EC funded under 6th Framework Programme Coordinated by KEMA

Goal: To demonstrate the technical and economical

possibilities of existing electricity storage

technologies.

– Realization of Transportable Flexible storage systems

– Realization of an Assessment tool for optimal distribution network

management

– Description of conceptual directions for EU regulatory framework

Grid Reliability and Operability with Distributed Generation using Transportable Storage

5

GROW-DERS

For more information regarding the project:

http://www.growders.eu

Workshop with visit

to the field test in Mannheim:

11 May 2011

7

Applications of Electricity Storage

T&D:

Asset management

Voltage control

Power quality

Grid stability

Trading/Generation:

Control / load following

Energy management

Peak generation

Load levelling

System operators:

Frequency control

Spinning reserve

Balancing

End user (industry) :

UPS / Ride Through / Shut down

Peak shaving

optimization of energy purchase by load shifting

(Reactive power)

Renewable:

Decoupling demand and source availability

Control and integration

8

Where is Storage Targeting?

• Integration of Renewables into Grid – Can help maintain grid operations

– Consensus being reached on exact level of penetration

– Bulk vs. Utility Scale, Centralized vs. Decentralized?

• Ancillary Services – Fast response capabilities allow devices to perform better than current

devices

– Should ISOs take advantage of fast response capabilities

• (PH)EVs – Battery in vehicle, when aggregated, can be a Smart Grid Tool

– Focus is currently on fleet applications!

• Community Energy Storage – Used for Distribution benefits

– Can be linked to EV secondary life applications =

9

Batteries NiCd and NiMH Lithium-ion High temperature batteries

– NaS– ZEBRA

(Advanced) Lead-Acid– Lead-Acid

Flow batteries– Vanadium Redox– Zinc Bromine– Zinc Flow

Flywheels Compressed Air Energy Storages (CAES)

Pumped Hydro Super Capacitors Superconducting Magnetic Energy Storage (SMES)

Summary of Technologies covered

10

Main activities in GROWDERS

Design Optimization Tool

Design Optimization Tool

Design Storage System

Design Storage System

Build Prototypes

Build Prototypes Field tests x 3Field tests x 3

Combined Pilot

Combined Pilot

Develop Optimization ToolDevelop Optimization Tool

Validation & OptimizationValidation & Optimization

Nov 20102007 June 2011

11

Field tests of GROWDERS

Zutphen, The Netherlands Zamudio, Spain Chambery, France Mannheim, Germany

12

Test site Bronsbergen (Zutphen)

13

Test site Bronsbergen (Zutphen)

LS RAIL 40.401.00

-150.20

GE HKL0.401.00

-150.20

Sectie Kast 20.401.00

-150.20

Sectie Kast 1 0.401.00

-150.20

MS rail 4 10.001.000.00

V

VDC STOR_002

0.000.000.00

V

VDC STOR_001

0.000.000.00

STO

R_0

02

0.00-0.000.00

STO

R_0

02

0.000.000.00

STO

R_0

01

0.00-0.000.00

STO

R_0

01

0.000.000.00

STO

R_0

03

0.00-0.000.00

STO

R_0

03

0.000.000.00

V

VDC STOR_003

0.000.000.00

Koppeling

45.4111.9367.82

Koppeling

-45.41-11.9367.82

Link

1Li

nk 1

Link

4Li

nk 4

Trf 3

92

45.4112.092.71

Trf 3

92

-45.41-11.9367.82

Line

73

(Gro

en)

6.191.739.29

Line

73

(Gro

en)

-6.19-1.739.29

Line

226

(Paa

rs)

12.973.11

19.27

Line

226

(Paa

rs)

-12.97-3.1119.27

Line

39

(Roz

e)

15.094.23

22.64

Line

39

(Roz

e)

-15.08-4.2222.64

Line

3 (B

lauw

)

11.152.8616.63

Line

3 (B

lauw

)

-11.15-2.8616.63

Link 402Link 402

Link

403

Link

403

Roelofs

45.41 kW12.09 kvar

0.97

DIg

SIL

EN

T

14

Holiday parc Bronsbergen in Zutphen

200 homes ~300 kW solar panels

15

Flywheel system

16

Results Zutphen

Flywheel system passed all tests All initial problems were solved (like communication

problems) Flywheel and inverter work well Harmonic compensation worked perfectly Reactive power supply was OK Islanding mode worked perfect

Noisy system Power losses of the system

17

Battery test site Zamudio and Chambery

18

Transportable storage systems

19

Results Chambery and Zamudio

System is transportable All initial problems were solved (like communication problems) Battery and inverter worked well together Energy management system is developed and tested in

Chambery Load alleviation is proven in these field tests

Communication problems caused a delay in these field tests Energy management system is a critical component of the system

20

Final field test Mannheim

3 storage systems at one location (2 Li-ion batteries

and 1 flywheel) A large PV solar power unit is installed at this location January 2011 – June 2011

21

Lessons learned GROWDERS

Transportation of storage systems is possible, but not always

easy to realize (heavy, strict regulations)

Technology is still in the demonstration phase (e.g. the energy

management system, communication between the components)

Financial benefits are for different stakeholders– this is not organized yet

Regulations are not clear and differ per country

Number of locations available for the grid operator is often limited

22

The need of storage in the grid

GROWDERS demonstrated that Transportable

(containerized) storage can be used on LV networks

when Distributed Energy Resources (DER) are

installed:– To alleviate potential network overloads

– For voltage control to avoid over/undervoltage

– To mitigate voltage dips

– To improve Power Quality (PQ) (Flicker, harmonics etc.)

Demand and DGNetwork Storage

23

Questions

Questions that need to be answered:

– What type and size of storage is required?

– Where on the network should storage be installed?

– When should the storage system be charged / discharged?

– What are the costs and benefits?

Demand and DGNetwork Storage ?? ?

24

Planning tool for optimizing storage (PLATOS)

Tool that assist network planners to optimize the

location, size and types of energy storage systems in

an electrical power system

LS RAIL 40.401.00

-150.20

GE HKL0.401.00

-150.20

Sectie Kast 20.401.00

-150.20

Sectie Kast 1 0.401.00

-150.20

MS rail 4 10.001.000.00

V

VDC STOR_002

0.000.000.00

V

VDC STOR_001

0.000.000.00

STOR

_002

0.00-0.000.00

STOR

_002

0.000.000.00

STOR

_001

0.00-0.000.00

STOR

_001

0.000.000.00

STOR

_003

0.00-0.000.00

STOR

_003

0.000.000.00

V

VDC STOR_003

0.000.000.00

Koppeling

45.4111.9367.82

Koppeling

-45.41-11.9367.82

Link

1Lin

k 1

Link

4Lin

k 4

Trf 3

92

45.4112.092.71

Trf 3

92

-45.41-11.9367.82

Line

73 (G

roen

)

6.191.739.29

Line

73 (G

roen

)

-6.19-1.739.29

Line

226

(Paa

rs)

12.973.11

19.27

Line

226

(Paa

rs)

-12.97-3.1119.27

Line

39 (R

oze)

15.094.23

22.64

Line

39 (R

oze)

-15.08-4.2222.64

Line

3 (B

lauw)

11.152.8616.63

Line

3 (B

lauw)

-11.15-2.8616.63

Link 402Link 402

Link

403

Link

403

Roelofs

45.41 kW12.09 kvar

0.97

DIgS

ILEN

T

•Weather forecast

•Trade price forecast

Generation & Load forecast

Network

Storage

specifications

Optimal

Storage

Solution

Genetic

Optimization

Calculate costs

and benefits

Load flow

analysis

3

1

4

Prediction module

Modeling module

Optimization module

Costs / benefits module

1

2

2

3

4

Complexity of Decision Making

Optimizing the use of storage

Decision methodology introductionfor distribution system planning

Focus: Application to storage

Implementation in GROWDERS (PLATOS)

26

Setting expectations

Learning goals:– Getting insight in overview of decisions

about storage applications

– Setting foundation for understanding implementation

for GROWDERS

General description of decision process– Introduction to decision process only

– Pitfall: not everything can be explained

Focus on implementation of principles and

choices made for GROWDERS (PLATOS)

27

Scenario 1

Scenario 2

Scenario n

Distribution system planning in general

Decision

process (Sub)Scenarios Optimal solutions

Optimal distribution network

Bottlenecks

PowerFactory 13.2.337

Projec t:

Graphic : Grid

Date: 4/17/2008

Annex:

Load Flow Balanced

Nodes

Line-Line Voltage, Magnitude [V]

Voltage, Magnitude [p.u.]

Voltage, Angle [deg]

Branches

Active Power [kW]

Reactive Power [kvar]

Current, Magnitude [A]

E

A

R

P

2-Winding..

199.9

496.9

36.4

122.2

2

-199.9

4-9

3.9

7312.8

922.2

2

STOR_002

0.000.000.00

STOR_003

0.000.000.00

STOR_001

0.000.000.00

5

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14

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28

Changing balance of objectives

(Risk management)

Avoid

ControlIntuition

Well funded(Net present value, Life cycle costing)

Certainty Customer oriented(Cost-benefit analysis)

COSTP

ER

FO

RM

AN

CE

RISK

29

Uncertainties in distribution system planning

Demand– Growth, size and location of energy demand

Production– Decentralized generation, type, location, size

– Sustainable generation patterns (wind & sun)

Other relevant factors– Legislation, regulatory processes (also European)

– Grid infrastructure: number, timing, place of failures

– Technological developments

– Incidents or chaos

– Environment

– Tariffs

30

RESIDENTIALDEMAND

ELECTRICALAPPLIANCES

ENERGYEFFICIENCY

CUSTOMERBEHAVIOURS

METERS &DISPLAYS

SUPPLIERTRANSACTIONS

DISTRIBUTIONNETWORK

TRANSMISSIONNETWORK

MICRO-GENERATION

DISTRIBUTEDGENERATION

CENTRALISEDGENERATION

INTERCONNECTIONS

smart metering

smart gridsElements influencing demand

ELECTRIC VEHICLES

CENTRALISEDSTORAGE

31

New development

Generate alternative solutions

Technical evaluation per alternative solution (check constraints)

Per alternative solution:Define optimal investment phasesEvaluate expected objectives (costs, reliability, image, …)

Decide (with uncertainty)

Inventory / problem definition

Scenarios + probabilities

Re-evaluate

Physical implementation of first step

Decision making process

32

What are the aspects of a good decision?

Objective Transferable

– Consistent terminology

– Separation between preparator and decision maker

Transparent / verifiable– Internally, to regulator, to public

Well founded– no unconscious decision making

New development

Generate alternative solutions

Technical evaluation per solution (check constraints)

Per alternative solution:

Define optimal investment phases

Evaluate expected objectives (costs, reliability, image, …)

Decide (with uncertainty)

Inventory / problem definition

Scenarios + probabilities

Re-evaluate

Physical implementation of first step

33

Important to distinguish I

Scenario

External

Uncertainty

It could happen to you

Possible problems

Not directly influenced

Alternative Internal

Certainty

Possible solution or action

Sought solution

You choose

New development

Generate alternative solutions

Technical evaluation per solution (check constraints)

Per alternative solution:

Define optimal investment phases

Evaluate expected objectives (costs, reliability, image, …)

Decide (with uncertainty)

Inventory / problem definition

Scenarios + probabilities

Re-evaluate

Physical implementation of first step

34

Important to distinguish II

Constraints

Requirements

You have to meet the requirements

Crossing the threshold gives

consequences

Good is good enough

Objectives Business values

Desired direction

This is the direction you (i.e. your

company) prefer(s)

More (less) is better

New development

Generate alternative solutions

Technical evaluation per solution (check constraints)

Per alternative solution:

Define optimal investment phases

Evaluate expected objectives (costs, reliability, image, …)

Decide (with uncertainty)

Inventory / problem definition

Scenarios + probabilities

Re-evaluate

Physical implementation of first step

35

Important to distinguish III

PlanningShort, medium, long term

Revisable plans

Decisions about assets

Normal configuration

Nominal or maximum values given

Assumptions about Operation

Operation Immediate to short time Direct effects Assets are given Configuration may change Actual parameters can be set Operational values are known,

alterations in procedures are possible

Projects

Strategy

Planning

Operations

36

Problem definition

A well described problem definitioncontains:– Uncertainties– Decision variables– Objectives– Constraints– Space– Time

General problem definition:What should I do (choice of decision variables) to obtain the objective(s) and satisfy the constraints, given the uncertainty, time and space?

Set borders Time

Complexity

New development

Generate alternative solutions

Technical evaluation per solution (check constraints)

Per alternative solution:

Define optimal investment phases

Evaluate expected objectives (costs, reliability, image, …)

Decide (with uncertainty)

Inventory / problem definition

Scenarios + probabilities

Re-evaluate

Physical implementation of first step

37

Problem definition in GROWDERS (1/2)

Given the grid and (expected) problems and

fluctuating demand and generation,

Optimize the use of storage in a distribution grid– Decide about number, location, type, size

– Maintaining all grid constraints

– Calculate (optimal) total costs / benefits

Post processing:– Compare result with conventional solutions

– Decide about (economic) duration of (optimal) storage solution

38

Problem definition in GROWDERS (2/2)

Solve grid problems– Under/over-voltages

– Overloaded components

– Mitigation of voltage dips

Postpone investment– Reduce uncertainty

Trading– Subject to grid constraints

Generic: long(er) term planning process with operational

(short term) constraints

39

Generating alternatives (general)

Alternative = possibility to solve the problem Brainstorm:

– Any potential idea is (initially) OK

– Selection will be done later

Set up a not too small number of alternatives– When there is less creativity in alternatives this can lead to no solution

or the lack of the optimal solution

Choose the best version per set of alternatives If needed, use phases

“DO NOTHING”is also an Alternativeto be considered!

40

Storage alternatives within GROWDERS

Use of Storage– Number

– Type(s)

– Size(s)

– Location(s)

Combination of multiple storage systems All simultaneously Number of potential solutions is combinatorial

Future:– Use of electric cars (V2G Vehicle to Grid)

– Mobile storage (short term mobility)

41

Added complexity through storage (1/2)

Storage is both demand and generation

Pattern not given or estimated but (second stage) result of controls after “normal” demand & generation

Has both operational & planning characteristics– And others, like economics, space

Possible conflicting interests (different stakeholders)– E.g. peak-shaving for grid operator, peak enhancing due to trading

company. Solved in GROWDERS by maximizing profit by trade, while keeping minimal grid constraints

42

Added complexity through storage (2/2)

Storage adds many new decisions When choosing 3 locations (begin/middle/end) in 4

feeders for small or larger storage (single type), this leads to theoretically 412 = 16.777.216 combinations

More realistic: 80 homes, available storage in 2 types, each 3 sizes: theoretically 680≈2*1062 options

When storage operations have to be optimized along the pattern, each trial solution requires a loadflow sweep for e.g. 8760 hours per year– loadflow means matrix inversion

Thus analytically impossible to solve

43

Other alternatives within GROWDERS“conventional” solutions Operational

– Change ratings, setting or use dynamic monitoring

– Demand side (& generation) management

– Load or generation curtailment (fined)

Planning (investments)– Add cables, lines

– Add transformer capacity

Reconfigure system (not within PLATOS) In GROWDERS (PLATOS) results are compared, not

simultaneously optimized

44

New development

Generate alternative solutions

Technical evaluation per alternative solution (check constraints)

Per alternative solution:Define optimal investment phasesEvaluate expected objectives (costs, reliability, image, …)

Decide (with uncertainty)

Inventory / problem definition

Scenarios + probabilities

Re-evaluate

Physical implementation of first step

Decision making process

45

Typical constraints DSO

Comply with regulation Grid Code (Legislation) (internal) Guidelines Safety Reliability Thermal rated capacity Short-circuit capacity Power Quality standards Environmental rules Financial budget

46

Constraints within GROWDERS

Usual loadflow and short circuit checks Time patterns through “sweep” function Uncertainty modeled through prediction error: Neural

Network used to determine prediction error of demand

Prediction error

Demand

Historical data from Bronsbergen (NL) used. Neural Network predicts on basis of:- Time of the day- Day of week- Which month- Public holidays- Meteorological data (temperature, cloudiness)

47

Different objectives

Objectives DSO Performance Indicators For example

– Economical (costs, profits) CAPEX + OPEX

– Reliability

– Social environment

– Risk (financial)

– Image

n

tt

t

iCF

NPV0 )1(

CF = Cashflow

i = interest

t = time (year)

n = period

n

tt

t

iCF

NPV0 )1(

CF = Cashflow

i = interest

t = time (year)

n = period

48

Objectives within GROWDERS

Net Present Value of all costs Trading: Maximized within grid limits Performance indicators translated to costs, using

prices– Overloading

– Over/under-voltage

– Voltage dipsStorage Solution Generator

Alleviation Algorithm:

Overloading

Alleviation Algorithm:

Over/undervoltage

Alleviation Algorithm:

Voltage Dips

Trading Algorithm

Solution Assessment

Output data processing

Performance Indicator 1

Performance Indicator 2

Performance Indicator 3

Performance Indicator 4

+ + +

Non-storage Solution Generator

Performance Indicator

49

New development

Generate alternative solutions

Technical evaluation per alternative solution (check constraints)

Per alternative solution:Define optimal investment phasesEvaluate expected objectives (costs, reliability, image, …)

Decide (with uncertainty)

Inventory / problem definition

Scenarios + probabilities

Re-evaluate

Physical implementation of first step

Summary decision making process

50

Additional options for decision making

Time dependent alternative solutions (strategies)

Sensitivity analysis, what-if, break-even, risk-analysis

Portfolio decision making

Stochastic decision making

42

42

break-even point

02000400060008000

10000120001400016000

0

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

probability of X

cost

alternative 1

alternative 2

alternative 3

Graph Break-even point probabilities

Break-even point

2 & 339

39

RBAM Risk Based Asset Management

Risk

inventory

and selection

Decision process

For each risk

Optimal plan

given budget

Maybe

next year?

Bottlenecks

Portfolio

management

34

34

Distribution of risk

distribution of risk alternative 2

0%10%20%30%40%50%60%70%

0 1000 2000 3000 4000 5000

cost (kEuro)

chan

ces

Expected costs2800 kEuro

Pro

ba

bili

ty

51

Summary

Adding storage to the conventional decision process

for in distribution planning process means added

complexity

Use of dedicated tools is unavoidable

The structured approach allows consistent,

quantitative and transparent decisions

52

ConclusionsDecision making about storage

Follow a complete and integrated approach– Define proper problem scope (size, time)– Consider temporary storage solutions– Consider conventional solutions

incl. “do nothing solution” and delays

– Combine objectives (economics, risk, environment)– Check all constraints (technical, legal)– Reflect upon uncertainties

scenarios and input data

– Make consistent choices

Use consistent data and tools– Method is used within PLATOS

What’s next ?

54

Next session

Tuesday 17th of February 2011, 16:00 – 17:30

– Main features of PLATOS

– Demonstration with the PLATOS tool

– Examples, results

Wednesday 11th May 2011, Mannheim (GE)

Workshop including site visit to the storage systems

55

Example of PLATOS

Thank you for your attention

www.growders.eu

Petra de Boer Roger Cremers Gabriël Bloemhof

+31 26 356 2552 + 31 26 356 3240 + 31 26 356 6150

petra.deboer@kema.com roger.cremers@kema.com gabriel.bloemhof@kema.com