simulation model for economic assessments of … · 2020-02-12 · kolloqium projektstudium 2010...

Kolloqium Projektstudium 2010 Smart Grid

13.08.2010 - 1 - Technische Universität Berlin Fachgebiet für Wirtschafts- und InfrastrukturPolitik

SIMULATION MODEL FOR ECONOMIC

ASSESSMENTS OF DEMAND

MANAGEMENT & STORAGE

Murk Creusen, Andreas Schröder, Jan Siegmeier

INFRADAY 9.10.2010

Technische Universität Berlin, DIW Berlin



Research question

3 options to reduce generation cost

• Grid Reinforcement

• Load Management

• Central Storage facilities

• Economic model with low-voltage grid representation

World with high RES, DG, EV

Not necessary

Not beneficial

Best option



2) Technical and cost parameters



2a) Grid Topology: Linear vs. Meshed Grid

Meshed Linear

Map

Grid Supply Point/ Storage location

Node 0 Node 0

DG/ load/ EV Nodes 1 and 2 Node 1



2b) Demand: Reference scenario

Comparison of winter urban standard load profile (week day) and „stochastic‟ profile of average

household and one electric vehicle charging in kW. (Sources: BDEW (2010), Bärwaldt and Kurrat (2008),

Leitinger (2009))



2c) Generation: Capacity

Generation parameters – available energy (sources: BDEW (2010); BMU (2009) for installed capacities, * = mean

values btw. years 2015 and 2020 for nuclear power; Kohler (2008); own calculations)

Available energy (per day, over all nodes)

demand peak, urban model [kW] 206

demand peak, rural model [kW] 225

Technology Wind PV CHP

(gas) bio-

mass hydro

nuclea

r *

lig-

nite

hard

coal gas Total

type time-

dep.

time-

dep.

time-

dep. flex. flex. flex. flex. flex. flex.

installed capacity (BRD 2020) [GW] 42 23 4 8 5 8 18 26 24 158

electr. generation (BRD 2020) [TWh] 96 20 20 51 25 63 123 100 88 586

- during summer (Apr-Sept) [TWh] 33 13 5,6

- during winter (Oct-Mar) [TWh] 63 7 14,4

capacity utilization (RES-E) 26% 10% 57%

technical avail. (non-RES-E) 88% 90% 93% 89% 89% 42%

installed capacity (urban) [kW] 127 70 12 24 16 24 55 78 73 478

installed capacity (rural) [kW] 139 77 13 26 17 27 60 85 90 523

Avail. energ. (urban summer) [kWh/d] 547 218 94 5801

Avail. energ. (urban winter) [kWh/d] 1050 114 238 502 335 541 1170 1662 732

1402

Avail. energ. (rural summer) [kWh/d] 599 239 102 6347

Avail. energ. (rural winter) [kWh/d] 1149 124 261 549 366 591 1281 1818 801

1534

Shares from Ministry of Environment: 2020 scenario

Calibrated to fit demand



From: Forschungsstelle für Energiewirtschaft e.V

Lignite

40€/ MWh

coal

60€/ MWh

Nuclear 9€/ MWh Ga

s a

nd

ste

am

70

€/ M

Wh

Gastu

rbin

es 1

00

€/

MW

h

Oil

125€/

MW

h

0 10.000 20.000 30.000 40.000 50.000 60.000 70.000

Load [MW]

Mar

gin

al C

ost

[€

/MW

h]

20

0

40

60

80

100

120

140

2c) Generation: Merit Order



2d) Demand Side Management:

Investment into Smart Meters

Metering and Appliances

Cost [€] Annual cost [€]

Installation 23.4 4

Meter 58.4 8.5

Add. cost/appliance for DSM device 8.8 1.5

av. 3,5 apps 32.2 5.25

Sum 112.4 19.25

(Source: own calculation, based on: Ecofys (2009), Destatis (2003))



2d) Demand Side Management: Calculated limits (H0)

0

0,02

0,04

0,06

0,08

0,1

0,12

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

kW

Reduction Limit

0

0,02

0,04

0,06

0,08

0,1

0,12

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

Increase Limit

electrical heater

cooling/ freezing

dishwasher

dryer

washing mashine

Source: Schubert, K. (2009) Stadler (2005), IFEU (2009), Klobasa (2007),

DSM: Household Potential



0

0,02

0,04

0,06

0,08

0,1

0,12

0,14

0,16

0,18

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

kW

Reduction Limit

0

0,02

0,04

0,06

0,08

0,1

0,12

0,14

0,16

0,18

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

Increase Limit

cooling/ freezingcommercial

air conditioning

cooling houses

cooling/ freezing smallsized

electrified heater

Source: Schubert, K. (2009) Stadler (2005), IFEU (2009), Klobasa (2007),

DSM: Commercial Potential

2d) Demand Side Management: Calculated limits (G0)



0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

kW

H0

min load with DSM

max load with DSM

2d) Demand Side Management: Potential

„boundary to perform Demand Changes“



2e) Storage: technical specification of selected device

Redox Flow Battery

Energy conversion efficiency 75 %

Power limit 30 kW

Capacity 100 kWh

Number of charge cycles 10.000

Investment cost 202.4 €/kWh

(Source: Prognos AG, 2009, Öko Institut, 2009)



3) Main scenarios



Scenarios

No Name Description

1 “Baseline” Original grid infrastructure; RE, DG and EV as projected

2 “DSM 1” High DSM investment

3 “DSM 2” Low DSM investment (25% penetration)

4 “Storage 1” central storage of 200kWh

5 “Storage 2” Low storage investment of 50kWh



4) Modeling



Basic idea

time

demand

• If the demand side is provided with real-time prices, there‟s an incentive for intertemporal load shifting -

the extent to which this is possible depends on the installed technology (e.g. smart metering, storage capacity, etc.)

• This can result in a better overall utilization of the more efficient generation technologies welfare improvements

High demand and prices at t = 1

demand already satisfied (or use stored energy)

Low demand and prices at t = 0

use or store more energy (more load online)

With demand-side

management technology

quantity

Demand

with DSM supply

price

p0*

p0

Demand

w/o

DSM DSM0

quantity

Demand

w/o demand-

side mgmt.

supply

price

p1

Demand

with

DSM

p1*

DSM1

+W0

- W1 W0 > W1

(* = the rationale for storage devices is similar)



pr1(q1)

DSM2

DSM1

q, q1, q2 - DSM1

p0r1

Welfare maximisation vs. Cost minimisation

p1(q1)

p0r2

p0r2 – m DSM2

pi(qi) = (p0ri - mDi) + mqi

pri (q) = p0r

i + mq

p2(q2) pr2(q2)



Welfare maximisation vs. Cost minimisation

Disontinuous inverse demand function - in fig. 3a, welfare contributions of DSM would cancel, so it is not

used, while in 3b, it improves welfare (source: own production).



Model set-up



Model set-up

lftl flow on line l at time t

B network susceptance

matrix

H weighted network matrix

(Hl,i = 1/xl LNl,n)

LN connection matrix

LNmax Network matrix

(l: single index for all non-

zero elements)

i Potential of node i

(choose 1=0)

Stin

storage inflow

Stout

storage outflow

Smaxin

storage inflow power limit

Smaxout

storage outflow power limit

Smaxcap

storage capacity

η storage efficiency (for initial

energy conversion only,

zero leakage from stotrage

assumed)



Model features

• Overall demand summed over day is fixed

• 9 generation technologies with time-dependent maximum capacity (RE, DG)

• Nodal energy balance captures 1st Kirchhoff rule (link btw. demand and generation)

• DSM and storage beneficial only if last generating unit switches

• DSM limits are time-dependent

• Asymmetric positive and negative DSM

• Storage capacity and power limit not time-dependent

- Charging a battery in one period increases demand (like DSM > 0)

- Discharge = energy supply in a later period acts like additional generation technology shift merit order of all

generation technologies with cgen > cstor to the right relative to demand function (similarly, DSM < 0 shifts the demand

function to the left)

- Storage with flexible location and scale, but less efficient (η) as opposed to DSM



5) Results



Model execution

• Executed in GAMS with EMP and CONOPT Solver

• Linear programming

• Runnung time 11.2 sec

• 3 MB work space allocated

• 6 - 8 Iterations

• 1327 * 937 variables



INVESTMENT

Scenario Baseline DSM 1 DSM 2 Storage 1 Storage 2

Scap_max [kWh] 0 0 0 200 50

DSM penetration 0% 100% 25% 0% 0%

Sin_max [kW] 0 0 0 30 30

Rural grid

Δ inv. cost [€] 0 40482 10120 40480 10120

Δ inv. cost/day [€] 0.00 18.99 4.75 4.05 1.01

Urban grid

Δ inv. cost [€] 0 40482 10120 40480 10120

Δ inv. cost/day [€] 0.00 18.99 4.75 4.05 1.01

COST MINIMISATION RESULTS

Rural grid Baseline DSM 1 DSM 2 Storage 1 Storage 2

Total cost [€/day] 42.05 37.55 40.07 38.03 39.79

Δ Total cost *[€/day] 0.00 4.50 1.98 4.02 2.26

? pay-off [€/day] 0.00 -14.48 -2.77 -0.03 1.25

pay-off time [years] 0.00 -7.66 -10.02 -4402.32 22.19

Urban grid

Total cost [€/day] 47.32 44.07 46.00 45.22 45.67

Δ Total cost * [€/day] 0.00 3.25 1.33 2.11 1.65

? pay-off [€/day] 0.00 -15.73 -3.42 -1.94 0.64

pay-off time [years] 0.00 -7.05 -8.11 -57.13 43.14

Results I



COST MINIMISATION

Rural grid Baseline DSM 1 DSM 2 Storage 1 Storage 2

Total cost 30.39 18.11 25.92 23.11 26.94

Δ Total cost * 0.00 12.28 4.47 7.28 3.45

? pay-off 0.00 -6.70 -0.28 3.23 2.44

pay-off time (years) 0 -17 -101 34 11

Urban grid

Total cost 30.42 17.90 26.08 21.61 26.52

Δ Total cost * 0.00 12.53 4.34 8.81 3.90

? pay-off 0.00 -6.46 -0.40 4.76 2.89

pay-off time (years) 0 -17 -69 23 10

Results II

Results in EUR based on a highly fluctuating demand profile



Results III

Pay-off storage devices of capacity Scap_max with investment break-even point (cost reduction curves are interpolated from several model runs).

0

2

4

6

8

10

12

0 50 100 150 200

Scap_max (kWh)

EU

R/d

ay

delta total cost (marg

cost)

investment cost 500

EUR/kWh

investment cost 200

EUR/kWh



6) Conclusion



Conclusion

• When modelling intertemporal load management, cost

minimisation more appropriate vs welfare maximisation

• Grid at 10 kV sufficiently equipped for high RES share

• Smart meters and related appliances not beneficial at

total cost of 112 EUR

• Storage facilities beneficial when cost is below 500

EUR/MWh capacity



Literature

• Klobasa, M. (2007): Dynamische Simulation eines Lastmanagements und Integration von Windenergie

in ein Elektrizitätsnetz auf Landesebene unter regelungstechnischen und Kostengesichtspunkten.

Promotion, ETH Zürich

• Leprich, U. (2010): Vollständige Dekarbonisierung des Energiesystems muss 2050 abgeschlossen sein,

BWK, Ed. 62 (2010), No. 4.

• Nestle, D., Ringelstein, J., Selzam, P. (2009): Einbindung von Stromkunden in ein intelligentes

Verteilnetz - Geschäftsmodelle und IT-Infrastruktur, Institut für Solare Energieversorgungstechnik

(ISET) e.V., Kassel.

• Nestle, D. (2007): Energiemanagement in der Niederspannungsversorgung mittels dezentraler

Entscheidung

• , Dissertation, Universität Kassel.

• Schill, W., Kemfert, C. (2010): The Effect of Market Power on Electricity Storage Utilization: The Case of

Pumped Hydro Storage in Germany. Discussion Paper DIW Berlin, 24 S., 947 / 2009.

• Schill, W. (2010): Elektromobilität in Deutschland - Chancen, Barrieren und Auswirkungen auf das

Elektrizitätssystem, Vierteljahresheft DIW Berlin (im Erscheinen).

• Schubert, K. (2005): Potential des Lastmanagements als Ersatz für Regelenergiekraftwerke bei einem

steigenden Anteil Erneuerbarer Energieträger, Diplomarbeit, Prof. Ziegler, TU Berlin, 2005

• Stadler, I. (2005): Demand Response - Nichtelektrische Speicher für Elektrizitätsversorgungssysteme

mit hohem Anteil erneuerbarer Energien, Berlin: dissertation.de - Verlag im Internet.

• Stadler, M. (2005): The relevance of demand-side-measures and elastic demand curves to increase

market performance in liberalized electricity markets: The case of Austria. Dissertation, Technische

Universität Wien



Back-up



Literature

• Strbac, G., Grenard, S., Cao, D., Pudjianto, D. (2006): Method for Monetarisation of Cost and Benefits

of DG Options, University of Manchester, Imperial CollegeLondon, DG GRID Projekt.

• Strunz, K., Fletsher, R. (2007): Optimal Distribution System Horizon Planning, IEEE Transactions on

Power Systems, Vol. 22, No.2, May 2007.

• Strunz, K., Knab, S., Lehmann, H. (2010): Smart Grid - The Central Nervous System for Power Supply,

TU Berlin Innovationszentrum Energie, Schriftenreihe No.2.

• Wittwer, C. (2010): Netzintegration von E-Fahrzeugen bei einem hohen Anteil erneuerbarer Energien,

Fraunhofer Institut für Solare Energiesysteme ISE, Presentation at Berliner Energietage, 11.May 2010.



Literature

• Modelling

• Rutherford (1995) on MCP and GAMS

• Gabriel, Leuthold (2009) on MPEC Stackelberg games

• Schill (2010), Sioshansi (2010), Kempton (2005) on storage facilities

• Distribution Network

• Strunz/Fletcher (2007) Technical parameters Distribution Network USA

• Strbac et al. (2006) on distributed generation in FIN + GB

• Regulation

• DG Grid Project (2006) Business Models and Regulation Distribution Network

• Agrell, Bogetoft (2010) on investment

• EcoFys/EnCT/BNetzAg (2009) on variable tariffs, smart metering

• BMU ( 2009): Langfristszenarien und Strategien für den Ausbau erneuerbarer Energien in Deutschland

unter Berücksich¬tigung der europäischen und globalen Entwicklung. Berlin, August 2009



Literature still to be reviewed (extract)

• Wolak, F. (2010): An Experimental Comparison of Critical Peak and Hourly Pricing: The PowerCentsDC

Program, Prepared for 2010 POWER Conference.

• ftp://zia.stanford.edu/pub/papers/dcpowercents.pdf [Retrieved 12 August 2010]

• Marketwatch (2010): eMeter to Brief Federal Officials on PowerCents DC Smart Grid Pilot Results

http://www.marketwatch.com/story/emeter-to-brief-federal-officials-on-powercents-dc-smart-grid-pilot-

results-2010-07-01?reflink=MW_news_stmp

• Kirschen, D., Strbac, G. (2004): Fundamentals of Power System Economics, Institution of Electric

Engineers, London.

• Prognos, Öko-Institut (2008):

• Sachverständigenrat, Baglatoni, A. (2010): The Super Smart - Grid : Paving the Way for a completely

renewable Energy System.

• Nadolni (2010): Diplomarbeit

• The Battle Group (2007): The Power of Five Percent - How Dynamic Pricing Can Save $35 Billion in

Electricity Costs, Discussion Paper Brattle Group, Faruqui, A., Hledik, R., Newell, S., Pfeifenberger, J.,

May 2007.

ftp://zia.stanford.edu/pub/papers/dcpowercents.pdf

http://www.marketwatch.com/story/emeter-to-brief-federal-officials-on-powercents-dc-smart-grid-pilot-results-2010-07-01?reflink=MW_news_stmp































Backup: Old slides

Reexamine carefully & improve before use!



Table of content

1. Introduction: Literature overview (Andreas)

2. Model description (Jan (+ Murk for network diagrams))

a. Basic idea / approach (1 slide)

b. Scenarios / major parameters in a distribution network: Grid topology (1 slide – input: Murk),

demand-side management, storage; RE / DG quota, demand patterns (1-2 slides)

c. Mathematical formulation: welfare maximization (1-2 slides)

3. Technical and cost parameters (Murk)

a. Sources & assumptions for demand (incl. EV), generation (esp. RE), grid, storage

b. Deriving the demand-side management limit

4. Main scenarios and expected behavior / trade-offs (Jan) (2-3 slides)

a. Baseline: “old” grid; RE, DG and EV as projected for 2030, but no active demand control

+ low investment / - high prices, RE “wasted”, failure rate (proxy: binding capacity limit)

b. High grid investment to accommodate RE & EV, but still no demand control (and no price signal)

- high investment , high prices

c. High DSM investment (worst test case: low exogenous demand at medium voltage low prices also at DN level high demand at DN

level (e.g. for EV) distribution network limit?)

- investment in DSM tech., maybe some grid reinforcements / + better utilization of RE, lower prices

d. High storage investment (central storage at grid supply point)

+/- similar to 4.c), but performing better for supply changes (RE), worse for demand changes (EV), with related effects for the grid hard to

quantify yet, depends on data / simulations!

e. Mix of DSM and storage – best of both worlds? +/- see 4.d)

f. Other alternatives: location of storage, different split of RE between high & medium voltage and DG (low voltage)

5. Some preliminary results

a. Grid: @400V: composition of households, commercial units / light industry, agriculture; @10kV: current capacity reserves, € per unit of

capacity extension (Murk)

b. Storage technologies: € per kWh, power characteristics, lifetime (Murk)

c. Demand-side management: current demand split, scope for demand shifts, features and performance of Smart Meters and other DSM

technology, € per unit (or lump sum for one-off control devices) – see also 3.) (Murk)

d. Simulation: … (Jan – no idea if I‟ll have something until Friday)

6. Discussion and remaining challenges

7. References (Andreas)



2.a) Grid Designs

households/

commerce:

Without / with

DSM

1a) Linear w/o distributed generation 1b) Linear with distributed generation

2b) Meshed with distributed generation 2a) Meshed w/o distributed generation

10 kV 400 V 10 kV 400 V

Without / with

storage distributed

generation



Symbols:

qt demand at time t

pDSM (qt ) inverse demand fct. with

demand-side management

p0 offset of inverse demand

function

A slope of inverse demand

function

DSMt shift of inverse demand fct.

through technologies

shifting demand between

periods (e.g. storage, smart

metering, load mgmt., etc.)

DSMtmax

maximum shift capacity

cs cost of using technology s

gs,t used capacity share of

technology s at time t

Gmaxs,t maximum capacity of

technology s at time t

2b) Mathematical formulation:Basic welfare maximization

model with demand-side management

)(,

0

,0,0

0

,0

~),~(max

0

maxmax

,

,

,

max

,

,,

0

function demandwith

:demand totalconst. 5.1)

:limit management side-demand 4.1)

:negativity-non 3.1)

:balanceEnergy 2)

:contraint Generation 1.1)

...subject to,

tttt

t

t

t

tts

s

ttts

tsts

t s

tss

s

tss

q

tttDSMgq

DSMqApDSMqp

DSM

tDSMDSMDSM

tsqg

tDSMqg

tsgG

gcgcqdDSMqpcontrolledpush

t

ttst



2b) Mostly the same as for a conventional grid…

Cost of generation

at period t

Benefit

at period t

Generation capacity is limited for each technology

Immediate consumption (or DSM. shift)

There„s no negative generation or consumption

More complicated (but still conventional) models could for example incorporate…

• grid topology

• physical properties of electricity networks, generators and loads (e.g. network capacity and losses, AC properties)

• additional terms in the welfare function (e.g. to account for environmental externalities or reliability)

)(,

0

,0,0

0

,0

~),~(max

0

maxmax

,

,

,

max

,

,,

0

function demandwith


:limit management side-demand 4.1)

:negativity-non 3.1)

:balanceEnergy 2)


...subject to,

tttt

t

t

t

tts

s

ttts

tsts

t s

tss

s

tss

q

tttDSMgq

DSMqApDSMqp

DSM

tDSMDSMDSM

tsqg

tDSMqg

tsgG

gcgcqdDSMqpcontrolledpush

t

ttst



4c) Mathematically: Welfare maximization, DSM and two-

way storage in a grid – our approach so far…

1),,(0

,

,,0,0,0,0

),(0,0

0

0

,0

,,0,0

,

,,0

)(0)(ˆ

~)

~(ˆmax

1

,max,max,

,

,

max

1

11

1

11

1

1

,,

,,

,

maxmax

max,max,

,

max,

,

1i 1

,

0

:flow 6.2.)

:limits flow line 6.1.)

:negativity-non 5.)

:limitscapacity Storage 4.)

:balance storage 3.2)


:balanceEnergy 2)

:limitspower Storage 1.3)

:limit mgmt. side demand 1.2)


...subject to and

function demandwith

,

slacktislack

HlfwheretlLFlfLF

tsiSinSoutqg

tiSSoutSinSinSout

SoutSin

DSM

LNHBwheretiBSinDSMqSoutg

tsiSinSinSoutSout

tiDSMDSMDSM

tsigG

SinDSMqQwhereQApQp

SoutcgcQdQp

i

ti

i

i

t

ill

t

ll

t

l

i

t

i

t

i

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i

ts

icapt

it

it

it

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t

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t

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t

l

iljl

ij

s j

j

t

iji

t

i

t

i

t

i

t

i

ts

i

t

ii

t

i

ii

t

i

i

ts

i

ts

i

t

i

t

i

t

i

t

i

t

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t

i

n T

t

i

tstor

s

i

tss

Q

t

i

tSoutSinDSMgq

it

it

it

it

it

its

it



New symbols:

SMtin

storage inflow

SMtout

storage outflow

SMmaxin

storage inflow power limit

SMmaxout

storage outflow power limit

SMmaxcap

storage capacity

η storage efficiency (for initial

energy conversion only,

zero leakage from stotrage

assumed)

2b) Mathematical formulation: Welfare maximization, DSM

and storage with feed-in

)(,,ˆ

0,0

)(0,0

,0,0,0,0

0

,0,0

,0

~),,~(ˆmax

0

11

max

1

11

1

11

maxmax

,

,

maxmax

,

max

,

,,

0

function demandwith

:demand totalconst. 2)-5.1

:limitscapacity Storage 4.2)


:negativity-non 3)-3.1

:balanceEnergy )2'



...subject to,

in

ttt

in

ttt

toutin

t

capt

outt

int

int

out

t

in

t

out

ttts

s

in

ttt

out

tts

in

t

inout

t

out

tsts

t s

out

tstortss

s

tss

q

t

in

tttSMSMDSMgq

SMDSMqApSMDSMqp

SMSMDSM

tSMSMSMSMSM

tDSMDSMDSM

tsSMSMqg

tSMDSMqSMg

tsSMSMSMSM

tsgG

SMcgcgcqdSMDSMqpcontrolledpush

t

outt

intttst



New symbols:



matrix


(Hl,i = 1/xl LNl,n)




zero elements)


(choose 1=0)

2b) Mathematical formulation: Welfare maximization, DSM

and storage with feed-in – in a grid

1),,(0

,

0,0

),(0,0

,

,,0,0,0,0

,0

,,0,0

,,0

)(0)(ˆ

~)

~(ˆmax

1

,max,max,

11

,

max

1

11

1

11

max,max,

,

,,

,,

,

maxmax

,

max,

,

1i

,,

0

:Flow 7.)

:limits flow line 6.)

:demand totalconst. 2)-5.1

:limitscapacity Storage 4.2)


:negativity-non 3)-3.1

:balanceEnergy )'2'

:limitspower Storage )1.2'

:contraint Generation )1.1'

...subject to and

function demandwith

,

slacktislack

HlfwheretlLFlfLF

SoutSinDSM

tiSSoutSinSinSout

tiDSMDSMDSM

tsiSinSoutqg


tsiSinSinSoutSout

tsigG

SinDSMqQwhereQApQp

SoutcgcgcQdQp

i

ti

i

i

t

ill

t

ll

t

l

tii

ti

icapt

it

it

it

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ts

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t s

i

tstor

i

tss

s

i

tss

Q

t

i

tSoutSinDSMgq

controlledpush

it

it

it

it

it

its

it



New symbols:



matrix


(Hl,i = 1/xl LNl,n)




zero elements)


(choose 1=0)

3c) Mathematical formulation: Welfare maximization, DSM

and storage with feed-in – in a grid

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:limits flow line 6.1.)

:negativity-non 5.)

:limitscapacity Storage 4.)

:balance storage 3.2)


:balanceEnergy 2)




...subject to and

function demandwith

,

slacktislack

HlfwheretlLFlfLF

tsiSinSoutqg

tiSSoutSinSinSout

SoutSin

DSM


tsiSinSinSoutSout

tiDSMDSMDSM

tsigG

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3) Potential for demand shifting by DSM

Source:

Stadler (2005)

Modellstadt Mannheim (2009)



3) DSMmax as a function of Investment

Standard SM 0,01

auto SM 0,11

0,1kW 0,21

0,2kW 0,31

0,3kW 0,41

0

20

40

60

80

100

120

140

160

180

200

0,000 0,050 0,100 0,150 0,200 0,250 0,300 0,350 0,400 0,450 0,500

∆d_realiseable

[kW]

Cost_var [€/HH/a]

Szenarien cost_var[€/HH/a] usage of ∆d_max [%] ∆d_realiseable [kW]

w/o Smart Meter 0 0,00% 0,000

standard Smart-Meter 14 10,00% 0,011

Smart Appliances Management 36,5 100,00% 0,113

storage device 0,1KW 76,5 188,57% 0,213

storage device 0,2kW 116,5 277,13% 0,313

storage device 0,3kW 156,5 365,70% 0,413



Model set-up

Set Description Unit Range

I Node - N={0,...,3}

T time period h T={0,...,24}

S generation technology - 9 technologies

L Line - 3 lines

Variable Description Unit Range

DSM it demand-side-management kWh Free

Sin it storage inflow kWh Positive

Sout it storage outflow kWh Positive

q it Demand kWh Positive

g is,t Generation kWh Positive

Δ it Phase angle difference (choose 1=0) 1 Free

Parameter Description Unit Range

cs variable generation cost (acc. to merit order) EUR/kWh 0.001 - 0.07

cstor variable cost of storage EUR/kWh 0.004

Η storage efficiency (zero leakage from storage) % 75

Sin imax storage inflow power limit kW 30

Sout imax storage outflow power limit kW 30

Scap,imax storage capacity kWh 0 – 100

DSMtmax load shift capacity kWh cf. Annex II

lfti electricity flow kW see LFmax

B network susceptance matrix 1/ see X

H weighted network matrix 1/ see X

LN incidence matrix 1 0 or 1

LFmax Maximal capacity for line flow kW 1850

slacki slack variable (with slack1=1) 1 0 or 1

X reactance of line 1/Ohm 0.4 - 0.5 per km

simulation model for economic assessments of … · 2020-02-12 · kolloqium projektstudium 2010...

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