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Faculty of Business and Economics , Chair of Energy Economics, Prof. Dr. Mst THE VALUE OF FLEXIBILITY OPTIONS FROM AN www.ee2.biz OPERATORS PERSPECTIVE Steffi Sch chrei eiber Co-Author: Theresa Mller 15 th IAEE European Conference 2017

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SLIDE 1

Faculty of Business and Economics, Chair of Energy Economics, Prof. Dr. Möst

www.ee2.biz

THE VALUE OF FLEXIBILITY OPTIONS FROM AN OPERATOR‘S PERSPECTIVE

Steffi Sch chrei eiber Co-Author: Theresa Müller

15th IAEE European Conference 2017 Session 4 G – Flexibility & Storage II Vienna, 05.09.2017

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SLIDE 2

TU Dresden, Chair of Energy Economics, Prof. Dr. Möst 2 05.09.2017

Agenda

Motivation 1 2 Value Definition and Methodological Approach 3 Flexibility Value at the Day-Ahead-Market 4 Conclusion

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SLIDE 3

TU Dresden, Chair of Energy Economics, Prof. Dr. Möst 3 05.09.2017

More incentives for operators to activate flexibility options with rising RES-share in future years

❶ Motivation ❷ Value Definition and Methodology ❹ Conclusion ❸ Flexibility Value at the DA-Market

Motivation Challenge Scope

  • balancing of load and generation
  • cost-benefit optimal operation mode (portfolio optimization)
  • provision of ancillary services
  • lower need of curtailment
  • be prepared for growing demand for flexibility

at present

  • low price signals at electricity market and German balancing power market
  • few incentives to activate or invest in flexibility options

in future years

  • rising demand for flexible assets because of rising share of renewable energy sources (RES) and

intermittent feed-in of photovoltaic and wind power plants

  • development of a methodology to determine the value of flexibility options (FO) from an
  • perator’s perspective
  • decision-making tool for activating or investing in FO

wind onshore photovoltaic battery storage CHP unit drinking water pumps

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SLIDE 4

TU Dresden, Chair of Energy Economics, Prof. Dr. Möst 4 05.09.2017

Agenda

Motivation 1 2 Value Definition and Methodological Approach 3 Flexibility Value at the Day-Ahead-Market 4 Conclusion

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SLIDE 5

TU Dresden, Chair of Energy Economics, Prof. Dr. Möst 5 05.09.2017

The flexibility value mainly consists of a technical and an economic value

❶ Motivation ❷ Value Definition and Methodology ❹ Conclusion ❸ Flexibility Value at the DA-Market

tech echnic ical l valu lue ec econ

  • nomic valu

lue flexibility value

  • activation time
  • maximal flexibility

provision time

  • load gradients
  • efficiency
  • availability
  • revenues at EPEX spot and

balancing power market

  • short-term activation

costs (variable) e.g. costs of provision, call and operation consequences

 

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SLIDE 6

TU Dresden, Chair of Energy Economics, Prof. Dr. Möst 6 05.09.2017

The flexibility value mainly consists of a technical and an economic value

❶ Motivation ❷ Value Definition and Methodology ❹ Conclusion ❸ Flexibility Value at the DA-Market planning horizon for activating FO flexibility value qualities of FO merchandising trails

contribution margin max = ෍ max sales revenues − ෍ min flexibility activation costs

under consideration

  • f technical

con

  • nstraints and
  • p
  • peration reg

egime

source: own illustration following Agricola et al. 2014

errors errors

day-ahead market preventive redispatch intraday and balancing power market curative redispatch

balancing of power

  • r price fluctuations

reaching high power and load gradients balancing of recognized forecasts errors balancing of residual forecasts errors short-term responding to unexpected changings in demand and supply plannable operating to balance fluctuations in demand and supply

D - 1 D

FO … Flexibility Option

D + 1 D

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SLIDE 7

TU Dresden, Chair of Energy Economics, Prof. Dr. Möst 7 05.09.2017

Estimation of the flexibility value of different assets at the day- ahead market in comparison to an inflexible operation mode

❶ Motivation ❷ Value Definition and Methodology ❹ Conclusion ❸ Flexibility Value at the DA-Market

Scenarios

contribution margin IST / FLEX operation mode IST FLEX IST FLEX IST FLEX IST FLEX

 at present not day-ahead marketed (marketed at the balancing power market as FCR)  water demand guided operation mode (restricted by filling level of water storage tanks)  revenues for pumping in periods of negative EPEX spot prices  costs of electricity purchase for pumping at positive EPEX spot prices

revenues short-term activation costs

 

 curtailment of RES if EPEX spot price is negative for a period of 6 hours  market premium will lapse (six-hour-rule § 51 EEG 2017)  revenues and costs structure as above (IST)  no curtailment in periods of negative EPEX spot prices  revenues for feed-in at positive EPEX spot price + market premium  costs for feed-in at negative EPEX spot price  cost-benefit optimal marketed at the day-ahead market  revenues for discharging at highest price level a day  costs for charging at lowest price level a day  heat guided operation mode  revenues for electricity feed-in at positive EPEX spot prices + CHP-remuneration  costs for gas purchasing and electricity feed-in at negative EPEX spot prices  electricity price guided operation mode  cost-benefit optimal operation mode and intelligent use of heat storage and heating boiler  revenues and costs structure as above (IST)  electricity price guided operation mode  load shifting in times of lowest electricity prices a day + optimal use of water storages  revenues and costs structure as above (IST)

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SLIDE 8

TU Dresden, Chair of Energy Economics, Prof. Dr. Möst 8 05.09.2017

Model formulation for optimization at the day-ahead market

❶ Motivation ❷ Value Definition and Methodology ❹ Conclusion ❸ Flexibility Value at the DA-Market

INPUT OPTIMIZATION OUTPUT

  • electricity price time series

(day-ahead forecast) 2017, 2020, 2030, 2040

  • temporal resolution of 8.760 h
  • gas price forecast

2017, 2020, 2030, 2040

  • CHP-remuneration
  • RES market premium of direct

marketing model

  • quarter-hourly generation and load

profiles (reference year 2016)

  • techno-economic characteristics of

considered assets

  • operation management regime /

constraints

  • heat demand (reference year 2016)

Target function 𝑑𝑝𝑜𝑢𝑠𝑗𝑐𝑣𝑢𝑗𝑝𝑜 𝑛𝑏𝑠𝑕𝑗𝑜 𝑛𝑏𝑦 = ෍

𝑛𝑏𝑦

𝑠𝑓𝑤𝑓𝑜𝑣𝑓𝑡 − ෍

𝑛𝑗𝑜

𝑔𝑚𝑓𝑦𝑗𝑐𝑗𝑚𝑗𝑢𝑧 𝑏𝑑𝑢𝑗𝑤𝑏𝑢𝑗𝑝𝑜 𝑑𝑝𝑡𝑢𝑡 in €/MW·a

  • under consideration of technical

constraints

profit maximization

  • optimized sales

revenues at the day-ahead market

cost-minimal generation

  • optimized

electricity and fuel procurement at the day-ahead market

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SLIDE 9

TU Dresden, Chair of Energy Economics, Prof. Dr. Möst 9 05.09.2017

Agenda

Motivation 1 2 Value Definition and Methodological Approach 3 Flexibility Value at the Day-Ahead-Market 4 Conclusion

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SLIDE 10

TU Dresden, Chair of Energy Economics, Prof. Dr. Möst 10 05.09.2017

Flexibility value of wind onshore and photovoltaic power plant at the day-ahead market

❶ Motivation ❷ Value Definition and Methodology ❹ Conclusion ❸ Flexibility Value at the DA-Market

  • FLEX value due to

curtailment of RES under consideration

  • f six-hour-rule

§ 51 EEG rather minor

  • cost savings of

curtailment in periods ≥ 6 hours of negative spot prices too low

  • no valuable cost-

benefit relationship

8 4 55 169 136 186 242 50 100 150 200 250 300 2017 [35%] 2020 [41%] 2030 [57%] 2040 [69%]

∆ contribution margin [FLEX - IST] [€/MW∙a] year [RES-share of brutto generation]

PV delta contribution margin wind onshore delta contribution margin

six-hour-rule § 51 EEG 2017  elimination of market premium (ex post) if the spot price is negative for a period of ≥ 6 hours 

  • pportunity costs of curtailment = 0 €

 reliable price forecast necessary

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SLIDE 11

TU Dresden, Chair of Energy Economics, Prof. Dr. Möst 11 05.09.2017

Flexibility value of a battery storage (lithium-polymer) at the day-ahead market

❶ Motivation ❷ Value Definition and Methodology ❹ Conclusion ❸ Flexibility Value at the DA-Market

13.911 8.517 23.612 34.457 10.000 20.000 30.000 40.000 2017 [35%] 2020 [41%] 2030 [57%] 2040 [69%]

  • max. contribution margin [FLEX]

[€/MW∙a] year [RES-share of brutto generation]

  • electricity price

guided operation mode of battery storage = increasing FLEX value with higher RES-share and rising price volatility

  • charging at lowest

electricity price a day

  • discharging at highest

electricity prices a day

  • cost-benefit optimal
  • peration mode at

the day-ahead market

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SLIDE 12

TU Dresden, Chair of Energy Economics, Prof. Dr. Möst 12 05.09.2017

Flexibility value of CHP unit and drinking water pumps at the day-ahead market

❶ Motivation ❷ Value Definition and Methodology ❹ Conclusion ❸ Flexibility Value at the DA-Market

5.214 4.431 15.119 22.006 5.000 10.000 15.000 20.000 25.000 2017 [35%] 2020 [41%] 2030 [57%] 2040 [69%]

∆ contribution margin [FLEX - IST] [€/MW∙a] year [RES-share of brutto generation]

9.044 7.644 20.455 24.467 20% 25% 30% 35% 40% 45% 50% 5.000 10.000 15.000 20.000 25.000 2017 [35%] 2020 [41%] 2030 [57%] 2040 [69%]

relative costs savings ∆ contribution margin [FLEX - IST] [€/MW∙a] year [RES-share of brutto generation]

∆ CM = CM [FLEX - IST] relative costs savings

value of electricity price guided operation mode [FLEX] higher than heat guided operation mode [IST] because of:

  • increasing electricity

sales revenues

  • lower gas costs of CHP

unit

  • optimal use of heat

storage and heat boiler

  • ca. 30% - 35% cost

savings because of daily load shifting [FLEX] in times of low electricity prices

  • optimal use of water

storage

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SLIDE 13

TU Dresden, Chair of Energy Economics, Prof. Dr. Möst 13 05.09.2017

Agenda

Motivation 1 2 Value Definition and Methodological Approach 3 Flexibility Value at the Day-Ahead-Market 4 Conclusion

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SLIDE 14

TU Dresden, Chair of Energy Economics, Prof. Dr. Möst 14 05.09.2017

Potential for electricity price guided operation mode of battery, CHP unit and pumps – flexibility value of RES rather minor

❶ Motivation ❷ Value Definition and Methodology ❹ Conclusion ❸ Flexibility Value at the DA-Market

results

  • growing flexibility value at the day-ahead market with rising RES-share and growing volatility of

electricity prices battery, drinking water pumps and CHP unit

  • contribution margin of electricity price guided operation mode [FLEX] always higher than

contribution margin of heat or water guided operation mode [IST] photovoltaic and wind onshore

  • RES with limited flexibility at the day-ahead market
  • curtailment [FLEX] not valuable under consideration of six-hour-rule §51 EEG 2017

photovoltaic wind onshore pumps battery storage CHP unit 5.000 10.000 15.000 20.000 25.000 30.000 35.000 2017 [35%] 2020 [41%] 2030 [57%] 2040 [69%] ∆ contribution margin [FLEX - IST] [€/MW∙a] year [RES-share of brutto generation]

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SLIDE 15

Faculty of Business and Economics, Chair of Energy Economics, Prof. Dr. Möst

www.ee2.biz

Thank you!

St Steffi i Sch Schreib iber TU Dresden, Chair of Energy Economics Phone: +49 351 463 39682 Email: steffi.schreiber@tu-dresden.de Th Ther eresa Mül üller TU Dresden, Chair of Energy Economics Phone: +49 351 463 39766 Email: theresa.mueller@tu-dresden.de Tuesday, 05.09.2017

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SLIDE 16

TU Dresden, Chair of Energy Economics, Prof. Dr. Möst 16 05.09.2017

References

Agri Agricola, A.; Seidl, H.; Heuke, R.; Roon, v. S.; Hinterstocker, M. und Eberl, B. (2014): dena-Analyse "Entwicklung der Erlösmöglichkeiten für Flexibilität auf dem Strommarkt". Endbericht. Deutsche Energie-Agentur GmbH (dena), Berlin. Bau auknecht, D.; Heinemann, C.; Koch, M.; Ritter, D.; Harthan, R.; Sachs, A.; Vogel, M.; Tröster, E. und Langanke, S. (2016): Systematischer Vergleich von Flexibilitäts- und Speicheroptionen im deutschen Stromsystem zur Integration von erneuerbaren Energien und Analyse entsprechender Rahmenbedingungen. Öko-Institut e.V., Freiburg/Darmstadt. Brunner, C.; Müller, T. und Heyder, B. (2015): Wettbewerb von Flexibilitätsoptionen zur besseren Integration Erneuerbarer Energien. In: bbr Leitungsbau Brunnenbau Geothermie, Spezial: Neue Leitungsnetze, S. 24-29. Künzel, T.; Klumpp, F. und Weidlich, A. (2016): Methodische Quantifizierung der Bereitstellungskosten flexibler Systemkomponenten im deutschen Stromsystem. In: Zeitschrift für Energiewirtschaft (03/2017), 41 (1), S. 1-22. Mül üller, T. und Brunner, C. (2015): Flexibilitätsoptionen zur Systemintegration erneuerbarer Energien im

  • Kostenvergleich. 9. Internationale Energiewirtschaftstagung an der TU Wien (IEWT 2015), S. 1-19, Wien.

Mül üller, T.; Michaelis, J.; Elsland, R.; Reiter, U.; Fermi, F.; Wyrwa, A.; Chen, Y.; Zöphel, C. und Kronthaler, N. (2016): REflex - Analysis of the European Energy System, Overview of Techno-Economic Characteristics of Different Options for System Flexibility Provision. Sch Schill, W. P. (2013): Systemintegration erneuerbarer Energien: Die Rolle von Speichern für die Energiewende. Vierteljahreshafte zur Wirtschaftsforschung, Deutsches Institut für Wirtschaftsforschung e.V. (DIW), 82. Jg. , S. 61- 88, Berlin.

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TU Dresden, Chair of Energy Economics, Prof. Dr. Möst 17 05.09.2017

Back-Up

time [h] load [MW] capacity deficits capacity surpluses

A

downward flexibility

  • ptions

B

upward flexibility

  • ptions

C

shifting flexibility

  • ptions

positive residual load [capacity deficits] negative residual load [RES surpluses]

source: own illustration following Bauknecht et al., 2014 and Schill, 2013

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SLIDE 18

TU Dresden, Chair of Energy Economics, Prof. Dr. Möst 18 05.09.2017

Back-Up

adaptation or flexibilisation of operation mode negative residual load [RES-surpluses] positive residual load [capacity deficits] temporal shifting spatial shifting

conventional power plants  lignite  hard coal  gas  oil  nuclear electricity price guided operation mode  CHP units  biogas / biomass plants  hydro power plants RES-curtailment  wind onshore  wind offshore  photovoltaic increase in demand  Power-to-Heat (electric boiler)  Power-to-Gas (water electrolysis)  Power-to-X (z.B. Power- to-Mobility, Power-to- Chemicals) load shedding energy-intensive industry  air separation  electrolysis  electric arc and induction furnace  etc. load shifting commercial and residential sector  heat pumps  night storage heater  air conditioning  ventilators  washing machines  cooling machines energy-intensive industry  paper industry (pulp production)  cement and raw mills electric mobility  batteries  plug-in-hybrid- vehicles energy storages  battery storage  A-CAES, CAES  heat storage  pumped storage  etc. electricity grid  regional shifting (national)  imports and exports  electricity grid

  • ptimization

 grid expansion  expansion of interconnection capacities demand-side-management

↑ upward flexibility options ↓ downward flexibility options ↔ shifting flexibility options

source: own illustration following Müller et al., 2016 and Brunner et al., 2015

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SLIDE 19

TU Dresden, Chair of Energy Economics, Prof. Dr. Möst 19 05.09.2017

Back-Up

costs of flexibility provision FIX initial costs VARIABLE activation costs investment costs [€/kW]

  • construction
  • development of

flexibility potential fix operating costs [€/kW∙a]

  • maintenance
  • servicing
  • staff costs

costs of provision [€/MWhel] costs of call [€/MWhel] costs of operation consequence [€/MWhel] Costs which occur one- time at construction or development of the FO. Costs which occur regular and independent from actual

  • peration of the FO.

Costs which occur only if FO operates.

source: own illustration following Müller and Brunner, 2015 and Künzel et al., 2016

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SLIDE 20

TU Dresden, Chair of Energy Economics, Prof. Dr. Möst 20 05.09.2017

equation 1: equation 2: equation 3: equation 4:

Back-Up: Calculation of market premium for RES which are in the direct marketing model

𝑛𝑝𝑜𝑢ℎ𝑚𝑧 𝑛𝑏𝑠𝑙𝑓𝑢 𝑤𝑏𝑚𝑣𝑓 = σ𝑔𝑗𝑠𝑡𝑢 ℎ𝑝𝑣𝑠 𝑝𝑔 𝑏 𝑛𝑝𝑜𝑢ℎ

𝑚𝑏𝑡𝑢 ℎ𝑝𝑣𝑠 𝑝𝑔 𝑏 𝑛𝑝𝑜𝑢ℎ 𝑡𝑞𝑝𝑢 𝑞𝑠𝑗𝑑𝑓ℎ ∙ 𝑕𝑓𝑜𝑓𝑠𝑏𝑢𝑗𝑝𝑜ℎ

∅ 𝑛𝑝𝑜𝑢ℎ𝑚𝑧 𝑡𝑞𝑝𝑢 𝑞𝑠𝑗𝑑𝑓 ∙ 𝑛𝑝𝑜𝑢ℎ𝑚𝑧 𝑔𝑓𝑓𝑒−𝑗𝑜 𝑛𝑏𝑠𝑙𝑓𝑢 𝑤𝑏𝑚𝑣𝑓 = 𝑛𝑏𝑠𝑙𝑓𝑢 𝑤𝑏𝑚𝑣𝑓 𝑔𝑏𝑑𝑢𝑝𝑠 ∙ ∅ 𝑡𝑞𝑝𝑢 𝑞𝑠𝑗𝑑𝑓 𝑏𝑞𝑞𝑚𝑗𝑓𝑒 𝑤𝑏𝑚𝑣𝑓 = 𝑔𝑓𝑓𝑒−𝑗𝑜 𝑠𝑓𝑛𝑣𝑜𝑓𝑠𝑏𝑢𝑗𝑝𝑜 + 𝑛𝑏𝑜𝑏𝑕𝑓𝑛𝑓𝑜𝑢 𝑞𝑠𝑓𝑛𝑗𝑣𝑛 𝑛𝑏𝑠𝑙𝑓𝑢 𝑞𝑠𝑓𝑛𝑗𝑣𝑛 = 𝑏𝑞𝑞𝑚𝑗𝑓𝑒 𝑤𝑏𝑚𝑣𝑓 − 𝑛𝑏𝑠𝑙𝑓𝑢 𝑤𝑏𝑚𝑣𝑓

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SLIDE 21

TU Dresden, Chair of Energy Economics, Prof. Dr. Möst 21 05.09.2017

Back-Up: Market value and market factor of RES

28 30 73 74

0,85 0,90 0,95 1,00 1,05 1,10 20 40 60 80 2017 [35%] 2020 [41%] 2030 [57%] 2040 [69%]

market factor market value [€/MWh] year [RES-share of brutto generation]

PV market value wind onshore market value PV market factor wind onshore market factor average yearly spot price

20 40 60 80 100 120 140

2017 [35%] 2020 [41%] 2030 [57%] 2040 [69%]

[€/MWh] year [RES-share of brutto generation]

applied value PV applied value wind onshore market value PV market value wind onshore market premium PV market premium wind onshore

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SLIDE 22

TU Dresden, Chair of Energy Economics, Prof. Dr. Möst 22 05.09.2017

Back-Up: Contribution margin of RES without [IST] and with curtailment [FLEX]

6 7 8 22 5 10 15 20 25 2017 [35%] [106 h] 2020 [41%] [96 h] 2030 [57%] [123 h] 2040 [69%] [369 h]

absolute frequency year [RES-share of brutto generation] [number of hours with negative electricity prices] absolute frequency of periods (≥ 6 hours) with negative electricity prices 10.000 20.000 30.000 40.000 50.000 60.000 70.000 80.000 2017 [35%] 2020 [41%] 2030 [57%] 2040 [69%] contribution margin [€/MW∙a] year [RES-share of brutto generation] PV CM [IST] wind onshore CM [IST] PV CM [FLEX] wind onshore CM [FLEX]

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SLIDE 23

TU Dresden, Chair of Energy Economics, Prof. Dr. Möst 23 05.09.2017

Back-Up: Revenues of electricity sales and market premium § 51 EEG 2017

5.119 12.015 18.477 50.533 17.876 4.077 49.621 2.169

10.000 20.000 30.000 40.000 50.000 60.000 2017 [35%] 2020 [41%] 2030 [57%] 2040 [69%] contribution margin [IST] [€/MW∙a] year [RES-share of brutto generation]

PV electricity sales revenues [IST] wind onshore electricity sales revenues [IST] PV revenues of market premium [IST] wind onshore revenues of market premium [IST] 5.119 12.069 18.646 50.775 17.876 4.077 49.621 2.169

10.000 20.000 30.000 40.000 50.000 60.000 2017 [35%] 2020 [41%] 2030 [57%] 2040 [69%] contribution margin [FLEX] [€/MW∙a] year [RES-share of brutto generation]

PV electricity sales revenues [FLEX] wind onshore electricity sales revenues [FLEX] PV revenues of market premium [FLEX] wind onshore revenues of market premium [FLEX]

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SLIDE 24

TU Dresden, Chair of Energy Economics, Prof. Dr. Möst 24 05.09.2017

Back-Up: Electricity price guided storage behaviour

5 10 15 20 25 30 35 0,5 1 1,5 2 2,5

01.01.2017 00:00:00 01.01.2017 01:00:00 01.01.2017 02:00:00 01.01.2017 03:00:00 01.01.2017 04:00:00 01.01.2017 05:00:00 01.01.2017 06:00:00 01.01.2017 07:00:00 01.01.2017 08:00:00 01.01.2017 09:00:00 01.01.2017 10:00:00 01.01.2017 11:00:00 01.01.2017 12:00:00 01.01.2017 13:00:00 01.01.2017 14:00:00 01.01.2017 15:00:00 01.01.2017 16:00:00 01.01.2017 17:00:00 01.01.2017 18:00:00 01.01.2017 19:00:00 01.01.2017 20:00:00 01.01.2017 21:00:00 01.01.2017 22:00:00 01.01.2017 23:00:00

EPEX spot price [€/MWh] charge- and discharge power [MW]

charge power [MW] discharge power [MW] EPEX spot price 2017

5 10 15 20 25 30 35 0,5 1 1,5 2 2,5

01.01.2017 00:00:00 01.01.2017 01:00:00 01.01.2017 02:00:00 01.01.2017 03:00:00 01.01.2017 04:00:00 01.01.2017 05:00:00 01.01.2017 06:00:00 01.01.2017 07:00:00 01.01.2017 08:00:00 01.01.2017 09:00:00 01.01.2017 10:00:00 01.01.2017 11:00:00 01.01.2017 12:00:00 01.01.2017 13:00:00 01.01.2017 14:00:00 01.01.2017 15:00:00 01.01.2017 16:00:00 01.01.2017 17:00:00 01.01.2017 18:00:00 01.01.2017 19:00:00 01.01.2017 20:00:00 01.01.2017 21:00:00 01.01.2017 22:00:00 01.01.2017 23:00:00

EPEX spot price [€/MWh] content of storage [MWh]

content of storage [MWh] EPEX spot price 2017

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SLIDE 25

TU Dresden, Chair of Energy Economics, Prof. Dr. Möst 25 05.09.2017

Back-Up: Costs and revenues of charge and discharge of a battery storage

14.176 36.430 28.087 70.888 10.000 20.000 30.000 40.000 50.000 60.000 70.000 80.000 2017 [35%] [106 h] 2020 [41%] [96 h] 2030 [57%] [123 h] 2040 [69%] [369 h]

costs and revenues [€/MW∙a] year [RES-share of brutto generation] [number of hours with negative electricity prices]

electricity procurement costs electricity sales revenues

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SLIDE 26

TU Dresden, Chair of Energy Economics, Prof. Dr. Möst 26 05.09.2017

Back-Up: Costs and revenues of heat and electricity price guided operation mode of a CHP unit

325.436 595.564 88.144 192.815 1.509 3.302 50.000 100.000 150.000 200.000 250.000 100.000 200.000 300.000 400.000 500.000 600.000 700.000 2017 [35%] 2020 [41%] 2030 [57%] 2040 [69%]

gas costs [IST] [€/MW∙a] electricity sales revenues [IST] [€/MW∙a] year [RES-share of brutto generation] heat guided operation mode [IST]

electricity sales revenues CHP(el) gas costs CHP(th) gas costs heat boiler(th) 329.454 612.196 86.716 186.401 1.741 4.341 50.000 100.000 150.000 200.000 250.000 100.000 200.000 300.000 400.000 500.000 600.000 700.000 2017 [35%] 2020 [41%] 2030 [57%] 2040 [69%]

gas costs [FLEX] [€/MW∙a] electricity sales revenues [FLEX] [€/MW∙a] year [RES-share of brutto generation] electricity price guided operation mode [FLEX]

electricity sales revenues CHP(el) gas costs CHP(th) gas costs heat boiler(th)

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SLIDE 27

TU Dresden, Chair of Energy Economics, Prof. Dr. Möst 27 05.09.2017

Back-Up: Delta-contribution margin of heat [IST] and electricity price guided [FLEX] operation mode

235.783 399.447 240.997 421.453

100.000 200.000 300.000 400.000 500.000 2017 [35%] 2020 [41%] 2030 [57%] 2040 [69%] contribution margin [€/MW∙a] year [RES-share of brutto generation] contribution margin [IST] contribution margin [FLEX]

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SLIDE 28

TU Dresden, Chair of Energy Economics, Prof. Dr. Möst 28 05.09.2017

Back-Up: Costs of electricity procurement of drinking water pumps with [FLEX] and without [IST] load shifting

  • 25.667
  • 27.391
  • 67.731
  • 70.016
  • 16.623
  • 19.747
  • 47.276
  • 45.549
  • 80.000
  • 70.000
  • 60.000
  • 50.000
  • 40.000
  • 30.000
  • 20.000
  • 10.000

2017 [35%] 2020 [41%] 2030 [57%] 2040 [69%] contribution margin [€/MW∙a] year [RES-share of brutto generation] pumps contribution margin [IST] pumps contribution margin [FLEX]

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SLIDE 29

TU Dresden, Chair of Energy Economics, Prof. Dr. Möst 29 05.09.2017

Back-Up: Flexibility value at the German balancing power market

photovoltaic wind onshore 3.037 pumps

74.841

battery storage 29.942 CHP unit 20.000 40.000 60.000 80.000 100.000 120.000

  • 75%
  • 50%
  • 25%

100% [2017] + 25% + 50%

  • max. balancing power revenues

[operator's amount] [€/MW∙a] sensitivity of balancing power prices photovoltaic SCR NEG wind SCR NEG pumps SCR POS battery storage PCR CHP unit SCR POS + NEG

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SLIDE 30

TU Dresden, Chair of Energy Economics, Prof. Dr. Möst 30 05.09.2017

Back-Up: Average capacity prices at the German balancing power market

190.000 129.000 105.000 52.300 38.400 29.500 24.300 8.300 6.800 5.000 8.300 6.000 15.700 7.400 5.300 20.000 40.000 60.000 80.000 100.000 120.000 140.000 160.000 180.000 200.000 2015 2016 forecast 2017

average capacity prices of balancing power [€/MW∙a] PCR SCR POS SCR NEG MCR POS MCR NEG

source: e2M, 2016

slide-31
SLIDE 31

TU Dresden, Chair of Energy Economics, Prof. Dr. Möst 31 05.09.2017

Back-Up

47% 82% 8% 40% 16% 15% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% photovoltaic wind power pumps battery storage CHP

  • max. availability [%/a]

day-ahead market

generation and charge of storage load and discharge of storage

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SLIDE 32

TU Dresden, Chair of Energy Economics, Prof. Dr. Möst 32 05.09.2017

Back-Up

36% 77% 12% 98% 36% 0% 0% 22% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% photovoltaic wind power pumps battery storage CHP

  • max. availability [%/a]

balancing power market

negative balancing power positive balancing power