Impact of Distributed Generation on the Cost Effectiveness of Large Power Plants

Transcription

1 Impact of Distributed Generation on the Cost Effectiveness of Large Power Plants Christopher Breuer and Reinhard Madlener Institute for Future Energy Needs and Behavior (FCN) / Chair of Energy Economics and Management 10th IAEE European Conference, Vienna 7-10 September 2009

2 Presentation Outline Introduction Framework conditions in Germany Impact of distributed generation Investment model Results Discussion and conclusions 9 Sep 2009 FCN, E.ON ERC/RWTH Aachen University 2

3 1. Introduction Motivation and goals of the study Motivation: Due to the increasing importance of distributed generation (DG), conventional large-scale power plants face new requirements and uncertainties, requiring an explicit consideration of these impacts in investment decisions. Especially the decreased utilization and increased partial-load operation could lead to marked changes in the cost-effectiveness of different types of power plants. Research goals: (1) investigating the main factors influencing the cost-effectiveness of power plants; (2) presenting an analysis of the impact of different DG technologies on centralized power generation; and (3) demonstrating the changes in the investment decision. 9 Sep 2009 FCN, E.ON ERC/RWTH Aachen University Christopher Breuer 3

4 2. Introduction Energy policy goals in Germany (roadmap) GHG mitigation goals from Kyoto-Protocol: -21% until % until 2020 Implementation in Germany: 27% share of renewable electricity generation by % share of cogeneration (CHP) by 2020 Strong trend towards distributed power generation! 9 Sep 2009 FCN, E.ON ERC/RWTH Aachen University Christopher Breuer 4

5 2. Framework conditions in Germany Capacity gap in Germany Currently Needed needed capacity capacity Capacity from planned additional CHP installations (25% goal) Capacity from Renewables (without hydro) Capacity from planned additional thermal power plants (high probability of realization) Capacity from newly built power plants (under construction or in op. after 2005 ) Currently installed capacity (thermal and hydro) Source: Deutsche Energie Agentur GmbH (dena) 2008 High investment needs in the coming years! 9 Sep 2009 FCN, E.ON ERC/RWTH Aachen University Christopher Breuer 5

6 2. Framework conditions in Germany Promotion of renewables (EEG) Priority feed-in of the electricity produced Constant prices over time No costs for grid connection Promotion of energy efficiency (esp. CHP) Priority feed-in of the electricity produced Time-independent remuneration with fixed annual tariff (+ mark-up) Liberalized electricity market (since 1998) 9 Sep 2009 FCN, E.ON ERC/RWTH Aachen University Christopher Breuer 6

7 3. Impact of distributed generation Overview distributed generation feed-in (RETs) Share of renewables in total power production: 14.2% (2007) 9 Sep 2009 FCN, E.ON ERC/RWTH Aachen University Christopher Breuer 7

8 3. Impact of distributed generation Illustrative feed-in of a regulatory zone (RETs) 9 Sep 2009 FCN, E.ON ERC/RWTH Aachen University Christopher Breuer 8

9 3. Impact of distributed generation Overview of decentralized feed-in (CHP) Source: BMU Report Leitszenario (2008) Share of cogeneration (CHP) plants in power generation: ca. 11% (2007) 9 Sep 2009 FCN, E.ON ERC/RWTH Aachen University Christopher Breuer 9

10 3. Impact of distributed generation Effects and possible consequences Two effects: Crowding out of conventional production (crowding out effect) Increased requirements to grid operation (fluctuation effect) Possible consequences result on, among others: Capacity utilization of the power plants Operation of the power plants Wholesale electricity price 9 Sep 2009 FCN, E.ON ERC/RWTH Aachen University Christopher Breuer 10

11 3. Impact of distributed generation Impact of the Merit-Order Effect The Merit-Order Effect denotes the negative impact on the wholesale electricity price of renewable energies Merit-Order (Reference) Merit-Order + 10 GW EEG Cumulated Capacity [MW] Lower and more volatile wholesale electricity prices in the short-term! 9 Sep 2009 FCN, E.ON ERC/RWTH Aachen University Christopher Breuer 11

12 3. Impact of distributed generation Impacts on power plant operation Problems from price impacts: Increased no. of plant ramp-ups / shut-downs Increased non-profitable partial load operation 9 Sep 2009 FCN, E.ON ERC/RWTH Aachen University Christopher Breuer 12

13 4. Investment model Discounted cash flow method Data basis: Electricity-/Fuel prices from Leitstudie 2008 Power plant data for investment decision Non-linear conversion efficiency curves Consideration of technical framework conditions (availability, operating ranges) Deterministic and stochastic Decision variables: NPV and power generation cost Goal: Analysis of the impact of increased partial load operation, increased ramp-up-/shut-down-processes and lowered capacity utilization 9 Sep 2009 FCN, E.ON ERC/RWTH Aachen University Christopher Breuer 13

14 4. Investment model Conversion efficiencies by capacity utilization Conversion efficiencies 9 Sep 2009 FCN, E.ON ERC/RWTH Aachen University 14 14

15 4. Investment model Types of power plants considered Unit Lignite Hard-coal CCGT Technical parameter Declared capacity MW Construction time Years Plant lifetime Years Minimum load MW 420 (40%) 320 (40%) 200 (40%) Energy consumption per ramp-up GJ / MW Economic parameter Specific investment cost / kw Fixed operating cost / kwa Variable operating cost / MWh Cost of abrasion per ramp-up / MW Discount rate: 8%/a 9 Sep 2009 FCN, E.ON ERC/RWTH Aachen University 15 15

16 4. Investment model Parameters Input parameters: Capacity utilization (full-load hours) [h/a] Ramp-ups [1/a] Minimum load share [%] Partial load share [%] State of operation Off Minimum load Partial load Standard load Model implementation No power production Electricity generated counts for minimum load share Electricity generated counts for partial load share 100% Minimum load share-partial load share 9 Sep 2009 FCN, E.ON ERC/RWTH Aachen University 16 16

17 4. Investment model Stochastic model extension Main elements: Mean reversion Increasing volatility Possible drift 9 Sep 2009 FCN, E.ON ERC/RWTH Aachen University 17 17

18 5. Results Calculations done NPV calculations Sensitivity analyses Scenario analysis Stochastic simulation 9 Sep 2009 FCN, E.ON ERC/RWTH Aachen University 18 18

19 5. Results Scenarios considered Full-load hours 9 Sep 2009 FCN, E.ON ERC/RWTH Aachen University 19 Minimum load share Partial load share No. of ramp-ups Basic scenario Lignite power plant 7,400 2% 3% 5 Hard-coal power plant 5,500 8% 8% 50 CCGT power plant 5,000 5% 5% 70 Scenario 1 Lignite power plant 7,300 4% 5% 5 Hard-coal power plant 5,000 10% 10% 80 CCGT power plant 4,500 6% 6% 100 Scenario 2 Lignite power plant 6,500 3% 4% 15 Hard-coal power plant 5,200 10% 10% 50 CCGT power plant 5,200 5% 5% 70 Scenario 3 Lignite power plant 6,000 6% 4% 15 Hard-coal power plant 4,500 12% 10% 80 CCGT power plant 4,200 6% 6%

20 5. Results Necessity of cost considerations Power generation cost by plant type +4% 9 Sep 2009 FCN, E.ON ERC/RWTH Aachen University 20 20

21 5. Results Sensitivity analysis: hard-coal power plant 9 Sep 2009 FCN, E.ON ERC/RWTH Aachen University 21 21

22 5. Results Sensitivity analysis: capacity utilization 9 Sep 2009 FCN, E.ON ERC/RWTH Aachen University 22 22

23 5. Results Sensitivity analysis: minimum load share 9 Sep 2009 FCN, E.ON ERC/RWTH Aachen University 23 23

24 5. Results Sensitivity analysis: partial load share 9 Sep 2009 FCN, E.ON ERC/RWTH Aachen University 24 24

25 5. Results Sensitivity analysis: annual start-ups 9 Sep 2009 FCN, E.ON ERC/RWTH Aachen University 25 25

26 5. Results Scenario evaluation 9 Sep 2009 FCN, E.ON ERC/RWTH Aachen University 26 26

27 5. Results Stochastic analysis 9 Sep 2009 FCN, E.ON ERC/RWTH Aachen University 27 27

28 6. Conclusions Partial load and ramp-ups Consideration of investment cost calculation needed Ramp-ups critical for lignite- and hard-coal power plants Partial load- and minimal load operation unfavorable for gas-fired plants Investment decision Flexible IGCC power plants are in almost all scenarios the preferred investment option Hard-coal power plants are, for some boundary conditions of the model, the inferior choice compared to lignite- and gas-fired power plants 9 Sep 2009 FCN, E.ON ERC/RWTH Aachen University 28 28

29 Any questions? Many thanks for your kind attention! Contact: Prof. Reinhard Madlener Tel Web: Student: cand. Wirt.-Ing. Christopher Breuer 9 Sep 2009 FCN, E.ON ERC/RWTH Aachen University 29 29

30 Back-up slides Parameter value determination Production in MW Hard-coal power plant Lignite power plant Gas turbine Hour of the week 9 Sep 2009 FCN, E.ON ERC/RWTH Aachen University 30 30

31 Back-up slides Politically expected RET development 9 Sep 2009 FCN, E.ON ERC/RWTH Aachen University 31 Source: BMU Leitszenario

32 Back-up slides Framework conditions in Germany Liberalized Market: Simple grid access for potential new power plant operators Former monopolists are big market players Liquid exchange-based electricity trading (EEX) 9 Sep 2009 FCN, E.ON ERC/RWTH Aachen University 32 32