Least-cost options for integrating intermittent renewables

Transcription

1 Copernicus Institute of Sustainable Development Least-cost options for integrating intermittent renewables Anne Sjoerd Brouwer

2 Introduction Stricter climate policies increasingly likely Power sector in a state of change More intermittent renewables: wind and solar PV Large CO 2 emission reductions: only a role for lowcarbon power plants Effect of intermittent renewables? Role of emerging technologies? Carbon Capture and Storage (CCS) Demand response Increased interconnection capacity Electricity storage

3 Structure 1. Background on intermittent RES (ires) 2. Methods 3. Results 4. Conclusions

4 Generation (GW) Electricity Price ( /MWh) Background on intermittent RES Intermittent RES important component of low-carbon power systems Specific properties 1. Intermittent 2. Partly unpredictable 3. Location specific Affect the whole power system Operation Economics Generation during week with minimum residual demand (6.4 GW) ma 17 jun di 18 jun wo 19 jun do 2 jun vr 21 jun za 22 jun zo 23 jun Nuclear Geothermal Wind Offshore Wind Onshore Hydro Solar PV NGCC-CCS NGCC Biothermal Pumped Hydro Gas Turbine Demand Response Electricity Price (LWA)

5 Methods Some technologies match better with intermittent renewables Goal of our research: identify which technologies may be part of a power system with: High reliability (LOLP <.1 day/year) Low emissions (>96% reduction CO 2 emissions) Low costs Power system with high shares of RES and ires Consider a future power system

6 Methods in Europe in the year 25 Six-node power system simulated with PLEXOS Three scenarios evaluated RES 4% 6% 8% ires 22% 41% 59% Quantify system costs British Isles (BR) United Kingdom, Ireland BR SC Scandinavia (SC) Norway, Sweden, Denmark France (FR) France FR GE IT Germany & Benelux (GE) Germany, The Netherlands, Belgium, Luxembourg IB Iberian Peninsula (IB) Spain, Portugal Italy & Alpine States (IT) Italy, Austria, Switzerland

7 Share of annual power generation (%) Study methods 1. Define non-fossil capacity per scenario 2. Define capacities of complementary options 3. Optimize fossil capacity per scenario [PLEXOS] 1% 8% 6% 4% 2% % Power Generation generation capacity per per scenario scenario 1% 2% 3% 14% 1% 6% 11% 2% 28% 6% 2% 1% 9% 2% 14% 6% 9% 2% 9% 9% 3% 13% 2% 1% 4% RES 6% RES 8% RES (Fossil capacity) Solar PV Wind onshore Wind offshore Geothermal Hydro Biomass Nuclear 4. Run hourly simulations [PLEXOS]

8 Results power generation Two aspects can reduce total system costs Least-cost power generation Efficient system operation First: what is the cheapest way to generate power in low-carbon power systems?

9 Electricity storage charging cost / Electricity price ( /MWh) 1. DR DR - Shed - Shed DR 1. DR - Shift - Shift Coal-CCS Results power generation Cheapest technology to generate low-carbon power? 2. GT Generation option with the lowest LCOE - 7 /tco2 NGCC At low capacity factors, DR is cheapest, but its potential is limited. GT is the next cheapest Demand Response has limited potential Storage can be attractive at low charging costs Electricity storage charging costs are too high Compressed Air Energy Storage NGCC-CCS Capacity factor (%) Pumped Hydro Storage Not realizable for electricity storage due to charging constraints NGCC GT NGCC GT NGCC-CCS NGCC-CCS PC-CCS PC-CCS DR-Shift DR-Shift DR-Shed DR-Shed PHS PHS CAES Electricity price The dotted line shows the typical charging cost (p1 of 6% RES electricity price)

10 Total Installed Capacity (GW) Power generation (TWh/yr) Power generation (TWh/yr) Results power generation Fossil capacity optimization: only natural gas capacity Combined cycle with CCS Gas turbines Existing Capacity (212) Installed capacity 4% RES (22% ires) 6% RES (41% ires) 8% RES (59% ires) % RES (% ires) Generation Oil Coal Demand Response Gas Turbine NGCC NGCC+CCS Solar PV Wind Onshore Wind Offshore Pumped Hydro Geothermal Hydro Biothermal 17% 4% RES 4% 6% RES 6% 8% RES 8% RES (22% (% Copernicus ires) (22% (41% ires) Institute (41% (59% ires) of (59% Sustainable ires) Nuclear Development

11 Generation (GW) Electricity Price ( /MWh) Results power generation NGCC-CCS generates power during the night in the summer Generation during week with minimum residual demand (6.4 GW) ma 17 jun di 18 jun wo 19 jun do 2 jun vr 21 jun za 22 jun zo 23 jun Nuclear Geothermal Wind Offshore Wind Onshore Hydro Solar PV NGCC-CCS NGCC Biothermal Pumped Hydro Gas Turbine Demand Response Electricity Price (LWA)

12 Generation (GW) Electricity Price ( /MWh) Results power generation NGCC-CCS baseload generation during winter time Gas turbines supply peak demand Generation during week with maximum residual demand (388 GW) Mon 14 Dec Tue 15 Dec Wed 16 Dec Thu 17 Dec Fri 18 Dec Sat 19 Dec Sun 2 Dec Nuclear Geothermal Wind Offshore Wind Onshore Hydro Solar PV NGCC-CCS NGCC Biothermal Pumped Hydro Gas Turbine Demand Response Electricity Price (LWA)

13 Annual system costs (bn /yr) Electricity cost or price ( /MWh) Results power generation Effect on costs: intermittent renewables increase total system costs 12% higher capital costs 18% reduction in electricity price % RES (22% ires) Total system costs per scenario 6% RES (41% ires) 8% RES (59% ires) Operational Costs (bn /yr) Fuel Costs (bn /yr) FO&M Costs (bn /yr) Invest DR (bn /yr) Invest NGCC/GT (bn /yr) Invest Solar PV (bn /yr) Invest Wind (bn /yr) Invest NGCC-CCS (bn /yr) Fixed scenario costs (bn /yr) Avg. electricity price ( /MWh) Avg. generation cost ( /MWh)

14 CO₂ emitted /stored (MtCO₂) Specific CO2 emissions (g/kwh) Results power generation CO 2 emission reduction target is met in all scenarios Specific emissions of <13g/kWh correspond to a >96% reduction in CO 2 emissions CO2 emissions and storage per scenario CO₂ stored Gas Turbine NGCC NGCC-CCS Specific emissions % RES 6% RES 8% RES

15 Results system operation Which options can decrease system costs by improving system efficiency? More efficient use of power plants Less curtailment

16 Total System Costs ( bn/y) Total System Costs ( bn/y) Results system operation Interconnects reduce costs up to a sweet spot Storage is expensive Effect of CAES and Interconnection Capacity on Total System Costs 95 GW CAES GW CAES GW CAES 26 GW CAES GW CAES 25 GW CAES 245 Current (37 GW) % RES Effect of CAES and Interconnection Capacity on Total System Costs 95 GW CAES 6% RES 95 GW CAES 47 GW CAES GW CAES 24 GW CAES 4% 5 GW CAES RES GW CAES 6% RES, 95 GW CAES 95 GW CAES 6% RES, 47 GW CAES 6% RES, 24 GW CAES Min Low Med* High Max (86 GW) (123 GW) (189 GW) (257 GW) (349 GW) 6% RES, 5 GW CAES GW CAES Interconnection Capacity 6% RES, GW CAES* Copernicus Institute of Sustainable 4% RES, Development 95 GW CAES

17 No DR 23 GW DR *47 GW DR 95 GW DR No DR 23 GW DR *47 GW DR 95 GW DR No DR 23 GW DR *47 GW DR 95 GW DR Total System Cost ( Bn/y) Results system operation Demand response also reduces costs by 2-3% Effect stronger at high RES penetration 3 25 Effect of DR on total system costs Generation Costs Fixed Costs % RES 6% RES 8% RES

18 Conclusions ires affect the operation of power systems, but their impacts are manageable. Larger reserves, effect on capacity factors of other generators Low-carbon power systems can be realized with various generation portfolios ires increase total system costs Natural-gas fired generation important in all scenarios DR & interconnections least-cost options Electricity storage too expensive

19 The bigger picture Future power systems will become increasingly complex New technologies More decentralized generation Cross-sectoral integration More uncertainty for investors Necessary investments in power plants will not be made with the current energy-only market design Business cases are unsound. Generation adequacy may become a key issues of future power systems

20 Thank you for your attention Any questions? Anne Sjoerd Brouwer Will Zappa Machteld van den Broek Anne Sjoerd Brouwer, Machteld van den Broek, William Zappa, Wim C. Turkenburg, André Faaij. Least-cost options for integrating intermittent renewables in low-carbon power systems. Applied Energy 161, pp (216)

21 6. Supplementary slides

22 Overview of method Input data Method Results 1. Low-carbon scenarios from previous studies 2. Projected load patterns 3. Projected ires production patterns 4. Projected power plant flexibility parameters 5. Projected balancing reserve patterns 6. Projected power plant techno economic data 7. Projected interconnection capacity 8. Projected demand response potential and costs 9. Projected techno-economic specifications of electricity storage 1. Define plausible non-fossil generation scenarios 2. Define capacities of complementary technologies (excluding fossil) 3. Run PLEXOS LT plan to optimize fossil generation capacity The full generation mix is now defined 4 Run PLEXOS MT and ST schedules to optimize hourly unit commitment and economic dispatch model for western Europe 1. Comparison of complementary technologies 2. Overview of full generation mixes 3. Costs and CO 2 emissions per scenario 4. Effect of demand response 5. Effect of electricity storage and interconnection capacity 6. ires integration costs: - Balancing costs - Profile costs 7. Profitability of generators 8. Sensitivity analyses

23 Key input parameters /GJ Coal Natural gas Uranium Fuel price Biomass /tco 2 Transport and storage CO 2 costs 13.5 France TWh/yr Britain Scandinavia Germany+ Iberian pen. Italy+ Load

24 Generator Input parameters of generation technologies Investment (TCR, /kw) Fixed O&M ( /kw/yr) Variable O&M ( /MWh) Efficiency Nuclear % Remarks PC-CCS % 9% CO 2 capture NGCC-CCS % 9% CO 2 capture NGCC % Biothermal % 1% biomass fired Geothermal Hydro Wind onshore CF: 22-26% Wind offshore CF: 4-43% Solar PV 7 17 CF: 11-21% Gas turbine % Pumped hydro % 8% round trip η Demand resp % 1-2 hrs of storage

25 Demand response potential Demand Response potential used in this study Shed - Electrolysis Italy and Alpine states Iberian peninsula Scandinavia Germany & Benelux France British Isles DR Potential (MW) Shed- Chloralkali Shed - Paper Shed - Steel Shed - Cement Shift - 2h by 1h Shift - 2h by 2h Shift - Airconditioning Shift - Washing machines / dryers Shift - Fridge and freezer Shift - Space/water heating

26 Detailed total system costs Annual system costs (bn /yr) % RES (% ires) 4% RES (22% ires) 6% RES (41% ires) 8% RES (59% ires) Electricity cost or price ( /MWh) Interconnection Cost (bn /yr) DR shedding cost (bn /yr) CO₂ Costs [tax/storage] (bn /yr) Start/stop costs (bn /yr) VO&M Costs (bn /yr) Fuel Costs (bn /yr) FO&M Costs (bn /yr) Invest DR (bn /yr) Invest NGCC/GT (bn /yr) Invest Solar PV (bn /yr) Invest Wind (bn /yr) Invest NGCC-CCS (bn /yr) Invest RES (bn /yr) Invest Nuclear (bn /yr) Reserve costs (bn /yr) Avg. electricity price ( /MWh) Avg. generation cost ( /MWh)

27 Capacity factors per month

28 Investment costs ( /kw) 1. Define non-fossil capacity per scenario 2. Define capacities of complementary options 2. Methods 3. Optimize fossil capacity per scenario [PLEXOS] 4. Run hourly simulations [PLEXOS] British Isles France Germany & Benelux Scandinavia Iberian peninsula Italy and Alpine states Interconnection capacity Deployment based on previous studies Costs of 28 k /MW/yr Demand response Potential based on other studies (11 categories) Crude costs of 2-5 /MWh (shedding) and 2-1 /kw (shifting) DR capacity per region Interconnection capacity per region Current (37 GW) Minimum (86 GW) Low (123 GW) Medium (189 GW, default) High (257 GW) Maximum (349 GW) Interconnection capacity (GW) British Isles France Germany & Benelux Scandinavia Iberian peninsula Italy and Alpine states 12, 1, 8, 6, 4, 2, Storage capacity: 8 hours Load shedding Load shifting Copernicus Institute of Sustainable DR capacity (GW) Development P s

29 1. Define non-fossil capacity per scenario 2. Define capacities of complementary options 2. Methods 3. Optimize fossil capacity per scenario [PLEXOS] 4. Run hourly simulations [PLEXOS] Two optimizations with PLEXOS Power system simulation and optimization tool Optimization of fossil capacity IEA projections of specifications 6 generator types Hourly simulations Chronological simulations with 876 steps Account for flexibility constraints Auxiliary reserves also included Curtailment of RES allowed

30 Profile Costs ( /MWh ires ) 3. Results power generation Total system costs are also increased by the integration costs of intermittent RES: Profile costs Balancing costs, grid costs 25 Profile integration costs of intermittent renewables % 1% 2% 3% 4% 5% 6% ires Penetration (%) Residual System Utilization Costs* Curtailment Costs Profile Costs

31 Required Capacity Payment ( /(kw.y)) Capacity Factor (%) 4% 6% 8% 4% 6% 8% 4% 6% 8% 4% 6% 8% 4% 6% 8% 4% 6% 8% 4% 6% 8% 4% 6% 8% 4% 6% 8% 4% 6% 8% 4% 6% 8% 4% 6% 8% 4% 6% 8% (3. Results economics) 4% RES 6% RES 8% RES Capacity Factor Hydro Geothermal Wind Offshore Wind Onshore Solar PV Nuclear NGCC NGCC+CCS Biothermal Gas Turbine Pumped Hydro Demand Response CAES (4.7GW)

32 (4. Sensitivities) Variations in input parameters are explored, as the input parameters determine the outcome of the study. Four tornado graphs depict the effect of variation in input parameters on: Total system costs Fuel use Generation capacities Electricity generation per generator type

33 (4. Sensitivities key findings) Results are overall robust. Results are only considerably affected by: High gas price (7.8 /GJ) shift of NGCC-CCS to PC-CCS Cheaper biomass (5.5 /GJ) biomass early in merit order Higher CO2 cap (18 Mt) shift of NGCC-CCS to NGCC Lower investment costs of ires lower system costs More flexibility in systems resulting from interconnections, DR or CAES leads to More base-load generation Less GT capacity being installed and used Investment costs of ires are a key factor. Reduction in ires investment costs has a large impact Overall ires cost reduction of >31% required to make 8% RES the cheapest scenario

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35 4. Discussion scope, assumptions This study only considers snapshots of possible future power systems in 25. No consideration is given to the dynamic transition from the current system Starting points of this study are pre-set emission reduction and reliability targets. Heating (demand, generation, storage) is not included. No transmission constraints are simulated within regions Assumed properties of DR capacity 49 GW of DR potential Shedding costs in this study: 2-5 /MWh Shifting investment cost in this study: 2-1 /kw

36 4. Discussion - caveats The scenario approach fixes 5-65% of the costs exogenously leaving less room for optimization. Thus systems are plausible, not necessarily optimum. This may be reflected in the costs. Capacity credits of ires are fixed. This assumption: Underestimates the benefits of interconnections (interconnectors can increase the capacity credit by spatial smoothing); Underestimates the profile costs in the 8% RES scenario compared to the 4% RES scenario (capacity credits decrease with higher ires capacity, requiring more firm capacity with low capacity factors). Significant uncertainties remain about the potential and costs of DR The model does not include specialized VOLL-values, or a detailed representation of super-peak generators (e.g. backup generators). These could improve the profitability of power plants by causing price spikes, which lead to big profits in a small amount of time.