Renewable Energy, Power Storage, and the Importance of Modeling Partitioned Power Markets

Transcription

1 Renewable Energy, Power Storage, and the Importance of Modeling Partitioned Power Markets 2009 GTSP Technical Workshop May 28, 2009 College Park, MD Marshall Wise, Kate Calvin, Page Kyle, Steve Smith Joint Global Change Research Institute Pacific Northwest National Laboratory PNNL-SA-66811

2 Motivation Our current modeled representation of the electricity system is based mainly on average annual behavior and economics. Has been an appropriate, useful level of detail for IA modeling. Current approach misses some important physical and structural detail when representing renewables. Timing and availability of wind and solar power Becomes more of a concern in high-renewable scenarios. We want to be able to new technology systems where the timing of electricity demand is critical. Plug-in electric vehicles demanding mostly off-peak Electric storage technologies Other technologies/policies/systems that would alter the timing profile of electric demand.

3 More Motivation Response of Electricity Prices under a Carbon Constraint Some of our other analysis has helped to illuminate the differential response to carbon policy of different time segments of the electricity market i.e., differences in off-peak vs peak. Following slides illustrate with an example from a study of ECAR (a mid-western US electric power control area), using a simple optimal dispatch model. Wise, Marshall A.; Dooley, James J. Baseload and Peaking Economics and the Resulting Adoption of Carbon Dioxide Capture and Storage Systems for Electric Power Plants. In, ES Rubin, DW Keith and CF Gilboy (Eds.), Greenhouse Gas Control Technologies, Volume I (pp ). Elsevier Science, 2005.

4 Where in the Dispatch Order are the ECAR Year 2050 Carbon Emissions? ECAR Electric Power Carbon Emissions Million tonnes Carbon/yr Base 2002 Case 1. Stabilize at 2002 to 2050 Baseload (CF > 60%) Intermediate and Peaking (CF <=60%) Case 2. Reduce to Half of 2002 by 2050 Case 3. Reduce to 1/3 of 2002 by 2050 In 2002, about 85% of the carbon emissions were in the baseload. Cases 1,2, 3 are three levels of emissions reductions. In Case 3, peak and intermediate emissions are just slightly below what they were in It is clear that these emissions are the costliest to remove. Wise and Dooley, 2005.

5 Generation Dispatch Curves: Current vs. CO 2 Policy in 2050 Dispatch Cost ($/MWh) ECAR Generation Dispatch Curve for 2005 (Modeled) PC PC Min Dispatch 50 pctile 90 pctile 10 Nuclear 0 Renewables Capacity (GW) ECAR s current capacity mix dominated by PC coal Gas CC built in last decade PC Gas CC Gas Steam Gas CT Gas CT and Steam used for Peaking PC ECAR Generation Dispatch Curve for 2050: Case 3. Emissions Reduced to 1/ Levels Dispatch Cost ($/MWh) IGCC CCS Min Load 50 Pctile 90 Pctile PeakLoad RtrFit PC Coal PC Coal Gas CC Gas CC CCS 10 Nuclear 0 Renewable Capacity (GW) Gas CT Costs Include $70/ton CO 2 price. Baseload and some intermediate load plants need to be decarbonized with CCS Distance between dispatch costs in baseload versus peak increases dramatically Wise and Dooley, 2005.

6 Project Objective Main Objective: Add sufficient structural detail to our integrated assessment modeling and analysis to Fairly and consistently analyze the relevant economics of renewable electricity technologies in the long term as technologies, policies, and the energy system evolve. Capture the special physical characteristics of renewables and integrate them with electricity demand. Analyze the impact of paradigm-changing technologies such as energy storage systems for electricity and plug-in vehicles. Represent the economics of the difficulty and resulting cost of mitigating CO2 emissions from electricity given the time-varying nature of electricity demand.

7 Approach (in Progress) Basic approach: explicitly model the temporal variation of electricity demand. But must be kept simple to function in an IA model. Incorporate the impact of intermittency on the relative economics of renewable in the market for generation. Detailed modeling of grid reliability not appropriate or even possible here, but the first order economic implications must be considered in a fair and consistent manner. Explicitly model storage technologies. Storage can expand the potential penetration of renewables. Explicitly model technologies and technology responses that can change the pattern of electricity demand. E.g., plug-in vehicles, peak demand reduction in Buildings.

8 Hourly Electricity Demand and Load Curve MW Hour of Year This variation in load is a key driver of the economic trade-offs involved in determining the mix of new capacity to be built. Modeling approach demand is sorted from highest to lowest. The resulting Load Duration Curve is then divided into segments of number of hours where demand is similar.

9 Load Duration Curve From the Demand Side MW On Peak Intermediate Off Peak Hour Demand for electricity is represented by vertical segments. These segments reflect the markets and electricity prices the demand side sees.

10 Electric Generation- Supply Side View Step 5. Solve for Incremental Generation needed for On Peak Step 4. Intermediate Generation Operating in On Peak Step 3. Solve for Incremental Generation needed for Interm ediate MW Step Step 2. Baseload Generation Operating in Higher Segments Step 1. Solve for Off Peak (and thus Baseload) Generation Mix Hour of the Year (Sorted) Supply is better represented as horizontal segments Use a heuristic rule to mimic an optimization approach: you operate the lowest variable cost stuff first so if you chose to operate off-peak, you will operate intermediate, and peak

11 Linked Electricity Supply Sectors Baseload sector output serves offpeak demand and Intermediate Generation Intermediate Generation has multiple inputs 1.supply provided by Baseload Generation 2. Incremental supply provided by a mix of technologies competing on economics at the appropriate lower capacity factor. Only the input from step 2 affects the price of this sector, since it is the margin. Peak Generation analogous to Intermediate except Intermediate is its first input. In Baseload and Intermediate sectors, prices are just the variable costs. Only in Peak sector are prices the sum of variable and capital costs.

12 Links from Demand to Supply Sectors Off-Peak Electricity Demands Intermediate Electricity Demands On-Peak Electricity Demands Intermediate Generation Total Sector Scaled by relative % in each segment On-Peak Generation Total Sector Scaled by relative % in each segment Baseload Generation Sector Intermediate Generation Margin Sector Peaking Generation Margin Sector Competing Technologies Competing Technologies Competing Technologies

13 Modeling of Payment for Contribution to Capacity Dispatchable capacity (e.g., fossil, nuclear, hydro with storage, anything with storage) has the advantage that it is available when needed. Whereas wind and solar are intermittent, can t always count on their full output when needed, which can reduce their value. In real world, some systems have separate markets for capacity. Others have explicit mandates for capacity reserves to get the same result. Wind is typically given reduced credit for its capacity from intermittency. We have modeled this by giving credit (reduction in cost) that is used in the logit competition. Dispatchables get full credit. Intermittent get less than full credit We have been implicitly doing something like this all along for wind and solar as we use low capacity factors to levelize their energy costs. E.g., if we assumed a 1 MW wind turbine had a 33% capacity factor, we assumed it only got paid for electricity (including capacity payment) on 33% of its capacity. We did not assume a full capacity payment on that 1 MW.

14 Other Modeling Considerations for Renewables and Storage Intermittent renewable resources will supply the respective electric segments based on their physical availability. Economic impact of intermittency: intermittent resources will receive reduced capacity revenues relative to conventional capacity (fossil and nuclear). Model will not explicitly force renewables to firm-up by mating with back-up. (Although there are some technological advantages such as hybrid CSP which are considered.) Electricity storage technologies consume mainly off-peak and excess renewable electricity as their energy sources. Compete with other generating technologies based on relative economics i.e., capital costs and energy costs. Storage technologies would receive full capacity payments. Storage likely to become more important under climate policy expanding renewables plus allowing use of lower-carbon, capital intensive technologies such as nuclear and CO2 capture.

15 Representative Results (for one set of technology assumptions): US Electric Production $25/tC Hotelling Tax Baseload Generation EJ/yr battery geothermal solar wind hydro nuclear bio w ccs bio oil w ccs oil gas w ccs gas coal w ccs coal

16 Representative Results (for one set of technology assumptions): US Electric Production $25/tC Hotelling Tax Intermediate Generation EJ/yr battery geothermal solar wind hydro nuclear bio w ccs bio oil w ccs oil gas w ccs gas coal w ccs coal

17 Representative Results (for one set of technology assumptions): US Electric Production $25/tC Hotelling Tax Peak Generation EJ/yr battery geothermal solar wind hydro nuclear bio w ccs bio oil w ccs oil gas w ccs gas coal w ccs coal

18 Further work Research/explore load shapes. Storage technologies/storage resources Intermittency challenge continue to improve modeling We have begun a collaborative effort with NREL and their group that builds the REEDs (formerly WinDS) model of electric power and renewables in the US. They have a regionally-disaggregated optimization model We are running a parallel set of technology and policy scenarios with our models to see what insights can be gained from comparing the two approaches.