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RGGI Renewable Energy Modeling Assumptions

RGGI Renewable Energy Modeling Assumptions. Bob Grace, Sustainable Energy Advantage, LLC Regina Jain, LaCapra Associates RGGI Stakeholder Meeting February 16, 2005 NY, NY. Overview. Overview of Renewable Energy Analysis Key Challenges: Demand Assumptions

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RGGI Renewable Energy Modeling Assumptions

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  1. RGGI Renewable Energy Modeling Assumptions Bob Grace, Sustainable Energy Advantage, LLC Regina Jain, LaCapra Associates RGGI Stakeholder Meeting February 16, 2005 NY, NY

  2. Overview • Overview of Renewable Energy Analysis • Key Challenges: Demand Assumptions • Differing RPS policies in states across region • Key Challenges: Supply Assumptions • Developing resource potential • Developing representative costs • Summary of Key Renewable Energy Inputs

  3. Development of Renewable Energy Modeling Inputs • Purpose: • Develop reasonable input assumptions for IPM renewable energy (RE) supply availability, cost and demand to: (i) determine baseline (ii) enable policy analysis of greenhouse gas initiative measures • Perspective: • “Middle of the Road,” neither conservative nor aggressive • Constraints: • Recent studies and sources • Consistency across the modeling region • Accommodate state/regional studies using different methodologies and assumptions, and filling numerous data gaps • Analysis years – 2005, 2010, 2015, 2020

  4. Modeling Challenges • Analysis and judgment required: • Selection of appropriate data sources (credible, recent, applicable to region) • Resolve conflicts, fill gaps, extrapolate • Avoid bias • Taking analysis to appropriate level of detail & materiality • Wide range of input parameters… Example: wind power (land-based) • Windy land area by wind speed class • Performance (c.f.) by wind speed class • Production profile • Land Exclusions • MW quantities • Distance from transmission (cost adders) • Representative costs (capital, operating, financing) • Project scale • Improved performance and cost over time • Additional system (integration) costs • Production tax credit availability • Resolution (# blocks modeled) • Phase-in availability (IPM would take all at once!)

  5. RPS: Resources Compete to Meet Demand Connecticut Class 1 (excl. fuel cells) Massachusetts New Jersey Class 1 (excl. solar tier) Rhode Island (less 2% existing) Maryland Tier 1 New York main tier Pennsylvania Tier 1 (excl. solar tier) RPS: Forced Quantities NY Customer Sited Tier NJ & PA Solar Tiers CT fuel cells (est.) RPS Ignored (existing RE): Maine CT Class 2 MD Tier 2 PA Tier 2 Demand Drivers for Incremental Renewable Energy • Green Power + Other • NY Executive Order 111 • State-by-state penetration of voluntary demand (H, M, L; NY 1% goal)

  6. Adjustments to RPS % Targets for… • Extrapolations MA, NJ beyond set targets • Load exemptions • Existing eligible supply in baseline • Bonus credits (MD) • Alternative Compliance Payments (MD) • Supply from outside modeling footprint • Coal-mine methane (PA) • Biomass retrofits (MA) • “Forced” supply (e.g. PV)

  7. Representing RPS Demand: “Standard” RPS Definitions • Challenge: Eligibility, geographical/delivery & vintage requirements differ • Approach  Simplify: • Approximate differing eligible resources and geographic requirements across RGGI states while relaxing the fewest possible constraints • Consolidate into 2 “standard” RPS policies

  8. Restricted analysis to subset of technologies: Proven Commercially available RPS-eligible Material contribution over analysis horizon Supply Curve Onshore wind Offshore wind Landfill Gas Biomass Co-firing Biomass Direct Fire (NOx control) Biomass Gasification Hydroelectric Forced, Fixed Quantities Solar/Photovoltaics Fuel Cells Supply Side Resources Modeled

  9. Resource Potential Philosophy “Developable Potential” Technical Potential Economic Potential Based on Demand Maximum Physical That portion of technical potential that could realistically be developed subject to real-world constraints in the presence of sufficiently high demand

  10. Key Challenges: Developing Resource Costs For each technology, we sought to characterize: • Capital Costs, $/kW • Fixed O&M, $/kW-year • Variable O&M, $/MWh • Heat Rate (Btu/kWh) • Performance (Capacity Factor) • Financing Assumptions (structure, cost of money, term) • Production Profile (electricity revenue determines need for REC revenue) Some examples follow….

  11. Wind Resource PotentialOf vast potential, how much is developable? On-Shore: • NREL performed analysis for RGGI (Oct. ’04) of windy land area by: • Wind speed, distance from transmission, 9 land use types • Excluded land with >20% slope, protected federal lands, etc. • We then: • Allocated between wind “farms” and “clusters” based on state-specific dynamics (landform, ownership, programs to encourage clusters)  drives cost due to scale economies • Applied substantial further land use exclusions Off-Shore: • No comprehensive study available, so Commissioned study for RGGI based on detailed GIS analysis (AWS Truewind) • Limited developable potential: • <100 ft. max depth beyond 3 miles of shore • Exclusions 87.5% of remainder to approximate shipping lanes, fisheries shoals, proximity to transmission, permitting difficulties, etc.

  12. Resource Costs: Wind Costs vary depending on: • Substantial economies of scale: wind farms (more than 10 wind turbines) vs. clusters (2 to 10 turbine configurations) • Distance from transmission: Near (<5 miles), far (5-20 mi.) or distant (>20 mi.)  106 “blocks” modeled • Source: NY RPS Cost Analysis, adjusted to reflect impact of steel markets & exchange rates • Canadian wind exported to Northern Tier subject to delivery cost adder Wind Integration • Large quantities of wind (a variable resource) may impose some upstream operating costs on system • Our analysis of available studies of such costs concluded additional operational costs ~ $1/MWh at low wind %, ~ $10/MWh when wind penetration reaches 20%.

  13. Resource Potential: Biomass • Biomass fuel availability is the constraint on amount of new biomass • Challenges: • 1) Est. amount of biomass fuel available for incremental power generation • 2) Based on total cost of energy, determine which technologies will likely be built • 3) Allocate fuel to technologies (no shared fuel curve in IPM) • 4) Power plant access to fuel supplies (delivery cost, logistics) • Fuel Availability and Cost • Data source for fuel quantities: Oak Ridge National Laboratory study (most credible study that covered all RGGI states) • Quantities described by four cost blocks (from $.70 to $3.15 per mmbtu) • Fuels considered: agriculture residues, forest residues, mill wastes, urban wastes, and dedicated crops (potential) • Remove fuel used in existing biomass facilities • Assumed not economic to transport fuel over state borders, except NYC/CT

  14. Resource Potential: Biomass New Construction of Biomass Facilities • Co-firing: Cheap, but limited by current coal capacity; • potential assumed to be 25% of existing coal facilities, 15% of output. • Gasification, direct-fire and fluidized bed total energy costs compared in each year • New build in each year is all most economic technology • Remaining fuel (after use by co-firing) allocated to these technologies Sustainable Biomass Requirements • No adjustments made for NJ and CT RPS fuel restrictions. • Assume NY and MD, which have minimal restrictions, can absorb RECs generated by such fuel by displacement.

  15. Other Resource Potential Challenges • Phase-in Availability • Can’t build everything overnight (but the model doesn’t know that) due to development & construction lead-time, infrastructure, public acceptance, etc. • Imports into RGGI Modeling footprint • From US: modeled WV Wind only • From Canada: large wind potential, constrained by transmission capacity • Costs associated with energy deliver requirements

  16. Other Resource Cost Challenges • Generalization into resource “blocks” vs. project-, site-specific factors • Minimize detail for modeling simplicity without removing meaningful distinctions • Variety of different sources • LFG: costs vary by with and without collection systems, scale • Hydro: costs vary between upgrades and powering existing dams without generation

  17. Major Cost Wildcard:Production Tax Credits (PTC) • Federal PTC • Set to expire 12/31/2005 • Major wildcard for future re: duration, value, applicability • Modeling Approach • Seek “middle ground” between further PTC extension after 2005 and no PTC extension

  18. Northern Tier Southern Tier Northern Tier includes MA and RI. Southern Tier includes. CT Class 1, NJ Class 1 Main Tier, NY Main Tier, MD Tier 1, and PA Tier 1.

  19. Summary of Inputs: Supply Curve • Caveats: • 2010 is last year of PTC • These are all-in COE, but premium varies based on regional electricity prices, production profile • More biomass potential is off the charts • Co-firing is an estimate for comparison purposes NH 2 NH 1 BIO HU 2 WC 4 WC 3 OW 5 OW 6 WF 4 WF 3 WF 5 WF 6 COF LFG w/o c LFG w/c 4,000 5,000 WC5, WC6, HU1

  20. Capital Cost Trajectory over Time

  21. Conclusions/Final Thoughts: Assessing RE Developable Potential • Targeted “middle of the road” estimates… try to avoiding bias of both skeptics and technologic optimists • Baseline demand driven by locked-in policies • Best data sources available balancing regionally consistent vs. current & unbiased • Many choices necessary to reflect feasible “developable potential” in the face of wide range of factors • Simplify for modeling without losing meaningful detail • PTC wildcard merits sensitivity analysis Caution: RE and GHG (& other air) policies not synchronized • Accounting systems for RECs not designed to track emission rights • RE mandates vary on eligibility of resources selling off emission rights: • RECs are RPS-eligible even if GHG benefits sold elsewhere (NJ; MA?) • All Environmental Attributes bundled for compliance (NY) • TBD (RI)

  22. The End(Extra reference slides follow…)

  23. On-Shore Wind Potential excluded this % of windy land… • Urban 99.5% • Agriculture 50.0% • Grassland 50.0% • Ridge Forest 75.0% • Non-ridge Forest 75.0% • Shrubland 50.0% • Water 100.0% • Wetland 99.0% • Other 50.0%

  24. Resource Potential: Phase-In Constraints • Can’t build everything overnight (but the model doesn’t know that) • Development & construction lead-time, infrastructure, public acceptance, etc. • Applied Phase-in to Developable Potential Totals • Default assumption: Limit of 25% of each resource block available in 2005 unless otherwise specified. • On-shore wind: each state assigned permitting difficulty based on judgment, near-transmission developed before far from transmission; also considered biggest demand drivers with locational bias • Offshore wind: 25% of developable potential phased in each 3 years starting 2009 modeling year • Biomass limited to 15% of max in 2006

  25. Developing Resource Potential: Imports to RGGI region • U.S. RE imports into RGGI modeling footprint ignored as immaterial… • Except West Virginia wind • Canadian Imports assumed: • Ignored as unlikely to be material: • Biomass: environmental constraints, economic options available • LFG: limited quantities, not likely to be available for export • Hydro: constrained by 30 MW, no new dam eligibility assumption • Offshore wind: uncompetitive vs. US off-shore + delivery cosrts • Considered onshore wind resources from Ontario and Quebec. • constrained by transmission capacity

  26. Resource Potential: Hydro & Landfill Gas Hydro • Assume no new dams built during study period • Assume 30 MW cutoff • Source: “U.S. Hydropower Resource Assessment Final Report,” Idaho National Engineering and Environmental Laboratory (INEEL), 1998. • Used quantities in INEEL, applying INEEL probability factors Landfill Gas • EPA’s Landfill Methane Outreach Program database of potential sources • Candidate landfills • Under construction projects • Shut-down projects • Estimated impact of increased new sources of waste offset (in part) by degradation of methane available in existing sources; resulted in 3.1% CAGR in MW available through 2020.

  27. Resource Costs: Other Sources Biomass • Costs taken from DOE/EPRI study, adjusted to reflect communication with manufacturers and developers. Hydro • DOE Hydropower Program database (INEEL) Landfill Gas • NY RPS Cost Study and NYSERDA Technology Assessment Fuel Cells • NJ Renewable Energy Market Assessment, Navigant Consulting, and NY RPS Cost Study. Solar PV • NY RPS Cost Study

  28. Cost Blocks in Each IPM Modeling Zone

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