Gregg, Jay Sterling

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Downloaded from orbit.dtu.dk on: Dec 20, 2017Modeling Future Potential of BioenergyGregg, Jay SterlingPublication date:2012Document VersionPublisher's PDF, also known as Version of recordLink back to DTU OrbitCitation (APA):Gregg, J. S. (2012). Modeling Future Potential of Bioenergy [Sound/Visual production (digital)].Modeling Future Potential of Bioenergy  Jay Sterling Gregg jsgr@risoe.dtu.dk Denmark Climate Center Systems Analysis Risø DTU, Danmarks Tekniske Universitet How do you model potential? • What do you need to know? • What do you have to assume? • How do you approach this topic?  Risø DTU, Danmarks Tekniske Universitet What is “potential”? •Theoretical Potential – total amount that theoretically can be produced •Supply (Technical) Potential – often used interchangeably with technical potential, but could also vary if one considers sustainability constraints •Demand (Market, Economic) Potential – amount of biomass demanded by the global market at a given price or under a given policy scenario, in consideration of other energy options  Risø DTU, Danmarks Tekniske Universitet Biomass Resources 1. Energy Crops Primary Residues 2. Agricultural crop harvest residues 3. Forest residues from industrial roundwood and fuelwood/charcoal production Secondary Residues 4. Food processing residues 5. Wood and other fiber processing residues (mill residues) 6. Animal Dung Tertiary Residues 7. non-eaten food (compost and municipal solid waste) 8. non-food organic waste (municipal solid waste) Aquatic Resources 9. Freshwater Algae 10. Seawater Algae Other 11. biomass presently used for fuel wood and charcoal 12. unspecified forest biomass 13. unspecified residues  Classification based on resource Risø DTU, Danmarks Tekniske Universitet Biomass Resources II  Hoogwijk et al. 2003 Different classification based on land use Risø DTU, Danmarks Tekniske Universitet Biomass Resources III 1. Traditional Biomass First Generation Bioenergy 2. Sugars and Starch to Alcohol fuel  3. Transesterification and biodiesel production 4. Waste oil to biodiesel 5. Syngas 6. Biogas Second Generation Bioenergy 7. Cellulosic Ethanol 8. Algael Biodiesel 9. Biohydrogen 10. Biomethanol  Etc. Different classification based on technology Risø DTU, Danmarks Tekniske Universitet Example: Agricultural Crop Residues Energy = [Production × Residue Ratio – (Residue Retention × Area)]  × Energy Content Residue Ratio Residue Retention Energy Content Total Residue Production Available Residue Crop Residue Left on Field ( ) 11 +−⋅= drydrywet HIcontentwaterHIHI( )11 −= −wetHIRatioResidueRisø DTU, Danmarks Tekniske Universitet Rokityanskiy et al. 2006 Constraints: Water, Land, Nutrients, etc. Risø DTU, Danmarks Tekniske Universitet  Dornburg et al. 2008 Risø DTU, Danmarks Tekniske Universitet What is “potential”? •Theoretical Potential – total amount that theoretically can be produced •Supply (Technical) Potential – often used interchangeably with technical potential, but could also vary if one considers sustainability constraints •Demand (Market, Economic) Potential – amount of biomass demanded by the global market at a given price or under a given policy scenario, in consideration of other energy options  Risø DTU, Danmarks Tekniske Universitet Science and Policy Analysis • We want to know how important this technology can be in addressing climate change, sustainable development, and energy security.   • How important will this option be in the future relative to other options?  • How much will it cost and what will be the effect on the economy?    • Challenges of modeling the future: • Is it possible for a model to            predict the future? • Is it possible to test the model by running    from a past date to the present?   Risø DTU, Danmarks Tekniske Universitet Differences between physical science and policy analysis For policy analysis to make sense, we have two philosophical assumptions:  1. Non-Determinism: • If we assume that whatever is going to happen is already predestined, then policy has no role. We have to assume that policy has the power to change the course we are on.  2. Non-Nihilism: • We have to assume that some outcomes are better than others and that there exists a criteria for deciding between the different outcomes. If not, policy again would have no purpose because every possible future would be equally desirable.  Risø DTU, Danmarks Tekniske Universitet Scenarios • Scenarios are created to bracket sets of outcomes. They are designed to answer specific types of questions while holding constant a set of assumptions about the future.   • Scenarios are not predictions or forecasts for the future! They are storylines about how a hypothetical future might develop, constructed to answer specific policy and economic questions.  • Scenarios allow for strategic   planning and decision making   when facing an uncertain future.    Risø DTU, Danmarks Tekniske Universitet Scenarios • Examples:   What if more economic growth occurs in China and India and less in the developed world? How will that change the regional distribution of energy consumption?  How will a climate agreement change the global energy portfolio versus a business-as-usual world? What if we only have a partial climate agreement (not all regions participating)?  What if there is twice as much biomass available in the world than we assume by default? What if there is only half as much available? What if it is twice as expensive? etc.  What are the key uncertainties in the scientific understanding of biomass production and which make the largest impact?   Risø DTU, Danmarks Tekniske Universitet Biomass Potential Scenarios 1. Technology • Investment, Domestic Development, Tech Transfer (e.g. CDM, JI) • Some tech can increase supply potential (e.g. fertilizer, pesticides increase yield, allow farming of marginal lands) • Some tech can change demand potential (e.g. tractors, equipment can reduce labor costs) • Investment in industrialization over ag can reduce supply potential  2. Sustainability Concerns • Can reduce technical supply • Can influence crop choice  3. Foreign Trade in biomass and food • Can increase or reduce supply potential based on profits to land owners  4. Economic Development • Affects cost of labor, labor mobility, and immigration (affects demand potential) • Affects international trade of bio-products (affects supply potential) • Affects tech development and tech transfer (affects supply and demand potential) Risø DTU, Danmarks Tekniske Universitet Specific Issues when Modeling Future Biomass Potential Biomass Potential Theoretical Technical (Supply) Market (Demand) •Land availability (crop land, forestland, urban, pasture, rangeland, marginal land)  •Water availability •Climate •Future ag yield •Harvest efficiency (Technology) •Sustainability criteria •Population •Diet •Crop Distribution •Animals  •Cost curves •Labor cost •Profits to land owners •Carbon price (land carbon) •Subsidies •Economies of Scale •Foreign trade   Risø DTU, Danmarks Tekniske Universitet Modeling Approaches for Bioenergy • Top Down: Maximize economic value of land, Benefit-Cost, or long term utility under a given carbon constraint Versus • Bottom Up: Obtain detailed information on technologies, costs and options for a given piece of land and then determine the carbon prices at which the various options become economic   • Integrated: a dynamic land allocation system is built into the model and calculates land distribution and economic land use endogenously (IMAGE, GCAM)      Versus • Soft Linked: Land distribution/ Land use scenarios/ Biomass production are derived exogenously and input into the Integrated Assessment Model (IAM) (Most IAMs)  Risø DTU, Danmarks Tekniske Universitet Modeling Approaches • Perfect foresight versus dynamic recursive: how the economic optimization works.  Perfect foresight Myopic foresight (Dynamic-recursive) 28 29 30 31 3203 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27Model horizonMilestoneyearPeriod28 29 30 31 3203 04 05 06 0708 09 10 11 1213 14 15 16 1718 19 20 21 2223 24 25 26 27Run1Run 2Run 3Run 4Run 5Run 6One optimization run over entire horizon Sequence of model runs Risø DTU, Danmarks Tekniske Universitet Integrated Assessment Models • IAMs provide a framework for understanding climate change from a point of view that takes into account economics, demographics, policy, technology, and other human factors.  • The world is represented as a set of regions, with each region having specific resources they are able to develop and trade with other regions. Regional information on population, economy and prices also demand to be modeled.  • IAMS allow the testing of different policy scenarios and market dynamics.  •  IAMs typically have a simple climate model built in that can estimate atmospheric GHG concentrations and the economically optimal schedule for emissions reductions.   Risø DTU, Danmarks Tekniske Universitet TIAM: Times Integrated Assessment Model Risø DTU, Danmarks Tekniske Universitet GCAM: Global Climate Assessment Model MiniCAM RegionsUSACanadaWestern EuropeJapanAustralia & NZFormer Soviet UnionCentrally Planned AsiaMiddle EastAfricaLatin AmericaSoutheast AsiaEastern EuropeSouth KoreaIndiaGCAM Regions Risø DTU, Danmarks Tekniske Universitet Example: Bioenergy and CCS in China • What is the potential for bioenergy and CCS in China under a reference scenario and 2-degree C climate policy scenario?  • How does China compare to the rest of the world in this respect?  • What is the most optimal use for biomass in China in a carbon constrained world? Risø DTU, Danmarks Tekniske Universitet Example: Biomass in primary energy  0%5%10%15%20%25%30%200520072012202020302040205020602070208020902100Share of Biomass in Primary Energy Consumption, World1a Ref1b Mit Default1c Mit No CCS2b Mit Pessimistic3b Mit Optimistic0%5%10%15%20%25%30%200520072012202020302040205020602070208020902100Share of Biomass in Primary Energy Consumption, China1a Ref1b Mit Default1c Mit No CCS2b Mit Pessimistic3b Mit OptimisticRisø DTU, Danmarks Tekniske Universitet Example: Biomass in primary energy  0%5%10%15%20%25%30%200520072012202020302040205020602070208020902100Share of Biomass in Primary Energy Consumption, World1a Ref1b Mit Default1c Mit No CCS2b Mit Pessimistic3b Mit Optimistic0%5%10%15%20%25%30%200520072012202020302040205020602070208020902100Share of Biomass in Primary Energy Consumption, China1a Ref1b Mit Default1c Mit No CCS2b Mit Pessimistic3b Mit OptimisticRisø DTU, Danmarks Tekniske Universitet Example: Biomass in transportation 0%10%20%30%40%50%200520072012202020302040205020602070208020902100Share of Biofuels in Transportation (World)1c Mit No CCS3b Mit Optimistic1b Mit Default2b Mit Pessimistic1a Ref0%10%20%30%40%50%200520072012202020302040205020602070208020902100Share of Biofuels in Transportation (China)1c Mit No CCS3b Mit Optimistic1b Mit Default2b Mit Pessimistic1a RefRisø DTU, Danmarks Tekniske Universitet Use of CCS technologies 0%10%20%30%40%50%60%70%80%90%100%2020 2030 2040 2050 2060 2070 2080 2090 2100Share of CCS in Power Production, World3b Mit Optimistic - Fossil1b Mit Default - Fossil2b Mit Pessimistic - Fossil3b Mit Optimistic - BECCS1b Mit Default - BECCS2b Mit Pessimistic - BECCS0%10%20%30%40%50%60%70%80%90%100%2020 2030 2040 2050 2060 2070 2080 2090 2100Share of CCS in Power Production, China3b Mit Optimistic - Fossil1b Mit Default - Fossil2b Mit Pessimistic - Fossil3b Mit Optimistic - BECCS1b Mit Default - BECCS2b Mit Pessimistic - BECCSRisø DTU, Danmarks Tekniske Universitet Example: China Biomass Conclusions • Chinese economic growth causes a dramatic increase in energy demand final energy demand grows 500-600% from 2010 to 2100  • Even with optimistic assumptions on future biomass, it can only cover around 10% of the Chinese primary energy consumption in 2050 and around 5% in 2100. Most of the available biomass in China is optimally used in the transport sector, thereby favoring CCS over BECCS.  • CCS is a key technology for China in an emissions constrained world  • The CCS storage potential in China is not a limiting factor  Risø DTU, Danmarks Tekniske Universitet Conclusions of Bioenergy Modeling  • The future potential for bioenergy will depend on both physical and human factors  • Policy can influence the future potential of bioenergy  • Estimating the future potential for bioenergy requires an integrated approach   

Modeling Future Potential of Bioenergy

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Modeling Future Potential of Bioenergy

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