Sida LEAP Training Lecture #3 and #4: Energy Supply and Emissions Modeling
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The document outlines the training workshop for the Low Emissions Analysis Platform (LEAP) hosted by the Stockholm Environment Institute, detailing the schedule and content for Day 2, which focuses on energy supply and emissions modeling. It provides guidelines for participants, including registration, connection information, and workshop etiquette, while also presenting an overview of LEAP’s modeling capabilities, including energy supply chain modeling, emissions analysis, and capacity expansion methods. Additionally, it discusses methodologies for integrating cost-benefit analyses and impacts of mitigation strategies in various scenarios.
Sida LEAP Training Lecture #3 and #4: Energy Supply and Emissions Modeling
- 1. SEI Asia Centre Training on Low Emissions Analysis Platform Day 2: 20 October 2021 Charlotte Wagner Scientist, Energy Modeling Program Stockholm Environment Institute charlotte.wagner@sei.org Jason Veysey Deputy Director, Energy Modeling Program Stockholm Environment Institute jason.veysey@sei.org
- 2. Workshop registration Please register your attendance daily Participants need to register for at least 3 days to be eligible for an attendance certificate Registration link day 2 https://tinyurl.com/SEIAsiaLEAPtraining-1450 Password: Day02#
- 3. Workshop connection information Web meetings https://tinyurl.com/SEIAsiaLEAPtraining Zoom meeting ID: 872 2041 5222 Zoom passcode: 353649 Shared files https://tinyurl.com/SEIAsiaLEAPMaterials Password: seiasia1021
- 4. • Please: • Enter your name in Zoom so meeting hosts can identify you in participant lists • Mute yourself when not speaking • Use your camera if possible • If you have a question, raise your hand in Zoom Zoom etiquette
- 5. Workshop overview • Day 1: Introduction to LEAP and energy demand modeling • Day 2: Energy supply and emissions modeling • Day 3: Cost-benefit analysis and optimization modeling • Day 4: Linking LEAP and WEAP and other advanced topics
- 6. Modeling energy supply with LEAP
- 7. Structure of a representative LEAP analysis Demographics Macro- Economics Demand Analysis Transformation Analysis Statistical Differences Stock Changes Land-Use Change and Land-Based Resources Integrated Cost-Benefit Analysis Non-Energy Sector Emissions Analysis Monetized Environmental Externalities Integrated Benefits Calculator (IBC): Mortality, Crop Losses, Temperature Change Other Resources (Fosssil, Hydro, etc.) SDG Indicators Environmental Loadings (Pollutant Emissions)
- 8. Supply modeling overview • LEAP supports modeling all links in the energy supply chain, from resource extraction to energy trade, energy conversion, and delivery to end users • Demand-driven, engineering-based simulation • Two main branches in LEAP tree: Resources and Transformation • Resources – Extraction of primary energy resources, imports and exports • Transformation – Conversion of one fuel (energy carrier) to another; transport, transmission, and distribution of fuels • Total primary energy supply, primary resource reserves (non-renewable) and annual yields (renewable), imports and exports tracked in Resources • Transformation structure: “modules” (energy-producing sectors), each containing one or more “processes” • Processes use feedstock (input) fuels to produce output fuels • Transformation modeling allows for simulation of process capacity: expansion and dispatch • Choice of two overall methodologies: rules-based simulation and optimization • Cost-benefit and emissions accounting can be integrated throughout supply model
- 10. Resources branch LEAP automatically adds branches for all primary and secondary fuels used in model Variables in Analysis View for specifying reserves, yields, required imports and exports
- 11. Transformation module layout • Simple (e.g., transmission lines) • Multi-output (e.g., petroleum refining) • Multi-process (e.g., electricity generation)
- 12. An electricity generation module
- 13. A petroleum refining module
- 14. Simple, non-dispatched transformation modules
- 15. Steps for a supply analysis in LEAP Map components of supply system to Transformation and Resources branches Sequence transformation modules Set transformation module properties, specify output fuels, and enter process data Enter data in Resources branch
- 16. Mapping supply system components • Ensure every supply source and activity is appropriately represented in LEAP • Key question: system boundary Transformation module or Resources branch? • Transformation – use to model energy conversion, energy losses, production capacity, dispatch of multiple production options, intra-annual variation in demand & supply, disaggregated production costs (e.g., capital, O&M) • Resources – use to model production of primary energy resources or required fuel imports and exports where none of Transformation conditions apply
- 17. Module ordering Energy requirements are imposed on transformation modules from higher-level branches, starting with final energy demand Modules satisfy all requirements imposed on them, subject to capacity limits Multiple modules can produce same fuel LEAP’s calculation “direction”
- 19. Module outputs and processes LEAP automatically creates categories (folder branches) for output fuels and processes User decides which outputs and processes to add to categories – i.e., what’s going to be modeled
- 20. Modeling process capacity Two major issues to consider: Capacity expansion • How much capacity to build and when? (MW) Dispatch • Once built, how should capacity be operated? (MWh)
- 21. Capacity modeling methods • Two main methods for modeling capacity expansion and process dispatch in LEAP • Rules-based simulation => user defines prioritization rules • Optimization => user defines cost and performance parameters, model finds least-cost solution • Optimization is more data-intensive and difficult to calibrate, but it expands functional possibilities – e.g., energy storage and transmission power flow
- 22. Rules-based capacity expansion • Exogenous capacity – assumed to exist in specified years • Endogenous capacity – prioritized options that LEAP may build if needed to maintain reserve margin • User specifies reserve margin target and order and addition size for endogenous capacity options Reserve margin: available capacity when system is at peak load
- 23. Rules-based capacity expansion • Exogenous capacity – assumed to exist in specified years • Endogenous capacity – prioritized options that LEAP may build if needed to maintain reserve margin • User specifies reserve margin and order and addition size for endogenous capacity options Reserve margin: available capacity when system is at peak load Reserve margin σ 𝑐𝑎𝑝𝑎𝑐𝑖𝑡𝑦 × 𝑐𝑎𝑝𝑎𝑐𝑖𝑡𝑦 𝑐𝑟𝑒𝑑𝑖𝑡 − 𝑝𝑒𝑎𝑘 𝑙𝑜𝑎𝑑 𝑝𝑒𝑎𝑘 𝑙𝑜𝑎𝑑 × 100
- 24. Rules-based process dispatch • Before first simulation year – historical production • In and after first simulation year – dispatch rule • Merit order – user-assigned priorities • Full capacity – 100% of available capacity (capacity x maximum availability) • Percent share – % of module output requirements • Running cost – in order of variable O&M + fuel cost • Proportional to capacity – output shares determined by available capacity Dispatch in and after first simulation year attempts to meet demand (MWh) and load (MW) in each year and time slice, given available capacity
- 25. Time slicing • Time slices divide the year into sub-annual periods • Used to model additional temporal detail in supply and demand of particular fuels (e.g., electricity) • One set of time slices per model – configure in General -> Time Slices • Various variables can be time sliced (e.g., maximum availability, merit order, key assumptions) • Module output requirements can also be time sliced • Exogenous – load shape attached to module • Endogenous – load shape attached to each final energy demand, LEAP sums demand shapes • If module requirements are not time sliced, dispatch is for an entire year at a time
- 26. Time slice options • LEAP streamlines creation of various time slice configurations • If time-sliced inputs are entered with hourly resolution, LEAP aggregates them by time slice • Load shapes, process maximum availabilities, etc. • Time slice configuration can then be modified, and LEAP will automatically re-aggregate all time-sliced inputs => this makes it extremely easy to change time slices
- 27. Load shapes and dispatch
- 28. A quantitative example • Two time slices: winter (40% of year) and summer (60% of year) • Annual demand = 100 GWh • Load shape: 70% of demand in winter, 30% in summer • Time sliced demands • Winter = 70% x 100 GWh = 70 GWh • Summer = 30% x 100 GWh = 30 GWh • Time sliced loads • Winter = 70 GWh / (8760h x 40%) = 20.0 MW • Summer = 30 GWh / (8760h x 60%) = 5.7 MW • Peak load = 20.0 MW
- 29. Putting demand & supply together: energy balances • Energy demand and supply results are combined in LEAP’s integrated framework • Results can be displayed as standard energy balance tables • Balances can be viewed for any year, scenario, or region in different units • Balance columns can be switched among fuels, fuel groupings, years, and regions • Balance rows are Demand and Transformation sectors/modules. Can optionally show sub- sectoral results • Balances can be viewed in table, chart, and Sankey diagram formats
- 31. Structure of a representative LEAP analysis Demographics Macro- Economics Demand Analysis Transformation Analysis Statistical Differences Stock Changes Land-Use Change and Land-Based Resources Integrated Cost-Benefit Analysis Non-Energy Sector Emissions Analysis Monetized Environmental Externalities Integrated Benefits Calculator (IBC): Mortality, Crop Losses, Temperature Change Other Resources (Fosssil, Hydro, etc.) SDG Indicators Environmental Loadings (Pollutant Emissions)
- 33. Energy-related emissions • Energy-related emissions in LEAP are based on energy production or consumption and emission factors • Can specify factors for any greenhouse gas (GHG) or pollutant • Factors can be entered in any physical unit and denominated by units of energy consumption, energy production, or distance traveled for transport (e.g., kg/tonne coal consumed, grams/mile traveled) • Expressions for factors can reference chemical composition of fuels • LEAP includes default IPCC Tier 1 emission factors in its Technology Database
- 34. Non-energy emissions • LEAP models can optionally include emissions from non-energy sources • This allows modeling of economy-wide emissions • Non-energy emissions based on user- defined expressions (i.e., formulas that return total annual emissions) • Expressions can reference other model variables (e.g., key assumptions) to ensure consistency across sectors
- 35. Emission results • Results can be shown for individual pollutants or summed to show overall global warming potential (GWP) • LEAP includes GWP conversion factors from all IPCC assessment reports • Direct emissions from demand, supply, and non-energy branches can be displayed by branch, fuel, year, and other dimensions • For demand branches, indirect GHG emissions (supply-side emissions attributable to final energy demands) can also be calculated • In national models, projected air pollution emissions can be used in LEAP’s Integrated Benefits Calculator (LEAP-IBC) to quantify impacts on human health, agriculture, and temperature
- 36. Motivation for LEAP-IBC: Integrated climate and air pollution assessments
- 37. LEAP-IBC calculation pathway Emissions Impacts: Health and vegetation Exposure ` Transport Impacts: Climate
- 38. LEAP-IBC results Key results include: • Air pollutant concentrations by origin (natural background, national emissions, rest of world emissions) • Premature deaths and years of life lost by pollutant, sex, age, disease, indoor vs. outdoor exposure, pollutant origin • Global temperature change due to national emissions by pollutant • Economic costs of premature mortality • Crop losses due to pollutant concentrations (major cereal crops)
- 39. Mapping emissions • In addition to using charts and tables to analyze emission results, users can optionally display them on maps • This feature is useful for identifying emerging emission hotspots and for tracking and monitoring progress on reducing emission burdens faced by different communities
- 40. Emission mitigation assessment • LEAP modeling can play a valuable role in emission mitigation assessment • Supports analytical steps in assessment process, allowing quantification of impacts of mitigation strategies • LEAP’s scenario-based architecture aligns well with typical assessment framework: comparing mitigation options to a baseline Organizational & preparatory Analytical 1. Assess situation & organize process 2. Define scope 3. Design methodology 4. Collect & calibrate data 5. Develop baseline scenario(s) 6. Identify & screen mitigation options 7. Develop mitigation scenario(s) and sensitivity analyses Assess impacts (social, economic, environmental)
- 41. Using LEAP for mitigation assessment Historical data (Current Accounts) Energy consumption and production, energy sector emission factors, and non-energy emissions Baseline scenario(s) Mitigation scenarios
- 42. Baseline scenarios • Baseline scenarios are often termed “business-as-usual (BAU)” scenarios, but BAU needs to be carefully defined • Does it include anticipated future changes? Does it include policies recently enacted? Recently announced? Does it only include policies not specifically aimed at reducing emissions? • There is no single commonly accepted definition • In principle, a baseline scenario should provide a plausible and consistent description of future developments in the absence of explicit new mitigation policies • Not a forecast of what will happen: future is inherently unpredictable • Development of a baseline scenario is a critically important analytical and policy task • Influences magnitude of emission benefits and relative cost of mitigation strategies • It can be useful to have multiple baseline scenarios, e.g., with and without existing policies (to reveal their emission benefits) • Not simply an extrapolation of past trends, a baseline scenario requires data and assumptions regarding factors such as: • Macroeconomic and demographic projections (e.g., population and GDP growth) • Structural shifts in the economy (e.g., relative growth of agricultural, industrial, and services sectors) • Planned investments and existing policies in individual sectors (e.g., power supply plans) • Evolution of technologies and practices, including saturation effects, fuel switching, and adoption rates of new technologies (e.g., share of households with air conditioning; use of combined heat and power in steel industry)
- 43. Developing mitigation scenarios • Mitigation scenarios reflect a future in which explicit policies and measures are adopted to reduce the sources (or enhance the sinks) of emissions • Mitigation scenarios should take into account: - Specific national and regional development priorities, objectives, and circumstances - Common but differentiated responsibilities of countries • Mitigation scenarios should not simply reflect current plans. Instead, they should assess what would plausibly be achievable based on the goals of the scenario Possible framing of scenarios An emission reduction target Specific options or technologies: included based on perceived technical and/or political feasibility All options up to a certain cost per unit of emissions reduction (equivalent to a carbon tax) “No regrets” (cost-effective) options only
- 44. Implementing mitigation scenarios in LEAP Bottom-up / end-use Hybrid / decoupled A common approach: measure-specific “mini” scenarios combined into overall mitigation strategies
- 45. Assessing impact of mitigation scenarios • Scenarios can be compared in terms of: • Emission savings • Impacts on energy security • Social impacts (e.g., development benefits or drawbacks) • Costs • Technical feasibility of options • Political plausibility … • LEAP facilitates such comparisons in Results view
- 46. Exercise 2: Energy supply and emissions modeling
- 47. Freedonia https://www.youtube.com/watch?v=cW87lWDABgc Workshop Exercises 1 and 2: Chapter 1 in Training Exercises document exercise_1_2_3 - LEAPTrainingExerciseEnglish2020.pdf
- 48. Freedonia 1.1. Overview of Freedonia 1.2. Settings 1.3. Demand 1.3.1. Data structures 1.3.2. Current Accounts 1.3.3. Viewing Results 1.3.4. Reference Scenario 1.4. Transformation 1.4.1. Transmission and Distribution 1.4.2. Electricity Generation 1.4.3. Viewing Results 1.5 Emissions 1.5.1. Viewing Results 1.6. A Second Scenario: Demand- Side Management 1.6.1. DSM Scenario Results Exercise 2
- 49. Saving and sharing models LEAP Areas Repository …DocumentsLEAP Areas One folder with multiple files per area .LEAP file One zipped file per area Backup Install Installing an area from a .LEAP file overwrites what’s in local LEAP areas repository Be careful, you can lose work!