Report

Using Energy Economy Models to Deliver Policy-Relevant Insight SAMSI Workshop 20 September 2011 Joe DeCarolis Assistant Professor Dept of Civil, Construction, and Environmental Engineering NC State University 1 Talk Outline 1. 2. 3. 4. 5. Motivation and description of EEO models Problems with model development and application Introduce the TEMOA project Describe our approach to uncertainty analysis Conclude 2 1. Motivation and description of EEO models 3 Energy Implications of Climate Change 50 Total Primary Power Required by 2100 Primary Power (1012 Watts) 45 40 Gas Oil Coal Carbon-Free 35 30 25 20 2005 Total Primary Power 15 2005 CarbonFree Primary Power 10 5 0 750 ppm (~3.6°C) 550 ppm (~3.1°C) 450 ppm (~2.6°C) Stabilization Target for Year 2100 Based on WRE carbon-cycle model. For details see: Hoffert et al (1998). “Energy implications of future stabilization of atmospheric CO2 content.” Nature, 395: 881-884. 4 Energy Economy Optimization (EEO) Models Energy economy optimization models refer to partial or general equilibrium models that minimize cost or maximize utility by, at least in part, optimizing the energy system over multiple decades Examine the competition among energy technologies, including renewables Expansive system boundaries and multi-decadal timescales Encoded with a set of structured, self-consistent assumptions and decision rules Such models have emerged as a key tool for the analysis 5 Model Types Computable General Equilibrium: Adjusts all prices until all supplies and demands in all markets are balanced simultaneously. Technologies often represented stylistically by production functions (output as a function of capital, labor, land, resources) Technology Explicit, Partial Equilibrium: Detailed technology representation using engineering-economics; demand fixed or elastic 6 Modeling Objectives Three broadly defined objectives for such models: 1. Prediction of future quantities “The price of oil in 2030 will be …” “Under a federal climate policy, the installed capacity of Technology x in 2050 will be …” 2. Prescriptive analysis for planning purposes “The following mechanisms should be included in federal climate policy …” “The following technologies should be deployed to minimize system-wide costs …” 3. Generation of insight “Increased use of natural gas for vehicle H2 could lead to poor air quality” “The widescale deployment of plug-in hybrid vehicles could lead to high coal consumption” 7 2. Problems with model development and application 8 Problems with energy modeling Inability to validate model results + Increasing availability of data + Increasing model complexity Inability to verify model results + Lack of openness Uncertainty analysis is difficult Moore’s Law 9 Lack of model validation Inability to validate model results + Increasing availability of data + Increasing model complexity Inability to verify model results + Lack of openness Uncertainty analysis is difficult Moore’s Law No practical way to validate energy-economy models → cannot be validated in the same way as models of physical processes Three validation options: 1. Wait 2. Backcast 3. Compare results with other models Little to guide the modeler and reign in efforts that do not improve model performance 10 Lack of openness Inability to validate model results + Increasing availability of data + Increasing model complexity Inability to verify model results + Lack of openness Uncertainty analysis is difficult Moore’s Law Most EEO models and datasets remain closed source. Why? • protection of intellectual property • fear of misuse by uninformed end users • inability to control or limit model analyses • implicit commitment to provide support to users • overhead associated with maintenance • unease about subjecting code and data to public scrutiny 11 Inability to verify model results Inability to validate model results + Increasing availability of data + Increasing model complexity Inability to verify model results + Lack of openness Uncertainty analysis is difficult Moore’s Law With a couple exceptions, energy-economy models are not open source Descriptive detail provided in model documentation and peer-reviewed journals is insufficient to reproduce a specific set of published results Reproducibility of results is fundamental to science Replication and verification of large scientific models can’t be achieved without source code and input data 12 Critique of scenario analysis Inability to validate model results + Increasing availability of data + Increasing model complexity Inability to verify model results + Lack of openness Uncertainty analysis is difficult Moore’s Law Source: IPCC Fourth Assessment Report, Synthesis Report, Chapter 3. Stretch one’s thinking about how the future may unfold Shell Group (2005): They are not forecasts, projections or predictions of what is to come. Nor are they preferred views of the future. Rather, they are plausible alternative futures: they provide reasonable and consistent answers to the ‘what if?’ questions relevant to business. Without subjective probabilities p(X|e), scenarios of little value 13 Critique of scenario analysis (continued) Cognitive heuristics play a role and can lead to misinterpretation of results. Availability heuristic: Probabilities of a future event or outcome assessed on the basis of how easily an individual can remember or imagine examples Anchoring and adjustment: People start with an initial value or “anchor” and then modify their judgment as they consider factors relevant to the specifics often insufficient adjustment A few highly detailed scenarios can create cognitively compelling storylines. Drawn from: Morgan G, Keith D. Improving the way we think about projecting future 14 energy use and emissions of carbon dioxide. Climatic Change 2008; 90; 189-215. 3. The TEMOA Project (Tools for Energy Model Optimization and Analysis) 15 The TEMOA Project Tools for Energy Model Optimization and Analysis Goal: Create a set of community-driven energy economy optimization models Our Approach: • Open source code (GNU Public License) • Open source data (GNU Public License) • No commercial software dependencies • Input and output data managed directly with a relational DB • Data and code stored in a web accessible electronic repository • A version control system • Programming environment with links to linear, mixed integer, and non-linear solvers • Built-in capability for sensitivity and uncertainty analysis 16 Version control with Subversion We are using a version control system called Subversion (SVN) http://subversion.apache.org/ http://svnbook.red-bean.com/ Why? Ensure the integrity, sustainability and traceability of changes during the entire software lifecycle. SVN enables: • Multiple developers to work simultaneously on software components; automatic integration of non-conflicting changes • Display the modifications to model source code • Create software snapshots (releases) that represent well-tested and clearly defined milestones • Utilize the release mechanism to take snapshots of the model code and data used to produce research publications. • Public access to snapshots of the code and data Works on all major (Unix, Windows, MacOS) platforms 17 Python Optimization Modeling Objects We're developing the model against the Pyomo API Why Pyomo? • Uses a full-featured modern programming language • Rich set of Python libraries that cover nearly every task • Active development; linkages between Pyomo and custom solvers are being developed within the COmmon Optimization Python Repository (COOPR) Pyomo developed at Sandia National Laboratories: https://software.sandia.gov/trac/coopr/wiki/Pyomo 18 COmmon Optimization Python Repository Pyomo is part of the COOPR package, which is in turn part of A Common Repository for Optimizers (ACRO) Two-language approach: high-level language for model formulation and efficient low-level languages for numerical computations (e.g., C, C++, Fortran) ACRO includes both libraries developed at Sandia and publicly available third-party libraries (e.g., GLPK and COIN-OR) Gives us the capability to formulate linear, mixed integer, and non-linear model formulations without commercial solvers Active collaboration with Discrete Math and Complex Systems Department at Sandia National Laboratories 19 Model Structure Resource Commodities Oil Coal P P Q Nat Gas Electricity P P Q Intermediate Commodities H2 P Q Q Coal-fired power Coal gasification End-Use Demands Gasoline Hot Water Vehicle Miles Q P P P Q Q Q Electric water heater H2 fuel cell vehicle Refinery Gas turbine CH4-H2O reforming Energy Technologies Gasoline vehicle Gas water heater Demand Technologies 20 Basic TEMOA Model Formulation Minimize present cost of energy Such that Supply ≥ Demand Commodity_Into_Process ≥ Commodity_Outof_Process Commodity_Produced ≥ Commodity_Consumed Capacity × Capacity_Factor ≥ Activity MARKAL ‘Utopia’ System Diagram Diagram generated using Graphviz: http://www.graphviz.org/ 22 MARKAL Objective value: 36,821 TEMOA Objective value: 38,502 Calibration to Utopia Installed Capacity of Process Technologies: 0.9 Capacity (GW) 0.8 MARKAL: Dark Colors; TEMOA: Light Colors E01 0.7 E01 0.6 E31 E31 0.5 E51 0.4 E51 0.3 E70 0.2 E70 0.1 SRE 0 SRE 1990 2000 Year 2010 23 Calibration to Utopia (continued) Installed Capacity of Demand Technologies: 100 Capacity (PJ/yr) 90 MARKAL: Dark Colors; TEMOA: Light Colors 80 RHO 70 RHO 60 RL1 50 RL1 40 TXD 30 TXD 20 TXG 10 TXG 0 1990 2000 Year 2010 24 Questions to address with TEMOA • How does uncertainty in technology-specific characteristics (e.g., capital cost of solar PV) affect outcomes of interest (e.g., fuel prices, fossil fuel consumption, air emissions)? • Which technologies and fuels appear to be robust options given uncertainty in future climate policy and rates of technology learning? • How much flexibility exists in energy system design and at what cost? 25 4. Approach to uncertainty analysis 26 Approach to uncertainty analysis Use the following techniques in series: Sensitivity analysis and Monte Carlo simulation → Determine key sensitivities Multi-stage stochastic optimization → Develop a hedging strategy Explore near-optimal, feasible region (MGA) → Test robustness of hedging strategy Uncertainties associated with renewables Easy • Capital costs • Marginal and fixed operational costs • Performance characteristics Hard • Dispatch (of intermittent renewables) • Representation of forecasting error 28 Residual load duration curve Taken from: Ueckerdt F, Brecha R, Luderer G, Sullivan P, Schmid E, Bauer N, Böttger D. (2011) “Variable Renewable Energy in Modeling Climate Change Mitigation Scenarios .” International Energy Workshop, Stanford, CA Implemented in REMIND‐D, a hybrid energy‐economy model of Germany 29 Stochastic Optimization Decision-makers need to make choices before uncertainty is resolved → requires an “act then learn” approach Need to make short-term choices that hedge against future risk → Sequential decision-making process that allows recourse Stochastic optimization • Build a scenario tree • Assign subjective probabilities to future outcomes • Optimize over all possibilities 30 Stochastic optimization of energy models Desirable features for energy models: • Multi-stage (greater than 2) • Multi-objective (e.g., cost, risk, emissions) • Mixed integer (esp. endogenous tech learning) Potential stochastic parameters: • Fuel prices (esp. crude oil, natural gas, coal) • Policy targets (e.g., CO2 constraints, subsidies) • Technology performance (e.g., capital cost, thermal eff) • End-use demand projections (e.g., heating, cooling) 31 Simple example of stochastic optimization Suppose we have two technologies, A and B. Let x and y represent the installed capacity in Stages 1 and 2, respectively. Stage 1 Decision Variables: x A , xB p1 s1 Stage 2 Decision Variables: y A , s1 , y B , s1 Scenario 1: s1 y A , s 2 , y B , s 2 Scenario 2: s2 p2 s2 N M inim ize: c x T ps d s ys T s 1 S ubject T o: t1 t2 Ax b Ts x W s y s hs for s 1, ..., N x0 ys 0 for s 1, ..., N 32 Stochastic optimization with PySP Python-based Stochastic Programming (PySP) is part of the COOPR package. To perform stochastic optimization, specify a Pyomo reference model and a scenario tree PySP offers two options: 1. runef: builds and solves the extensive form of the model. “Curse of dimensionality” → memory problems 2. runph: builds and solves using a scenario-based decomposition solver (i.e., “Progressive Hedging) based on Rockafellar and Wets (1991). Can be implemented in a compute cluster environment; more complex scenario trees possible. R.T. Rockafellar and R. J-B. Wets. Scenarios and policy aggregation in optimization under uncertainty. Mathematics of Operations Research, pages 119–147, 1991. 33 A Test Case of the US Electric Sector Time periods: 2010-2040, 5-year increments 2030 and after, 2 possible CO2 emissions levels, 3 possible natural gas prices Electric sector CO2 emissions in 2010: 2340 MmtCO2 BAU CO2: 0.6% annual increase, CO2 Constrained: 4.7% annual decrease [-50% to +20% change in CO2 emissions in 2040 relative to 2010] Natural gas prices in 2010: 4.45 $/GJ Low: 1.1% annual decrease, Constant, High: 8.4% annual increase [Price ranges from 3.8 to 15 $GJ in 2040] BAU CO2, Low Gas Price Uniform Probabilities BAU CO2, natural gas price constant Constrained CO2, Low Gas Price BAU CO2, Constant Gas Price 2010 2015 2020 Constrained CO2, Constant Gas Price 2025 BAU CO2, Low Gas Price 63 = 216 scenarios 60+61+62+63 = 259 nodes 2030 Constrained CO2, Low Gas Price 34 Technology Cost and Performance Characteristics Annual growth in electricity demand of 0.6% based on the reference case in the Annual Energy Outlook 2009. Technology a Pulverized Coal IGCC IGCC-CCS GTCC-CCS Nuclear Geothermal GTCC GT Hydro Wind-Onshore Wind-Offshore Solar Thermal Solar PV Efficiency (%) Capacity Factor (%) Average Cost ($/kWh) Baseload / Shoulder / Peak (B/S/P) 0.0459 39 95 0.043 B 0.0292 0.0444 0.0294 0.0049 0.00 0.0200 0.0317 0.0243 0.00 0.00 0.00 0.00 46 41 46 33 11 54 40 34 34 34 34 34 90 90 90 95 90 95 95 65 35 40 40 30 0.045 0.066 0.086 0.054 0.044 0.062 0.076 0.047 0.076 0.14 0.17 0.25 B B B B B Any Any Any S S S S Capital Cost ($/kW) Fixed O&M ($/kW∙yr) Variable O&M ($/kWh) 2058 27.5 2378 3496 1890 3318 1711 948 634 2242 1923 3851 5021 6038 38.7 46.1 19.9 90.0 165 11.7 10.5 13.6 30.3 89.5 56.8 11.7 Capacity Constraintb (GW) 23 2 8000 800 100 Source: EIA (US Energy Information Administration), Office of Integrated Analysis and Forecasting, US Department of Energy. Assumptions to the Annual Energy Outlook 2009. DOE/EIA-0554(2009); 35 Washington DC; US Government Printing Office; 2009b. Natural Gas Price in 2040 vs. Total Cost 1.04E+07 1.03E+07 System Cost ($) 1.03E+07 1.02E+07 1.02E+07 1.01E+07 1.01E+07 1.00E+07 9.95E+06 9.90E+06 9.85E+06 0 2 4 6 8 10 12 14 16 Natural Gas Price in 2040 ($/GJ) 36 Constant Nat Gas Prices, Increasing CO2 600 Electricity Generation (GWyr) 500 geothermal 400 nuclear hydro 300 cc gas turbine sc gas turbine coal 200 100 0 2010 2015 2020 2025 2030 2035 2040 Year CO2 emissions allowed to grow 0.6% annually Natural gas prices remain constant at 4.5 $/GJ 37 High Nat Gas Prices, Decreasing CO2 600 Electricity Generation (GWyr) 500 onshore wind geothermal 400 nuclear hydro 300 cc gas turbine sc gas turbine 200 coal 100 0 2010 2015 2020 2025 2030 2035 2040 Year CO2 emissions decrease 4.6% annually from 2030-2040 Natural gas prices increase 8.3% annually from 2030-2040 38 Modeling to Generate Alternatives Need a method to test the robustness of a hedging strategy → “Modeling to Generate Alternatives”† MGA generates alternative solutions that are maximally different in decision space but perform well with respect to modeled objectives The resultant MGA solutions provide modelers and decisionmakers with a set of alternatives for further evaluation †Brill (1979), Brill et al. (1982), Brill et al. (1990) 39 Hop-Skip-Jump (HSJ) MGA Brill et al. (1982) Steps: 1. Obtain an initial optimal solution by any method 2. Add a user-specified amount of slack to the value of the objective function 3. Encode the adjusted objection function value as an additional upper bound constraint 4. Formulate a new objective function that minimizes the decision variables that appeared in the previous solutions 5. Iterate the re-formulated optimization 6. Terminate the MGA procedure when no significant changes to decision variables are observed in the solutions 40 HSJ MGA Mathematical formulation where: min p x k kK s.t. f j (x) T j x X j K represents the set of indices of decision variables with nonzero values in the previous solutions fj x is the jth objective function Tj is the target specified for the jth modeled objective X is the set of feasible solution vectors 41 Conclusions Most EEO models and model-based analyses are opaque to external parties The TEMOA project represents a new, transparent modeling framework designed for rigorous uncertainty analysis Combine sensitivity analysis, stochastic optimization, and modeling-to-generate-alternatives to identify robust hedging strategies Critical to analyze the deployment of renewables in a systems context 42 Acknowledgments • Kevin Hunter, MS student, Civil Engineering, NCSU • Sarat Sreepathi, PhD student, Computer Science, NCSU • Jean-Paul Watson and Bill Hart, Sandia National Laboratory This work would made possible through the generous support of the National Science Foundation. CAREER: Modeling for Insights with an Open Source Energy Economy Optimization Model. Award #1055622