Lecture 15 Review and Summary

Gang He

December 5, 2022

Start with two quotes

George Box:

All models are wrong, but some are useful.

Albert Einstein:

Everything should be made as simple as possible, but not simpler.

Energy systems

What we have learned

• Energy systems
  • Data
  • Economics
  • Technology
  • Supply
  • Demand
  • Power

• Tools/skills
  • Data analysis
  • Economic analysis
  • Life cycle analysis
  • Energy-economy-environment (nexus)

How modelers think about an energy system

• System components, structure, and relations
  
• Networks (transmission, transport)
  
• Objectives and constraints (if do optimization)
  
• Validation and calibration
  
• Intepretation and communication

Data quality

• Availability
  
• Accessibility
  
• Credibility

Comparison, Validation, Verification

Project economics

• Project economics is useful for basic cost-benefit analysis
  
• Getting the price (discounting) right
  
• Understanding technology dynamics will help to model future projections
  
• Aware of the limitations

Energy sources and technologies

• Theory - learn and understand the physics of energy technologies:
  • thermaldynamics (fossil)
    
  • kinematics (wind)
    
  • light and semiconductor (solar)
    
  • gravity (hydro, tidal)
    
  • atomic (nuclear)
    
• Practice - learn all kinds of corrections based on real-world situation
  
• The physics doesn’t change, corrections help us to do better job in simulation and projections

Energy demand

• Energy demand is shaped by dynamic factors: populations, GDP, technology, regulation, and human behavior
  
• Understanding those drivers will help us learn the magnitude and shape of our demand
  
• Load forecast integrates all insights to the show

Energy, environment, and human health

• Environmental and health impacts are the externalities of energy systems
  
• Energy-air-pollution-human health framework of analysis
  
• Energy-water-carbon nexus
  
• System impacts show the constraint in systems modeling

Energy and climate change

• IAM framework and models to analyze energy, emissions, and climate impacts
  
• Understanding the evolution of climate policy instruments
  
• Learn the applications and limitations of social cost of carbon
  
• Climate change affects energy supply and demand

Power sector analysis

• Power sector’s central role in decarbonization
  
• Capacity expansion model to analyze the optimized investment decisions
  
• Decisions in the real world is much more complicated
  
• Emerging trends in the power sector

Energy transition

• Diverse factors that drive energy transition
  
• Common and differentiated solutions to energy
  
• Leverage the advantages
  
• Addressing uncertainties: geopolitics, wars, pandemic, and more

Energy efficiency

• There are barriers for energy efficiency and inforamtion and standards can help address the energy efficiency gap
  
• Human behavior is complicated, and understanding it will help us to design policy
  
• Rebound is real but might be overplayed, rebound could be good if improve welfare
  
• Sustainable consumption and production within the limit

Energy poverty, access, and justice

• Electricity/energy access is fundamental to other modern services: education, health, and information
  
• Energy access also has gender equity, health cobenefits, and other implications
  
• Energy justice is important to achieve just transition
  
• An reflection on our approaches to energy justice

AI and big data for clean energy

• AI and big data has transformed how we live, and how can we save the planet
  
• AI does not automatic add new insights, but it provides powerful tools to analyze increasingly available big data
  
• Learning those new tools and skills becomes necessary to work in the field
  
• The potential of using AI/big data to analyze our understanding of energy supply/demand and human behavior is enomorous.

Limitations of models

• Know the limitations of models
  
• Use model appropriately
  
• Interpret/communicate the results effectively
  
• Modeling for social good

End with two quotes

Bill Hogan:

It is not the individual results of a model that are so important; it is the improved user appreciation of the policy problem that is the greatest contribution of modeling.

Huntington, Weyant, Sweeney “Modeling for insights, not numbers”:

The primary goal of policy modeling should be the insights quantitative models can provide, not the precise-looking projections –i.e. numbers – they can produce for any given scenario.

References

Hogan, William W. 2002. “Energy Modeling for Policy Studies.” Operations Research 50 (1): 89–95. https://doi.org/10.1287/opre.50.1.89.17803.

Huntington, Hillard G, John P Weyant, and James L Sweeney. 1982. “Modeling for Insights, Not Numbers: The Experiences of the Energy Modeling Forum.” Omega 10 (5): 449–62. https://doi.org/10.1016/0305-0483(82)90002-0.