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.


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