Energy Systems Analysis - Lecture 1 Introduction

US passed historical climate bill

How to evaluate its impact to the emission reduction?

Why EST603?

Quantitative skills/tools
- Research
- Jobs
Evidence based energy/environmental policy making

Organization

Syllabus
Brightspace
Course website: https://drganghe.github.io/est603-energy-systems-analysis-2022-fall/
Homework (4): 40%
Proposal: 10%
Final presentations: 20%
Final paper/policy memo: 30%

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.

What is energy systems analysis

We also call it energy modeling, using computing models to simulate, design, and operate energy systems. Answering questions such as:

What will be our energy demand?
What technology to invest to satify that demand?
What infrastructure do they require?
Where/when should we build?
What are the environmental/climate impacts?
How much will it cost?

Why models

Prediction/Projection
Simulation
Optimization
Control
Stochastic/dynamic
Policy: Scenarios

Models help us think

Modeling is hard

Why?

Risk: System structural change could break our core data and assumptions that are based on historical experience. Example: shale gas revolution.
Uncertainty: The exact timing and character of pivotal events and technology changes is unpredictable. Example: renewables cost changes, fussion.

Why: energy systems are complicated

The Energy Trillemma: Balancing trade-offs

Energy security: Ensure reliable energy supply

Energy equity: Provide universal access to reliable, affordable, and abundant energy

Environmental Sustainability: Avoid environmental harm or climate impact

Those goals sometimes conflict with each other, and decisions has to make trade-offs between them

Energy, economy, and environment

Economy: Decent living? Growth? Degrowth?

Environment: Emissions, ecosystems constraints/goals

Energy: Work within constraints

Energy great achievement

Energy grand challenges: SDGs

Energy grand challenges: net-zero

Modeling can be useful/insightful (an example)

Build the structure
Demonstrate the relation
Visualize the changes
Inform the impacts

Renewables are achieving grid parity: a structural change

Implications are profound

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

Grant, Neil, Adam Hawkes, Tamaryn Napp, and Ajay Gambhir. 2021. “Cost Reductions in Renewables Can Substantially Erode the Value of Carbon Capture and Storage in Mitigation Pathways.” One Earth 4 (11): 1588–1601. https://doi.org/10.1016/j.oneear.2021.10.024.

He, Gang, Jiang Lin, Froylan Sifuentes, Xu Liu, Nikit Abhyankar, and Amol Phadke. 2020. “Rapid Cost Decrease of Renewables and Storage Accelerates the Decarbonization of China’s Power System.” Nature Communications 11 (1): 2486. https://doi.org/10.1038/s41467-020-16184-x.

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.

IEA. 2021. “Net Zero by 2050.” International Energy Agency.

IRENA. 2021. “Renewable Power Generation Costs in 2021.” Abu Dhabi: International Renewable Energy Agency. https://www.irena.org/publications/2022/Jul/Renewable-Power-Generation-Costs-in-2021.

Lecture 1 Introduction