# Presentation: 223371

## Energy Modeling Overview

- Monisha Shah
- June 2004
- South Asian Regional Initiative – Energy
- Kathmandu, Nepal

## Model

-  What is a model?
- Runway model?
- The model of a car?
-  A set of relationships used to simulate a situation or system.
-  An organized set of assumptions

## Why Use a Model?

- Reflects complex systems in an understandable form.
- Helps to organize large amounts of data.
- Provides a consistent framework for testing hypotheses.

## Considerations for Building a Model

- Objectives, scope, priorities
- Data availability and reliability
- Resources available – financial, time, labor, technology

**Notes:**
Use example of EIA’s new international model

## Models vs. Decision Support Systems

- Model methodology is only one (albeit important) issue for analysts, planners and decision makers.
- They also require the full range of assistance provided by decision support systems including: data and scenario management, reporting, units conversion, documentation, and online help and support.
- See example of SAGE

## SAGE Model Mechanics

- Balances

- Templates (Region/Sector) for
- Existing Equipment Stock
- Multiple Excel files

- Region
- Year
- Sector
- Fuel

- Matrix Generator
-  (GAMS)/Solver (CPLEX)

- VEDA-BE
- Analyze, Compare
-  and Report Results

- New Technology
- Data Repository

- Baseline Economic
- Projections

- Scenario Specific
-  Assumptions:
- GDP, Industrial Growth
- Regional technology availability,
-   and regionalized parameter estimates
- Regionalized technology
-   level discount rates
- Energy/Carbon Trading Links
-  and level constraints
- User-defined technology market
-  share constraints
- Technology market behavioral
-   assumptions
- Etc.

- VEDA-FE
- Set up Scenario
- Run and ‘SOLVE’

- Results in Excel
- and Word format

**Notes:**
The Templates estimate energy use from the existing equipment stock
	Currently baseline future service demand is also estimated there 
	this is priority 1 for changing

The New technology data repository characterizes  provides baseline estimates for a large set of end use and conversion technologies—investment cost, efficiency, capacity factor, variable O&M etc.

The modeler, however, is free to choose which technologies are applicable to a particular region and how the characteristics of each technology may vary in application in different parts of the world.

## Putting our Models into Context

- Electricity Demand
- Bottom up
- Top down

## Top Down Models

*Computable general equilibrium *(C.G.E.) models represent an entire economy by a relatively small number of aggregate variables and equations.
- Partial equilibrium where GDP is not included in energy sector
- Answers are at the top and the other results/solutions flow down from there.
- Capture the interaction between energy policies and macroeconomic performance of the economy.
-  Often include variables that account for macroeconomic factors; able to link to macroeconomic modes to obtain feedback results.

**Notes:**
production function parameters are calculated for each sector such that inputs and outputs reproduce a single base historical year.
In policy runs, the mix of inputs required to produce one unit of a sector’s output is allowed to vary according to user-selected elasticities of substitution.
Technology: the autonomous energy efficiency index (AEEI) and the elasticity of substitution (ESUB).

## Top Down Models (2)

*General equilibrium *(G.E.) models represent an entire economy by a relatively small number of aggregate variables and equations.
- production function parameters are calculated for each sector such that inputs and outputs reproduce a single base historical year.
- In policy runs, the mix of inputs required to produce one unit of a sector’s output is allowed to vary according to user-selected elasticities of substitution.

## Two Kinds of Top Down:

- Compute equilibrium solutions.
- Partial equilibrium models:
- Optimization: compute a least cost (or maximum profit) path for the energy system.
- Simulation: emphasis is on representing a system not governed purely by profit or utility maximizing behavior.

- simulate the flows of capital and other monetary quantities between sectors.
- econometrically derived input-output coefficients to compute the impacts of these flows on the main sectoral indicators, including economic output (GDP) and other variables (labor, investments).
- Sectoral variables are then aggregated into national indicators of consumption, interest rate, GDP, labor, and wages.

- Computable General Equilibrium

- Macro-econometric

**Notes:**
CGE: production functions are linear or linearized. Everything is calculated simultaneously. MERGE, SGM, etc. Good examples. 
Partial Eq: energy segment doesn’t include GDP. 

## Bottom Up Models

*technology explicit *models that focus only on the energy sector of an economy.
- Energy-using technologies are identified by a detailed description of the inputs, outputs, unit costs, and several other technical and economic characteristics.
- a sector is constituted by a (usually large) number of logically arranged technologies, linked together by their inputs and outputs (which may be energy forms or *carriers*, materials, and/or emissions).

## Reference Energy System

- Demand: devices that generate end-use services
- Conversion: electricity and district heat
- Process: convert energy from one form to another; i.e. refinery
- Resource: how energy enters and exits the system; mining, exports, etc.

- Energy Carriers = Green arrows
- Interconnect the technologies

**Notes:**
Energy carriers interconnect the technologies represented in an energy system.  A high conversion refinery (process technology), for example, will consume crude oil (energy carrier) to produce refined products such as liquefied petroleum gases, gasoline, light and heavy distillate, jet fuel, and kerosene.  Each refinery output is classified as separate energy carrier, which is subsequently consumed by other technologies.  Gasoline produced by high conversion refineries and other technologies may be consumed by cars and buses (demand technologies) in order to generate transportation energy services; some gasoline may also exit the energy system as an export (resource technology).  Likewise, distillate may be distributed among demand devices for the provision of transportation energy services, and may also be consumed by oil combustion turbines (conversion technology) in order to generate electricity (energy carrier).  Figure 1 maps the aggregate flow of energy carriers across the user-defined network of technologies, terminating with energy consumption by mutually-exclusive end-use demand categories.      

## Bottom Up Models (2)

-  some compute a partial equilibrium via maximization of the total net [consumer and producer] surplus;
- while others simulate other types of behavior by economic agents.
- one unit of sectoral output (e.g., a billion vehicle kilometers of heavy truck service or a Petajoule of residential cooling service) is produced using a mix of individual technologies’ outputs.
- the production function of a sector is *implicitly *constructed.

## Top Down vs. Bottom Up 

- Top-down models encompass macroeconomic variables beyond the energy sector proper, such as wages, consumption, and interest rates
- Bottom-up models have a rich representation of the variety of technologies (existing and/or future) available to meet energy needs, and, they often have the capability to track a wide variety of traded commodities.
- What approach you choose to take depends on your objectives and priorities, etc.

**Notes:**
Issues of aggregation, endogenization, technology representation. Also intangible costs and market barriers. 
Models that endogenize behavior are able to predict actual outcomes, whereas models that exogenize behavior are better suited to simulate effects of changes in historical patterns 

## Techniques for Modeling

-  Simulation Models
-  Accounting Frameworks
-  Optimization Models

## Simulation Models

- Simulate behavior of energy consumers and producers under various signals (e.g. price, income levels, limits on rate of stock turnover).
-  Ex.:
- 	S(t) = State variable at time, *t*
- S (t+1) = S(t) + [Inflow(t) – Outflow(t)]
- (see Vensim Greens example) – maybe make a trade example

**Notes:**
Show the basic VENSIM diagram. 
ENPEP – Argonne National Labs. 
NEMS (Economy Market Equilibrium) 
Dale Nesbit; Jim Pierrenbum

## Simulation Models (2)

- Pros:
- Not limited by assumption of “optimal” behavior.
- Do not assume energy is the only factor affecting technology choice (e.g. BALANCE uses a market share algorithm based on price and  “premium multipliers” indicating quality of energy services).
- Cons:
- Tend to be complex and data intensive.
- Behavioral relationships can be controversial and hard to parameterize.
- Future forecasts can be very sensitive to starting conditions and parameters.
- Examples: ENPEP/BALANCE, Energy 20/20

## Accounting Frameworks (1)

- Physical description of energy system, costs & environmental impacts optional.
- Rather than simulating _decisions_ of energy consumers and producers, modeler explicitly accounts for _outcomes of decisions_ *So instead of calculating market share based on prices and other variables, Accounting Frameworks simply examine the implications of a scenario that achieves a certain market share.*
- Explores the resource, environment and social cost implications of alternative future “what if” energy scenarios.
**Example: **“What will be the costs, emissions reductions and fuel savings if we invest in more energy efficiency & renewables vs. investing in new power plants?”
- Examples: LEAP, MEDEE, MESAP

**Notes:**
EPRI with the automated RES; Change rules at each node to see the effect on the model results. 

## Accounting Frameworks (2)

- Pros:
- Simple, transparent & flexible, lower data requirements
- Does not assume perfect competition.
- Capable of examining issues that go beyond technology choice or are hard to cost.
- Especially useful in capacity building applications.
- Cons:
- Does not automatically identify least-cost systems: less suitable where systems are complex and a least cost solution is needed.
- Does not automatically yield price-consistent solutions (e.g. demand forecast may be inconsistent with projected supply configuration).

## Optimization Models (1)

- Typically uses linear programming to identify energy systems that provide the least cost means of providing an exogenously specified demand for energy services.
- Optimization is performed under constraints (e.g. technology availability, supply = demand, emissions, etc.)
- Model chooses between technologies based on their lifecycle costs.
- Least-cost solution also yields estimates of energy prices (the “dual” solution).
- Examples: MARKAL, EFOM, WASP (electric sector).

## Optimization Models (2)

**Pros:**
- Powerful & consistent approach for a common type of analysis called **Backcasting**. E.g. What will be the costs of meeting a certain policy goal?
- Especially useful where many options exist. E.g. : What is the least cost combination of efficiency, fuel switching, pollution trading, scrubbers and low sulfur coal for meeting a SOx emissions cap?
**Cons:**
- Questionable fundamental assumption of perfect competition (e.g., no monopolistic practices, no market power, no subsidies, all markets in equilibrium).
- Not well suited to simulating how systems behave in the real world.
- Assumes energy is only factor in technology choice.  *Is a Ferrari the same as a Ford?  *Tends to yield extreme allocations, unless carefully constrained.
- Not well suited to examining policy options that go beyond technology choice, or hard-to-cost options.  E.g. *To reduce CO**2** you can either (a) use a large hybrid car, or (b) drive a smaller car. *
- Relatively complex, opaque and data intensive: hard to apply for less expert users, so less useful in capacity building efforts.

## Types of Optimization Models

-  Linear
-  Nonlinear models – other functional forms for objective function or constraints
- Integer or mixed-integer models – some or all of the variables are integers

## Linear Programs

-  Objective function:
*min or max Z = f(x**1**, x**2**, x**3**, ..., x**n**)*
-  Decision variables (must be non-negative):
***x**** = x**1**, x**2**, x**3**, ..., x**n*
-  Constraints
-  determine the feasible region
-  must be convex

## Example LP

- Problem Statement: Maximize profit of company by selling product 1 and product 2 by determining the optimal production strategy
- x1 = number of tons of product 1 each week
- x2 = number of tons of product 2 each week

- Resource Requirements and Availability

## Example LP

- Objective Function:
- 			Max Z = 140 x1 + 160 x2
- Subject to: 	5x1+5x2 ≤ 50
- 				2x1+4x2 ≤ 28
- 				x1 ≤ 8
- 				x2 ≤ 6
- 				x1 , x2  0

## Feasible Region

- 5x1+5x2 ≤ 50

- 2x1+4x2 ≤ 28

- x2 ≤ 6

- x1 ≤ 8

- 5x1+5x2 ≤ 50
- 2x1+4x2 ≤ 28
- x1 ≤ 8
- x2 ≤ 6
- x1 , x2 ≤ 0

## Objective Functions (Max)z = 140 x1 + 160 x2

- Optimal Solution:
- X1 = 6, x2 = 4

## Issues with LPs

-  It is possible to over-constrain a model
- This leads to an infeasible solution
- LP models are solved using a variety of algorithms based on matrix algebra
- A feature of the solution are the “shadow prices” of the constraints
- The shadow prices are the marginal values of the constraints and indicate how much the objective function would change if the constraints were changed by one unit

## Summary

-  Many types of energy models
-  Need to determine objectives and questions need to answer with the model
-  Also need to determine data and software support available and required
-  Models – organized set of assumptions

## Other Sources

- Energy Modeling Forum
- ETSAP (MARKAL)
- Tellus Institute
- Carnegie Mellon University
- MITUniversity of California, Berkeley
- Resources for the Future
- Argonne National Laboratories
- Energy Policy Journal

## Thank You

- Monisha Shah
- monisha.shah@eia.doe.gov