Post on 22-Dec-2015
transcript
Govinda R. TimilsinaThe World Bank, Washington, DC
Skopje, MacedoniaMarch 1, 2011
Sectoral Models for Energy and Climate Policies
Presentation Outline
► Introduction
► Typology of models
► Energy Demand Models
► Energy supply models
► Energy system models
Introduction
Energy modelling has a long history(Since the early 1970s, a wide variety of models became available for analysing energy systems or sub-systems, such as the power system)
Energy modelling has multiple purposes(Better understanding of current and future markets – supply, demand, prices; facilitating a better design of energy supply systems in short, medium and long term; ensuring sustainable exploitation of scarce energy resources; understanding of the present and future interactions energy and the rest of the economy; understanding of the potential implications to environmental quality)
Based on different theoretical foundations(Engineering, economics, operations research, and management science)
Apply different techniques(Linear programming, econometrics, scenario analysis)
Classifying Energy Models
Energy Model
Model for energy market forecast
Energy demand model
End-use accounting model
Econometric model
Energy supply model
Optimization model
Simulation model
Energy system model
Model for energy – economic interaction
Input-Output model
General equilibrium model
Methodologies for Energy Demand
Forecasting
Methodologies for Energy Demand Forecasting
End-use Approach
Bottom-up or engineering approach
Use physical or engineering relationship between energy and energy utilizing devices and processes (e.g., capacity, efficiency, utilization rate)
Follows growth of driving variables (i.e., devices and processes), which are derived often scenario analysis or economic models
Could produce more disaggregated (i.e., end-use and sector) and the forecasts are relatively precise
Complex and data consuming; more appropriate for long-term
Econometric Approach
Econometric approach
Use historically established relationships between energy demand and economic variables (e.g., GDP, population, household income)
Follows growth of driving variables (i.e., economic variables)
Estimation are made at more aggregated level or at sectoral level but not at end-use level
Simple but relatively less accurate; more appropriate for short-term
Methodologies for Energy Demand Forecasting
End-use Approach
Normally do not account pricing effect on demand, which is very critical when demand for a fuel is highly elastic
Econometric Approach
This approach normally considers single fuel or aggregate energy (gasoline, electricity) and does not account substitution possibilities between fuels
Use of flexible functional forms (e.g., translog, normalized quadratic ) is growing
They are unable to account technology specific features which are key determinants of fuel consumption
Comparison of some energy demand forecasting models
Criteria DTI NEMS MAED/ MEDEE
LEAP POLES
Type Top-Down Hybrid Bottom-up Bottom-up HybridApproach Econometric AccountingGeography National Flexible GlobalLevel of disaggregation
Domestic, transport, service, industry Agriculture is also included
Technology coverage
Renewable and conventional
Both conventional and renewable
Data need Time series and survey
Energy Supply Models
Energy Supply Models
These models either stand alone (e.g., MARKAL, WASP) or serve as a module of a energy system
model (e.g., electricity market module, coal market module in US NEMS model)
Demand forecasts, energy resources and technologies characteristics, costs are the key driving variables
Can accommodate any policy instruments or constraints such as emission constraints
Methodologies for Energy Supply Planning
Optimization
Ensure cost minimization meeting all constraints such as resource availability, system reliability, environmental quality (if desired)
More appropriate when a large number of supply alternatives are available
Example: MARKAL, EFOM, WASP
Simulation
Simulates behavior of energy consumers and producers under various signals (e.g. price, income levels)
Forecasts can be sensitive to starting conditions and behavioral parameters
Example: ENPEP/BALANCE, Energy 20/20
Energy Supply Model: MARKAL
MARKAL is a “bottom-up” model with detailed representation of energy resources and production technologies
It follows the principal of reference energy system and finds a least cost set of technologies to satisfy end-use energy service demands and user-specified constraints
MARKAL is found extensively used for both academic and consulting studies
The MARKAL Energy PerspectiveThe MARKAL Energy Perspective
Industry, e.g.-Process steam-Motive power
Services, e.g.-Cooling-Lighting
Households, e.g.-Space heat-Refrigeration
Agriculture, e.g.-Water supply
Transport, e.g.-Person-km
Demand for Energy Service
Industry, e.g.-Steam boilers-Machinery
Services, e.g.-Air conditioners-Light bulbs
Households, e.g.-Space heaters-Refrigerators
Agriculture, e.g.-Irrigation pumps
Transport, e.g.-Gasoline Car-Fuel Cell Bus
End-UseTechnologies
ConversionTechnologies
Primary Energy Supply
Fuel processingPlants e.g.-Oil refineries-Hydrogen prod.-Ethanol prod.
Power plants e.g.-ConventionalFossil Fueled
-Solar-Wind-Nuclear-CCGT-Fuel Cells-Combined Heat
and Power
Renewables e.g. -Biomass-Hydro
Mining e.g.-Crude oil-Natural gas-Coal
Imports e.g.-crude oil -oil products
Exports e.g.-oil products-coal
Stock changes
(Final Energy) (Useful Energy)
MARKAL: MARKet ALlocation)
Developed under the Energy Technology Systems Analysis Program of IEA
Linear programming type optimization ; based on Reference Energy System
Detailed modeling of energy resources and supply chains
Includes electricity generation and transmission planning
Energy Supply Model: MARKAL
Energy Supply Model: MARKAL
Total OECD Countries = 21Total Developing Countries =
23Total Other Countries = 13
Electricity Supply Model: WASP
WASP stands for Wien Automatic System Planning
It was originally developed by the Tennessee Valley Authority and Oak Ridge National Laboratory of the US for International Association of Atomic Energy
It is the most well-known and widely used optimization model for examining medium- to long-term expansion options for
electrical generating systems
The software is distributed for use by electric utilities and regulation agencies in over 90 countries, as well as to 12 international organizations including The World Bank
Electricity Supply Model: WASP
Countries Using WASP
Energy System Models
Energy System Modeling
Energy system models combine both demand and supply, they can be also used for:
Energy market projections Energy policy analysis Projections of environmental pollution (e.g., GHG, SOx, NOx) from the energy system and policies for their mitigation
They can employ different methodologies for the demand and supply blocks (e.g., end-use or econometric for demand and optimization or simulation for supply)
ENPEP – Optimization for supply; econometric for demand
LEAP uses end-use accounting approach for demand and simulation approach for supply
NEMS uses optimization modules for the electricity sector and simulation approaches for each demand sector
Name Developer
NEMS US DOE
ENPEP Argonne National Laboratory
LEAP Stockholm Environmental Institute
TIMES Energy Technology Systems Analysis Program (ETSAP) of the International Energy Agency (IEA),
MESSAGE International Institute for Applied Systems Analysis, Austria
POLES LEPII (formerly IEPE - Institute of Energy Policy and Economics), Grenoble, France
ENERGY 2020 Systematic Inc. (a US private company)
Energy System Models - Examples
MAED
LOAD
WASP IV
BALANCE
WASP IV
MACRO-E
Capacity Expansion Plan
Load Dispatching
Electricity Generation
Fuel Consumption
Emissions
Emissions
Energy Demand (excluding electricity)
- Detailed evaluation of energy demands by sector, sub-sector, fuels and useful energy
- Representation of resource availability and costs
- Detailed evaluation of the power system configurations
Energy System Model - ENPEP
Energy System Model – US NEMS
The National Energy Modeling System (NEMS) is the tool the Energy Information Administration (EIA) of the United States has been using since 1994 to project US energy market and to analyze various energy-economic, environmental and energy security policies
NEMS projects the production, imports, conversion, consumption, and prices of energy, subject to assumptions on macroeconomic and financial factors, world energy markets, resource availability and costs, behavioral and technological choice criteria, cost and performance characteristics of energy technologies, and demographics
Based on NEMS results the EIA publishes its Annual Energy Outlook every year; it has also been used for a number of special analyses at the request of the Administration, U.S. Congress, other offices of DOE and other government agencies:
Energy Market and Economic Impacts of H.R. 2454, the American Clean Energy and Security Act of 2009, requested by Chairman Henry Waxman and Chairman Edward Markey
Impacts of a 25-Percent Renewable Electricity Standard as Proposed in the American Clean Energy and Security Act, requested by Senator Markey
Source: EIA, USDOE (http://www.eia.doe.gov/oiaf/aeo/overview/figure_2.html)
Energy System Model – US NEMS (Model Structure)
Long Range Energy Alternatives Planning System
Developed by Stockholm Environmental Institute
Scenario-based energy accounting model
It accommodates a Technology and Environmental Database
Energy demands by sectors, sub-sectors end-uses and equipment Energy transformation sectors included (e.g., electricity, refinery,
charcoal)
Energy System Model – LEAP
Energy System Model – LEAP(Overall Model Structure)
Dem ographicsMacro-
Econom ics
Dem andAnalysis
Transform ationAnalysis
StatisticalD ifferences
StockChanges
ResourceAnalysis
Integrated Cost-B
enefit AnalysisE
nviro
nmen
tal L
oadi
ngs
(Pol
luta
nt E
mis
sion
s)
Non-Energy SectorEm issions Analysis
Environm entalExternalities
Energy System Model – LEAP(Global Application)
MESSAGE stands for Model for Energy Supply Strategy Alternatives and their General Environmental Impact; it is the International Institute for Applied Systems Analysis, Austria
It is a systems engineering optimization model used for medium- to long-term energy system planning, energy policy analysis, and scenario development
It is a scenario-based energy system model; scenarios are developed through minimizing the total systems costs under the constraints imposed on the energy system; this information and other scenario features such as the demand for energy services, the model configures the evolution of the energy system from the base year to the end of the time horizon
Energy System Model – MESSAGE
Energy System Model – MESSAGE(Overall Model Structure)
Comparison of Selected Energy System Models
Criteria RESGEN EFOM MARKAL TIMES MESAP LEAP Approach Optimisation Accounting
Geographical coverage
Country Local - national
Country - multi-country
National Local - global
Activity coverage
Energy Energy & trading
Energy
Sector Pre-defined User defined Pre-defined Technology Good Extensive
Data need Variable Extensive Variable Skill
requirement Limited High Limited
Documentation
Limited Good Extensive Good Extensive
Thank You
Govinda R. Timilsina
Sr. Research Economist
Environment & Energy Unit
Development Research Group
The World Bank
1818 H Street, NW
Washington, DC 20433, USA
Tel: 1 202 473 2767
Fax: 1 202 522 1151
E-mail: gtimilsina@worldbank.org