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Sectoral Models for Energy and Climate Policies. Govinda R. Timilsina The World Bank, Washington , DC Skopje, Macedonia March 1, 2011. Presentation Outline. ► Introduction ► Typology of models ► Energy Demand Models ► Energy supply models ► Energy system models.
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SectoralModels for Energy and Climate Policies GovindaR. TimilsinaThe World Bank, Washington, DC Skopje, Macedonia March 1, 2011
Presentation Outline ► Introduction ► Typology of models ► Energy Demand Models ► Energy supply models ► Energy system models
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) Introduction
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
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
Energy Supply Model: MARKAL • MARKAL: MARKetALlocation) • 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 Total OECD Countries = 21 Total Developing Countries = 23 Total 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 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
Energy System Model - ENPEP • - 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 Global Use of 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
Energy System Model – US NEMS (Model Structure) Source: EIA, USDOE (http://www.eia.doe.gov/oiaf/aeo/overview/figure_2.html)
Energy System Model – LEAP • 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 – MESSAGE • 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
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