EPA6 Texas workshop

MARKAL Modeling Workshop : 

MARKAL Modeling Workshop Presented by John C. Lee jcl@bnl.gov Brookhaven National Laboratory U.S. Department of Energy June 5-6, 2006 University of North Texas, Denton Texas

Workshop Agenda: 

Workshop Agenda MARKAL Model Overview History General Structure Extended MARKAL Features Users and Applications MARKAL Mathematical Foundations Case Studies National: USDOE & Taiwan Municipal - Local Advanced Planning : New York City Multi-regional: Quebec-Northeast, Colombia-Switzerland, Central America, and IEA-Global MARKAL Input and Output Structure Computer Laboratory Exercise Reference System Formulation (ANSWER) Data, Scenario & Output Management Extended Topics & Panel Discussions

What Is a Model?: 

What Is a Model? A model is a simplification of complex behaviors. Models may range from complex computer based forms to simple ‘back of the envelope’ calculations. In the analysis of climate/energy related policies, two general types of models have been used: (1) Top-down (e.g., general equilibrium or macro-economic frameworks); and (2) Bottom-Up (e.g., energy system models).

Caution Points with Models: 

Caution Points with Models Models provide insights. But rarely are the forecasted numbers right on! Model results are only as good as the assumptions used. Care needs to be taken in selecting parameters used in the modeling effort. Understanding how the nature and structure of a model affects the answer is an important part of the process.

Caution Points with Models (cont.): 

Caution Points with Models (cont.) Is there an underlying theory behind the model? Increased levels of detail do not necessarily make a model more accurate. Can uncertainty be handled in the modeling framework? Particularly for the analysis of GHG mitigation options, how is technological change handled?

Evolution of MARKAL: 

Evolution of MARKAL Pioneering efforts in the field of energy systems analysis at Brookhaven National Laboratory (BNL): Reference Energy System network concept (1975) Brookhaven Energy System Optimization Model(BESOM) and its variants (Time sequenced TESOM, Regional RESOM) (1976 – 1978) Implementing Agreement under the auspice of the International Energy Agency – Energy Technology Systems Analysis Programme (IEA-ETSAP) to develop new modeling framework (1978 - 2001) BNL/KFA Parallel OMNI/Fortran Development Teams produce first production MARKAL model (1980)

Evolution of MARKAL (cont.): 

Evolution of MARKAL (cont.) Migration from Mainframe to PC (1989) Development of first data handling and analysis “shell,” MARKAL User’s Support System (1990) Development of MARKAL-MACRO, and migration to GAMS (1992) Migration to ANSWER Windows-based “shell” (1999)

Evolution of MARKAL (cont.): 

Evolution of MARKAL (cont.) Continued development of model variants (1996 – 2001) MICRO, Material Flows, Elastic_Demands, Stochastics, Multi-region, Endogenous Technology Learning, Goal Programming, MARKAL_TimeStep Development of World-MARKAL_ED by the U.S. Department of Energy Energy Information Administration for the International Energy Outlook (2001) Development of TIMES, The Integrated MARKAL EFOM System (1999 - 2001)

MARKAL Model OverviewGeneral Structure: 

MARKAL Model Overview General Structure

Integrated Market Based/Technology Specific Approach: The MARKAL Model: 

Integrated Market Based/Technology Specific Approach: The MARKAL Model Utilizes a bottom-up approach to represent and characterize technology specific portfolios of the entire energy - materials flow system – synergies, offset and feedback effects Provides a dynamic integrated framework to assess market competition, technology diffusion and emission/waste accounting Facilitates Program Managers in selecting technology mix over the entire energy - materials system based on life cycle accounting and least cost Solves as a mathematical programming problem

MARKAL Building Blocks - Data Categories: 

MARKAL Building Blocks - Data Categories 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-Use Technologies Conversion Technologies Primary Energy Supply Fuel processing Plants e.g. -Oil refineries -Hydrogen prod. -Ethanol prod. Power plants e.g. -Conventional Fossil 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)

Demonstrative MARKAL Reference Energy System: 

Demonstrative MARKAL Reference Energy System Utilizes a bottom-up approach to identify an optimal technology mix over the entire energy system Consists of a dynamic integrated framework to assess market competition, technology diffusion and emission accounting. Technology choices are made based on least cost.

Modeling Biomass/Hydrogen Economy in MARKAL: 

Modeling Biomass/Hydrogen Economy in MARKAL

Components of MARKAL: 

Components of MARKAL

Basic Software & Hardware Requirements: 

Basic Software & Hardware Requirements MARKAL requires commercial software (ANSWER) for management of input-output data & execution of model runs, and software (e.g. MINOS) for compiling/executing the source code and solving the model. MARKAL runs in a DOS-box under Windows and is installed as source code using GAMS . This means that the knowledgeable user can alter the code. High-end PCs are the preferred hardware platform with 1gb or more RAM.

MARKAL(Relative Advantages and Disadvantages): 

MARKAL (Relative Advantages and Disadvantages) Advantages MARKAL has a well-developed support network around the world through ETSAP (Energy Technology Systems Analysis Programme of the International Energy Agency). MARKAL is highly flexible and scalable. MARKAL has a user friendly shell which interfaces with the Office suite. Open data and model architecture. Well understood modeling paradigm, least-cost optimization.

MARKAL(Relative Advantages and Disadvantages, Cont.): 

MARKAL (Relative Advantages and Disadvantages, Cont.) Disadvantages MARKAL is data intensive. Characterization of technologies and a country’s reference energy system can be initially labor intensive. Results sometimes sensitive to small changes in data (cost) assumptions. Relatively long learning curve. Software costs.

Slide18: 

MARKAL Users Total OECD Countries = 21 Total Developing Countries = 29 Total Other Countries = 13

MARKAL Model OverviewExtended MARKAL Features : 

MARKAL Model Overview Extended MARKAL Features

Extended MARKAL Features: 

Extended MARKAL Features Standard MARKAL has been extended to handle a number of specialized problems. Extended versions of MARKAL include: Material Flows Multi-Region Endogenous Technology Learning Stochastic MARKAL Elastic_Demands MARKAL-MACRO

Slide22: 

Modeling Nuclear Energy in MARKAL

Slide23: 

Water Sources

Slide24: 

Irrigation water Aquifer Recharge

Multi-region Models: 

Multi-region Models Traditionally, MARKAL has been applied to a single geographic area, normally a country, state or municipality. However, multiple MARKAL-MM/MED models can be combined. With the linked version, trading of emissions or energy products can occur freely or according to defined patterns. Macroeconomic effects are achieved through including flows of other materials in addition to energy.

Multi-Region MARKAL: 

Multi-Region MARKAL

Endogenous Technology Learning: 

Endogenous Technology Learning ‘Learning by doing’ can be modeled in MARKAL using mixed integer programming (MIP). Modeling of this process uses estimated relationships between cumulative world-wide sales and technology investment costs, which decline with increased sales. ‘Key technologies’ which are components of other technologies (e.g., gas turbines, fuel cells, and boilers) are selected for application of ‘learning by doing.’

ETL: Stepwise Specific Cost : 

ETL: Stepwise Specific Cost

Stochastic MARKAL: Event Tree : 

Stochastic MARKAL: Event Tree

MARKAL Elastic–Demand (MED): 

MARKAL Elastic–Demand (MED) Standard MARKAL solutions may be interpreted as a partial equilibrium results: one market-clearing price per commodity, no excess demand in that market, and efficient market pricing. In MED, exogenously defined demands, have been replaced with demand curves. The sum of consumer and producer surplus are maximized in the objective function, and the equilibrium between supply and demand is derived.

Elastic–Demand: Trade-Off Curve: 

Elastic–Demand: Trade-Off Curve

What is MARKAL-MACRO?: 

What is MARKAL-MACRO? MARKAL-MACRO (M-M) is an extension of the MARKAL model that simultaneously solves the energy and economic systems. M-M merges the “bottom-up” engineering and “top-down” macroeconomic approaches. M-M has price responsive demands (i.e., determined endogenously), while MARKAL is not (i.e., demands are exogenously defined). M-M maximizes consumer welfare over the solution period, optimizes aggregate investment in the economy and provides least cost energy system configurations to meet endogenously determined demands.

MARKAL-MACRO Overview: 

MARKAL-MACRO Overview

What MARKAL Does?: 

What MARKAL Does? Identifies the most cost-effective pattern of resource use and technology deployment over time. balancing all supply/demand requirements, ensuring proper process/operation, monitoring capital stock turnover, and adhering to environmental & policy restrictions. Quantifies the sources of emissions and waste flows from the associated energy system. Provides a framework for evaluating alternative futures, and role (cost-benefit) of various technologies, trade and policy options.

What MARKAL Does?: 

What MARKAL Does? Generates integrated local energy and waste management planning; Establishes GHG baseline inventory Evaluates mitigation projects and the value (cost) of carbon rights determines the value of regional and international cooperation 

Who Is Using MARKAL; and Why?: 

Who Is Using MARKAL; and Why? More than 90 Research institutions and universities in over 60 countries around the world; to look at sustaining economic development in the context of energy/environmental issues ranging from Climate Change to local air pollution. The International Energy Agency (IEA) for the Energy Technology Perspective (ETP) project; to bring technology detail to future World Energy Outlook publications. Research institutions in the 16 countries currently participating in the IEA – Energy Technology Systems Analysis Programme (ETSAP); to support their national governments’ energy/environmental planning. (WWW.ETSAP.ORG)

Who Is Using MARKAL; and Why?: 

Who Is Using MARKAL; and Why? Selected by EERE and NE of the US Department of Energy for strategic planning in technology R&D and Government Performance and Result Act (GPRA) Analysis. Selected by the Energy Information Administration as the primary model to generate the International Energy Outlook Selected by US EPA for multi-regional analysis in GHG mitigation and trade Selected by the Government of Kuwait as the primary tool to formulate optimal strategies in petroleum economy development and trade

Who Is Using MARKAL; and Why?: 

Who Is Using MARKAL; and Why? Selected by ASEAN and Central American countries for energy planning, water & waste management, and carbon trade (Clean Development Mechanism) analysis Selected by US EPA Region 2 to study energy conservation and electricity load management strategies in office buildings in lower Manhattan of New York City. Research institutions and universities in 5 European countries participating in the IEA – Annex 33: Advanced Local Energy Planning; to apply MARKAL at the municipal level. Used by US DOE Policy Office for quick response and long-term policy formulation.

MARKAL Mathematical Foundations : 

MARKAL Mathematical Foundations

MATHEMATICAL PROGRAMMING PROGRAM: 

MATHEMATICAL PROGRAMMING PROGRAM MAX F(x) subject to g(x) < b, x > 0 x or MIN F(x) subject to g(x) > c, x > 0 x MAX F(x1, x2, . . . ,xn) x1, x2, . . . , xn subject to g1 (x1, x2, . . . , xn) < b1 g2 (x1, x2, . . . , xn) < b2 gm (x1, x2, . . . , xn) < bm x1 > 0, x2 > 0, . . . , xn > 0 . . .

LINEAR PROGRAMMING PROGRAM: 

LINEAR PROGRAMMING PROGRAM MAX F(x) = c x subject to Ax < b, x > 0 x MAX F (x1, x2, . . . xn) = c1x1 + c2x2 + . . . + cnxn x1,x2,…,xn subject to a11x1 + a12x2 + . . . + a1nxn < b1 a21x1 + a22x2 + . . . + a2nxn < b2 . . . . . . . . .. . . . . am1x1 + am2x2 + . . . + amnxn < bm x1 > 0, x2 > 0, . . . , xn > 0

Slide42: 

PRIMAL MAX F(x) = c x x subject to A x < b x > 0 DUAL MIN G(y) = y b y subject to y A > c y > 0 DUAL PROBLEM OF LINEAR PROGRAMMING

Slide43: 

PRIMAL MAX F(x1, x2) = 3x1 + 2x2 x1,x2 subject to 2x1 + < 6 + 2x2 < 8 x1 > 0, x2 > 0 SOx limit NOx limit DUAL MIN G(y1, y2) = 6y1 + 8y2 y1,y2 subject to 2y1 + > 3 + 2y2 > 2 y1 > 0, y2 > 0

SOLUTIONS: 

SOLUTIONS PRIMAL X*1 = 3, X*2 = 4 , DUAL Y1 = 1.5 , y2 = 1 F* = 17 G* = 17

Slide45: 

If the SOx limit is increased by I unit, then F* = 18.5 (an increase of 1.5) If the NOx limit is increased by I unit, then F* = 18 (an increase of 1) If the producer is given an additional unit of emission Right (SOx or NOx), which one should he/she accept?

Slide46: 

PRIMAL MAX F(x1, x2) = 3x1 + 2x2 x1,x2 subject to 2x1 + x2 < 6 x1 + 2x2 < 8 x1 > 0, x2 > 0 SOx limit NOx limit DUAL MIN G(y1, y2) = 6y1 + 8y2 y1,y2 subject to 2y1 + y2 > 3 y1 + 2y2 >2 y1 > 0, y2 > 0

SOLUTIONS: 

SOLUTIONS PRIMAL , DUAL , If the producer is forced to use at least 2 units of X1, what happens?

Slide48: 

PRIMAL MAX F(x1, x2) = 3x1 + 2x2 x1,x2 subject to 2x1 + x2 < 6 x1 + 2x2 < 8 x1 > 2, x2 > 0 SOx limit NOx limit DUAL MIN G(y1, y2) = 6y1 + 8y2 y1, y2 subject to 2y1 + y2 > 3 y1 + 2y2 >2 y1 > 0, y2 > 0

SOLUTIONS: 

SOLUTIONS PRIMAL X*1 = 2, X*2 = 2 , DUAL Y1 = 2 , y2 = 0 F* = 10 G* = 10 Note: y2 = 0 because the NOx limit is non-binding. If the producer is allowed to “relax” the use of x1 by one unit, F* goes back to 32/3, an increase of 2/3.

FOR NON-LINEAR PROGRAMMING: 

FOR NON-LINEAR PROGRAMMING m m b F Y ¶ ¶ = * * where is the lagrangian multiplier of the mth constraint. > 0 if the mth constraint is binding = 0 if not binding bm is the RHS of the mth constraint

MARKAL Matrix Structure: 

MARKAL Matrix Structure

Case Studies: NationalU.S. Department of Energy: 

Case Studies: National U.S. Department of Energy

Slide53: 

Estimating Benefits of Publicly Funded Energy Technology Research: U.S. GPRA Benefits Analysis Brookhaven National laboratory US Department of Energy June 5, 2006

Government Performance & Results Act: 

Government Performance & Results Act The Government Performance and Results Act (GPRA) of 1993 seeks to shift the focus of government decision-making and accountability away from a preoccupation with the activities that are undertaken - such as grants dispensed or inspections made - to a focus on the results of those activities, such as real gains in employability, safety, responsiveness, or program quality. Under the Act, agencies are to develop multiyear strategic plans, annual performance plans, and annual performance reports.

MARKAL-GPRA Analysis for Energy Programs:Multi-year Strategic Planning: 

MARKAL-GPRA Analysis for Energy Programs: Multi-year Strategic Planning Official tool to conduct GPRA analysis for EERE, NE, & FE Offices in DOE Comparison of relative cost-effectiveness among energy R&D programs Coordinated planning for maximizing synergy among energy R&D programs Identification of energy system bottlenecks and disparities from integration of energy R&D programs

The Ground Rules: 

The Ground Rules Benefit estimates based on stated program output goals that are achievable with current funding levels. For the budget, benefits are counted for only future program activities. One baseline for all Programs Energy prices, demographics, GDP, industrial output Demands for energy services Improvements in competing technologies Assesses value of realizing Program goals Benefits = difference between how energy system evolves with and without realizing the program goal. No new policies

Slide57: 

If you give me some $$, I’ll do something with it, making some progress each year until I produce a product which will provide the benefits you want Budget, resources, inputs Key activities Targets, milestones Outputs, program goals, end points Outcomes, impacts, Benefits The GPRA Simple “logic chain” Program performance Portfolio benefits

EERE GPRA Benefits Logic Chain: 

EERE GPRA Benefits Logic Chain The GPRA process uses the outputs developed by the Program Offices to project technology adoption rates and resulting benefits. Source: John Mortensen

NAS/NRC Benefits Framework: 

NAS/NRC Benefits Framework

The Current Metrics: 

The Current Metrics Economic Energy expenditure savings (mid-term) Total energy system savings (long-term) Environment Primary non-renewable energy savings Carbon emission reductions Security/Reliability Oil savings Natural gas savings Reductions in conventional, central power capacity additions

Sample GPRA MetricsFY2005 Budget Request: 

Sample GPRA Metrics FY2005 Budget Request The full documentation of the GPRA benefits estimates for the FY2005 budget request can be found at http://www.eere.energy.gov/office_eere/gpra_estimates_fy05.html

Measurement of Benefits – Nuclear Energy Prospective Benefits Matrix : 

Reduction in System Cost – 119 Billion $, Oil price drops by 3% in 2040 Reduction in Carbon – 12 BT, SOx – 2 BT and NOx – 50 MT Reduction in Oil Imports – 16 Bbl Imports Share Dropped from 82% to77% in 2040 Feasible & Lower Carbon Reduction Cost Further Reduction in System Cost and Oil prices Further Reduction in dependency on Imported Oil, dropping to 74% in 2040 Improvements in High Temperature Materials Minimization of Nuclear Waste Diversity of Fuel Supply Measurement of Benefits – Nuclear Energy Prospective Benefits Matrix * Source: Lee, J.; Bhatt, V.; Friley, P.; Horak, W.; Reisman, A. (2004). Hydrogen: Adding Value and Flexibility to the Nuclear Power Industry, Americas Nuclear Energy Symposium 2004, October 3-6; Miami Beach, Florida.

MARKAL-MACRO - AN INTEGRATED APPROACH FOR THE EVALUATION OF THE ENERGY STAR PROGRAM: THE CASE OF TAIWAN: 

MARKAL-MACRO - AN INTEGRATED APPROACH FOR THE EVALUATION OF THE ENERGY STAR PROGRAM: THE CASE OF TAIWAN John C. Lee1 and Edward Linky2 Brookhaven National Laboratory1 Upton, New York 11973, USA US Environmental Protection Agency, Region II2 290 Broadway, New York, New York 10007-1866 USA

OBJECTIVES AND APPROACH: 

OBJECTIVES AND APPROACH Demonstrates the Use of MARKAL- MACRO to Assess The Energy Star Program Establishment of Baseline and Alternative Scenarios Evaluation of Energy System Impact Quantification of Carbon Reduction/Avoidance Assessment of Benefit and Cost Realization of Sustainable Development

DEFINITION OF MARKAL-MACRO CASE RUNS: 

DEFINITION OF MARKAL-MACRO CASE RUNS Case Name Case Description BASE Baseline, No CO2 Constraint. EPASB EPA Energy Star Buildings Scenario, No CO2 Constraint BASECL Baseline, 10 Ton CO2 Per Capita Emission Limit by 2025. EPASBCL EPA Energy Star Buildings Scenario, 10 Ton CO2 Per Capita Emission Limit by 2025.

Base Case vs. Energy Star - Commercial Lighting: 

Base Case vs. Energy Star - Commercial Lighting

Slide68: 

Base Case vs. Energy Star - Commercial AC

Slide69: 

Cost & Benefits of Energy Star Buildings Program

Slide70: 

Marginal Reduction Cost of Carbon Carbon Emission & Mitigation Cost: Base Case vs. Energy Star

Slide71: 

Gross Domestic Product: Base Case vs. Energy Star

Case Studies: Municipal - Local Advanced Planning New York City: 

Case Studies: Municipal - Local Advanced Planning New York City

A Portfolio Approach to Local Energy and Environmental Planning A Case Study of New York City : 

A Portfolio Approach to Local Energy and Environmental Planning A Case Study of New York City John Lee & Vatsal Bhatt Brookhaven National Laboratory Owen Carroll SUNY Stony Brook Edward Linky U.S. Environmental Protection Agency Annex IX Technical Conference, ETSAP Taipei, Taiwan, 4-7 April 2005

What is Advanced Local Energy Planning?: 

What is Advanced Local Energy Planning? Evaluation of energy and environmental policies at the state or community level within the context of the entire energy system Local energy planning addresses: Global issues translated into National commitments which require local actions. GHG and local air quality and pollution concerns require restructuring & improving the efficiency of energy systems on a project by project basis. What are reasonable goals for community energy, transportation and environmental policy? How can consensus be built among the various stakeholders to implement the plan?

New York City Energy Issues: 

New York City Energy Issues Deteriorating System reliability: load requirement at 9 GW in 2004, increasing to 9.6 GW in 2009, 3 GW in shortfall without demand response & out of state capacities Frequent overload & congestion at load pockets/substations, with limited distributed generation capacity to support critical services Blackout

Objective & Scope of the Study: 

Objective & Scope of the Study Evaluate the impact of efficiency improvements in buildings and mitigation measures in urban heat island effect on electricity & power demand in hot spots of NYC Integrate the system-wide changes in energy - power demand, benefit/cost, and environmental emissions due to the impacts from hot-spots Development of a portfolio approach to:

Case Studies: Multi-regional Quebec - Northeast: 

Case Studies: Multi-regional Quebec - Northeast

References on MARKAL Quebec - NortheastMulti-regional Studies: 

References on MARKAL Quebec - Northeast Multi-regional Studies "Assessing the dividends of power exchange between Quebec and New-York State", with C. Berger, R. Dubois, A. Haurie, E. Lessard, Int. J. Energy Research, vol. 14, 253-273, 1990 "A Two-Player Model of Power Cogeneration in New England", with A. Haurie and G. Savard, IEEE trans. Aut. Control, vol. 37, No 9, 1451-1456, September 1992 Loulou, R., and Waaub, J.P., "CO2 Control with Cooperation in Québec and Ontario: A MARKAL Perspective", Energy Studies Review, v. 4, 1992, pp. 278-296. (Berger, C., Dubois, R., Haurie, A.), "Modelling Electricity Trading in the Northeast" Proceedings of the 12th Annual Conference of the International Association for Energy Economics, Ottawa, October 1-3 1990, pp. 304-315

MECANISMO DE DESARROLLO LIMPIOESTRATEGIAS Y ACCIONESJUAN PABLO BONILLA, Ph. D - Viceministro de Medio Ambiente : 

MECANISMO DE DESARROLLO LIMPIO ESTRATEGIAS Y ACCIONES JUAN PABLO BONILLA, Ph. D - Viceministro de Medio Ambiente Presentado por Angela Ines Cadena San Juan de Puerto Rico - Septiembre de 2002 República de Colombia Ministerio del Medio Ambiente

Switzerland & Colombia (CO2 Emissions (million tonnes) and Undiscounted Marginal Reduction Cost (USD per tonne CO2): 

Switzerland & Colombia (CO2 Emissions (million tonnes) and Undiscounted Marginal Reduction Cost (USD per tonne CO2)

Switzerland/Colombia CDM Projects(O. Bahn, et al., 1998): 

Switzerland/Colombia CDM Projects (O. Bahn, et al., 1998) Industrial clean technology (e.g., for boilers, furnaces and motors in the industry sector: efficiency improvements on the one hand, use of cleaner fuels on the other) Compressed natural gas urban transport (e.g., buses) Liquefied petroleum gas instead of commercial (non-renewable) wood for cooking in the rural residential areas not connected to the natural gas Advanced power plants for electricity generation (e.g., us industrial cogeneration plants, gas turbine combined cycle, and small- and medium-scale hydropower plants)

Cooperation Results(O. Bahn, et al., 1998): 

Cooperation Results (O. Bahn, et al., 1998)

Case Studies: Multi-regionalCentral America: 

Case Studies: Multi-regional Central America

Integrated Energy Planning and GHG Emissions Reduction in Central America: Development of a Regional MARKAL Model: 

Integrated Energy Planning and GHG Emissions Reduction in Central America: Development of a Regional MARKAL Model Ta-Hsiung Lin, Soon-Ching Ho, & Hui-Chen Chien, Taiwan Environmental Protection Administration James Lu & Marsha Tsai Industrial Technology Research Institute John Lee & Vatsal Bhatt Brookhaven National Laboratory Annex IX Technical Conference, ETSAP Taipei, Taiwan, 4-7 April 2005

History and Background: 

The modeling activity is part of follow-up efforts to fulfill the common interests expressed in the “Declaration of Meeting of Ministers of the Environment between The Republic of China and the Countries of the Central American Isthmus” signed by Taiwan and governments in Central America on April 27th, 2000 in San Salvador, El Salvador. The Declaration’s main goal is to promote international cooperation among the participating countries in energy planning and environmental management under sustainable development. History and Background

Objectives: 

Objectives In the short term, to build an analytical tool for Central America to examine current regional/multinational issues in energy demand-supply and environmental emission reductions. In the long run, to apply the integrated capability for formulating regional energy policies, environmental emissions and waste management (materials flow) strategies under sustainable development.

Regional Issues in Energy and Environment : 

Regional Issues in Energy and Environment Regionalized electricity grid and capacity expansion plans for higher efficiency and stability Coordinated production and distribution of petroleum products to improve regional balance of trade Bilateral and regional share of renewable resources at lower costs to meet utilization goals Promotion and diffusion of efficient technologies developed locally for low-cost energy conservation Consistent energy policies and environmental regulations to encourage international cooperation

Electricity/Power Market: 

Electricity/Power Market

Regional Petroleum Market: 

Regional Petroleum Market

Renewable Resource Market: 

Renewable Resource Market

Regionally Developed Technologies: 

Regionally Developed Technologies

Energy Policies & Environmental Regulations: 

Energy Policies & Environmental Regulations

Initial Analysis and Scenario Development: 

Initial Analysis and Scenario Development Baseline Base case economic and energy projection under current government development plans, including recommended power sector expansion plans. Energy Efficient & Renewable Energy Scenario Accelerated penetration of renewable energy resources, inclusion of major CDM projects under consideration, and introduction of efficient demand-side technologies

Slide117: 

Primary Energy Demand: Baseline vs. Energy Efficiency & Renewable Scenario Panama El Salvador Honduras

Slide118: 

Industrial Demand: Baseline vs. Energy Efficiency & Renewable Scenario El Salvador Honduras Panama

Slide119: 

Commercial Demand: Baseline vs. Energy Efficiency & Renewable Scenario El Salvador Honduras Panama

Slide120: 

El Salvador Honduras Panama Residential Demand: Baseline vs. Energy Efficiency & Renewable Scenario

Slide121: 

El Salvador Honduras Panama Transportation Demand: Baseline vs. Energy Efficiency & Renewable Scenario

Slide122: 

Honduras El Salvador Panama Electricity Supply: Baseline vs. Energy Efficiency & Renewable Scenario

Slide123: 

El Salvador Panama Carbon Emissions: Baseline vs. Energy Efficiency & Renewable Scenario Honduras

Slide124: 

El Salvador Honduras Panama Marginal Cost of Carbon Emission Reduction at the Emission Level of the Energy Efficiency & Renewable Scenario

Case Studies: Multi-regionalInternational Energy Agency - Global: 

Case Studies: Multi-regional International Energy Agency - Global

A milestone Study Based on 15-region Global MARKAL: 

A milestone Study Based on 15-region Global MARKAL

MARKAL Input and output Structure : 

MARKAL Input and output Structure

Primary MARKAL Input Data Requirements: 

Primary MARKAL Input Data Requirements Useful Energy Demands/Energy consumption by demand sector/end-use/fuel Costs Resource, capital, fixed & variable O&M, fuel delivery Technology Profiles (by plant or specific resource) Fuels in/out, efficiency, availability Resource supply steps, cumulative resources limits, installed capacity, new investment Environmental emission coefficients System and other parameters Discount rate, seasonal/day-night fractions, electric reserve margin

MARKAL Data Structures: 

MARKAL Data Structures To describe an energy system, MARKAL uses items, sets, and parameters in a database format. Items are elements of the energy system (commodities and technologies) grouped into component types through sets. A set is a collection of similar entities e.g., demands sectors, fossil fuels, base-load power plants. Parameters are time-series that identify the various data characterizing an item (e.g., investment cost, lifespan, emission level), by time period (years). Completeness in parameter specification is important to properly characterize an item.

MARKAL Naming Conventions: Limitations: 

MARKAL Naming Conventions: Limitations Demand Sector names may be up to 10 characters, but are usually limited to 3 (so that the associated demand devices may incorporate the sector name). Technologies names may be up to 10 characters. Energy Carrier, Material and Emission Indicator names may be up to 10 characters, but are usually limited to 6 (so that the relevant resource options can incorporate the commodity name). The name of an item must be unique to ALL items. A name of an item cannot contain single/double quotation marks. Descriptions of up to 50 characters can be provided for an item.

MARKAL Naming Conventions: Guidelines: 

MARKAL Naming Conventions: Guidelines Demand Sectors and the devices serving them traditionally begin with the same characters. The first character of the Demand Sectors is assumed to mean: I = Industry T = Transportation R = Residential N = Non-Energy C = Commercial A = Agriculture

MARKAL Naming Conventions:Guidelines (cont.): 

MARKAL Naming Conventions: Guidelines (cont.) Names for Resource Supply Options indicate the source or type of resource. The first three characters of a Resource Supply Option MUST be: MIN = Domestic Extraction STK = Stockpiles EXP = Exports RNW = Renewables IMP = Imports

MARKAL Naming Conventions:Guidelines (cont.): 

MARKAL Naming Conventions: Guidelines (cont.) Power sector technologies usually begin with Exxxx. Establish a consistent approach to names of technologies within groups to distinguish: Existing from new technologies Fuel groups Establish a root-name for a commodity stream, changing the last characters as the fuel/material moves through the network.

Example of a Set: 

Example of a Set SETS NAMED LISTS OF ITEMS SET BAS BASE-LOAD POWER PLANTS E01 E02 -- BITUMINOUS COAL STEAM ELECTRIC SUB-BITUMINOUS COAL STEAM ELECTRIC Etc.

Primary Commodity Sets: 

Primary Commodity Sets Demand Sectors [DM] Energy Carriers [ENT] Environmental Indicators [ENV] Materials [MAT]

Hierarchy of Commodity Sets: 

Hierarchy of Commodity Sets ENT LTH ELC ENC EFS ESY ENU ERN SLD LIQ GAZ EHC MAT MVO MWT DM ENV

Secondary Energy Carrier/Material Sets: 

Secondary Energy Carrier/Material Sets [EFS] All fossil energy carriers [EHC] High-temperature heat and cooling [ELC] All electricity [ENC] All energy carriers other than electricity [ELC] and district heat [LTH] [ENT] All energy carriers [ENU] Nuclear energy carriers [ERN] Renewable energy carriers other than fuels [ESY] Synthetic energy carriers

Secondary Energy Carrier/Material Sets (cont.): 

Secondary Energy Carrier/Material Sets (cont.) [GAZ] Gaseous fossil and synthetic energy carriers [LIQ] Liquid fossil and synthetic energy carriers [LTH] Low-temperature heat [MAT] All materials [MVO] Materials by weights [MWT] Materials by weight [SLD] Solid fossil and synthetic energy carriers

Primary Technology Sets: 

Primary Technology Sets [CON] Power plants producing electricity [ELC] and/or district heat [LTH], including storage [STG] [DMD] All demand technologies [PRC] All process technologies [SRCENCP] Resource Supply Options

Hierarchy of Technology Sets: 

Hierarchy of Technology Sets TCH PRC DMD NST NST Others Others CON FOS NUC REN STG ELE CPD HPL CEN DCN NLM BAS Others SRCENCP

Secondary Technology Sets: 

Secondary Technology Sets [BAS] Base-load technologies [CEN] Centralized conversion technologies (includes transmission and distribution costs [CPD] Coupled-production technologies (produce both electricity [ELC] and district heat [LTH] [DCN] Decentralized conversion technologies except storage [CON – STG] [ELE] Conversion technologies producing only electricity [CON - HPL]

Technology Sets (cont.): 

Technology Sets (cont.) [FOS] Fossil-fuel-using conversion technologies [HDE] Hydro-electric power plants [HLK] Link technologies connecting heating grids [HPL] Conversion technologies producing only heat [CON - ELE] [LNK] Link technologies connecting electricity grids [NLM] Non-load-managed conversion technologies [NST] Night-storage processes and demand devices

Technology Sets (cont.): 

Technology Sets (cont.) [NUC] Nuclear technologies [REN] Conversion technologies using renewable energy carriers [STG] Storage technologies [TCH] All technologies [PRC + CON + DMD] [XLM] Externally load managed conversion technologies [XPR] Externally load managed process technologies

Example of a Parameter: 

Example of a Parameter PARAMETERS “SPREADSHEETS” OF DATA E01 BITUMINOUS COAL STEAM ELECTRIC Technology Characteristics CAPUNIT LIFE START AF BOUNDUP INVCOST -- 31.536 8.0 1.0 1.0 etc. 0.78 41.0 0.78 250.0 TID 1990 2030

Emissions Characteristics: 

Emissions Characteristics Cumulative Emissions Limit: [ENV_CUMMAX] Bound on Emissions: [ENV_BOUND/MAXEM]

Report Table Organization: 

Report Table Organization T01 – Scenario Identifiers D. discounted costs (with D.TOTCOST the MARKAL OBJ) UTILITY is the MACRO OBJ EMMISSION for total emissions by indicator no grouping for other T02 – Summary D. are the discounted costs NET. lines are the net imports U. are the undiscounted costs TOT. energy totals

Report Table Organization(cont.): 

Report Table Organization (cont.) T03 – Primary Energy EXPFOS export of fossil IMPELC net import-export of electricity IMPFOS fossil imports MINFOS domestic fossil extraction STKFOS stockpiling of fossil fuels R. renewables TOT. grand totals TOT.USExxx total by energy type group

Report Table Organization(cont.): 

Report Table Organization (cont.) T04 – Energy Output of Technologies at Gate EXPELC export of electricity IMPELC import of electricity NETELC/LTH net exchange of electricity/heat OUTELC/LTH/MVO/MWT total output of electricity/heat/materials OUTOTH output of other energy carriers by commodity type

Report Table Organization(cont.): 

Report Table Organization (cont.) T05 – Final Energy Use by Fuel/Demand Sector FUELC_ENT energy carrier consumption by demand FUELC_AGR consumption for agriculture by group FUELC_COM consumption for commercial by group FUELC_IND consumption for industry by group FUELC_N-E consumption for non-energy by group FUELC_RES consumption for residential by group FUELC_TRN consumption for transport by group

Report Table Organization(cont.): 

Report Table Organization (cont.) T06 – Useful Energy (Services) from Demand Devices USENRG_TTL total consumption USENRG_AGR useful energy to agriculture USENRG_COM useful energy to commercial USENRG_IND useful energy to industry USENRG_N-E useful energy to non-energy USENRG_RES useful energy to residential USENRG_TRN useful energy to transport

Report Table Organization(cont.): 

Report Table Organization (cont.) T08 – Use of Energy Carriers FUELUSE.grp use of energy carrier by resource group FUELUSE.TCH/TOT use of energy carrier by each technology/total MATVO/WT.USE.TCH/TOT use of material by each technology/total

Report Table Organization(cont.): 

Report Table Organization (cont.) T09 – Shadow Price (Marginal Cost) of Energy Carriers and Emissions EMISSION emissions indicator EQ.BASELOAD baseload constraint by electricity/heat grid EQ.PEAK.ELC/LTH peaking constraint by electricity/heat grid FUEL.ELC/ENC energy carrier balance constraint MATL.MVO/MWT material balance constraint for volumn/weight materials

Report Table Organization(cont.): 

Report Table Organization (cont.) T11 – Marginals for Technologies, Resources Demands, Cumulative Limits, ADRATIOs CAPACITY marginal cost of total capacity of a technology DEMAND marginal cost of each demand EMISSION marginal cost of each emissions indicator EQ.ADRATIO marginal cost of each user-defined constraint EQ_CUMRES marginal cost of any cumulative resource constraint RESOUCE marginal cost of each limited resource

Report Table Organization(cont.): 

Report Table Organization (cont.) T25 – Annualized Costs of Technologies & Resources AC.INV.RESID annualized cost of residual capacity (for MACRO) AC.INVEST annualized cost of investment in new capacity AC.O&M.FIXED/VAR/VARLTH fixed and variable operating and maintenance costs (LTH for heat component of coupled heat and power plants) AC.RESOURCE resource costs

Report Table Organization(cont.): 

Report Table Organization (cont.) T26 – Refinery Blending TOT.WV total by weight/volumn TOT.PJ total by energy units BLE each blending stream T27 – Annual Environmental Indicators D. total discounted emission by indicator EMIS.RESOURCE emissions from resources by indicator EMIS.TCH.ACT/CAP/INV/TOT emission arising from technology activity/capacity/investment/total by indicator EMISSION.L total emissions by indicator

Report Table Organization(cont.): 

Report Table Organization (cont.) T30 – Elastic Demands RE/GR.INPUT input cost of changing demand RE/GR.COST cost of reducing/increasing demand RE/GR.STEP amount demand increased/reduced at each step RE/GR.TOT total amount demand increased/reduced ACT – Activity of Each Technology ACTIVITY.L individual process activity

Report Table Organization(cont.): 

Report Table Organization (cont.) CAP – Capacity of Each Technology CAPACITY.L individual technology capacity level CAPACITY.UNUSD unused capacity (%) of conversion technologies LOAD.ANNUAL annual load (%) of conversion/process technologies LOAD.SEASON.ELC/LTH seasonal electric/heat load OUTAGE.ANNUAL annual outage (%) of conversion technologies OUTPUT.ELC/LTH output of electricity/heat from conversion technologies

Report Table Organization(cont.): 

Report Table Organization (cont.) COSTBEN – Cost/Benefit Ratios COSTBEN the cost benefit ratio for each selected (in COSTBEN.DD) technology DEMAND – Useful Energy (Service) Demand by Sector DEMAND.L level individual demand sectors GDP - M-M Macroeconomic Results CONSUMPTION total consumption ENERGY–COSTS total energy costs GDP gross domestic product INVESTMENT total investment TOT.DEMAND total useful energy demand

Report Table Organization(cont.): 

Report Table Organization (cont.) INV – Investment in Each Technology INVEST.COST cost of investing in new capacity INVEST.F full cost for technologies whose investment cost was adjusted by M-M differential costing algorithm INVEST.L level of investment in new capacity INVEST.M marginal cost of new investment MC – M-M Marginal Cost of Demands (Plus Reference Price Differences) DEMAND marginal cost of the individual demand sectors PREFDIFF marginal cost of demands in 2nd period – input PREFs; if substantially different for a Reference then recalibration of MACRO may be necessary

Report Table Organization(cont.): 

Report Table Organization (cont.) SUPPLY – Activity of Each Resource Supply Option RESOURCE individual resource supply step levels

Computer Laboratory Exercise: 

Computer Laboratory Exercise

Training Model “Exam” Activities: 

Training Model “Exam” Activities Open the TRAIN_EX database Review the existing Reference Energy System Run the Base Scenario, Import and analyze results Construct the Energy Star Scenario using New and Copy operations to add EST’s Run Energy Star Scenario and compare results against the Base Scenario Construct Ethanol Scenario, the Carbon Tax and Carbon Limit Scenarios Run Scenarios and perform sensitivity analyses Answer multi-case comparison questions

Training Model Base RES: 

Training Model Base RES