Combining Top-Down and Bottom-up in Energy Policy Analysis: A Decomposition Approach

The formulation of market equilibrium problems as mixed complementarity problems (MCP) permits integration of bottom-up programming models of the energy system into top-down general equilibrium models of the overall economy. Despite the coherence and logical appeal of the integrated MCP approach, implementation cost and dimensionality both impose limitations on its practical application. A complementarity representation involves both primal and dual relationships, often doubling the number of equations and the scope for error. When an underlying optimization model of the energy system includes upper and lower bounds on many decision variables the MCP formulation may suffer in robustness and efficiency. While bounds can be included in the MCP framework, the treatment of associated income effects is awkward. We present a decomposition of the integrated MCP formulation that permits a convenient combination of top-down general equilibrium models and bottom-up energy system models for energy policy analysis. We advocate the use of complementarity methods to solve the top-down economic equilibrium model and quadratic programming to solve the underlying bottom-up energy supply model. A simple iterative procedure reconciles the equilibrium prices and quantities between both models. We illustrate this approach using a simple stylized model. --Mathematical Programming,Mixed Complementarity,Top-Down/Bottom-Up

Decomposition Methods and the Computation of Spatial Equilibria: An Application to Coal Supply and Demand Models

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Energy demand models for policy formulation : a comparative study of energy demand models

This paper critically reviews existing energy demand forecasting methodologies highlighting the methodological diversities and developments over the past four decades in order to investigate whether the existing energy demand models are appropriate for capturing the specific features of developing countries. The study finds that two types of approaches, econometric and end-use accounting, are used in the existing energy demand models. Although energy demand models have greatly evolved since the early 1970s, key issues such as the poor-rich and urban-rural divides, traditional energy resources, and differentiation between commercial and non-commercial energy commodities are often poorly reflected in these models. While the end-use energy accounting models with detailed sector representations produce more realistic projections compared with the econometric models, they still suffer from huge data deficiencies especially in developing countries. Development and maintenance of more detailed energy databases, further development of models to better reflect developing country context, and institutionalizing the modeling capacity in developing countries are the key requirements for energy demand modeling to deliver richer and more reliable input to policy formulation in developing countries.Energy Production and Transportation,Energy Demand,Environment and Energy Efficiency,Energy and Environment,Economic Theory&Research

Carbon Footprint Stock Analysis of U.S. Manufacturing: A Time Series Input-Output LCA

The purpose of this paper is to provide an input-output life cycle assessment model to estimate the carbon footprint of US manufacturing sectors. To achieve this, the paper sets out the following objectives: develop a time series carbon footprint estimation model for US manufacturing sectors; analyze the annual and cumulative carbon footprint; analyze and identify the most carbon emitting and carbon intensive manufacturing industries in the last four decades; and analyze the supply chains of US manufacturing industries to help identify the most critical carbon emitting industries

Decomposition methods for mathematical programming/economic equilibrium energy planning models

"9-112-77."Bibliography: leaves 21-22.Supported in part by the U.S. Army Research Office (Durham) under contract no. DAAG29-76-C-0064by J. F. Shapiro

Integrated hybrid multi-regional input-output for assessing life cycle air emissions of the Italian power system

The air emissions of the Italian power system, as well as national emissions between 2010 and 2017 and projections to 2040, have been assessed from a lifecycle perspective, using an integrated hybrid two-region input-output model of Italy versus the rest of the world. The Italian economy is divided into 42 sectors, including electricity, which is further disaggregated into seven technologies. Detailed electricity sector data, from Istat, are fed into the EXIOBASE input-output database. NAMEA tables represent overall air emissions, while the Ecoinvent database is used for the electricity sector. Electricity transition scenarios from Terna and Snam have been integrated into input-output and air emission databases. Demand and emissions were tracked within the electricity sector over medium-term, and the findings showed a sharp decrease between 2017 and 2025, from 97.5 MtCO2 to 32.6 MtCO2. By 2040, air emissions from the electricity sector are expected to grow gradually, compared to those of 2030, from 22.2 MtCO2 to 25.9 MtCO2, suggesting that the demand between 2030 and 2040 grows faster than the decarbonization effort during the same period. There is an overall, gradual downtrend between 2010 and 2040, with all air emission categories declining by half from both production and consumption-based perspectives in this period

General Equilibrium, Electricity Generation Technologies and the Cost of Carbon Abatement

Abstract and PDF report are also available on the MIT Joint Program on the Science and Policy of Global Change website (http://globalchange.mit.edu/)Electricity generation is a major contributor to carbon dioxide emissions, and a key determinant of abatement costs. Ex-ante assessments of carbon policies mainly rely on either of two modeling paradigms: (i) partial equilibrium models of the electricity sector that use bottom-up engineering data on generation technology costs, and (ii) multi-sector general equilibrium models that represent economic activities with smooth top-down aggregate production functions. In this paper, we examine the structural assumptions of these numerical techniques using a suite of models sharing common technological features and calibrated to the same benchmark data. First, our analysis provides evidence that general equilibrium effects of an economy-wide carbon policy are of first-order importance to assess abatement potentials and price changes in the electricity sector, suggesting that the parametrization of Marshallian demand in a partial equilibrium setting is problematic. Second, we find that top-down technology representations produce fuel substitution patterns that are inconsistent with bottom-up cost data, mainly because of difficulties in capturing the temporal and discrete nature of electricity generation by means of aggregate substitution elasticities. Our analysis highlights the difficulty to parameterize numerical models used for policy projections, and suggests that the integration of a bottom-up electricity sector model into a general equilibrium framework provides an attractive structural alternative for ex-ante policy modeling.This study received support from the MIT Joint Program on the Science and Policy of Global Change, which is funded by a consortium of government, industry and foundation sponsors

Combining Top-Down and Bottom-up in Energy Policy Analysis: A Decomposition Approach

The formulation of market equilibrium problems as mixed complementarity problems (MCP) permits integration of bottom-up programming models of the energy system into top-down general equilibrium models of the overall economy. Despite the coherence and logical appeal of the integrated MCP approach, implementation cost and dimensionality both impose limitations on its practical application. A complementarity representation involves both primal and dual relationships, often doubling the number of equations and the scope for error. When an underlying optimization model of the energy system includes upper and lower bounds on many decision variables the MCP formulation may suffer in robustness and efficiency. While bounds can be included in the MCP framework, the treatment of associated income effects is awkward. We present a decomposition of the integrated MCP formulation that permits a convenient combination of top-down general equilibrium models and bottom-up energy system models for energy policy analysis. We advocate the use of complementarity methods to solve the top-down economic equilibrium model and quadratic programming to solve the underlying bottom-up energy supply model. A simple iterative procedure reconciles the equilibrium prices and quantities between both models. We illustrate this approach using a simple stylized model

Effects of fuel cost uncertainty on optimal energy flows in U.S.

The research is motivated by the need for economic efficiency and risk management in the national electric system. Stochastic costs of natural gas are introduced in a generalized network flow model of the integrated power energy system to explore the effects of uncertain fuel costs on the optimal energy flows in U.S. The fuel costs are modeled as discretely distributed random variables and a rolling two-stage approach is applied to solve the stochastic recourse problem. All the data are derived from publicly available information for the year 2002. The natural gas price forecasts by the Energy Information Administration are adapted to generate scenarios that are considered in the stochastic problem. Compared to the expected value solution from the deterministic model, the recourse problem solution obtained from the stochastic model has higher total cost, lower natural gas consumption and less subregional power trade but a flow mix which is closer to the 2002 real data. Surprisingly, increasing the uncertainty level of the scenarios leads to a recourse problem solution with slightly lower total cost but this effect may be distributed to the inaccuracy of the forecasts. The comparison demonstrates the stochastic model\u27s capability of forecasting energy flows. The stochastic model assists decision makers to better understand how the uncertain fuel costs would affect future flows within the national electric energy system

Capturing Natural Resource Dynamics in Top-Down Energy‑Economic Equilibrium Models

Top-down energy-economic modeling approaches often use deliberately simple techniques to represent heterogeneous resource inputs to production. We show that for some policies, such as feed-in tariffs (FIT) for renewable electricity, detailed representation of renewable resource grades is required to describe the technology more precisely and identify cost-effective policy designs. We extend a hybrid approach for modeling heterogeneity in the quality of natural resource inputs required for renewable energy production in a stylized computable general equilibrium (CGE) framework. Importantly, this approach resolves nearflat or near-vertical sections of the resource supply curve that translate into key features of the marginal cost of wind resource supply, allowing for more realistic policy simulation. In a second step, we represent the shape of a resource supply curve based on more detailed data. We show that for the case of onshore wind development in China, a differentiated FIT design that can only be modeled with the hybrid approach requires less than half of the subsidy budget needed for a uniform FIT design and proves to be more cost-effective.This work was supported by Eni S.p.A., ICF International, the French Development Agency (AFD), and Shell, founding sponsors of the MIT-Tsinghua China Energy and Climate Project. We are further thankful for support provided by the MIT Joint Program on the Science and Policy of Global Change through a consortium of industrial sponsors and U.S. federal grants. In particular, this work was supported by the DOE Integrated Assessment Grant (DE-FG02-94ER61937)