73,305 research outputs found

    Time-dependent opportunities in energy business : a comparative study of locally available renewable and conventional fuels

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    This work investigates and compares energy-related, private business strategies, potentially interesting for investors willing to exploit either local biomass sources or strategic conventional fuels. Two distinct fuels and related power-production technologies are compared as a case study, in terms of economic efficiency: the biomass of cotton stalks and the natural gas. The carbon capture and storage option are also investigated for power plants based on both fuel types. The model used in this study investigates important economic aspects using a "real options" method instead of traditional Discounted Cash Flow techniques, as it might handle in a more effective way the problems arising from the stochastic nature of significant cash flow contributors' evolution like electricity, fuel and CO(2) allowance prices. The capital costs have also a functional relationship with time, thus providing an additional reason for implementing, "real options" as well as the learning-curves technique. The methodology as well as the results presented in this work, may lead to interesting conclusions and affect potential private investment strategies and future decision making. This study indicates that both technologies lead to positive investment yields, with the natural gas being more profitable for the case study examined, while the carbon capture and storage does not seem to be cost efficient with the current CO(2) allowance prices. Furthermore, low interest rates might encourage potential investors to wait before actualising their business plans while higher interest rates favor immediate investment decisions. (C) 2009 Elsevier Ltd. All rights reserved

    Optimal operation of MEA-based post-combustion carbon capture for natural gas combined cycle power plants under different market conditions

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    Carbon capture for fossil fuel power generation attracts an increasing attention in order to address the significant challenge of global climate change. This study aims to explore the optimal operation under different market conditions for an assumed existing natural gas combined cycle (NGCC) power plant integrated with MEA-based post-combustion carbon capture (PCC) process. The steady state process models for NGCC power plant, PCC process and COā‚‚ compression train were developed in Aspen PlusĀ® to give accurate prediction of process performance. Levelised cost of electricity (LCOE) is formulated as the objective function in optimization studies. Economic evaluation was carried out for the base case of the integrated system including COā‚‚ transport and storage (T&S). The optimal operations were investigated for the carbon capture level under different carbon price, fuel price and COā‚‚ T&S price. The study shows that carbon price needs to be over ā‚¬100/ton COā‚‚ to justify the total cost of carbon capture from the NGCC power plant and needs to be ā‚¬120/ton COā‚‚ to drive carbon capture level at 90%. Higher fuel price and COā‚‚ T&S price would cause a higher operating cost of running carbon capture process thus a higher carbon price is needed if targeted carbon capture level is to be maintained

    An Agent-Based Model of Multifunctional Agricultural Landscape Using Genetic Algorithms

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    Landowner characteristics influence his/her willingness to change landuse practices to provide more or less environmental benefits. However, most studies of agricultural/environmental polices identify landowners as homogenous. And, the primary cause of failure of many environmental and other polices is the lack of knowledge on how humans may respond to polices based on changes in their behavior (Stern, 1993). From socioeconomic theory and empirical research, landowners can be identified as individuals who make agricultural landuse decisions independently based on their objectives. Identifying possible classes of landowners, assessing how each would potentially respond to policy alternatives, and the resulting pattern of land uses in a watershed or a riparian corridor would be very useful to policy makers as they evaluated alternatives. Agricultural landscapes are important producers of ecosystem services. The mix of ecosystem services and commodity outputs of an agricultural landscape depends on the spatial pattern of land uses emerging from individual land use decisions. However, many empirical studies show that the production of ecosystem services from agricultural landscapes is declining. This is consistent with research conducted over the last few decades showing there is a narrow range of social circumstances under which landowners are willing to make investments in the present to achieve public benefits in the future through investing in natural capital resulting in public goods which are frequently produced as ecosystem services. In this study an agent-based model within a watershed planning context is used to analyze the tradeoffs involved in producing a number of ecosystem services and agricultural commodities given price and policy scenarios while assuming three different types of agents in terms of their goals. The agents represent landowners who have been divided into a number of different groups based on their goals and the size of their farm operations. The multi-agent-based model is developed using a heuristic search and optimization technique called genetic algorithm (GA) (Holland), which belongs to a broader class of evolutionary algorithms. GAs exhibit three properties (1) they start with a population of solution, (2) they explore the solution space through recombination and mutation and (3) they evaluate individual solutions based on their appropriate fitness value(s), for example given profit maximizing agents this would be gross margin. A GA is a heuristic stochastic search and optimization method, which works by mimicking the evolutionary principles and chromosomal processing in natural genetics. The three economic agents that are modeled are based on variations in their objective functions and constraints. This study will help in identifying the tradeoffs associated with various agents in the provision of ecosystem services and agricultural commodities. The agent model developed here will help policy and decision maker identify the various agents within the watershed and assess various policy options based on that information. The study will also help to understand the interaction and feedback between the agents and their environment associated with various policy initiatives. The results of the study indicate that the agent model correctly predicts the actual landuse landcover map by 75 percent.Multifunctional agriculture, Agent based modeling, Genetic Algorithm, Environmental Economics and Policy, Land Economics/Use,

    The European Carbon Market in Action: Lessons from the First Trading Period Interim Report

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    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/).The European Union Emissions Trading Scheme (EU ETS) is the largest greenhouse gas market ever established. The European Union is leading the world's first effort to mobilize market forces to tackle climate change. A precise analysis of the EU ETS's performance is essential to its success, as well as to that of future trading programs. The research program "The European Carbon Market in Action: Lessons from the First Trading Period," aims to provide such an analysis. It was launched at the end of 2006 by an international team led by Frank Convery, Christian De Perthuis and Denny Ellerman. This interim report presents the researchers' findings to date. It was prepared after the research program's second workshop, held in Washington DC in January 2008. The first workshop was held in Paris in April 2007. Two additional workshops will be held in Prague in June 2008 and in Paris in September 2008. The researchers' complete analysis will be published at the beginning of 2009.The research program ā€œThe European Carbon Market in Action: Lessons from the First Trading Periodā€ has been made possible thanks to the support of: Doris Duke Charitable Foundation, BlueNext, EDF, Euronext, Orbeo, Suez, Total, Veolia

    Integrated Generation Management for Maximizing Renewable Resource Utilization

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    Two proposed methods to reduce the effective intermittency and improve the efficiency of wind power generation in the grid are spatial smoothing of wind generation and utilization of short term electrical storage to deal with lulls in production. In this thesis, based on a concept called integrated generation management (IGM), we explore the impact of spatial smoothing and the use of emerging plug-in hybrid electric vehicles (PHEVs) as a potential storage resource to the smart-grid. IGM combines nuclear, slow load-following coal, fast load-following natural gas, and renewable wind generation with an optimal control method to maximize the renewable generation and minimize the fossil generation. With the increasing penetration of PHEVs, the power grid is seeing new opportunities to make itself smarter than ever by utilizing those relatively large batteries. Based on current projections of PHEV market penetration and various wind generation scenarios, we demonstrate the potential for efficient wind integration at levels of approaching 30% of the aver- age electrical load with utilization efficiency exceeding 65%. At lower levels of integration (e.g. 15%), efficiencies are possible exceeding 85%

    Sharing the Burden of GHG Reductions

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    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/).The G8 countries propose a goal of a 50% reduction in global emissions by 2050, in an effort that needs to take account of other agreements specifying that developing countries are to be provided with incentives to action and protected from the impact of measures taken by others. To help inform international negotiations of measures to achieve these goals we develop a technique for endogenously estimating the allowance allocations and associated financial transfers necessary to achieve predetermined distributional outcomes and apply it in the MIT Emissions Prediction and Policy Analysis (EPPA) model. Possible burden sharing agreements are represented by different allowance allocations (and resulting financial flows) in a global cap-and-trade system. Cases studied include agreements that allocate the burden based on simple allocation rules found in current national proposals and alternatives that specify national equity goals for both developing and developed countries. The analysis shows the ambitious nature of this reduction goal: universal participation will be necessary and the welfare costs can be both substantial and wildly different across regions depending on the allocation method chosen. The choice of allocation rule is shown to affect the magnitude of the task and required emissions price because of income effects. If developing countries are fully compensated for the costs of mitigation then the welfare costs to developed countries, if shared equally, are around 2% in 2020, rising to some 10% in 2050, and the implied financial transfers are largeā€”over 400billionperyearin2020andrisingtoaround400 billion per year in 2020 and rising to around 3 trillion in 2050. For success in dealing with the climate threat any negotiation of long-term goals and paths to achievement need to be grounded in a full understanding of the substantial amounts at stake.Development of the EPPA model used has been supported by the U.S. Department of Energy, U.S. Environmental Protection Agency and U.S. National Science Foundation, and by a consortium of industry and foundation sponsors of the MIT Joint Program on the Science and Policy of Global Change
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