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This paper conveniently skips any controversy associated with the science of climate change. On the assumption that greenhouse gas emissions are causing climate change that is detrimental to humanity, the paper focuses on some economic dimensions of the issue which seem to be poorly understood by Australian media commentators, policy analysts, interest groups and the political parties. Using a neoclassical welfare economics framework the paper explores the costs and benefits of greenhouse gas abatement with reference to the findings of the Stern Report, the setting of greenhouse gas targets by Australian political parties, the danger of the government “picking winners” and the emerging carbon theory of value. The paper concludes with a brief review of the relative merits of a carbon tax and a cap and trade approach. Key Words: This paper conveniently skips any controversy associated with the science of climate change. On the assumption that greenhouse gas emissions are causing climate change that is detrimental to humanity, the paper focuses on some economic dimensions of the issue which seem to be poorly understood by Australian media commentators, policy analysts, interest groups and the political parties. Using a neoclassical welfare economics framework the paper explores the costs and benefits of greenhouse gas abatement with reference to the findings of the Stern Report, the setting of greenhouse gas targets by Australian political parties, the danger of the government “picking winners” and the emerging carbon theory of value. The paper concludes with a brief review of the relative merits of a carbon tax and a cap and trade approach.

This paper conveniently skips any controversy associated with the science of climate change. On the assumption that greenhouse gas emissions are causing climate change that is detrimental to humanity, the paper focuses on some economic dimensions of the issue which seem to be poorly understood by Australian media commentators, policy analysts, interest groups and the political parties. Using a neoclassical welfare economics framework the paper explores the costs and benefits of greenhouse gas abatement with reference to the findings of the Stern Report, the setting of greenhouse gas targets by Australian political parties, the danger of the government “picking winners” and the emerging carbon theory of value. The paper concludes with a brief review of the relative merits of a carbon tax and a cap and trade approach. Key Words: This paper conveniently skips any controversy associated with the science of climate change. On the assumption that greenhouse gas emissions are causing climate change that is detrimental to humanity, the paper focuses on some economic dimensions of the issue which seem to be poorly understood by Australian media commentators, policy analysts, interest groups and the political parties. Using a neoclassical welfare economics framework the paper explores the costs and benefits of greenhouse gas abatement with reference to the findings of the Stern Report, the setting of greenhouse gas targets by Australian political parties, the danger of the government “picking winners” and the emerging carbon theory of value. The paper concludes with a brief review of the relative merits of a carbon tax and a cap and trade approach.

Lignite-fired power plants in Northern Greece produce about 50% of the electricity and account for nearly 60% of the allocated CO2 emissions. The implementation of biomass co-firing is considered a cost effective and efficient method for minimizing GHG emissions. However, despite a favorable legislative framework, co-firing in Greece has not progressed since low lignite costs and distance of power plants from harbor facilities limit access to internationally traded solid biomass. The improvement and optimization of local biomass supply chains is thus a strategic priority in the national context. The purpose of the present work is to present preliminary investigations of a wheat straw supply chain for a Greek lignite-fired power plant to be converted into biomass co-firing operation. On¬field demonstration data are analyzed in order to estimate costs for supply chains involving either pelletized straw or straw bales. Information of energy consumption is also presented for different cases and GHG emissions calculated according to the methodology of (COM 2010)11. Results indicate that the cost of straw delivered at the power plant varies depending on whether pelletization is included as a supply chain step. Overall, over short distances, transfer of baled biomass is more economic and results in lower GHG emissions, however the increase in the cost of the supply chain suggest that a pelletization step should be considered for longer distances. The transport cost of the supply chain also depends on whether the vehicles are assumed to return empty at their starting point or loaded. Also, compared to imported or domestic wood pellets, straw pellets have lower prices and could be more attractive to plant operators. Long-term fuel contracts are essential in order to ensure the financial viability of co-firing, especially if a reduction in the feed-in tariff is expected. Proceedings of the 20th European Biomass Conference and Exhibition, 18-22 June 2012, Milan, Italy, pp. 136-142

Lignite-fired power plants in Northern Greece produce about 50% of the electricity and account for nearly 60% of the allocated CO2 emissions. The implementation of biomass co-firing is considered a cost effective and efficient method for minimizing GHG emissions. However, despite a favorable legislative framework, co-firing in Greece has not progressed since low lignite costs and distance of power plants from harbor facilities limit access to internationally traded solid biomass. The improvement and optimization of local biomass supply chains is thus a strategic priority in the national context. The purpose of the present work is to present preliminary investigations of a wheat straw supply chain for a Greek lignite-fired power plant to be converted into biomass co-firing operation. On¬field demonstration data are analyzed in order to estimate costs for supply chains involving either pelletized straw or straw bales. Information of energy consumption is also presented for different cases and GHG emissions calculated according to the methodology of (COM 2010)11. Results indicate that the cost of straw delivered at the power plant varies depending on whether pelletization is included as a supply chain step. Overall, over short distances, transfer of baled biomass is more economic and results in lower GHG emissions, however the increase in the cost of the supply chain suggest that a pelletization step should be considered for longer distances. The transport cost of the supply chain also depends on whether the vehicles are assumed to return empty at their starting point or loaded. Also, compared to imported or domestic wood pellets, straw pellets have lower prices and could be more attractive to plant operators. Long-term fuel contracts are essential in order to ensure the financial viability of co-firing, especially if a reduction in the feed-in tariff is expected. Proceedings of the 20th European Biomass Conference and Exhibition, 18-22 June 2012, Milan, Italy, pp. 136-142

Exhaust hoods are commonly used to capture all emissions from stationary combustion systems that are open to the environment, such as residential heaters or stoves. For experimental purposes, emissions are sampled at one, or more, discrete locations downstream in the exhaust duct. Point-wise measurements in the duct are often taken with the assumption that the emissions are homogeneously distributed across the duct cross-section, because the flow is turbulent and therefore believed to be thoroughly mixed. However, the length of such systems is rarely sufficient to ensure fully-developed flow, and the actual homogeneity is seldom assessed. In the present work the mixing within the duct is investigated by simulating the emissions distribution within various hood and duct configurations. The simulations include a straight duct with and without baffles and two different exhaust hood configurations, namely at the Stove Testing Lab at Lawrence Berkeley National Laboratory (LBNL) and at the University of Adelaide that meet standard requirements. The air flow in the ducts was simulated using Reynolds-averaged (RANS) turbulence modelling, with carbon monoxide (CO) as a representative combustion product, injected at three locations in the straight duct and two locations (centre and side) in the exhaust hoods. Simulations predict that, in isolation, neither a straight duct without baffles, nor a hood with a 90° elbow followed by a straight duct without baffles, provide sufficient mixing to achieve a near uniform distribution of CO at the sampling locations. However, simulations show that adequate mixing of dilution air and CO is achieved with baffles-induced flow detachment and recirculation, not from turbulent mixing in the straight section of the duct itself. The simulations also suggest that elbows, baffles, expansions or other geometrical features are needed to induce thorough mixing. For example, in the Stove Testing Lab at LBNL, flow disturbance is induced by an expansion into a larger diameter straight duct immediately downstream of the hood and the 90° elbow. Although these two systems demonstrate sufficient mixing of CO within the exhaust, the RANS simulations in this study suggest that other systems relying solely on mixing within a specified duct length (viz. 8–12 diameters) may not be sufficient. Refereed/Peer-reviewed

Exhaust hoods are commonly used to capture all emissions from stationary combustion systems that are open to the environment, such as residential heaters or stoves. For experimental purposes, emissions are sampled at one, or more, discrete locations downstream in the exhaust duct. Point-wise measurements in the duct are often taken with the assumption that the emissions are homogeneously distributed across the duct cross-section, because the flow is turbulent and therefore believed to be thoroughly mixed. However, the length of such systems is rarely sufficient to ensure fully-developed flow, and the actual homogeneity is seldom assessed. In the present work the mixing within the duct is investigated by simulating the emissions distribution within various hood and duct configurations. The simulations include a straight duct with and without baffles and two different exhaust hood configurations, namely at the Stove Testing Lab at Lawrence Berkeley National Laboratory (LBNL) and at the University of Adelaide that meet standard requirements. The air flow in the ducts was simulated using Reynolds-averaged (RANS) turbulence modelling, with carbon monoxide (CO) as a representative combustion product, injected at three locations in the straight duct and two locations (centre and side) in the exhaust hoods. Simulations predict that, in isolation, neither a straight duct without baffles, nor a hood with a 90° elbow followed by a straight duct without baffles, provide sufficient mixing to achieve a near uniform distribution of CO at the sampling locations. However, simulations show that adequate mixing of dilution air and CO is achieved with baffles-induced flow detachment and recirculation, not from turbulent mixing in the straight section of the duct itself. The simulations also suggest that elbows, baffles, expansions or other geometrical features are needed to induce thorough mixing. For example, in the Stove Testing Lab at LBNL, flow disturbance is induced by an expansion into a larger diameter straight duct immediately downstream of the hood and the 90° elbow. Although these two systems demonstrate sufficient mixing of CO within the exhaust, the RANS simulations in this study suggest that other systems relying solely on mixing within a specified duct length (viz. 8–12 diameters) may not be sufficient. Refereed/Peer-reviewed

{"references": ["U.S. Energy Information Administration. \"How much energy is\nconsumed in residential and commercial buildings in the United States?\"\nAvailable at: http://www.eia.gov/tools/faqs/faq.cfm?id=86&t=1", "S. Darby, \"The effectiveness of feedback on energy consumption.\"\nEnvironmental Change Institute, University of Oxford, 2006. Available\nat: http://www.globalwarmingisreal.com/energyconsump-feedback.pdf.\nVisited: September 2015", "J. S. John, \"Putting energy disaggregation tech to the test,\" November,\n2013. Greentech Media. Available at:\nhttp://www.greentechmedia.com/articles/read/putting-energydisaggregation-tech-to-the-test.\nVisited: September 2015", "A. Zoha, A. Gluhak, M. A. Imran, S. Rajasegarar, \"Non-intrusive load\nmonitoring approaches for disaggregated energy sensing: a survey,\"\nSensors, vol. 12, no. 12, pp. 16838-16866, December 2012.", "G. W. Hart, \"Nonintrusive appliance load monitoring,\" in Proc. of the\nIEEE, vol. 80, pp. 1870-1891, December 1992.", "M. Baranski, J. Voss, \"Non-intrusive appliance load monitoring based\non Optical Sensor,\" IEEE Bologna PowerTech Conference, Bologna,\nItaly, June 2003. Available at:\nhttp://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1304732", "L. Farinaccio, R. Zmeureanu, \"Using a pattern recognition approach to\ndisaggregate the total electricity consumption in a house into the major\nen-uses,\" Elsevier, Energy and Buildings, vol. 30, no. 3, pp. 245-259,\nAugust 1999.", "J. M. Abreu, F. C. Pereira, P. Ferr\u00e3o, \"Using pattern recognition to\nidentify habitual behavior in residential electricity consumption,\"\nElsevier, Energy and Buildings, vol. 49, pp. 479-487, June 2012.", "C. Beckel, L. Sadamori, S. Santini, \"Automatic socio-economic\nclassification of households using electricity consumption data,\" in\nProc. of the 4th international conference on future energy systems, New\nYork, 2013, pp. 75-86.\n[10] H. Zhao, F. Magoul\u00e8s, \"A review on the prediction of building energy\nconsumption,\" Elsevier, Renewable and Sustainable Energy Reviews,\nvol. 16, no. 6, pp. 3586-3592, August 2012.\n[11] G. K. F. Tso, K. K. W. Yau, \"Predicting electricity energy consumption:\nA comparison of regression analysis, decision tree and neural networks,\"\nElsevier, Energy, vol. 32, no. 9, pp. 1761-1768, September 2007.\n[12] F. Farzan, S. A. Vaghefi, K. Mahani, M. A. Jafari, J. Gong, \"Operational\nplanning for multi-building portfolio in an uncertain energy market,\"\nElsevier, Energy and Buildings, vol. 103, pp. 271-283, September 2015."]} Energy disaggregation has been focused by many energy companies since energy efficiency can be achieved when the breakdown of energy consumption is known. Companies have been investing in technologies to come up with software and/or hardware solutions that can provide this type of information to the consumer. On the other hand, not all people can afford to have these technologies. Therefore, in this paper, we present a methodology for breaking down the aggregate consumption and identifying the highdemanding end-uses profiles. These energy profiles will be used to build the forecast model for optimal control purpose. A facility with high cooling load is used as an illustrative case study to demonstrate the results of proposed methodology. We apply a high level energy disaggregation through a pattern recognition approach in order to extract the consumption profile of its rooftop packaged units (RTUs) and present a forecast model for the energy consumption.

{"references": ["U.S. Energy Information Administration. \"How much energy is\nconsumed in residential and commercial buildings in the United States?\"\nAvailable at: http://www.eia.gov/tools/faqs/faq.cfm?id=86&t=1", "S. Darby, \"The effectiveness of feedback on energy consumption.\"\nEnvironmental Change Institute, University of Oxford, 2006. Available\nat: http://www.globalwarmingisreal.com/energyconsump-feedback.pdf.\nVisited: September 2015", "J. S. John, \"Putting energy disaggregation tech to the test,\" November,\n2013. Greentech Media. Available at:\nhttp://www.greentechmedia.com/articles/read/putting-energydisaggregation-tech-to-the-test.\nVisited: September 2015", "A. Zoha, A. Gluhak, M. A. Imran, S. Rajasegarar, \"Non-intrusive load\nmonitoring approaches for disaggregated energy sensing: a survey,\"\nSensors, vol. 12, no. 12, pp. 16838-16866, December 2012.", "G. W. Hart, \"Nonintrusive appliance load monitoring,\" in Proc. of the\nIEEE, vol. 80, pp. 1870-1891, December 1992.", "M. Baranski, J. Voss, \"Non-intrusive appliance load monitoring based\non Optical Sensor,\" IEEE Bologna PowerTech Conference, Bologna,\nItaly, June 2003. Available at:\nhttp://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1304732", "L. Farinaccio, R. Zmeureanu, \"Using a pattern recognition approach to\ndisaggregate the total electricity consumption in a house into the major\nen-uses,\" Elsevier, Energy and Buildings, vol. 30, no. 3, pp. 245-259,\nAugust 1999.", "J. M. Abreu, F. C. Pereira, P. Ferr\u00e3o, \"Using pattern recognition to\nidentify habitual behavior in residential electricity consumption,\"\nElsevier, Energy and Buildings, vol. 49, pp. 479-487, June 2012.", "C. Beckel, L. Sadamori, S. Santini, \"Automatic socio-economic\nclassification of households using electricity consumption data,\" in\nProc. of the 4th international conference on future energy systems, New\nYork, 2013, pp. 75-86.\n[10] H. Zhao, F. Magoul\u00e8s, \"A review on the prediction of building energy\nconsumption,\" Elsevier, Renewable and Sustainable Energy Reviews,\nvol. 16, no. 6, pp. 3586-3592, August 2012.\n[11] G. K. F. Tso, K. K. W. Yau, \"Predicting electricity energy consumption:\nA comparison of regression analysis, decision tree and neural networks,\"\nElsevier, Energy, vol. 32, no. 9, pp. 1761-1768, September 2007.\n[12] F. Farzan, S. A. Vaghefi, K. Mahani, M. A. Jafari, J. Gong, \"Operational\nplanning for multi-building portfolio in an uncertain energy market,\"\nElsevier, Energy and Buildings, vol. 103, pp. 271-283, September 2015."]} Energy disaggregation has been focused by many energy companies since energy efficiency can be achieved when the breakdown of energy consumption is known. Companies have been investing in technologies to come up with software and/or hardware solutions that can provide this type of information to the consumer. On the other hand, not all people can afford to have these technologies. Therefore, in this paper, we present a methodology for breaking down the aggregate consumption and identifying the highdemanding end-uses profiles. These energy profiles will be used to build the forecast model for optimal control purpose. A facility with high cooling load is used as an illustrative case study to demonstrate the results of proposed methodology. We apply a high level energy disaggregation through a pattern recognition approach in order to extract the consumption profile of its rooftop packaged units (RTUs) and present a forecast model for the energy consumption.

The European Union’s external energy policy architecture is very important for further energy security and economic development. European normative power on its neighbours represents the most efficient way of integrating neighbouring energy markets, with the EU’s emerging internal market and, in perspective, through economic interdependence and complementarities, there are chances of creating an European geo-energy space. EU’s tools for shaping the geo-energy space are becoming more effective in an extended European economic area that would allow it to act as the main actor in a multilateral interconnected system of energy producer and transit countries. The result of the paper is materialized in a new paradigm for EU’s external energy policy, which can provide future security of supply through market institutions and an active economic diplomacy in the resource energy countries.