Paradigm shift in urban energy systems through distributed generation: Methods and models
Paradigm shift in urban energy systems through distributed generation: Methods and models
The path towards energy sustainability is commonly referred to the incremental adoption of available technologies, practices and policies that may help to decrease the environmental impact of energy sector, while providing an adequate standard of energy services. The evaluation of trade-offs among technologies, practices and policies for the mitigation of environmental problems related to energy resources depletion requires a deep knowledge of the local and global effects of the proposed solutions. While attempting to calculate such effects for a large complex system like a city, an advanced multidisciplinary approach is needed to overcome difficulties in modeling correctly real phenomena while maintaining computational transparency, reliability, interoperability and efficiency across different levels of analysis. Further, a methodology that rationally integrates different computational models and techniques is necessary to enable collaborative research in the field of optimization of energy efficiency strategies and integration of renewable energy systems in urban areas. For these reasons, a selection of currently available models for distributed generation planning and design is presented and analyzed in the perspective of gathering their capabilities in an optimization framework to support a paradigm shift in urban energy systems. This framework embodies the main concepts of a local energy management system and adopts a multicriteria perspective to determine optimal solutions for providing energy services through distributed generation.
Distributed generation, Local energy planning, Multicriteria decision-making, Multidisciplinary optimization, Multiobjective optimization
1032-1048
Manfren, Massimiliano
f2b8c02d-cb78-411d-aed1-c4d056365392
Caputo, Paola
675d96e8-c617-411e-a27c-2afb1630a5c4
Costa, Gaia
58d2359e-cd25-4c59-a5c0-dcb37a2047f6
2011
Manfren, Massimiliano
f2b8c02d-cb78-411d-aed1-c4d056365392
Caputo, Paola
675d96e8-c617-411e-a27c-2afb1630a5c4
Costa, Gaia
58d2359e-cd25-4c59-a5c0-dcb37a2047f6
Manfren, Massimiliano, Caputo, Paola and Costa, Gaia
(2011)
Paradigm shift in urban energy systems through distributed generation: Methods and models.
Applied Energy, 88 (4), .
(doi:10.1016/j.apenergy.2010.10.018).
Abstract
The path towards energy sustainability is commonly referred to the incremental adoption of available technologies, practices and policies that may help to decrease the environmental impact of energy sector, while providing an adequate standard of energy services. The evaluation of trade-offs among technologies, practices and policies for the mitigation of environmental problems related to energy resources depletion requires a deep knowledge of the local and global effects of the proposed solutions. While attempting to calculate such effects for a large complex system like a city, an advanced multidisciplinary approach is needed to overcome difficulties in modeling correctly real phenomena while maintaining computational transparency, reliability, interoperability and efficiency across different levels of analysis. Further, a methodology that rationally integrates different computational models and techniques is necessary to enable collaborative research in the field of optimization of energy efficiency strategies and integration of renewable energy systems in urban areas. For these reasons, a selection of currently available models for distributed generation planning and design is presented and analyzed in the perspective of gathering their capabilities in an optimization framework to support a paradigm shift in urban energy systems. This framework embodies the main concepts of a local energy management system and adopts a multicriteria perspective to determine optimal solutions for providing energy services through distributed generation.
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Published date: 2011
Keywords:
Distributed generation, Local energy planning, Multicriteria decision-making, Multidisciplinary optimization, Multiobjective optimization
Identifiers
Local EPrints ID: 414098
URI: http://eprints.soton.ac.uk/id/eprint/414098
ISSN: 0306-2619
PURE UUID: 340691b7-243b-4271-ab72-6921e521e532
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Date deposited: 14 Sep 2017 16:31
Last modified: 06 Jun 2024 01:59
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Author:
Paola Caputo
Author:
Gaia Costa
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