An inexact optimization model for regional energy systems planning in the mixed stochastic and fuzzy environment

Cai, Yanpeng; Huang, Guohe; Tan, Qian

doi:10.1002/er.1483

Cited by 48 publications

(22 citation statements)

References 69 publications

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“…However, it should be pointed out that many other disciplines and methods are vital for actual urban management, like system science, landscape ecology, network analysis methods, multi-objective programming methods [32][33][34], ecological suitability analysis methods, sensitivity analysis methods, and cost-benefit analysis methods.…”

Section: Discussionmentioning

confidence: 99%

Energy Pattern Analysis of a Wastewater Treatment Plant

2016

Improving Urban Environments

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Urban ecosystem health assessments can be applied extensively in urban management to evaluate the status quo of the urban ecosystem, identify the limiting factors, identify key problems, optimize the scheme and guide ecological regulation. Regarding the multi-layer roles of urban ecosystems, urban ecosystem health should be assessed at different scales with each assessment providing a specific reference to urban management from its own viewpoint. Therefore, a novel framework of multi-scale urban ecosystem health assessment is established on global, national, regional and local scales. A demonstration of the framework is shown by using a case study in Guangzhou City, China, where urban ecosystem health assessment is conducted in the order of global, national, regional, and local scales, from macro to micro, and rough to detailed analysis. The new multi-scale framework can be utilized to generate a more comprehensive understanding of urban ecosystem health, more accurate orientation of urban development, and more feasible regulation and management programs when compared with the traditional urban ecosystem health assessment focusing at the local scale.

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Section: Discussionmentioning

confidence: 99%

Energy Pattern Analysis of a Wastewater Treatment Plant

2016

Improving Urban Environments

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“…At the same time, a number of optimization modeling methods were proposed for supporting the management of GHG emission mitigation particularly through the adoption of renewable energies [54,[125][126][127][128][129][130][131][132][133][134][135][136][137][138][139][140][141][142][143]. In the middle of the 1970s, Duff (1975) presented an optimization model to design solar thermal energy systems in accordance with the minimum system cost [144].…”

Section: Optimization Of Ghg Emission Mitigationmentioning

confidence: 99%

A Review on Optimization Modeling of Energy Systems Planning and GHG Emission Mitigation under Uncertainty

et al. 2011

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Abstract:Energy is crucial in supporting people's daily lives and the continual quest for human development. Due to the associated complexities and uncertainties, decision makers and planners are facing increased pressure to respond more effectively to a number of energy-related issues and conflicts, as well as GHG emission mitigation within the multiple scales of energy management systems (EMSs). This quandary requires a focused effort to resolve a wide range of issues related to EMSs, as well as the associated economic and environmental implications. Effective systems analysis approaches under uncertainty to successfully address interactions, complexities, uncertainties, and changing conditions associated with EMSs is desired, which require a systematic investigation of the current studies on energy systems. Systems analysis and optimization modeling for low-carbon energy systems planning with the consideration of GHG emission reduction under uncertainty is thus comprehensively reviewed in this paper. A number of related methodologies and applications related to: (a) optimization modeling of GHG emission mitigation; (b) optimization modeling of energy systems planning under uncertainty; and (c) model-based decision support tools are examined. Perspectives of effective management schemes are investigated, demonstrating many demanding areas for enhanced research efforts, which include issues of data availability and reliability, concerns in OPEN ACCESSEnergies 2011, 4 1625 uncertainty, necessity of post-modeling analysis, and usefulness of development of simulation techniques.

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“…And the environmental issues have neither been comprehensively considered. Only a few atmospheric pollutants related to energy consumption have been modeled, such as GHG (Cai et al, 2011(Cai et al, , 2009Hashim et al, 2005;Sirikitputtisak et al, 2009), without considering many other contaminants created in processes of energy production and transportation. Correspondingly, in most cases, only limited environmental influences due to GHG emissions have been assessed, such as global warming (Ren et al, 2010), while other influences posed by other contaminants have been neglected.…”

Section: Introductionmentioning

confidence: 99%