Decision support system for exploiting local renewable energy sources: A case study of the Chigu area of southwestern Taiwan

Yue, Cheng-Dar; Yang, Grant Gwo-Liang

doi:10.1016/j.enpol.2005.11.035

Cited by 44 publications

(21 citation statements)

References 3 publications

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“…For the last 20 years GIS applications have been successfully used to assess environmental and economic constraints, and to select suitable sites for energy projects [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30]. The suitability of GIS to serve for this purpose was proposed earlier [12], while its performance and shortcomings having been evaluated more recently [31].…”

Section: Use Of Gis In Site Selectionmentioning

confidence: 99%

Assessment of Energy Production Potential from Tidal Streams in the United States

Haas

Fritz

French

et al. 2011

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Executive SummaryTidal stream energy is one of the alternative energy sources that are renewable and clean. With the constantly increasing effort in promoting alternative energy, tidal streams have become one of the more promising energy sources due to their continuous, predictable and spatially-concentrated characteristics. However, the present lack of a full spatial-temporal assessment of tidal currents for the U.S. coastline down to the scale of individual devices is a barrier to the comprehensive development of tidal current energy technology. This project created a national database of tidal stream energy potential, as well as a GIS tool usable by industry in order to accelerate the market for tidal energy conversion technology.Tidal currents are numerically modeled with the Regional Ocean Modeling System and calibrated with the available measurements of tidal current speed and water level surface. The performance of the model in predicting the tidal currents and water levels is assessed with an independent validation. The geodatabase is published at a public domain via a spatial database engine and interactive tools to select, query and download the data are provided. Regions with the maximum of the average kinetic power density larger than 500 W/m 2 (corresponding to a current speed of ~1 m/s), surface area larger than 0.5 km 2 and depth larger than 5 m are defined as hotspots and list of hotspots along the USA coast is documented. The results of the regional assessment show that the state of Alaska (AK) contains the largest number of locations with considerably high kinetic power density, and is followed by, Maine (ME), Washington (WA), Oregon (OR), California (CA), New Hampshire (NH), Massachusetts (MA), New York (NY), New Jersey (NJ), North and South Carolina (NC, SC), Georgia (GA), and Florida (FL). The average tidal stream power density at some of these locations can be larger than 8 kW/m 2 with surface areas on the order of few hundred kilometers squared, and depths larger than 100 meters. The Cook Inlet in AK is found to have a substantially large tidal stream power density sustained over a very large area. BackgroundTidal streams are high velocity sea currents created by periodic horizontal movement of the tides, often magnified by local topographical features such as headlands, inlets to inland lagoons, and straits. As tides ebb and flow, currents are often generated in coastal waters. In many places the shape of the seabed forces water to flow through narrow channels, or around headlands. Tidal stream energy extraction is derived from the kinetic energy of the moving flow; analogous to the way a wind turbine operates in air, and as such differs from tidal barrages, which create a head of water for energy extraction. A tidal stream energy converter extracts and converts the mechanical energy in the current into a transmittable energy form. A variety of conversion devices are currently being proposed or are under active development, from a water turbine similar to a scaled wind turbine, driving a...

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Section: Use Of Gis In Site Selectionmentioning

confidence: 99%

Assessment of Energy Production Potential from Tidal Streams in the United States

Haas

Fritz

French

et al. 2011

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“…Besides, fuzziness (UT2) and subjectiveness (UT4). The remaining two types of uncertainty, UT1-Random uncertainty (e.g., [183,214]) and UT3-Grey uncertainty (e.g., [19,88]) are less treated by researchers in strategic EPDM (11% and 16%, respectively). Therefore, in most case studies, the uncertainties have been handled throughout (1) fuzzy linguistic (38%) or (2) fuzzy (23%) decision-making environments while considering other types of preferences' representation (e.g., intervals [199,215], intuitionistic [111,197], or trapezoidal fuzzy numbers [41]).…”

Section: Results and In-depth Analysismentioning

confidence: 99%

“…Strategic C1.1. Energy planning regional [18,22,24,26,61,62], local [19,30,[63][64][65][66][67], urban [68][69][70], rural [71][72][73][74][75][76][77] C1.2. Energy policy planning [37,78,79], evaluation [43,52,80,81], frameworks [82,83] C1.3.…”

Section: An Overview Of Reviews On Epdmmentioning

confidence: 99%

Towards next-generation energy planning decision-making: An expert-based framework for intelligent decision support

Sellak¹,

Ouhbi²,

Frikh

et al. 2017

Renewable and Sustainable Energy Reviews

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Achieving sustainable energy planning and development involves complex decision-making processes. The energy planning decision-making (EPDM) field relies on a plethora of decision analysis methods that offered many solutions to process a variety of energy management and strategic decision-making problems. However, current EPDM solutions are unable to overcome the increasing complexity of strategic energy planning situations involving a large number of stakeholders in uncertain, dynamic, and distributed environments. This raises significant new challenges for researchers in both decision sciences and renewable and sustainable energy planning. On the basis of a representative assortment of peer-reviewed related literature selected by querying multiple electronic databases and indexed in Scopus and Web of Science databases domain journals over the last 12 years, this paper exhaustively highlights and discusses limitations of existing strategic EPDM solutions. The analysis is based on a classification specially developed by holistically harmonizing important domain concepts to categorize the considered representative sample of the field of interest. Additionally, this paper integrates results and conclusions from some recent and most cited literature reviews to (i) formulate essential evidence as well as practical and conclusive literature's supportalongside with the formulated representative sample -to this paper's subsequent insights and statements, and (ii) guarantee that no relevant articles have been excluded. A total of 78 related works is gathered and analyzed to provide a general view and discussion on major complexities found in classical/traditional strategic EPDM solutions and challenges for next-generation EPDM solutions. Moreover, a comparative analysis of the two solutions and a set of "quality indexes" of a next-generation EPDM solution were identified and some proposals were made to improve future applicative research. As an original result coming from the "quality indexes" identified through the review process, an intelligent expert-based framework for next-generation EPDM solutions is developed for enhanced renewable and sustainable energy planning.

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“…In order to identify the most appropriate set of energy options for providing sufficient power to fulfill local demands and improve rural livelihoods, Cherni et al developed a new multi-criteria DSS [292]. Yue and Yang established a DSS for strengthening the utilization of renewable energy resources and meeting new international environmental requirements and providing self-sufficient domestic energy supplies in Taiwan [293]. Blanco et al developed a DSS for the planning of micro-hydro power plants in the Amazon region under a sustainable development perspective [294].…”

Section: Model-based Decision Support Toolsmentioning

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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Decision support system for exploiting local renewable energy sources: A case study of the Chigu area of southwestern Taiwan

Cited by 44 publications

References 3 publications

Assessment of Energy Production Potential from Tidal Streams in the United States

Assessment of Energy Production Potential from Tidal Streams in the United States

Towards next-generation energy planning decision-making: An expert-based framework for intelligent decision support

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

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