The Experts below are selected from a list of 355902 Experts worldwide ranked by ideXlab platform
G H Huang - One of the best experts on this subject based on the ideXlab platform.
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a review on optimization modeling of energy Systems Planning and ghg emission mitigation under uncertainty
Energies, 2011Co-Authors: Yong Zeng, G H Huang, Y P Cai, Jing DaiAbstract: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 uncertainty, necessity of post-modeling analysis, and usefulness of development of simulation techniques.
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energy and environmental Systems Planning under uncertainty an inexact fuzzy stochastic programming approach
Applied Energy, 2010Co-Authors: Yang Li, G H Huang, Ying Li, Xiaohong ChenAbstract:In this study, an inexact fuzzy-stochastic energy model (IFS-EM) is developed for Planning energy and environmental Systems (EES) management under multiple uncertainties. In the IFS-EM, methods of interval parameter fuzzy linear programming (IFLP) and multistage stochastic programming with recourse (MSP) are introduced into a mixed-integer linear programming (MILP) framework, such that the developed model can tackle uncertainties described in terms of interval values, fuzzy sets and probability distributions. Moreover, it can reflect dynamic decisions for facility-capacity expansion and energy supply over a multistage context. The developed model is applied to a case of Planning regional-scale energy and environmental Systems to demonstrate its applicability, where three cases are considered based on different energy and environmental management policies. The results indicate that reasonable solutions have been generated. They are helpful for supporting: (a) adjustment or justification of allocation patterns of regional energy resources and services, (b) formulation of local policies regarding energy consumption, economic development and environmental protection, and (c) in-depth analysis of tradeoffs among system cost, satisfaction degree and environmental requirement under multiple uncertainties.
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community scale renewable energy Systems Planning under uncertainty an interval chance constrained programming approach
Renewable & Sustainable Energy Reviews, 2009Co-Authors: Y P Cai, Q G Lin, G H Huang, Zhifeng Yang, Q TanAbstract:In this study, an inexact community-scale energy model (ICS-EM) has been developed for Planning renewable energy management (REM) Systems under uncertainty. This method is based on an integration of the existing interval linear programming (ILP), chance-constrained programming (CCP) and mixed integer linear programming (MILP) techniques. ICS-EM allows uncertainties presented as both probability distributions and interval values to be incorporated within a general optimization framework. It can also facilitate capacity-expansion Planning for energy-production facilities within a multi-period and multi-option context. Complexities in energy management Systems can be systematically reflected, thus applicability of the modeling process can be highly enhanced. The developed method has then been applied to a case of long-term renewable energy management Planning for three communities. Useful solutions for the Planning of energy management Systems have been generated. Interval solutions associated with different risk levels of constraint violation have been obtained. They can be used for generating decision alternatives and thus help decision makers identify desired policies under various economic and system-reliability constraints. The generated solutions can also provide desired energy resource/service allocation and capacity-expansion plans with a minimized system cost, a maximized system reliability and a maximized energy security. Tradeoffs between system costs and constraint-violation risks can also be tackled. Higher costs will increase system stability, while a desire for lower system costs will run into a risk of potential instability of the management system. They are helpful for supporting (a) adjustment or justification of allocation patterns of energy resources and services, (b) formulation of local policies regarding energy consumption, economic development and energy structure, and (c) analysis of interactions among economic cost, system reliability and energy-supply security.
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identification of optimal strategies for energy management Systems Planning under multiple uncertainties
Applied Energy, 2009Co-Authors: Y P Cai, G H Huang, Zhifeng Yang, Q TanAbstract:Management of energy resources is crucial for many regions throughout the world. Many economic, environmental and political factors are having significant effects on energy management practices, leading to a variety of uncertainties in relevant decision making. The objective of this research is to identify optimal strategies in the Planning of energy management Systems under multiple uncertainties through the development of a fuzzy-random interval programming (FRIP) model. The method is based on an integration of the existing interval linear programming (ILP), superiority-inferiority-based fuzzy-stochastic programming (SI-FSP) and mixed integer linear programming (MILP). Such a FRIP model allows multiple uncertainties presented as interval values, possibilistic and probabilistic distributions, as well as their combinations within a general optimization framework. It can also be used for facilitating capacity-expansion Planning of energy-production facilities within a multi-period and multi-option context. Complexities in energy management Systems can be systematically reflected, thus applicability of the modeling process can be highly enhanced. The developed method has then been applied to a case of long-term energy management Planning for a region with three cities. Useful solutions for the Planning of energy management Systems were generated. Interval solutions associated with different risk levels of constraint violation were obtained. They could be used for generating decision alternatives and thus help decision makers identify desired policies under various economic and system-reliability constraints. The solutions can also provide desired energy resource/service allocation and capacity-expansion plans with a minimized system cost, a maximized system reliability and a maximized energy security. Tradeoffs between system costs and constraint-violation risks could be successfully tackled, i.e., higher costs will increase system stability, while a desire for lower system costs will run into a risk of potential instability of the management system. Moreover, multiple uncertainties existing in the Planning of energy management Systems can be effectively addressed, improving robustness of the existing optimization methods.
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iftem an interval fuzzy two stage stochastic optimization model for regional energy Systems Planning under uncertainty
Energy Policy, 2009Co-Authors: Q G Lin, G H Huang, B Bass, Xiaosheng QinAbstract:The development of optimization models for energy Systems Planning has attracted considerable interest over the past decades. However, the uncertainties that are inherent in the Planning process and the complex interactions among various uncertain parameters are challenging the capabilities of these developed tools. Therefore, the objective of this study is to develop a hybrid interval-fuzzy two-stage stochastic energy Systems Planning model (IFTEM) to deal with various uncertainties that can be expressed as fuzzy numbers, probability distributions and discrete intervals. The developed IFTEM is then applied to a hypothetical regional energy system. The results indicate that the IFTEM has advantages in reflecting complexities of various system uncertainties as well as dealing with two-stage stochastic decision problems within energy Systems.
Albert L Lederer - One of the best experts on this subject based on the ideXlab platform.
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rapid business and it change drivers for strategic information Systems Planning
European Journal of Information Systems, 2008Co-Authors: Henry E Newkirk, Albert L Lederer, Alice M JohnsonAbstract:AbstractToday's organizations increasingly plan new information Systems (IS) to better compete. Through such Planning, they attempt to align their IS strategy and their business strategy. This study tested the impact of business and information technology (IT) change on strategic information Systems Planning (SISP) horizon, of horizon on the Planning itself, and of the Planning on the alignment of IS strategy and business strategy. A questionnaire defined business change, IT change, and alignment as multi-item scaled questions, and Planning horizon as a single, nonscaled one. It defined a multi-item scaled SISP measure as both a second-order construct and as single-order constructs for its individual phases. A postal survey collected data from 161 IS executives. Constructs were extensively validated. The analysis used structural equation modeling, and surprisingly found that business change predicted longer SISP horizons, but IT change predicted neither longer nor shorter ones. Planning horizon predicted ...
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Rapid business and IT change: drivers for strategic information Systems Planning?
European Journal of Information Systems, 2008Co-Authors: Henry E Newkirk, Albert L Lederer, Alice M JohnsonAbstract:Today's organizations increasingly plan new information Systems (IS) to better compete. Through such Planning, they attempt to align their IS strategy and their business strategy. This study tested the impact of business and information technology (IT) change on strategic information Systems Planning (SISP) horizon, of horizon on the Planning itself, and of the Planning on the alignment of IS strategy and business strategy. A questionnaire defined business change, IT change, and alignment as multi-item scaled questions, and Planning horizon as a single, nonscaled one. It defined a multi-item scaled SISP measure as both a second-order construct and as single-order constructs for its individual phases. A postal survey collected data from 161 IS executives. Constructs were extensively validated. The analysis used structural equation modeling, and surprisingly found that business change predicted longer SISP horizons, but IT change predicted neither longer nor shorter ones. Planning horizon predicted SISP itself (as a second-order construct and as all of its phases), and such Planning (as a second-order construct, and as strategic awareness and strategy conception phases) predicted alignment of IS strategy and business strategy. These findings suggest that practitioners more carefully assess their own degree of caution in setting Planning horizons in response to business and IT change. In fact, the findings suggest it may not be necessary for practitioners to shorten Planning horizons in a rapidly changing environment.
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the effectiveness of strategic information Systems Planning under environmental uncertainty
Information & Management, 2006Co-Authors: Henry E Newkirk, Albert L LedererAbstract:Researchers have suggested that more extensive strategic information Systems Planning (SISP) in an uncertain environment produces greater Planning success. Managers must decide whether, and if so when, to perform such SISP. Our study tested the effect of SISP phases on Planning success in more and less uncertain environments.A questionnaire assessed SISP in terms of strategic awareness, situation analysis, strategy conception, strategy formulation, and strategy implementation Planning phases. It inquired about environmental uncertainty as dynamism, heterogeneity, and hostility. Finally, it measured SISP success as a composite of alignment, analysis, cooperation, and capabilities. One hundred and sixty-one IS executives provided data in a postal survey.More extensive strategy formulation uniformly predicted successful Planning in more uncertain environments, whereas strategic awareness generally predicted it in less uncertain ones. Strategy conception predicted it in neither more nor less uncertain environments. More extensive Planning is thus not uniformly successful in either environment but depends on the nature of the uncertainty.
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toward a theory of strategic information Systems Planning
Journal of Strategic Information Systems, 1996Co-Authors: Albert L Lederer, Salmela HannuAbstract:Abstract Strategic information Systems Planning is the process of identifying a portfolio of computer-based applications that will assist an organization in executing its business plans and realizing its business goals. Carrying it out is a critical challenge for many information Systems and business executives. Despite its importance to them, the absence of a theory of strategic information Systems Planning impedes research in the area. An input-process-output model provides the initial basis for such a theory. Constructs in the final version of a theory are: (1) the external environment, (2) the internal environment, (3) Planning resources, (4) the Planning process, (5) the information plan, (6) the implementation of the information plan, and (7) the alignment of the information plan with the organization's business plan. The constructs exhibit causal relationships among each other. Hypotheses illustrate the relationships. The theory has value for both researchers and practitioners.
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Meeting the challenges of information Systems Planning.
Long range planning, 1992Co-Authors: Albert L Lederer, Vijay SethiAbstract:Strategic information Systems Planning (SISP) is the process of deciding the objectives of computing for an organization and then identifying the applications that the organization should computerize. SISP has become increasingly important as information Systems have begun to play a more critical role in implementing business strategies. However, SISP is beset with problems that hinder organizations from determining their computing objectives and applications. This article identifies the impediments to SISP and offers some constructive actions for business planners to take to increase their chances of success. It also suggests that planners may face greater difficulties implementing their information Systems plans than in initially creating them.
Alice M Johnson - One of the best experts on this subject based on the ideXlab platform.
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rapid business and it change drivers for strategic information Systems Planning
European Journal of Information Systems, 2008Co-Authors: Henry E Newkirk, Albert L Lederer, Alice M JohnsonAbstract:AbstractToday's organizations increasingly plan new information Systems (IS) to better compete. Through such Planning, they attempt to align their IS strategy and their business strategy. This study tested the impact of business and information technology (IT) change on strategic information Systems Planning (SISP) horizon, of horizon on the Planning itself, and of the Planning on the alignment of IS strategy and business strategy. A questionnaire defined business change, IT change, and alignment as multi-item scaled questions, and Planning horizon as a single, nonscaled one. It defined a multi-item scaled SISP measure as both a second-order construct and as single-order constructs for its individual phases. A postal survey collected data from 161 IS executives. Constructs were extensively validated. The analysis used structural equation modeling, and surprisingly found that business change predicted longer SISP horizons, but IT change predicted neither longer nor shorter ones. Planning horizon predicted ...
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Rapid business and IT change: drivers for strategic information Systems Planning?
European Journal of Information Systems, 2008Co-Authors: Henry E Newkirk, Albert L Lederer, Alice M JohnsonAbstract:Today's organizations increasingly plan new information Systems (IS) to better compete. Through such Planning, they attempt to align their IS strategy and their business strategy. This study tested the impact of business and information technology (IT) change on strategic information Systems Planning (SISP) horizon, of horizon on the Planning itself, and of the Planning on the alignment of IS strategy and business strategy. A questionnaire defined business change, IT change, and alignment as multi-item scaled questions, and Planning horizon as a single, nonscaled one. It defined a multi-item scaled SISP measure as both a second-order construct and as single-order constructs for its individual phases. A postal survey collected data from 161 IS executives. Constructs were extensively validated. The analysis used structural equation modeling, and surprisingly found that business change predicted longer SISP horizons, but IT change predicted neither longer nor shorter ones. Planning horizon predicted SISP itself (as a second-order construct and as all of its phases), and such Planning (as a second-order construct, and as strategic awareness and strategy conception phases) predicted alignment of IS strategy and business strategy. These findings suggest that practitioners more carefully assess their own degree of caution in setting Planning horizons in response to business and IT change. In fact, the findings suggest it may not be necessary for practitioners to shorten Planning horizons in a rapidly changing environment.
Q G Lin - One of the best experts on this subject based on the ideXlab platform.
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A dynamic inexact energy Systems Planning model for supporting greenhouse-gas emission management and sustainable renewable energy development under uncertainty—A case study for the City of Waterloo, Canada
Renewable & Sustainable Energy Reviews, 2009Co-Authors: Q G Lin, Gordon HuangAbstract:In this study, a dynamic interval-parameter community-scale energy Systems Planning model (DIP-CEM) was developed for supporting greenhouse-gas emission (GHG) management and sustainable energy development under uncertainty. The developed model could reach insight into the interactive characteristics of community-scale energy management Systems, and thus capable of addressing specific community environmental and socio-economic features. Through integrating interval-parameter and mixed-integer linear programming techniques within a general optimization framework, the DIP-CEM could address uncertainty (expressed as interval values) existing in related costs, impact factors and system objectives as well as facilitate dynamic analysis of capacity-expansion decisions under such a uncertainty. DIP-CEM was then applied to the City of Waterloo, Canada to demonstrate its applicability in supporting decisions of community energy Systems Planning and GHG-emission reduction management. One business-as-usual (BAU) case and two GHG-emission reduction cases were analyzed with desired plans of GHG-emission reduction. The results indicated that the developed DIP-CEM could help provide sound strategies for dealing with issues of sustainable energy development and GHG-emission reduction within an energy management system.
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community scale renewable energy Systems Planning under uncertainty an interval chance constrained programming approach
Renewable & Sustainable Energy Reviews, 2009Co-Authors: Y P Cai, Q G Lin, G H Huang, Zhifeng Yang, Q TanAbstract:In this study, an inexact community-scale energy model (ICS-EM) has been developed for Planning renewable energy management (REM) Systems under uncertainty. This method is based on an integration of the existing interval linear programming (ILP), chance-constrained programming (CCP) and mixed integer linear programming (MILP) techniques. ICS-EM allows uncertainties presented as both probability distributions and interval values to be incorporated within a general optimization framework. It can also facilitate capacity-expansion Planning for energy-production facilities within a multi-period and multi-option context. Complexities in energy management Systems can be systematically reflected, thus applicability of the modeling process can be highly enhanced. The developed method has then been applied to a case of long-term renewable energy management Planning for three communities. Useful solutions for the Planning of energy management Systems have been generated. Interval solutions associated with different risk levels of constraint violation have been obtained. They can be used for generating decision alternatives and thus help decision makers identify desired policies under various economic and system-reliability constraints. The generated solutions can also provide desired energy resource/service allocation and capacity-expansion plans with a minimized system cost, a maximized system reliability and a maximized energy security. Tradeoffs between system costs and constraint-violation risks can also be tackled. Higher costs will increase system stability, while a desire for lower system costs will run into a risk of potential instability of the management system. They are helpful for supporting (a) adjustment or justification of allocation patterns of energy resources and services, (b) formulation of local policies regarding energy consumption, economic development and energy structure, and (c) analysis of interactions among economic cost, system reliability and energy-supply security.
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iftem an interval fuzzy two stage stochastic optimization model for regional energy Systems Planning under uncertainty
Energy Policy, 2009Co-Authors: Q G Lin, G H Huang, B Bass, Xiaosheng QinAbstract:The development of optimization models for energy Systems Planning has attracted considerable interest over the past decades. However, the uncertainties that are inherent in the Planning process and the complex interactions among various uncertain parameters are challenging the capabilities of these developed tools. Therefore, the objective of this study is to develop a hybrid interval-fuzzy two-stage stochastic energy Systems Planning model (IFTEM) to deal with various uncertainties that can be expressed as fuzzy numbers, probability distributions and discrete intervals. The developed IFTEM is then applied to a hypothetical regional energy system. The results indicate that the IFTEM has advantages in reflecting complexities of various system uncertainties as well as dealing with two-stage stochastic decision problems within energy Systems.
Q Tan - One of the best experts on this subject based on the ideXlab platform.
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community scale renewable energy Systems Planning under uncertainty an interval chance constrained programming approach
Renewable & Sustainable Energy Reviews, 2009Co-Authors: Y P Cai, Q G Lin, G H Huang, Zhifeng Yang, Q TanAbstract:In this study, an inexact community-scale energy model (ICS-EM) has been developed for Planning renewable energy management (REM) Systems under uncertainty. This method is based on an integration of the existing interval linear programming (ILP), chance-constrained programming (CCP) and mixed integer linear programming (MILP) techniques. ICS-EM allows uncertainties presented as both probability distributions and interval values to be incorporated within a general optimization framework. It can also facilitate capacity-expansion Planning for energy-production facilities within a multi-period and multi-option context. Complexities in energy management Systems can be systematically reflected, thus applicability of the modeling process can be highly enhanced. The developed method has then been applied to a case of long-term renewable energy management Planning for three communities. Useful solutions for the Planning of energy management Systems have been generated. Interval solutions associated with different risk levels of constraint violation have been obtained. They can be used for generating decision alternatives and thus help decision makers identify desired policies under various economic and system-reliability constraints. The generated solutions can also provide desired energy resource/service allocation and capacity-expansion plans with a minimized system cost, a maximized system reliability and a maximized energy security. Tradeoffs between system costs and constraint-violation risks can also be tackled. Higher costs will increase system stability, while a desire for lower system costs will run into a risk of potential instability of the management system. They are helpful for supporting (a) adjustment or justification of allocation patterns of energy resources and services, (b) formulation of local policies regarding energy consumption, economic development and energy structure, and (c) analysis of interactions among economic cost, system reliability and energy-supply security.
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identification of optimal strategies for energy management Systems Planning under multiple uncertainties
Applied Energy, 2009Co-Authors: Y P Cai, G H Huang, Zhifeng Yang, Q TanAbstract:Management of energy resources is crucial for many regions throughout the world. Many economic, environmental and political factors are having significant effects on energy management practices, leading to a variety of uncertainties in relevant decision making. The objective of this research is to identify optimal strategies in the Planning of energy management Systems under multiple uncertainties through the development of a fuzzy-random interval programming (FRIP) model. The method is based on an integration of the existing interval linear programming (ILP), superiority-inferiority-based fuzzy-stochastic programming (SI-FSP) and mixed integer linear programming (MILP). Such a FRIP model allows multiple uncertainties presented as interval values, possibilistic and probabilistic distributions, as well as their combinations within a general optimization framework. It can also be used for facilitating capacity-expansion Planning of energy-production facilities within a multi-period and multi-option context. Complexities in energy management Systems can be systematically reflected, thus applicability of the modeling process can be highly enhanced. The developed method has then been applied to a case of long-term energy management Planning for a region with three cities. Useful solutions for the Planning of energy management Systems were generated. Interval solutions associated with different risk levels of constraint violation were obtained. They could be used for generating decision alternatives and thus help decision makers identify desired policies under various economic and system-reliability constraints. The solutions can also provide desired energy resource/service allocation and capacity-expansion plans with a minimized system cost, a maximized system reliability and a maximized energy security. Tradeoffs between system costs and constraint-violation risks could be successfully tackled, i.e., higher costs will increase system stability, while a desire for lower system costs will run into a risk of potential instability of the management system. Moreover, multiple uncertainties existing in the Planning of energy management Systems can be effectively addressed, improving robustness of the existing optimization methods.
