The Experts below are selected from a list of 144 Experts worldwide ranked by ideXlab platform
Zhifeng Yang - One of the best experts on this subject based on the ideXlab platform.
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an integrated approach for climate change impact analysis and adaptation planning under multi level uncertainties part ii case study
Renewable & Sustainable Energy Reviews, 2011Co-Authors: G H Huang, Zhifeng YangAbstract:In this study, a large-scale integrated modeling system (IMS) was developed for supporting climate-change impact analysis and adaptation planning under multi-level uncertainties. A number of methodologies were seamlessly incorporated within IMS, including fuzzy-interval inference method (FIIM), inexact energy model (IEM), and uncertainty analysis. The system could (i) encompass multiple technologies, energy resources, and sub-sectors, and climate change impact analysis into a general modeling framework, (ii) address interactions of climate change impacts on multiple energy sub-sectors and resources within an EMS, (iii) identify optimal adaptation strategies of an EMS to climate change impact through a two-step procedure, (iv) deal with multiple levels of uncertain information associated with processes of climate change impact analysis and adaptation planning, and (v) seamlessly combine climate change impact analysis results with inexact adaptation planning. It could provide decision makers a comprehensive view on the EMS as well as the corresponding adaptation schemes under climate change, greatly improving the robustness and completeness of the decision-making processes. The generated solutions can provide desired energy resource/Service Allocation with a minimized system cost, a maximized system reliability and a maximized energy security under varied climate change impact levels, as well as multiple levels of uncertainties. In a companion paper, the developed method is applied to a real case for the long-term planning of waste management in the Province of Manitoba, Canada.
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planning of community scale renewable energy management systems in a mixed stochastic and fuzzy environment
Renewable Energy, 2009Co-Authors: Y P Cai, G H Huang, Q Tan, Zhifeng YangAbstract:Abstract In this study, an interval-parameter superiority–inferiority-based two-stage programming model has been developed for supporting community-scale renewable energy management (ISITSP-CREM). This method is based on an integration of the existing interval linear programming (ILP), two-stage programming (TSP) and superiority–inferiority-based fuzzy-stochastic programming (SI-FSP). It allows uncertainties presented as both probability/possibilistic distributions and interval values to be incorporated within a general optimization framework, facilitating the reflection of multiple uncertainties and complexities during the process of renewable energy management systems planning. ISITSP-CREM can also be used for effectively addressing dynamic interrelationships between renewable energy availabilities, economic penalties and electricity-generation deficiencies within a community scale. Thus, complexities in renewable energy management systems can be systematically reflected, highly enhancing applicability of the modeling process. 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 renewable energy management systems have been generated. Interval solutions associated with different energy availabilities and economic penalties 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 plans with a minimized system cost (or economic penalties), a maximized system reliability level and a maximized energy security. Tradeoffs between system costs and energy security can also be tackled. Higher costs will increase potential energy generation amount, while a desire for lower system costs will run into a risk of energy deficiency. They are helpful for supporting: (a) adjustment or justification of Allocation patterns of renewable energy resources and Services, (b) formulation of local policies regarding energy utilization, economic development and energy structure under various energy availabilities and policy interventions, and (c) analysis of interactions among economic cost, system reliability and energy-supply shortage.
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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.
G H Huang - One of the best experts on this subject based on the ideXlab platform.
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an integrated approach for climate change impact analysis and adaptation planning under multi level uncertainties part ii case study
Renewable & Sustainable Energy Reviews, 2011Co-Authors: G H Huang, Zhifeng YangAbstract:In this study, a large-scale integrated modeling system (IMS) was developed for supporting climate-change impact analysis and adaptation planning under multi-level uncertainties. A number of methodologies were seamlessly incorporated within IMS, including fuzzy-interval inference method (FIIM), inexact energy model (IEM), and uncertainty analysis. The system could (i) encompass multiple technologies, energy resources, and sub-sectors, and climate change impact analysis into a general modeling framework, (ii) address interactions of climate change impacts on multiple energy sub-sectors and resources within an EMS, (iii) identify optimal adaptation strategies of an EMS to climate change impact through a two-step procedure, (iv) deal with multiple levels of uncertain information associated with processes of climate change impact analysis and adaptation planning, and (v) seamlessly combine climate change impact analysis results with inexact adaptation planning. It could provide decision makers a comprehensive view on the EMS as well as the corresponding adaptation schemes under climate change, greatly improving the robustness and completeness of the decision-making processes. The generated solutions can provide desired energy resource/Service Allocation with a minimized system cost, a maximized system reliability and a maximized energy security under varied climate change impact levels, as well as multiple levels of uncertainties. In a companion paper, the developed method is applied to a real case for the long-term planning of waste management in the Province of Manitoba, Canada.
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planning regional energy system in association with greenhouse gas mitigation under uncertainty
Applied Energy, 2011Co-Authors: G H Huang, Xingquan ChenAbstract:Greenhouse gas (GHG) concentrations are expected to continue to rise due to the ever-increasing use of fossil fuels and ever-boosting demand for energy. This leads to inevitable conflict between satisfying increasing energy demand and reducing GHG emissions. In this study, an integrated fuzzy-stochastic optimization model (IFOM) is developed for planning energy systems in association with GHG mitigation. Multiple uncertainties presented as probability distributions, fuzzy-intervals and their combinations are allowed to be incorporated within the framework of IFOM. The developed method is then applied to a case study of long-term planning of a regional energy system, where integer programming (IP) technique is introduced into the IFOM to facilitate dynamic analysis for capacity-expansion planning of energy-production facilities within a multistage context to satisfy increasing energy demand. Solutions related fuzzy and probability information are obtained and can be used for generating decision alternatives. The results can not only provide optimal energy resource/Service Allocation and capacity-expansion plans, but also help decision-makers identify desired policies for GHG mitigation with a cost-effective manner.
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planning of community scale renewable energy management systems in a mixed stochastic and fuzzy environment
Renewable Energy, 2009Co-Authors: Y P Cai, G H Huang, Q Tan, Zhifeng YangAbstract:Abstract In this study, an interval-parameter superiority–inferiority-based two-stage programming model has been developed for supporting community-scale renewable energy management (ISITSP-CREM). This method is based on an integration of the existing interval linear programming (ILP), two-stage programming (TSP) and superiority–inferiority-based fuzzy-stochastic programming (SI-FSP). It allows uncertainties presented as both probability/possibilistic distributions and interval values to be incorporated within a general optimization framework, facilitating the reflection of multiple uncertainties and complexities during the process of renewable energy management systems planning. ISITSP-CREM can also be used for effectively addressing dynamic interrelationships between renewable energy availabilities, economic penalties and electricity-generation deficiencies within a community scale. Thus, complexities in renewable energy management systems can be systematically reflected, highly enhancing applicability of the modeling process. 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 renewable energy management systems have been generated. Interval solutions associated with different energy availabilities and economic penalties 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 plans with a minimized system cost (or economic penalties), a maximized system reliability level and a maximized energy security. Tradeoffs between system costs and energy security can also be tackled. Higher costs will increase potential energy generation amount, while a desire for lower system costs will run into a risk of energy deficiency. They are helpful for supporting: (a) adjustment or justification of Allocation patterns of renewable energy resources and Services, (b) formulation of local policies regarding energy utilization, economic development and energy structure under various energy availabilities and policy interventions, and (c) analysis of interactions among economic cost, system reliability and energy-supply shortage.
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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.
Q Tan - One of the best experts on this subject based on the ideXlab platform.
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planning of community scale renewable energy management systems in a mixed stochastic and fuzzy environment
Renewable Energy, 2009Co-Authors: Y P Cai, G H Huang, Q Tan, Zhifeng YangAbstract:Abstract In this study, an interval-parameter superiority–inferiority-based two-stage programming model has been developed for supporting community-scale renewable energy management (ISITSP-CREM). This method is based on an integration of the existing interval linear programming (ILP), two-stage programming (TSP) and superiority–inferiority-based fuzzy-stochastic programming (SI-FSP). It allows uncertainties presented as both probability/possibilistic distributions and interval values to be incorporated within a general optimization framework, facilitating the reflection of multiple uncertainties and complexities during the process of renewable energy management systems planning. ISITSP-CREM can also be used for effectively addressing dynamic interrelationships between renewable energy availabilities, economic penalties and electricity-generation deficiencies within a community scale. Thus, complexities in renewable energy management systems can be systematically reflected, highly enhancing applicability of the modeling process. 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 renewable energy management systems have been generated. Interval solutions associated with different energy availabilities and economic penalties 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 plans with a minimized system cost (or economic penalties), a maximized system reliability level and a maximized energy security. Tradeoffs between system costs and energy security can also be tackled. Higher costs will increase potential energy generation amount, while a desire for lower system costs will run into a risk of energy deficiency. They are helpful for supporting: (a) adjustment or justification of Allocation patterns of renewable energy resources and Services, (b) formulation of local policies regarding energy utilization, economic development and energy structure under various energy availabilities and policy interventions, and (c) analysis of interactions among economic cost, system reliability and energy-supply shortage.
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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.
Y P Cai - One of the best experts on this subject based on the ideXlab platform.
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planning of community scale renewable energy management systems in a mixed stochastic and fuzzy environment
Renewable Energy, 2009Co-Authors: Y P Cai, G H Huang, Q Tan, Zhifeng YangAbstract:Abstract In this study, an interval-parameter superiority–inferiority-based two-stage programming model has been developed for supporting community-scale renewable energy management (ISITSP-CREM). This method is based on an integration of the existing interval linear programming (ILP), two-stage programming (TSP) and superiority–inferiority-based fuzzy-stochastic programming (SI-FSP). It allows uncertainties presented as both probability/possibilistic distributions and interval values to be incorporated within a general optimization framework, facilitating the reflection of multiple uncertainties and complexities during the process of renewable energy management systems planning. ISITSP-CREM can also be used for effectively addressing dynamic interrelationships between renewable energy availabilities, economic penalties and electricity-generation deficiencies within a community scale. Thus, complexities in renewable energy management systems can be systematically reflected, highly enhancing applicability of the modeling process. 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 renewable energy management systems have been generated. Interval solutions associated with different energy availabilities and economic penalties 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 plans with a minimized system cost (or economic penalties), a maximized system reliability level and a maximized energy security. Tradeoffs between system costs and energy security can also be tackled. Higher costs will increase potential energy generation amount, while a desire for lower system costs will run into a risk of energy deficiency. They are helpful for supporting: (a) adjustment or justification of Allocation patterns of renewable energy resources and Services, (b) formulation of local policies regarding energy utilization, economic development and energy structure under various energy availabilities and policy interventions, and (c) analysis of interactions among economic cost, system reliability and energy-supply shortage.
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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.
Nima Jafari Navimipour - One of the best experts on this subject based on the ideXlab platform.
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Service Allocation in the cloud environments using multi-objective particle swarm optimization algorithm based on crowding distance
Swarm and Evolutionary Computation, 2017Co-Authors: Fereshteh Sheikholeslami, Nima Jafari NavimipourAbstract:Abstract Cloud computing is an emerging Internet-based computing paradigm, with its built-in elasticity and scalability. In cloud computing field, a Service provider offers a large number of resources like computing units, storage space, and software for customers with a relatively low cost. As the number of customer increases, fulfilling their requirements may become an important yet intractable matter. Therefore, Service Allocation is one of the most challenging issues in the cloud environments. The problem of Service Allocation in the cloud computing is thought to be a combinatorial optimization problem to a large company for numbers of their customers and owned resources could be huge enough. This paper considers three conflicting objectives, namely maximizing revenue for users and providers as well as finding the optimal solution at desired time. We use a Multi-Objective Particle Swarm Optimization based on Crowding Distance (MOPSO-CD) to solve the problem because MOPSO-CD is highly competitive in converging towards the Pareto front and generates a well-distributed set of non-dominated solutions. In addition, fuzzy set theory is employed to specify the best compromise solution. We simulate the proposed method using Matlab and compare the performance of the method against the performance of two other multi-objective algorithms, in order to prove that the proposed method is highly competitive with respect to them. Finally, the experiments results show that the method improves the speed of the execution of the resources Allocation algorithm while generating high revenue for both the users and the providers and increasing the resource utilization.
