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Articles by G.H. Huang
Total Records ( 3 ) for G.H. Huang
  Y.P. Cai , G.H. Huang , Z.F. Yang and Q. Tan
  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.
  M.F. Cao , G.H. Huang and Q.G. Lin
  Uncertainty attached to municipal power systems has long been crucial considerations for the related planners. Such an uncertainty could be expressed as random-boundary intervals (RBIs). In this study, an integer programming with random-boundary intervals (IPRBI) approach was developed for municipal electricity-supply management under uncertainty. A concept of random-boundary interval (RBI) was introduced to reflect dual uncertainties that exist in many system components. A solution method named two-boundary approach (TBA) was proposed to solve the IPRBI model. A case study was provided for demonstrating applicability of the developed method. The results indicated that the RBI and integer-interval concepts were effective in dealing with highly uncertain parameters. The IPRBI method solutions could be used for generating efficient electricity-supply schemes under various complexities. They can also be used for analyzing tradeoffs between system cost and electricity-shortage risk. Compared with the existing methods, IPRBI was advantageous in reflecting the complexities of system uncertainties that were presented as RBIs, integer-intervals and intervals.
  Y.F. Li , Y.P. Li , G.H. Huang and X. Chen
  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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