IFTEM: An interval-fuzzy two-stage stochastic optimization model for regional energy systems planning under uncertainty

Lin, Q.G.; Huang, Guohe; Bass, Brad; Qin, Xiaosheng

doi:10.1016/j.enpol.2008.10.038

Cited by 61 publications

(37 citation statements)

References 41 publications

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“…In TSP, a decision is first undertaken before values of random variables are known and after the random events have happened and their values are known, a second decision can be made in order to minimize ''penalties'' that may appear due to any infeasibility (Birge and Louveaux 1988;Ruszczynski 1993;Huang and Loucks 2000;Maqsood et al 2005;Lu et al 2008;Lin et al 2009). For example, Pereira and Pinto (1985) suggested a stochastic optimization approach for planning a multi-reservoir hydroelectric system under uncertainty.…”

Section: Introductionmentioning

confidence: 98%

Factorial two-stage stochastic programming for water resources management

Zhou

Huang

2010

Stoch Environ Res Risk Assess

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This study presents a factorial two-stage stochastic programming (FTSP) approach for supporting water resource management under uncertainty. FTSP is developed through the integration of factorial analysis and two-stage stochastic programming (TSP) methods into a general modeling framework. It can handle uncertainties expressed as probability distributions and interval numbers. This approach has two advantages in comparison to conventional inexact TSP methods. Firstly, FTSP inherits merits of conventional inexact two-stage optimization approaches. Secondly, it can provide detailed effects of uncertain parameters and their interactions on the system performance. The developed FTSP method is applied to a hypothetical case study of water resources systems analysis. The results indicate that significant factors and their interactions can be identified. They can be further analyzed for generating water allocation decision alternatives in municipal, industrial and agricultural sectors. Reasonable water allocation schemes can thus be formulated based on the resulting information of detailed effects from various impact factors and their interactions. Consequently, maximized net system benefit can be achieved.

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Section: Introductionmentioning

confidence: 98%

Factorial two-stage stochastic programming for water resources management

Zhou

Huang

2010

Stoch Environ Res Risk Assess

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“…For example, the Market Allocation Model (MARKAL) was developed as a large-scale energy system planning model and applied to many regions in the world (Fishbone and Abilock 1981;Unger and Ekvall 2003;Henning et al 2006); the Energy Flow Optimization Model (EFOM) was built and applied to regional energy systems planning and GHG-emission management (Khella 1997;Grohnheit and Mortensen 2003;Cormio et al 2003). Recently, Lin and Huang (2009a, b), Lin et al (2009), , and Lin et al (2004) reported a number of studies on energy systems management in Canada. To address various uncertainties associated with energy systems management, one optimization technique that received attention was two-stage stochastic programming (TSP) (Anderson 1968;Birge 1985;Gassmann 1990;Edirisinghe and Ziemba 1994;Ruszczynski and Swietanowski 1997;Huang and Loucks 2000;Maqsood et al 2004).…”

Section: Introductionmentioning

confidence: 99%

Interval-fuzzy stochastic optimization for regional energy systems planning and greenhouse-gas emission management under uncertainty—a case study for the Province of Ontario, Canada

Lin

Huang

2010

Climatic Change

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Greenhouse gas (GHG) emission reduction is usually associated with energy systems management. Management of regional energy systems is a complex task due to the strong interactions among energy supply, demand and conversion activities, as well as those among energy, environmental and economic factors. These complexities may be further compounded due to the presence of uncertainties in a variety of processes and the related costs, impact factors and objectives. Therefore, the objective of this study is to develop a dynamic interval-fuzzy two-stage stochastic regional energy systems planning model (DIFT-REM) and analysis GHG-emission reduction policies within a general energy management systems framework. The developed model is then applied to the Province of Ontario to demonstrate its applicability in supporting regional energy systems management and GHG-emission reduction analysis under uncertainty. The results indicated that DIFT-REM could address not only interactions among multiple energy-related activities, but also uncertainties in multiple forms and dynamics within a multi-period, multi-facility, multi-scale and multi-uncertainty context. The results also suggested that, when GHG-emission-credit trading is available for Ontario, the task of GHG-emission reduction could be accomplished with a lower system cost.

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“…Abadie and Chamorro (2008) used Monte Carlo approach to evaluate natural gas resources investments. Lin et al (2009) developed a hybrid interval-fuzzy two-stage stochastic energy systems planning model and applied the model to a hypothetical regional energy system.…”

Section: Energy Investmentsmentioning

confidence: 99%

Fuzzy Real Options Valuation for Oil Investments

Uçal

Kahraman

2009

Technological and Economic Development of Economy

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abstract. Traditional valuation methods are less viable under uncertainty. Hence, other methods such as real options valuation models, which can minimize uncertainty, have become more important. In this study, the hybrid approach suggested by Carlsson and Fuller is examined for the case of discrete compounding as this approach better models risky cash flows. A new real options valuation model that will evaluate the investment in a more realistic way is suggested by postponing the defuzzification of parameters in early stages. The suggested model has been applied to the data of an oil field investment and in conclusion the loss of information caused by early-defuzzification has been determined.

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IFTEM: An interval-fuzzy two-stage stochastic optimization model for regional energy systems planning under uncertainty

Cited by 61 publications

References 41 publications

Factorial two-stage stochastic programming for water resources management

Factorial two-stage stochastic programming for water resources management

Interval-fuzzy stochastic optimization for regional energy systems planning and greenhouse-gas emission management under uncertainty—a case study for the Province of Ontario, Canada

Fuzzy Real Options Valuation for Oil Investments

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