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| 1 |
ID:
092580
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2009.
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| Summary/Abstract |
In this study, an inexact coupled coal and power management (ICCPM) model was developed for planning coupled coal and power management systems through integrating chance-constrained programming (CCP), interval linear programming (ILP) and mixed integer linear programming (MILP) techniques. The ICCPM model can effectively handle uncertainties presented in terms of probability density functions and intervals. It can also facilitate dynamic analysis of capacity expansions, facility installation and coal inventory planning within a multi-period and multi-option context. Complexities in coupled coal and power management systems can be systematically reflected in this model, thus applicability of the modeling process would be highly enhanced. The developed ICCPM model was applied to a case of long-term coupled coal and power management systems planning in north China. Interval solutions associated with different risk levels of constraint violations have been obtained, which can be used for generating decision alternatives and helping identify desired policies. The generated results can also provide desired solutions for coal and power generation, capacity initiation and expansion, and coal blending with a minimized system cost, a maximized system reliability and a maximized coal transportation security. Tradeoffs between system costs and constraint-violation risks can also be tackled.
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| 2 |
ID:
107511
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2011.
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| Summary/Abstract |
In this study, an interval-valued minimax regret analysis (IMRA) method is proposed for planning greenhouse gas (GHG) abatement under uncertainty. The IMRA method is a hybrid of interval-parameter programming (IPP) and minimax regret analysis (MMR) techniques. The developed method is applied to support long-term planning of GHG mitigation in an energy system under uncertainty. Mixed integer linear programming (MILP) technique with fixed-charge cost function is introduced into the IMRA framework to facilitate dynamic analysis for decisions of timing, sizing and siting in planning capacity expansions for power-generation facilities. The results obtained indicate that replacing fossil fuels with renewable energy sources (i.e. hydro, wind and solar power) can effectively facilitate reducing the GHG emissions. They can help decision makers identify an optimal strategy that can facilitate reducing the worst regret level incurred under any outcome of the uncertain GHG-abatement target.
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| 3 |
ID:
092614
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| Publication |
2009.
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| Summary/Abstract |
Concerns over increasing energy price, exacerbating power shortage and changing climatic conditions are emerging associated with municipal energy management systems. Most of the previous studies could hardly address multiple uncertainties that exist in such systems; this may lead to incomplete, simplified or even false solutions, and could jeopardize the robustness of management decisions. This study is to develop a dynamic inexact stochastic energy systems planning model (DITS-MEM) for managing municipal energy systems and greenhouse-gas (GHG) emissions under uncertainty. Through integrating mixed-integer, interval-parameter and two-stage stochastic programming techniques, DITS-MEM can handle not only the uncertainties expressed as interval values and probabilistic distributions associated with GHG-emission reduction targets, but also the dynamics of capacity-expansion issues. The developed model is then applied to the Beijing Municipality. The results suggest that the DITS-MEM model is applicable in reflecting complexities of multi-uncertainty, dynamic and interactive municipal energy management systems, and capable of addressing two-stage stochastic problem of GHG-emission reduction. The obtained solutions can provide decision bases for formulating GHG-reduction policies and assessing the associated economic implications in purchasing emission credits or bearing economic penalties.
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| 4 |
ID:
093522
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2010.
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| Summary/Abstract |
In this study, a multistage interval-stochastic regional-scale energy model (MIS-REM) is developed for supporting electric power system (EPS) planning under uncertainty that is based on a multistage interval-stochastic integer linear programming method. The developed MIS-REM can deal with uncertainties expressed as both probability distributions and interval values existing in energy system planning problems. Moreover, it can reflect dynamic decisions for electricity generation schemes and capacity expansions through transactions at discrete points of a multiple representative scenario set over a multistage context. It can also analyze various energy-policy scenarios that are associated with economic penalties when the regulated targets are violated. A case study is provided for demonstrating the applicability of the developed model, where renewable and non-renewable energy resources, economic concerns, and environmental requirements are integrated into a systematic optimization process. The results obtained 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 energy structure, and (c) analysis of interactions among economic cost, environmental requirement, and energy-supply security.
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