2011
DOI: 10.3390/en4101657
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An Inexact Mix-Integer Two-Stage Linear Programming Model for Supporting the Management of a Low-Carbon Energy System in China

Abstract: Abstract:In view of the great contribution of coal-fired units to CO 2 emissions, the coupled coal and power system with consideration of CO 2 mitigation is a typical sub-system of the highly emitting Chinese energy system for low-carbon studies. In this study, an inexact mix-integer two-stage programming (IMITSP) model for the management of low-carbon energy systems was developed based on the integration of multiple inexact programming techniques. Uncertainties and complexities related to the carbon mitigatio… Show more

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Cited by 22 publications
(13 citation statements)
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“…Li et al [10] developed a multistage interval-stochastic regional-scale energy model to reflect dynamic decisions for power generation schemes. Liu et al [38] introduced an inexact mix-integer two-stage programming model for management of low carbon energy systems to handle complexities related to carbon mitigation issues which can be effectively reflected. Zhang and et al [39] compared new-types of bricks make of fly ash and coal gangue and conventional types of bricks to take as examples to analyze the energy saving.…”
Section: Research Backgroundmentioning
confidence: 99%
“…Li et al [10] developed a multistage interval-stochastic regional-scale energy model to reflect dynamic decisions for power generation schemes. Liu et al [38] introduced an inexact mix-integer two-stage programming model for management of low carbon energy systems to handle complexities related to carbon mitigation issues which can be effectively reflected. Zhang and et al [39] compared new-types of bricks make of fly ash and coal gangue and conventional types of bricks to take as examples to analyze the energy saving.…”
Section: Research Backgroundmentioning
confidence: 99%
“…It is easy to explain the incremental cost with respect to power changes in Equation (8). In this paper, a simplex method that chooses the minimum IC values to compute the new vertices and moves towards the optimum point iteratively is used to perform the DSM.…”
Section: Description Of the Optimal Dispatch Problemmentioning
confidence: 99%
“…For evolutionary algorithms, it is not easy to produce stable solutions in different runs due to the premature convergence problem [31][32][33]. By contrast with the aforementioned methods, MILP-based approaches can obtain the globally optimal solution within a finite number of iterations when the objective function and physical constraints become the linear functions of decision variables [34][35][36]. Besides, numerous softwares have been developed to solve large-scale MILP problems and the users can gain access to the information on the proximity to the optimal solution in the search process.…”
Section: Introductionmentioning
confidence: 99%