A modularized framework for solving an economic-environmental power generation mix problem

Haoxiang Xia, Michihisa Koyama, Geoff Leyland, Steven Kraines

    Research output: Contribution to journalArticlepeer-review

    9 Citations (Scopus)

    Abstract

    This paper presents a modularized simulation modelling framework for evaluating the impacts on economic cost and CO2 emissions resulting from the introduction of a solid oxide fuel cell (SOFC) system into the existing mix of centralized power generation technologies in Japan. The framework is comprised of three parts: a dual-objective linear programming model that solves the generation best-mix problem for the existing power generation technologies; a nonlinear SOFC system model in which the economic cost and CO2 emissions by the SOFC system can be calculated; and the Queuing Multi-Objective Optimizer (QMOO), a multi-objective evolutionary algorithm (MOEA) developed at the EPFL in Switzerland as the overall optimizer of the combined power supply system. Thus, the framework integrates an evolutionary algorithm that is more suitable for handling nonlinearities with a calculus-based method that is more efficient in solving linear programming problems. Simulation experiments show that the framework is successful in solving the stated problem. Moreover, the three components of the modularized framework can be interconnected through a platform-independent model integration environment. As a result, the framework is flexible and scalable, and can potentially be modified and/or integrated with other models to study more complex problems.

    Original languageEnglish
    Pages (from-to)769-784
    Number of pages16
    JournalInternational Journal of Energy Research
    Volume28
    Issue number9
    DOIs
    Publication statusPublished - Jul 1 2004

    All Science Journal Classification (ASJC) codes

    • Renewable Energy, Sustainability and the Environment
    • Nuclear Energy and Engineering
    • Fuel Technology
    • Energy Engineering and Power Technology

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