A two-stage multi-criteria decision for distributed wind and solar integration

Tongdan Jin, Yi Chen

    Research output: Contribution to conferencePaper

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    Abstract

    Distributed electric system integrating wind and solar generation is reshaping the landscape of power industry. Distributed renewable generation allows the utility companies to lower the carbon footprints as well as deferring the expansion of grid infrastructure. However, power intermittency and equipment cost are the main hurdles confining the large adoption of wind- and solar-based energy solutions. This paper proposes a multistage, multicriteria approach maximizing the renewable energy throughput, while minimizing the levelised cost of energy. In particular, we optimize the sizing, siting, and maintenance of renewable sources under stringent reliability, power quality, and environmental constraints. A two-stage meta-heuristics, consisting of genetic algorithms and the gradient method, is developed, in order to search for the non-dominant solution set. A 13-node distribution network is used to demonstrate the performance of the proposed planning model. The results are compared with simulations and other meta-heuristics, and it is shown that the genetic algorithm excels in terms of computational time and quality of the results.
    Original languageEnglish
    Publication statusPublished - Jul 2015

    Fingerprint

    Genetic algorithms
    Carbon footprint
    Gradient methods
    Power quality
    Electric power distribution
    Costs
    Industry
    Throughput
    Planning

    Keywords

    • distributed wind integration
    • distributed solar integration
    • distributed power service
    • wind power
    • Scotland

    Cite this

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    title = "A two-stage multi-criteria decision for distributed wind and solar integration",
    abstract = "Distributed electric system integrating wind and solar generation is reshaping the landscape of power industry. Distributed renewable generation allows the utility companies to lower the carbon footprints as well as deferring the expansion of grid infrastructure. However, power intermittency and equipment cost are the main hurdles confining the large adoption of wind- and solar-based energy solutions. This paper proposes a multistage, multicriteria approach maximizing the renewable energy throughput, while minimizing the levelised cost of energy. In particular, we optimize the sizing, siting, and maintenance of renewable sources under stringent reliability, power quality, and environmental constraints. A two-stage meta-heuristics, consisting of genetic algorithms and the gradient method, is developed, in order to search for the non-dominant solution set. A 13-node distribution network is used to demonstrate the performance of the proposed planning model. The results are compared with simulations and other meta-heuristics, and it is shown that the genetic algorithm excels in terms of computational time and quality of the results.",
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    A two-stage multi-criteria decision for distributed wind and solar integration. / Jin, Tongdan ; Chen, Yi.

    2015.

    Research output: Contribution to conferencePaper

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    T1 - A two-stage multi-criteria decision for distributed wind and solar integration

    AU - Jin, Tongdan

    AU - Chen, Yi

    PY - 2015/7

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    AB - Distributed electric system integrating wind and solar generation is reshaping the landscape of power industry. Distributed renewable generation allows the utility companies to lower the carbon footprints as well as deferring the expansion of grid infrastructure. However, power intermittency and equipment cost are the main hurdles confining the large adoption of wind- and solar-based energy solutions. This paper proposes a multistage, multicriteria approach maximizing the renewable energy throughput, while minimizing the levelised cost of energy. In particular, we optimize the sizing, siting, and maintenance of renewable sources under stringent reliability, power quality, and environmental constraints. A two-stage meta-heuristics, consisting of genetic algorithms and the gradient method, is developed, in order to search for the non-dominant solution set. A 13-node distribution network is used to demonstrate the performance of the proposed planning model. The results are compared with simulations and other meta-heuristics, and it is shown that the genetic algorithm excels in terms of computational time and quality of the results.

    KW - distributed wind integration

    KW - distributed solar integration

    KW - distributed power service

    KW - wind power

    KW - Scotland

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