• DocumentCode
    3090848
  • Title

    Stochastic Dynamic Economic Emission Dispatch considering Wind Power

  • Author

    Abarghooee, Rasoul Azizipanah ; Aghaei, Jamshid

  • Author_Institution
    Dept. Electron. & Electr., Shiraz Univ. of Technol., Shiraz, Iran
  • Volume
    1
  • fYear
    2011
  • fDate
    8-9 Sept. 2011
  • Firstpage
    158
  • Lastpage
    161
  • Abstract
    Renewable energy recourses and Wind Power Generators (WPGs) are playing an ever-increasing role in power generation. In this paper, WPGs are being considered in multiobjective day-ahead Dynamic Economic Emission Dispatch (DEED) problem which minimize total fuel cost and emission, simultaneously. Besides, a two stage scenario-based approach is implemented for Stochastic DEED (SDEED) problem considering hourly load/wind forecast uncertainty. Firstly, employs Roulette Wheel Mechanism (RWM) along with Probability Distribution Function (PDF) to model the load/wind forecast error wherein the SDEED is converted into its respective deterministic equivalents (scenarios). In the second stage, for each deterministic scenario, a multiobjective optimization algorithm based on Particle Swarm Optimization (PSO) is implemented to extract the best solution for the deterministic DEED problem. The proposed method is tested on a power system having 5-unit in order to measure its efficiency and feasibility.
  • Keywords
    particle swarm optimisation; power generation dispatch; stochastic processes; wind power plants; multiobjective optimization algorithm; particle swarm optimization; probability distribution function; roulette wheel mechanism; stochastic dynamic economic emission dispatch; wind power generators; Economics; Fuels; Heuristic algorithms; Indexes; Stochastic processes; Wind forecasting; Wind speed; Particle Swarm Optimization; Stochastic Dynamic Economic Emission Dispatch (SDEED); Wind Power;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Power Engineering and Automation Conference (PEAM), 2011 IEEE
  • Conference_Location
    Wuhan
  • Print_ISBN
    978-1-4244-9691-4
  • Type

    conf

  • DOI
    10.1109/PEAM.2011.6134825
  • Filename
    6134825