Probabilistic Optimal PV Capacity Planning for Wind Farm Expansion Based on NASA Data

Yongji Cao, Yi Zhang, Hengxu Zhang, Xiaohan Shi, Vladimir Terzija

Research output: Contribution to journalArticlepeer-review

49 Citations (Scopus)

Abstract

Considering the complementary characteristics of wind and solar energy, expanding an existing wind farm with photovoltaic panels can significantly smooth fluctuation of output power and improve operation economy. This paper proposes a two-stage approach to optimize the wind farm expansion. Based on the National Aeronautics and Space Administration data, modified meteorological models are developed considering the correlation between wind speed and solar irradiation. Taking into account fluctuation of output power, utilization of electrical equipment, and losses of renewable energy, a multi-objective optimization model is established. Two scenarios with different transformer ratings are analyzed to determine whether to expand electrical equipment. The Monte Carlo simulation is utilized to generate meteorological data in the first stage. The Pareto optimal solution set is searched by the multi-objective particle swarm optimization algorithm to determine the final solution in the second stage. A case study was conducted to validate the proposed approach.

Original languageEnglish
Article number7869394
Pages (from-to)1291-1300
Number of pages10
JournalIEEE Transactions on Sustainable Energy
Volume8
Issue number3
DOIs
Publication statusPublished - Jul 2017
Externally publishedYes

Keywords

  • Capacity expansion
  • Monte Carlo simulation (MCS)
  • multi-objective particle swarm optimization algorithm (MOPSO)
  • National Aeronautics and Space Administration (NASA)
  • photovoltaic (PV)
  • two-stage approach
  • wind energy

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