Optimal sizing of a hybrid wind-photovoltaic-battery plant to mitigate output fluctuations in a grid-connected system

Abdullah Al Shereiqi*, Amer Al-Hinai, Mohammed Albadi, Rashid Al-Abri

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

Abstract

A novel optimization strategy is proposed to achieve a reliable hybrid plant of wind, solar, and battery (HWSPS). This strategy's purpose is to reduce the power losses in a wind farm and at the same time reduce the fluctuations in the output of HWSPS generation. In addition, the proposed strategy is different from previous studies in that it does not involve a load demand profile. The process of defining the HWSPS capacity is carried out in two main stages. In the first stage, an optimal wind farm is determined using the genetic algorithm subject to site dimensions and spacing between the turbines, taking Jensen's wake effect model into consideration to eliminate the power losses due to the wind turbines' layout. In the second stage, a numerical iterative algorithm is deployed to get the optimal combination of photovoltaic and energy storage system sizes in the search space based on the wind reference power generated by the moving average. The reliability indices and cost are the basis for obtaining the optimal combination of photovoltaic and energy storage system according to a contribution factor with 100 different configurations. A case study in Thumrait in the Sultanate of Oman is used to verify the usefulness of the proposed optimal sizing approach.

Original languageEnglish
Article numberen13113015
JournalEnergies
Volume13
Issue number11
DOIs
Publication statusPublished - Jun 2020
Externally publishedYes

Keywords

  • Energy management
  • Hybrid system
  • Sizing optimization
  • Smoothing
  • Turbines layout
  • Wake effect

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • Energy Engineering and Power Technology
  • Energy (miscellaneous)
  • Control and Optimization
  • Electrical and Electronic Engineering

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