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Lifetime optimization framework for a hybrid renewable energy system based on receding horizon optimization

Behzadi Forough, A ; Sharif University of Technology | 2018

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  1. Type of Document: Article
  2. DOI: 10.1016/j.energy.2018.02.158
  3. Publisher: Elsevier Ltd , 2018
  4. Abstract:
  5. In this work, a novel convex sequence framework for real-time receding horizon operation optimization of a hybrid renewable energy system integrated with optimal sizing is presented to increase the penetration rate of renewable energy in supplying the demand. The proposed framework optimizes the entire lifetime cost of a system consisting of two main steps which are 1) design & installation and 2) operation as two sequence modules. This framework is applied to a hybrid renewable energy system which includes PV, wind turbine, batteries and a diesel generator. In the operation optimization, receding horizon strategy is used to optimize the operation schedule. Mixed integer convex programming method is applied in order to achieve the optimal operation. The hybrid renewable energy system is installed to actualize the design optimization outputs and to measure the required data for real-time operation optimization. The results show the proposed framework can be applied to facilitate the reliable real-time operation using real optimal input data for taking better advantage of the renewable energy resources. The effect of length of the horizon on optimal scheduling is also investigated. The results indicate that increasing of prediction horizon length enhances the economic performance and increases the share of renewable energy in the hybrid renewable energy system (from 68.5% to 81.4%). © 2018 Elsevier Ltd
  6. Keywords:
  7. Design/operation optimization ; Hybrid renewable energy system ; Lifetime optimization ; Receding horizon optimization ; Convex optimization ; Energy resources ; Integer programming ; Real time systems ; Wind turbines ; Design optimization ; Economic performance ; Hybrid renewable energy systems ; Mixed integer convex programming ; Operation optimization ; Optimization framework ; Real-time operation ; Renewable energy resources
  8. Source: Energy ; Volume 150 , 1 May , 2018 , Pages 617-630 ; 03605442 (ISSN)
  9. URL: https://www.sciencedirect.com/science/article/abs/pii/S036054421830392X