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Developing a multi-objective framework for expansion planning studies of distributed energy storage systems (DESSs)

Saber, H ; Sharif University of Technology | 2018

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  1. Type of Document: Article
  2. DOI: 10.1016/j.energy.2018.06.081
  3. Publisher: Elsevier Ltd , 2018
  4. Abstract:
  5. This paper presents a framework for expansion planning studies of distributed energy storage systems (DESSs) in high wind penetrated power systems. The main objective is to find optimal location and capacity of DESSs in the viewpoint of independent system operator (ISO) while ensuring the maximum usage of wind farms output generation. Three different criteria are introduced for expansion planning studies. Minimizing wind curtailment cost together with transmission congestion cost are considered to properly deal with the issues associated with the curtailment of wind energy and constraints of transmission network. Furthermore, the minimum normalized profit for all DESSs' owners needs to be maximized to model the requirements of DESSs' owners in the studies. These all the crucial aspects of the DESSs expansion problem are treated via a well-organized posteriori multi-objective (MO) optimization algorithm, i.e. the Non-dominated Sorting Genetic Algorithm II (NSGA-II). The proposed method is applied to the modified IEEE 24-bus test system, and the results are presented to verify the applicability and efficiency of the proposed DESSs planning in a renewable-based power system. © 2018 Elsevier Ltd
  6. Keywords:
  7. Distributed energy storage system (DESS) ; Load aggregator ; Mixed-integer linear programming (MILP) ; Non-dominated sorting genetic algorithm II (NSGA-II) ; Transmission network congestion ; Wind energy curtailment ; Data storage equipment ; Distributed computer systems ; Electric utilities ; Energy storage ; Genetic algorithms ; Integer programming ; Traffic congestion ; Wind power ; Distributed energy storage systems ; Expansion planning ; Independent system operators ; Mixed integer linear programming (MILP) ; Non dominated sorting genetic algorithm ii (NSGA II) ; Optimization algorithms ; Transmission congestion ; Wind energy curtailments ; Electric power system planning ; Genetic algorithm ; Linear programing ; Optimization ; Power generation ; Smart grid
  8. Source: Energy ; Volume 157 , 2018 , Pages 1079-1089 ; 03605442 (ISSN)
  9. URL: https://www.sciencedirect.com/science/article/abs/pii/S0360544218311460