Variable Grid Resolution based Evolutionary Multi-objective optimization towards Micro-siting

Mittal, Prateek and Mitra, Kishalay (2019) Variable Grid Resolution based Evolutionary Multi-objective optimization towards Micro-siting. In: IEEE Congress on Evolutionary Computation (CEC), 10-13 June 2019, Wellington, New Zealand.

Full text not available from this repository. (Request a copy)


The rapid diminishing of fossil fuels and their adverse climatic impacts have forced researchers to search for renewable sources of energy to meet the ever growing energy demand. Wind energy, the rapidly growing energy source, is generally harvested using wind turbines in a wind farm. However, the determination of optimal number and locations of wind turbines in such layouts is challenging task due to power loss or velocity deficits caused by the wake interactions among these energy generators. Binary and continuous variables being both present, this problem classifies itself into an MINLP, which are hard to solve. This problem is commonly solved by discretizing the given wind farm area into uniform grids, where efficacy of the solution depends on finer resolution of grids. However, with increasing resolution of grids, the problem becomes computationally intractable. In this study, a novel space decomposition with variable resolution (sDVR) approach has been proposed to solve the problem more efficiently thereby providing a better balance between solution quality and execution time. The proposed approach shows promises when validated against benchmark and realistic case studies. Moreover, solving the problem in multi-objective fashion helps the decision maker to have several alternative Pareto optimal solutions compared to single solution provided by the existing reported formulations.

[error in script]
IITH Creators:
IITH CreatorsORCiD
Mitra, Kishalay
Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: micro-siting,wind,Pareto,genetic algorithm,grid,variable resolution
Subjects: Chemical Engineering
Divisions: Department of Chemical Engineering
Depositing User: Team Library
Date Deposited: 16 Aug 2019 04:39
Last Modified: 16 Aug 2019 04:39
Publisher URL:
Related URLs:

Actions (login required)

View Item View Item
Statistics for RAIITH ePrint 5938 Statistics for this ePrint Item