Optimization of wind power plant sizing and placement by the application of multi-objective genetic algorithm (GA) in Madhya Pradesh, India
Authors:
- Manoj VermaMVManoj Verma
- Harish Kumar GhritlahreHGHarish Kumar Ghritlahre
- Prem Kumar ChaurasiyaPCPrem Kumar Chaurasiya
- Siraj AhmedSASiraj Ahmed
- Surendra BajpaiSBSurendra Bajpai
- 1 December 2021
- journal article
- research article
- Published by Elsevier in Sustainable Computing: Informatics and Systems
- Vol. 32
Abstract
No abstract availableKeywords
This publication has 35 references indexed in Scilit:
- Wind turbine layout optimization using multi-population genetic algorithm and a case study in Hong Kong offshoreJournal of Wind Engineering and Industrial Aerodynamics, 2015
- Optimum sizing of wind-battery systems incorporating resource uncertaintyApplied Energy, 2010
- Current status of research on optimum sizing of stand-alone hybrid solar–wind power generation systemsApplied Energy, 2010
- Optimal positioning of wind turbines on Gökçeada using multi‐objective genetic algorithmWind Energy, 2009
- Optimal sizing method for stand-alone hybrid solar–wind system with LPSP technology by using genetic algorithmSolar Energy, 2008
- Methodology for optimal sizing of stand-alone photovoltaic/wind-generator systems using genetic algorithmsSolar Energy, 2006
- Multi-objective optimization using genetic algorithms: A tutorialReliability Engineering & System Safety, 2006
- A fast and elitist multiobjective genetic algorithm: NSGA-IIIEEE Transactions on Evolutionary Computation, 2002
- Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approachIEEE Transactions on Evolutionary Computation, 1999
- Optimal mix of renewable energy resources to meet the electrical energy demand in villages of Madhya PradeshEnergy Conversion and Management, 1998