A novel hybrid MOL-TLBO optimized techno-economic-socio analysis of renewable energy mix in island mode

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• 作者：Ranjeeta Khare ; ^{rkhare18@gmail.com" class="auth_mail" title="E-mail the corresponding author} ; Yogendra Kumar
• 关键词：Annual cost of the system ; Loss of power supply probability ; Hybrid MOL&ndash ; TLBO ; Many optimizing liaisons (MOL) ; Simple genetic algorithm ; Teaching&ndash ; learning based optimization
• 刊名：Applied Soft Computing
• 出版年：2016
• 出版时间：June 2016
• 年：2016
• 卷：43
• 期：Complete
• 页码：187-198
• 全文大小：1652 K

文摘

In this paper, a novel hybrid many optimizing liaisons (MOL) and teaching–learning based optimization (TLBO) i.e. MOL–TLBO based optimization of integrated hybrid renewable energy sources (IHRES) is proposed for techno-economic-socio analysis. IHRES consists of PV, wind turbine, battery storage banks and diesel generator. The methodology was constructed by using meteorological data from Renewable Energy Laboratory of electrical engineering department, MANIT, Bhopal situated in Madhya Pradesh, India. Optimal sizing of IHRES is done on the basis of solar irradiation, wind speed, demand load, reliability index, loss of load probability (LOLP) and CO₂ emission through diesel generator. Three different crystalline silicon PV modules are considered: ASE-300(mi-Si based EFG), Kyocera-120(mc-Si based wafer) and Astro-Power AP-120(thin-film Si). Results show that AP-120 gives better result as compared with other two for PV-wind-battery-DG combination. Annual cost of the system (ACS) is evaluated by using the proposed hybrid MOL and TLBO (MOL–TLBO) algorithm. Supremacy of the suggested MOL–TLBO hybrid method for optimal sizing of hybrid system is proved by comparing the results with other optimization techniques i.e. TLBO, ITLBO, PSO, MOL and SGA. Finally it is seen that MOL–TLBO based techno-economic-socio analysis for IHRES exhibit superior performance and fast as compared to SGA, PSO, TLBO, ITLBO and MOL.