Optimization and performance enhancement of renewable energy microgrid energy system using pheasant bird optimization algorithm

• Published in: Sustainable energy technologies and assessments Vol. 66; p. 103801
• Main Authors: Verma, Ranu, Bhatia, Rinkoo, Raghuwanshi, Santosh S.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.06.2024
• Subjects: Battery; Biomass; Microgrid; Net present cost; Pheasant bird; Reliability; Biomass; Microgrid; Reliability; Pheasant bird; Battery; Net present cost
• ISSN: 2213-1388
• DOI: 10.1016/j.seta.2024.103801

•This study aims to design an optimal MG system by considering several aspects including technological, economic, environmental, social, and reliability parameters.•The mathematical modelling of the MG system and its components has been thoroughly examined.•The novel PBOA algorithm is proposed and aims to determine the optimal size for a MGs consisting of PV, WT, BMG, BB and DG, with seven different configurations i.e. PV/BMG/BB/DG, PV/WT/BB/DG, PV/WT/BMG, PV/WT/BMG/BB, PV/WT/BMG/BB/DG, PV/WT/DG and WT/BMG/BB/DG.•The multi-objective functions of the research are minimizing NPC, COE, CE, economic growth and job creation (EGJC), renewable factor (RF), LPSP parameters.•The performance and efficiency of the proposed PBOA are validated by renowned algorithm such as PSO, GA, ACO, CSA and HOMER Pro. The main aim of this research work is to design and select an optimal renewable energy resource based microgrid (MG) system for rural area electrification of India. MG system consists of diesel generator (DG), battery bank (BB), wind turbine (WT), biomass generator (BMG) and solar photovoltaic (PV) modules with seven different configurations. The optimal system is selected based on technological, economic, environmental, social and reliability parameters. For this objective, a novel algorithm called pheasant bird optimization algorithm (PBOA) is presented and compared with particle swarm optimization (PSO), genetic algorithm (GA), ant colony optimization (ACO), cuckoo search algorithm (CSA) and HOMER Pro. Results show that PV/WT/BMG/BB/DG is an optimal configuration with sizes of 1148 units of PV, 42 units of WT, 20 units of BMG, 36 units of BB and 1 unit of DG. PBOA has determined the optimal resource size for the MG system. In terms of social and reliability parameters for the PV/WT/BMG/BB/DG system, PBOA generated values of 93.58 %, for RF, 0.08754 for EGJC and 0.01 % for LPSP. For the optimal configuration, PBOA estimated NPC, COE, CE values are ₹66185779, ₹9.3 and 650901 kg/yrs respectively. PBOA reaches the minimal NPC of ₹66185779 in just 180 iterations, whereas PSO, ACO, GA, and CSA required 360, 200, 360 and 250 iterations respectively. PBOA also achieved minimal COE at a rate of 1.52, 1.11, 1.11 and 1.94 times faster than PSO, GA, CSA and ACO respectively. The contribution of PV and WT sources to the total generated electricity is approximately 50 % and 35 % respectively. PBOA exhibits faster convergence time and iteration compared to the PSO, ACO, GA and CSA algorithm. The comparison results indicate that the optimal PV/WT/BMG/BB/DG configuration select for rural areas. This research provides valuable information for policy makers and investors on the implementation of MG system in rural regions.