Multi-objective particle swarm optimization of component size and long-term operation of hybrid energy systems under multiple uncertainties

2018 ◽  
Vol 10 (1) ◽  
pp. 015902 ◽  
Author(s):  
M. Abdoos ◽  
M. Ghazvini
Keyword(s):  
Particle Swarm Optimization ◽  
Energy Systems ◽  
Swarm Optimization ◽  
Hybrid Energy ◽  
Term Operation ◽  
Component Size ◽  
Multi Objective ◽  
Hybrid Energy Systems ◽  
Multiple Uncertainties

Multi-objective optimisation of renewable hybrid energy systems with desalination

Energy ◽  
2015 ◽  
Vol 88 ◽  
pp. 457-468 ◽  
Author(s):  
Daniel P. Clarke ◽  
Yasir M. Al-Abdeli ◽  
Ganesh Kothapalli
Keyword(s):  
Energy Systems ◽  
Hybrid Energy ◽  
Multi Objective ◽  
Hybrid Energy Systems

scholarly journals A Multi-Criteria Decision Maker for Grid-Connected Hybrid Renewable Energy Systems Selection Using Multi-Objective Particle Swarm Optimization

2019 ◽  
Vol 11 (4) ◽  
pp. 1188 ◽  
Author(s):  
David Konneh ◽  
Harun Howlader ◽  
Ryuto Shigenobu ◽  
Tomonobu Senjyu ◽  
Shantanu Chakraborty ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Renewable Energy ◽  
Sierra Leone ◽  
Particle Swarm ◽  
Energy Systems ◽  
Sub Saharan Africa ◽  
Biomass Combustion ◽  
Swarm Optimization ◽  
Renewable Energy Systems ◽  
Multi Objective

Combating climate change issues resulting from excessive use of fossil fuels comes with huge initial costs, thereby posing difficult challenges for the least developed countries in Sub-Saharan Africa (SSA) to invest in renewable energy alternatives, especially with rapid industrialization. However, designing renewable energy systems usually hinges on different economic and environmental criteria. This paper used the Multi-Objective Particle Swarm Optimization (MOPSO) technique to optimally size ten grid-connected hybrid blocks selected amongst Photo-Voltaic (PV) panels, onshore wind turbines, biomass combustion plant using sugarcane bagasse, Battery Energy Storage System (BESS), and Diesel Generation (DG) system as backup power, to reduce the supply deficit in Sierra Leone. Resource assessment using well-known methods was done for PV, wind, and biomass for proposed plant sites in Kabala District in Northern and Kenema District in Southern Sierra Leone. Long term analysis was done for the ten hybrid blocks projected over 20 years whilst ensuring the following objectives: minimizing the Deficiency of Power Supply Probability (DPSP), Diesel Energy Fraction (DEF), Life Cycle Costs (LCC), and carbon dioxide (CO 2 ) emissions. Capacity factors of 27.41 % and 31.6 % obtained for PV and wind, respectively, indicate that Kabala district is the most feasible location for PV and wind farm installations. The optimum results obtained are compared across selected blocks for DPSP values of 0–50% to determine the most economical and environmentally friendly alternative that policy makers in Sierra Leone and the region could apply to similar cases.

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