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.

scholarly journals Multi-Objective Particle Swarm Optimization-Based Decision Support Model for Integrating Renewable Energy Systems in a Korean Campus Building

2021 ◽  
Vol 13 (15) ◽  
pp. 8660
Author(s):  
Minjeong Sim ◽  
Dongjun Suh ◽  
Marc-Oliver Otto
Keyword(s):  
Decision Making ◽  
Particle Swarm Optimization ◽  
Renewable Energy ◽  
Energy Savings ◽  
Energy Systems ◽  
Energy Policies ◽  
Swarm Optimization ◽  
Renewable Energy Systems ◽  
Multi Objective ◽  
Proposed Model

Renewable energy systems are an alternative to existing systems to achieve energy savings and carbon dioxide emission reduction. Subsequently, preventing the reckless installation of renewable energy systems and formulating appropriate energy policies, including sales strategies, is critical. Thus, this study aimed to achieve energy reduction through optimal selection of the capacity and lifetime of solar thermal (ST) and ground source heat pump (GSHP) systems that can reduce the thermal energy of buildings including the most widely used photovoltaic (PV) systems. Additionally, this study explored decision-making for optimal PV, ST, and GSHP installation considering economic and environmental factors such as energy sales strategy and electricity price according to energy policies. Therefore, an optimization model based on multi-objective particle swarm optimization was proposed to maximize lifecycle cost and energy savings based on the target energy savings according to PV capacity. Furthermore, the proposed model was verified through a case study on campus buildings in Korea: PV 60 kW and ST 32 m2 GSHP10 kW with a lifetime of 50 years were found to be the optimal combination and capacity. The proposed model guarantees economic optimization, is scalable, and can be used as a decision-making model to install renewable energy systems in buildings worldwide.

scholarly journals An Enhanced Multi-Objective Particle Swarm Optimization in Water Distribution Systems Design

Water ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 1334
Author(s):  
Mohamed R. Torkomany ◽  
Hassan Shokry Hassan ◽  
Amin Shoukry ◽  
Ahmed M. Abdelrazek ◽  
Mohamed Elkholy
Keyword(s):  
Particle Swarm Optimization ◽  
Local Search ◽  
Distribution Systems ◽  
Water Distribution ◽  
Water Distribution Systems ◽  
Particle Swarm ◽  
Large Network ◽  
Swarm Optimization ◽  
Original Algorithm ◽  
Multi Objective

The scarcity of water resources nowadays lays stress on researchers to develop strategies aiming at making the best benefit of the currently available resources. One of these strategies is ensuring that reliable and near-optimum designs of water distribution systems (WDSs) are achieved. Designing WDSs is a discrete combinatorial NP-hard optimization problem, and its complexity increases when more objectives are added. Among the many existing evolutionary algorithms, a new hybrid fast-convergent multi-objective particle swarm optimization (MOPSO) algorithm is developed to increase the convergence and diversity rates of the resulted non-dominated solutions in terms of network capital cost and reliability using a minimized computational budget. Several strategies are introduced to the developed algorithm, which are self-adaptive PSO parameters, regeneration-on-collision, adaptive population size, and using hypervolume quality for selecting repository members. A local search method is also coupled to both the original MOPSO algorithm and the newly developed one. Both algorithms are applied to medium and large benchmark problems. The results of the new algorithm coupled with the local search are superior to that of the original algorithm in terms of different performance metrics in the medium-sized network. In contrast, the new algorithm without the local search performed better in the large network.

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