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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Renewable Energy Development
Nguyen, Thang Trung
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Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering

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Jellyfish search algorithm for economic load dispatch under the considerations of prohibited operation zones, load demand variations, and renewable energy sources Trong, Hien Chiem; Nguyen, Thuan Thanh; Nguyen, Thang Trung
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i1.pp74-81

Abstract

This paper suggests a modified version of the former economic load dispatch (MELD) problem with the integration of wind power plant (WPP) and solar power plants (SPP) into thermal units (TUs). The target of the whole study is to cut the total producing electricity cost (TPEC) as much as possible. Three meta-heuristic algorithms, including particle swarm optimization (PSO), jellyfish search (JS) and salp swarm algorithm (SSA), are applied to solve the MELD. The real performance of these optimization tools is tested on the first system with six thermal units considering prohibited zones, and the second system with the combination of the first system and one solar, and two WPPs. In addition, the variation of load demand in 24 hours per day is also taken into account in the second system. JS is proved to be the most effective method for dealing with MELD. Furthermore, JS can also reach lower or the same TPEC as other previous algorithms. Hence, JS is a recommended to be a strong computing method for dealing with the MELD problem. 
Maximize the total electric sale profit for a hybrid power plant with fifteen thermal units and a 100-MW solar photovoltaic farm under a 20-year power generation project Tran, Dao Trong; Nguyen, Thang Trung
International Journal of Renewable Energy Development Vol 14, No 3 (2025): May 2025
Publisher : Center of Biomass & Renewable Energy (CBIORE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61435/ijred.2025.60943

Abstract

This study investigates the effectiveness of two recently proposed meta-heuristic methods, the Weighted Average Algorithm (WAA) and Electric Eel Foraging Optimization (EEFO), to maximize the total profit of a hybrid power system. The considered system comprises fifteen thermal generating units (TGUs) and a 100-MW solar photovoltaic farm (SPP) operating over a 20-year period. Initially, the problem is solved under conditions of fixed load demand and rated power supply from the renewable energy source while accounting for prohibited operating zone constraint and system power losses. Comparative results obtained from both algorithms demonstrate that EEFO exhibits superior performance in terms of stability and convergence speed. Specifically, EEFO demonstrates a lower fluctuation in overall electricity generation cost (OEGC) across multiple independent runs compared to WAA. Furthermore, EEFO achieves better minimum, mean, and maximum OEGC values of $0.266, $58.890, and $214.225, respectively. Subsequently, EEFO is reapplied to maximize the profit of the hybrid power system, incorporating load demand variations and real solar radiation data. This analysis includes the evaluation of initial capital expenditure (CAPEX) and operation and maintenance (O&M) costs for the SPP over the 20-year period. Current electricity and solar power prices are utilized to illustrate the cumulative profit over time. The results indicate that the hybrid system experienced the highest loss in the first year, with the minimum loss occurring after 9 years for the TGUs and 7 years for the SPP. Profitability is achieved after 10 years for the TGUs and 7 years for the SPP. The cumulative profit over 20 years amounts to $14.2 billion for the TGUs and $0.207 billion for the SPP, representing approximately 83% and 127% of their respective total costs.
Co-Authors Nguyen, Thuan Thanh Tran, Dao Trong Trong, Hien Chiem