Artificial cell swarm optimization and vapor liquid equilibrium for energy management system in smart grid

• Published in: International journal of numerical modelling 2022-09, Vol.35 (5), p.n/a
• Main Authors: Ganesan, P., Xavier, S. Arockia Edwin
• Format: Article
• Language: English
• Subjects: artificial cell swarm optimization; Cost analysis; Distributed generation; Electric power demand; Electrical loads; Energy management; Impact prediction; Maintenance costs; micro grid; Optimization; Power flow; renewable energy source; Renewable energy sources; Root-mean-square errors; Smart grid; steady and stable output power; vapor liquid equilibrium; Wind turbines
• ISSN: 0894-3370; 1099-1204
• DOI: 10.1002/jnm.3015

This article proposes a hybrid approach for energy management system in smart grid. The smart grid system contains photovoltaic, wind turbine, micro turbine, battery. The proposed hybrid approach is the combination of artificial cell swarm optimization (ACSO) and vapor liquid equilibrium (VLE); therefore, it is termed as ACSO‐VLE. The aim of the proposed approach is minimization of fuel cost, operation and maintenance cost, hourly power variation in the grid connected micro grid system. The necessary load demand of grid connected micro grid system is continually monitored by ACSO. The VLE is enhanced the perfect consolidation of micro grid with respect to predicted load demand. During the micro grid operation, the first approach is focused the scheduling of various renewable energy sources to lessen the cost of electricity. The aim of the second method is to balance the power flow and diminish the impacts of predicting errors depending on rule summarized from the scheduled power reference. The proposed model is carried out in MATLAB; its efficiency is examined to with and without grid of micro grid system. The effectiveness of ACSO‐VLE technique is analyzed through the comparison analysis using the existing techniques. The proficiency is analyzed utilizing cost analysis including power generation of photovoltaic, micro and wind turbine, battery. The Root mean square error (RMSE), MAPE and Mean bias error (MBE) under 50 counts of trails of the proposed technique are 9.3, 4.2 and 2.7, l. Likewise, the RMSE, MAPE and MBE under 100 counts of trails are 13.5, 3.9 and 5.7. The mean, median, standard deviation attains 0.9681, 0.9062, and 0.1099.