Open AccessOptimal Operation of Micro-Grids to Reduce Energy Production Costs and Environmental Pollution Using Ant Colony Optimization Algorithm (ACO)
Author(s) -
Giulio Lorenzini,
Mehrdad Ahmadi Kamarposhti,
Ahmed A. A. Solyman
Publication year - 2021
Publication title -
journal européen des systèmes automatisés/journal européen des systèmes automaitsés
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.16
H-Index - 20
eISSN - 2116-7087
pISSN - 1269-6935
DOI - 10.18280/jesa.540102
Subject(s) - ant colony optimization algorithms , renewable energy , wind power , computer science , environmental pollution , reliability (semiconductor) , grid , production (economics) , energy management , electricity generation , electric potential energy , electric power , reliability engineering , energy (signal processing) , mathematical optimization , algorithm , power (physics) , environmental science , engineering , electrical engineering , environmental protection , physics , geometry , mathematics , statistics , quantum mechanics , economics , macroeconomics
With increasing demand for electric energy and requesting higher quality by subscribers, the electric power industry has moved towards using new technologies. Many modern societies seek to use the systems of new energy management in order to reduce environmental pollutants and operational costs in electrical energy systems. Therefore, exploiting various resources of renewable energies, micro-grids can be considered as a significant tool to attain these objectives. According to the subject, Ant Colony Optimization algorithm (ACO) is used in this paper to optimize one micro-grid sample in order to reduce the cost of generating power and to reduce environmental pollution to increase reliability. The recommended algorithm has been done in two scenarios and each in two sections. In the first scenario, energy management will be conducted for all distributed generation resources and in the second scenario it is assumed that wind and solar products, produce their maximum power and energy management are conducted for the reminder elements and the results are compared with other optimization algorithms.