PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Coot algorithm for optimization and management of residential power demand

Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
PL
Algorytm Coota do optymalizacji i zarządzania zapotrzebowaniem na energię w budynkach mieszkalnych
Języki publikacji
EN
Abstrakty
EN
One of the major issues that investigators are working on is the rise in global electricity consumption. The main objective of this work is minimizing the total electricity cost of a residential house. In this current research a new metaheuristic algorithm that is inspired by the Coot swarm's behavior is applied. In addition to that, a comparison algorithm analysis is conducted using various metaheuristic methods. The results showed that employing the Coot optimization approach led to the lowest reduction in overall electricity daily cost.
PL
Jednym z głównych problemów, nad którymi pracują śledczy, jest wzrost globalnego zużycia energii elektrycznej. Głównym celem pracy jest minimalizacja całkowitego kosztu energii elektrycznej domu mieszkalnego. W obecnych badaniach zastosowano nowy algorytm metaheurystyczny, zainspirowany zachowaniem roju Łysek. Ponadto przeprowadzana jest analiza algorytmu porównawczego z wykorzystaniem różnych metod metaheurystycznych. Wyniki pokazały, że zastosowanie podejścia optymalizacyjnego Coota doprowadziło do najmniejszej redukcji całkowitego dziennego kosztu energii elektrycznej.
Słowa kluczowe
Rocznik
Strony
249--255
Opis fizyczny
Bibliogr. 26 poz., rys.
Twórcy
  • Departement of Electrotechnics, Faculty of Electrical Engineering, University of Science and Technology of Oran “Mohamed Boudiaf”, Oran, Algeria
  • Departement of Automatic, Faculty of Electrical Engineering, University of Science and Technology of Oran “Mohamed Boudiaf”, Oran, Algeria
  • Departement of Automatic, Faculty of Electrical Engineering, University of Science and Technology of Oran “Mohamed Boudiaf”, Oran, Algeria
Bibliografia
  • [1] P.A. Owusu and S.A. Sarkodie, «A review of renewable energy sources, sustainability issues and climate change mitigation», Cogent Engineering, (2016), 1-14
  • [2] REmap team at IRENA’s Innovation and Technology Centre, «GLOBAL ENERGY TRANSFORMATION», (2018)
  • [3] M.A.M. Mladjao, I.El Abbassi, M.El Ganaoui and M. Darcherif, “Modelisation et optimisation de systemes multi sources/Multi charges pour la ville durable” Ecobat Sciences & Techniques, (2014), 210-220
  • [4] M. Kharrich, S. Kamel, M. Abdeen, O.H. Mohammed, M. Akherraz, T. Khurshaid and S.B. Rhee, «Developed Approach Based on Equilibrium Optimizer for Optimal Design of Hybrid PV /Wind/ Diesel/Battery Microgrid in Dakhla, Morocco», IEEE, (2021)
  • [5] P. Roy, J. He and Y. Liao, «Cost Minimization of battery Supercapacitor Hybrid Energy Storage for Hourly Dispatching Wind-Solar Hybrid Power System», IEEE, (2020) 210099-210115
  • [6] J. Martinez-Rico, E. Zulueta, I. Rouiz de Argandona, U. Fernandez-Gamiz and M. Armendia, «Multi-Objective Optimization of Production Scheduling Using Particle Swarm Optimization Algorithm for Hybrid Renewable Power Plants with Battery Energy Storage System», Journal of modern power systems and clean energy, (2021), 285-294
  • [7] S. Saib, A. Gherbi, R. Bayindir and A. Kaabeche, «Multi Objective Optimization of a Hybrid renewable energy System with a Gas Micro-turbine and a Storage Battery», arabian Journal for Science and Enegineering, (2019)
  • [8] J. Pahasa and I. Ngamroo, «Tow-Stage Optimization based on SOC Control of SMES Installed in Hybrid Wind/PV System for Stabilizing Voltage and Power Fluctuations», IEEE, (2021)
  • [9] L. Wang and C. Singh, «Multicriteria Design of Hybrid PowerGeneration Systems Based on a Modified Particle Swarm optimization Algorithm», Transactions on Energy Conversion, IEEE, (2009), 163-172
  • [10] I. Çetinbaş, B. Tamyürek and M.Demirtaş, «The Hybrid Harris Hawks Optimizer-Arthmetic Optimization Algorithm : A New Hybrid Algorithm for Sizing Optimization and Design of Microgrids», IEEE, (2022), 19254-19283
  • [11] M.B. Danoune, A. Djafour, Y. Wang and A. Gougui, «The Whale Optimization Algorithm for efficient PEM fuel cells modeling»,Elsevier, (2021)
  • [12] A. Xavier, S.S. Ajitha and Shalini N M, «Multi Objective Sailfish Optimization Algorithm for Balancing Tradeoff amongst Cost and QoS for Resource Allocation in Cloud Computing», Journal of soft computing and engineering applications, (2020), 8-18
  • [13] S.J. Lee and Y. Yoon, «Electricity Cost Optimization in Energy Storage Systems by Combining a Genetic Algorithm with Dynamic Programming», Mathematics, (2020), 1-20
  • [14] L. Rao, X. Liu, L. Xie and W. Liu, «Minimizing Electricity Cost : Optimization of Distributed Internet Data Centers in a Multi Electricity-Market Environment», IEEE, (2010)
  • [15] A. Houbbadi, E.R. Iglesias, R. Trigui, S. Pelissier and T. Bouton, «Optimal Charging Strategy to Minimize Electricity Cost and Prolong Battery Life of Electric Bus Fleet», IEEE, (2020)
  • [16] Z. Qu, N. Qu, Y. Liu, X.Y. Qu, W. Wang, and J. Han, «Multi Objective Optimization Model of Electricity Behavior Considering the Combination of Household Appliance Correlation and Comfort», J Electr Eng Technol, (2018), 1821-1830
  • [17] C. Paul P.K. Roy V. Mukherjee, «Chaotic whale optimizationalgorithm for optimal solution of combined heat and power economic dispatch problem incorporating wind», Journal pre proof, (2020)
  • [18] L. Qin, T. Xu , S. Li, Z. Chen, Q. Zhang, J. Tian and Y. Lin, «Coot Algorithm for Optimal Carbon-Energy Combined Flow of Power Grid With aluminum Plants», Frontiers, (2022)
  • [19] L.C. Kien, T.T.B. Nga, T.M. Phan and T.T. Nguyen, «Coot Optimization Algorithm for Optimal Placement of Photovoltaic Generators in Distribution Systems Considering Variation of Load and Solar Radiation», Hindawi Mathematical Problems in Engineering, (2022), 1-17
  • [20] F.Y. Melhem, O. Grunder, Z. Hammoudan and N. Moubayed, “Optimization and Energy Mangement in Smart Home Considering Photovoltaic, Wind, and Battery Storage System With Integration of Electric Vehicles”, Canadian journal of electrical and computer engineering, IEEE, (2017), 128-138
  • [21] I. Naruei and F.Keynia, «A new optimization method based on COOT bird natural life model», Elsevier, (2021)
  • [22] S. Mirjalili and A. Lewis, “The Whale Optimization Algorithm”, Advances in engineering Software, Elsevier, (2016), 51-67
  • [23] J. Kennedy and R. Eberhart “Particle Swarm Optimization”, International Conference on Neural Networks, IEEE, (1995), 1942–1948
  • [24] M. Kharrich, O.H. Mohammed and M. Akherraz, “Design of Hybrid Microgrid PV/Wind/Diesel/Battery System: Case Study for Rabat and Baghdad”, EAI Endorsed Transactions on Energy Web, (2020), 1-9
  • [25] A. Bouakkaz, S. Haddad and A.J.G. Mena, “Optimal Peak Power Shaving Through Household Appliance Scheduling in off-grid Renewable Energy System”, EEEIC / I&CPS Europe, IEEE, (2019)
  • [26] S. Shadravan, H.R. Naji and V.K. Bardsiri, “The Sailfish Optimizer: A novel nature-inspired metaheruristic algorithm forsolving constrained engineering optimization problems”, Engineering Applications of Artificial Intelligence, Elsevier, (2019), 20-34
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-7b64b167-984a-4b49-b2b5-026838c766a8
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.