Sci. Tech. Energ. Transition
Volume 79, 2024
Emerging Advances in Hybrid Renewable Energy Systems and Integration
Numéro d'article 29
Nombre de pages 15
Publié en ligne 7 mai 2024
  • Blanco H., Faaij A. (2018) A review at the role of storage in energy systems with a focus on power to gas and long-term storage. Renew. Sustain. Energy Rev. 81, 1049–1086. [Google Scholar]
  • Ziadeh A., Abualigah L., Elaziz M.A., Şahin C.B., Almazroi A.A., Omari M. (2021) Augmented grasshopper optimization algorithm by differential evolution: A power scheduling application in smart homes, Multimed. Tools Appl. 80, 21, 31569–31597. [Google Scholar]
  • Abbassi A., Mehrez R.B., Touaiti B., Abualigah L., Touti E. (2022) Parameterization of photovoltaic solar cell double-diode model based on improved arithmetic optimization algorithm, Optik 253, 168600. [Google Scholar]
  • Eltigani D., Masri S. (2015) Challenges of integrating renewable energy sources to smart grids: A review, Renew. Sustain. Energy Rev. 52, 770–780. [Google Scholar]
  • Ma R., Chen H.-H., Huang Y.-R., Meng W. (2013) Smart grid communication: Its challenges and opportunities, IEEE Trans. Smart Grid 4, 1, 36–46. [Google Scholar]
  • Ayodele E., Misra S., Damasevicius R., Maskeliunas R. (2019) Hybrid microgrid for microfinance institutions in rural areas – a field demonstration in West Africa, Sustain. Energy Technol. Assess. 35, 89–97. [Google Scholar]
  • Vardakas J.S., Zorba N., Verikoukis C.V. (2015) A survey on demand response programs in smart grids: Pricing methods and optimization algorithms, IEEE Commun. Surv. Tutor. 17, 1, 152–178. [Google Scholar]
  • Judge M.A., Khan A., Manzoor A., Khattak H.A. (2022) Overview of smart grid implementation: Frameworks, impact, performance and challenges, J. Energy Storage 49, 104056. [Google Scholar]
  • Gelazanskas L., Gamage K.A.A. (2014) Demand side management in smart grid: A review and proposals for future direction, Sustain. Cities Soc. 11, 22–30. [Google Scholar]
  • Esther B.P., Kumar K.S. (2016) A survey on residential demand side management architecture, approaches, optimization models and methods, Renew. Sustain. Energy Rev. 59, 342–351. [Google Scholar]
  • Judge M.A., Manzoor A., Maple C., Rodrigues J.J., ul Islam S. (2021) Price-based demand response for household load management with interval uncertainty, Energy Rep. 7, 8493–8504. [Google Scholar]
  • Manzoor A., Javaid N., Ullah I., Abdul W., Almogren A., Alamri A. (2017) An intelligent hybrid heuristic scheme for smart metering based demand side management in smart homes, Energies 10, 9, 1258. [Google Scholar]
  • Zhou B., Li W., Chan K.W., Cao Y., Kuang Y., Liu X., Wang X. (2016) Smart home energy management systems: Concept, configurations, and scheduling strategies, Renew. Sustain. Energy Rev. 61, 30–40. [Google Scholar]
  • Bayram I.S., Ustun T.S. (2017) A survey on behind the meter energy management systems in smart grid, Renew. Sustain. Energy Rev. 72, 1208–1232. [Google Scholar]
  • Rathor S.K., Saxena D. (2020) Energy management system for smart grid: An overview and key issues, Int. J. Energy Res. 44, 6, 4067–4109. [Google Scholar]
  • Mourshed M., Robert S., Ranalli A., Messervey T., Reforgiato D., Contreau R., Becue A., Quinn K., Rezgui Y., Lennard Z. (2015) Smart grid futures: Perspectives on the integration of energy and ICT services, Energy Proc. 75, 1132–1137. [Google Scholar]
  • Khalili T., Nojavan S., Zare K. (2019) Optimal performance of microgrid in the presence of demand response exchange: A stochastic multiobjective model, Comput. Electr. Eng. 74, 429–450. [Google Scholar]
  • Muralitharan K., Sakthivel R., Shi Y. (2016) Multiobjective optimization technique for demand side management with load balancing approach in smart grid, Neurocomputing 177, 110–119. [Google Scholar]
  • Silva B.N., Han K. (2019) Mutation operator integrated ant colony optimization based domestic appliance scheduling for lucrative demand side management, Future Gener. Comput. Syst. 100, 557–568. [Google Scholar]
  • Sunil A., Venkaiah C. (2024) Multi-objective adaptive fuzzy campus placement based optimization algorithm for optimal integration of DERs and DSTATCOMs, J. Energy Storage 75, 109682. [Google Scholar]
  • Cortes-Arcos T., Bernal-Agustín J.L., Dufo-Lopez R., Lujano-Rojas J.M., Contreras J. (2017) Multi-objective demand response to real-time prices (RTP) using a task scheduling methodology, Energy 138, 19–31. [Google Scholar]
  • Taha H.A., Alham M., Youssef H.K. (2022) Multi-objective optimization for optimal allocation and coordination of wind and solar DGS, BESSs and capacitors in presence of demand response, IEEE Access 10, 16225–16241. [Google Scholar]
  • Lu Q., Zhang Y. (2022) Demand response strategy of game between power supply and power consumption under multi-type user mode, Int. J. Electr. Power Energy Syst. 134, 107348. [Google Scholar]
  • Liu Y., Ćetenović D., Li H., Gryazina E., Terzija V. (2022) An optimized multi-objective reactive power dispatch strategy based on improved genetic algorithm for wind power integrated systems, Int. J. Electr. Power Energy Syst. 136, 107764. [Google Scholar]
  • Noor S., Yang W., Guo M., van Dam K.H., Wang X. (2018) Energy demand side management within micro-grid networks enhanced by blockchain, Appl. Energy 228, 1385–1398. [Google Scholar]
  • Wang J., Zhang L., Liu Z., Niu X. (2022) A novel decomposition-ensemble forecasting system for dynamic dispatching of smart grid with submodel selection and intelligent optimization, Expert Syst. Appl. 201, 117201. [Google Scholar]
  • Vaish J., Tiwari A.K., Kaimal S. (2024) Multi-objective optimization of distributed energy resources based microgrid using random forest model, Bull. Electr. Eng. Inform. 13, 1, 67–75. [Google Scholar]
  • Yang Q., Dong N., Zhang J. (2021) An enhanced adaptive bat algorithm for microgrid energy scheduling, Energy 232, 121014. [Google Scholar]
  • Ahmad M., Javaid N., Niaz I.A., Almogren A., Radwan A. (2021) A bioinspired heuristic algorithm for solving optimal power flow problem in hybrid power system, IEEE Access 9, 159809–159826. [Google Scholar]
  • Makhadmeh S.N., Al-Betar M.A., Alyasseri Z.A., Abasi A.K., Khader A.T., Damaševičius R., Mohammed M.A., Abdulkareem K.H. (2021) Smart home battery for the multi-objective power scheduling problem in a smart home using grey wolf optimizer, Electronics 10, 4, 447. [Google Scholar]
  • Sanajaoba S. (2019) Optimal sizing of off-grid hybrid energy system based on minimum cost of energy and reliability criteria using firefly algorithm, Sol. Energy 188, 655–666. [Google Scholar]
  • Singh S., Chauhan P., Singh N. (2020) Capacity optimization of grid connected solar/fuel cell energy system using hybrid ABC-PSO algorithm, Int. J. Hydrogen Energy 45, 16, 10070–10088. [Google Scholar]
  • Ullah I., Hussain S. (2019) Time-constrained nature-inspired optimization algorithms for an efficient energy management system in smart homes and buildings, Appl. Sci. 9, 4, 792. [Google Scholar]
  • Ullah H., Khan M., Hussain I., Ullah I., Uthansakul P., Khan N. (2021) An optimal energy management system for university campus using the hybrid firefly lion algorithm (fla), Energies 14, 19, 6028. [Google Scholar]
  • Ma X., Mu Y., Zhang Y., Zang C., Li S., Jiang X., Cui M. (2022) Multiobjective microgrid optimal dispatching based on improved bird swarm algorithm, Glob. Energy Interconnect. 5, 2, 154–167. [Google Scholar]
  • Iqbal M.M., Zia M.F., Beddiar K., Benbouzid M. (2020) Optimal scheduling of grid transactive home demand responsive appliances using polar bear optimization algorithm, IEEE Access 9, 222285–222296. [Google Scholar]
  • Połap D., Woźniak M. (2017) Polar bear optimization algorithm: Meta-heuristic with fast population movement and dynamic birth and death mechanism, Symmetry 9, 10, 203. [Google Scholar]
  • Madathil D., Pandi V.R., Nair M.G., Jamasb T., Thakur T. (2021) Net zero energy in a residential building using heuristic optimization solution, J. Control Autom. Electr. Syst. 32, 2, 458–471. [Google Scholar]
  • Mouassa S., Tostado-Véliz M., Jurado F. (2021) Efficient power scheduling in smart homes using a novel artificial ecosystem optimization technique considering two pricing schemes, Int. J. Emerg. Electr. Power Syst. 22, 643–660. [Google Scholar]
  • Navarro-Caceres M., Herath P., Villarrubia G., Prieto-Castrillo F., Kumar Venyagamoorthy G. (2018) An evaluation of a metaheuristic artificial immune system for household energy optimization, Complexity 2018. [Google Scholar]
  • Safaie A.A., Bidgoli M.A., Javadi S. (2022) A multi-objective optimization framework for integrated electricity and natural gas networks considering smart homes in downward under uncertainties, Energy 239, 122214. [Google Scholar]
  • Lu Q., Zhang Y. (2022) A multi-objective optimization model considering users’ satisfaction and multi-type demand response in dynamic electricity price, Energy 240, 122504. [Google Scholar]
  • Deng W., Zhang X., Zhou Y., Liu Y., Zhou X., Chen H., Zhao H. (2022) An enhanced fast non-dominated solution sorting genetic algorithm for multi-objective problems, Inf. Sci. 585, 441–453. [Google Scholar]

Les statistiques affichées correspondent au cumul d'une part des vues des résumés de l'article et d'autre part des vues et téléchargements de l'article plein-texte (PDF, Full-HTML, ePub... selon les formats disponibles) sur la platefome Vision4Press.

Les statistiques sont disponibles avec un délai de 48 à 96 heures et sont mises à jour quotidiennement en semaine.

Le chargement des statistiques peut être long.