E3S Web Conf.
Volume 209, 2020ENERGY-21 – Sustainable Development & Smart Management
|Number of page(s)||8|
|Section||Session 1. Towards Intelligent Energy Systems|
|Published online||23 November 2020|
- C.W. Gellings. The Smart Grid: enabling energy efficiency and demand response. (Lilburn, CA: Fairmont Press, 2009). [Google Scholar]
- V.Z. Manusov, N. Khasanzoda, P.V. Matrenin. Application of artificial intelligence methods to energy management in Smart Grids (Novosibirsk, 2019) [In Russian] [Google Scholar]
- X. Fang, S. Misra, G. Xue, D. Yang. Managing smart grid information in the cloud: Opportunities model and applications. IEEE Netw., 26.4, 32-38 (2012). [Google Scholar]
- R. Zafar, et. al. Prosumer based energy management and sharing in smart grid. Renewable and Sustainable Energy Reviews, 82.1, pp. 1675-1684 (2018) [CrossRef] [Google Scholar]
- A.G. Azar, et. al. A Non-Cooperative Framework for Coordinating a Neighborhood of Distributed Prosumers. IEEE Transactions on Industrial Informatics, 15.5, pp. 2523-2534 (2019) [Google Scholar]
- L. Ma, N. Liu, J. Zhang, L. Wang. Real-Time Rolling Horizon Energy Management for the Energy-Hub-Coordinated Prosumer Community From a Cooperative Perspective. IEEE Transactions on Power Systems, 34.2, pp. 1227-1242 (2019) [CrossRef] [Google Scholar]
- A.C. Luna, N.L. Diaz, M. Graells, J.C. Vasquez, J.M. Guerrero. Cooperative energy management for a cluster of households prosumers. IEEE Transactions on Consumer Electronics, 62.3, pp. 235-242 (2016) [CrossRef] [Google Scholar]
- H. Mortaji, S. Siew, M. Moghavvemi, H. Almurib. Load Shedding and Smart-Direct Load Control Using Internet of Things in Smart Grid Demand Response Management. IEEE Transactions on Industry Applications, 53.6, pp. 5155-5163 (2017) [Google Scholar]
- S.R. Etesami, W. Saad, N.B. Mandayam, H.V. Poor. Stochastic Games for the Smart Grid Energy Management with Prospect Prosumers. IEEE Transactions on Automatic Control, 63.8, pp. 2327-2342 (2018) [Google Scholar]
- G. El Rahi, et. al. Managing Price Uncertainty in Prosumer-Centric Energy Trading: A Prospect-Theoretic Stackelberg Game Approach. Transactions on Smart Grid, 10.1, pp. 702-713 (2019) [CrossRef] [Google Scholar]
- N. Liu, X. Yu, C. Wang, J. Wang. Energy Sharing Management for Microgrids with PV Prosumers: A Stackelberg Game Approach. IEEE Transactions on Industrial Informatics, 13.3, pp. 1088-1098 (2017) [Google Scholar]
- Y. del Valle, et al. Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems. IEEE Transactions on Evolutionary Computation, 12.2, pp. 171-195 (2008) [CrossRef] [Google Scholar]
- V.Z. Manusov, P.V. Matrenin, L.S. Atabaeva. Firefly Algorithm to Optimal Distribution of Reactive Power Compensation Units. International Journal of Electrical and Computer Engineering, 8.3, pp. 1758-1765 (2018) [Google Scholar]
- V.Z. Manusov, et al Optimization of Power Distribution Networks in Megacities. IOP Conference Series: Earth and Environmental Science. International Conference on Sustainable Cities, 72, id 012019 (2017) [Google Scholar]
- V.Z. Manusov, P.V. Matrenin, S.E. Kokin. Swarm Intelligence Algorithms for The Problem of The Optimal Placement and Operation Control of Reactive Power Sources into Power Grids. International Journal of Design & Nature and Ecodynamics, 12.1, pp.101-112 (2017) [CrossRef] [Google Scholar]
- N. Khasanzoda. Optimization of power consumption modes in intelligent networks with a two-way flow of energy using artificial intelligence methods (Novosibisk, 2019) [In Russian] [Google Scholar]
- L.A. Rastrigin, Modern principles of management of complex objects (Moskov, 1980) [In Russian] [Google Scholar]
- J. Kennedy, R. Eberhart. Particle swarm optimization. IEEE International Conference on Neural Networks, Perth, WA, Australia. pp. 1942–1948 (1995) [Google Scholar]
- R.C. Eberhart, Y. Shi. Particle swarm optimization: developments, applications and resources. Congress on Evolutionary Computation; Seoul, South Korea. pp. 81-86 (2001) [Google Scholar]
- D.T. Pham, et. al. The bees algorithm – a novel tool for complex optimisation problems (Cardiff, UK, 2005) [Google Scholar]
- X. Yang. Firefly algorithm, Stochastic Test Function and Design Optimization. International Journal of Bio-Inspired Computation. 2.2, pp. 78-84 (2010) [CrossRef] [Google Scholar]
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