Abstract
Nowadays a correct use of energy is a crucial aspect, in fact cost and energy waste reduction are the main goals that must be achieved. To reach this objective an optimal energy management must be obtained through some techniques and optimization algorithms, in order to provide the best solution in terms of cost. In this work a comparison between different methods for energy scheduling is proposed and some analytical results are reported, in order to offer a clear overview for each technique, in terms of advantages and disadvantages. A residential scenario is considered for computer simulations, in which a system storage and renewable resources are available and exploitable to match the user load demand.
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References
Harley, R.G., Liang, J.: Computational Intelligence in Smart Grids. In: IEEE SSCI 2011 Symposium Series on Computational Intelligence CIASG (2011)
Venayagamoorthy, G.K.: Potentials and Promises of Computational Intelligence for Smart Grids. In: 2009 IEEE Power and Energy Society General Meeting, pp. 1–6 (2009)
De Angelis, F., Boaro, M., Fuselli, D., Squartini, S., Piazza, F., Wei, Q., Wang, D.: Optimal Task and Energy Scheduling in Dynamic Residential Scenarios. In: Wang, J., Yen, G.G., Polycarpou, M.M. (eds.) ISNN 2012, Part I. LNCS, vol. 7367, pp. 650–658. Springer, Heidelberg (2012)
Morais, H., Kádár, P., Faria, P., Vale, Z.A., Khodr, H.M.: Optimal scheduling of a renewable micro-grid in an isolated load area using mixed-integer linear programming. Renewable Energy - An International Journal 35(1), 151–156 (2009)
Gudi, N., Wang, L., Devabhaktuni, V., Depuru, S.S.S.R.: A Demand-Side Management Simulation Platform Incorporating Optimal Management of Distributed Renewable Resources. In: IEEE/PES Power Systems Conference and Exposition (PSCE), pp. 1–7 (2011)
Liang, R.H., Liao, J.H.: A Fuzzy-Optimization Approach for Generation Scheduling with Wind and Solar Energy Systems. IEEE Transactions on Power Systems. Journals and Magazines 22(4), 1665–1674 (2007)
Vale, Z.A., Faria, P., Morais, H., Khodr, H.M., Silva, M., Kadar, P.: Scheduling Distributed Energy Resources in an Isolated Grid –An Artificial Neural Network Approach. In: IEEE Power and Energy Society General Meeting, pp. 1–7 (2010)
Welch, R.L., Venayagamoorthy, G.K.: Energy Dispatch Controllers for a Photovoltaic System. Engineering Applications of Artificial Intelligence, 249–261 (2008)
Fuselli, D., De Angelis, F., Boaro, M., Liu, D., Wei, Q., Squartini, S., Piazza, F.: Optimal Battery Management with ADHDP in Smart Home Environments. In: Wang, J., Yen, G.G., Polycarpou, M.M. (eds.) ISNN 2012, Part II. LNCS, vol. 7368, pp. 355–364. Springer, Heidelberg (2012)
Del Valle, Y., Venayagamoorthy, G.K., Mohagheghi, S., Hernandez, J.C., Harley, R.G.: Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems. IEEE Transactions on Evolutionary Computation. Journals and Magazines 12(2), 171–195 (2008)
Werbos, P.J.: Approximate Dynamic Programming for Real-Time Control and Neural Modeling. In: Handbook of Intelligent Control (1992)
Huang, T., Liu, D.: Residential Energy System Control and Management using Adaptive Dynamic Programming. In: Intenational Joint Conference on Neural Networks (IJCNN), pp. 119–124 (2011)
National Renewable Energy Laboratory (NREL) of U.S. Department of Energy, http://www.nrel.gov/rredc/ , Office of Energy Efficiency and Renewable Energy, operated by the Alliance for Sustainable Energy, LLC
Markvart, T.: Solar Electricity, 2nd edn. Wiley, New York (2000)
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De Angelis, F., Boaro, M., Fuselli, D., Squartini, S., Piazza, F. (2013). A Comparison between Different Optimization Techniques for Energy Scheduling in Smart Home Environment. In: Apolloni, B., Bassis, S., Esposito, A., Morabito, F. (eds) Neural Nets and Surroundings. Smart Innovation, Systems and Technologies, vol 19. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35467-0_31
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DOI: https://doi.org/10.1007/978-3-642-35467-0_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35466-3
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