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Stochastic Genetic Algorithm and its Application as a Demand Control Tool for Houses with Thermal Energy Storage Systems
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DOI: https://doi.org/10.15866/iremos.v8i3.6045
Abstract
This paper presents a load control tool for houses with Thermal Energy Storage systems (TES) within the framework of direct load control. A control vector’s creation method is presented: this vector – that works as a time switch to the electric heating system of the house – is daily given to the customer, offering the most economic 24-hour loading pattern that satisfies his heating demand and allows to estimate the actual storage level of the house. The loading pattern is generated by using the Stochastic Genetic Algorithm (SGA), an application of the standard Genetic Algorithm. As a complement of the SGA, it is as well presented a storage level updating tool that allows tracking the customer’s behaviour regarding to his adjustment to the given control.
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References
Leena Storgårds, Energy consumption in households 2012, Statistics Finland, 13 November 2013.
J. W. D. Chvala, Jr., Technology Potential of Thermal Energy Storage (TES) Systems in Federal Facilities, U.S. Department of Energy, July 2001, pp. 4 – 6.
http://dx.doi.org/10.2172/15020978
C. S. T. M. A. M. Fortunato B., Simple Mathematical Model of a Thermal Storage with PCM, 2012 AASRI Conference of Power and Energy Systems, Volume 2, 2012, p. 241-248, Bari, Italy.
http://dx.doi.org/10.1016/j.aasri.2012.09.041
P. Ihma, M. Krartia, G.P. Henzeb, Development of a thermal energy storage model for EnergyPlus, Elsevier, vol. 36 n. 8, August 2004, pp. 807 – 814.
V. Vélez, Design of a Control Strategy for Optimal Operation of Autonomous Distributed Generation System for Electricity and Heat, Master's of Science Thesis, Elect. Eng., Math. and Comp. Sci., Faculty of App. Sci., Delf Univ. of Tech., 2010.
C. A. Cruickshank, Evaluation of a Stratified Multi-Tank Thermal Storage for Solar Heating Applications, Bacherlor's Thesis, Dep. Mech. and Mat. Eng., Queen's Univ., Ontario, Canada, 2009.
N. Li, S-X. Yang, Power Demand Forecast based on Optimized Neural Networks by improved Genetic Algorithm, The 5th International Conference on Machine Learning and Cybernetics, August 13-16, 2006, Dalian, China.
http://dx.doi.org/10.1109/icmlc.2006.259073
O. Alamos, H. Rudnick, Genetic Algorithm model to control peak demand to feeder capacity investment, Power and Energy Society General Meeting, Institute of Electrical Engineering and Electronics (IEEE), July 22–26, 2012, San Diego, USA.
http://dx.doi.org/10.1109/pesgm.2012.6344706
M. Choi. S-J Kim, A Genetic Algorithm for Server Location and Storage Allocation in Multimedia-on-Demand Network, Mobile Future and Symposium on Trends in Communications, SympoTIC '03, Institute of Electrical Engineering and Electronics (IEEE), October 2003, pp. 79 – 83.
http://dx.doi.org/10.1109/tic.2003.1249094
J. J. Cárdenas, A. Garcia, J.L. Romeral, F. Andrade, A Genetic Algorithm Approach to Optimization of Power Peaks in an Automated Warehouse, 35th Annual Conference of IEEE, 3–5 November 2009, pp. 3297–3302, Porto, Portugal.
http://dx.doi.org/10.1109/iecon.2009.5415200
E. De Santis, A. Rizzi, A. Sadeghian, F.M.F. Mascioli, Genetic Optimization of a Fuzzy Control System for Energy Flow Management in Micro-Grids, IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS), Institute of Electrical Engineering and Electronics (IEEE), 24-28 June 2013, pp. 418–423.
http://dx.doi.org/10.1109/ifsa-nafips.2013.6608437
A. Mubbashir, The Role of Electric Space Heating in Demand Response, Master’s Thesis, Dept. of Elec. Eng., Aalto Univ., Espoo, Finland.
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