Identyfikatory
Warianty tytułu
Energy demand calculation in active building energy management systems
Konferencja
XLVIII Międzyuczelniana Konferencja Metrologów MKM 2016 (XLVIII; 05.09-07.09.2016; Kraków, Polska)
Języki publikacji
Abstrakty
Efektywne zarządzanie energią w budynkach to kluczowy element inteligentnych sieci elektroenergetycznych z koncepcją zarządzania popytem na energię (Demand Side Management). Znajomość wartości poziomu popytu jest istotna z punktu widzenia organizacji aktywnych systemów zarządzania energią w budynkach. Systemy automatyzacji i sterowania budynkami (BACS) mogą dostarczać i gromadzić informacje o pobieranej mocy i energii przez odbiorniki, wraz z możliwością ich dynamicznego sterowania. W artykule zaproponowano różne algorytmy wyznaczania poziomu popytu, z wykorzystaniem mechanizmów harmonogramowania lub obsługi zdarzeń, charakterystycznych dla systemów BACS. Funkcjonowanie algorytmów poddano weryfikacji doświadczalnej. Przeprowadzono również analizę opracowanych algorytmów pod kątem ich możliwości aplikacyjnych, wskazując ich wady i zalety.
Buildings with implemented Building Energy Management Systems (BEMS) are crucial part of smart grids with demand-response mechanism. Mostly the BEMS are based on Building Automation and Control Systems (BACS). Devices, controllers and meters integrated in the BACS could be used to provide data about energy consumption, instantaneous power and actively control loads in buildings. An energy demand value is essential for the BEMS with an active demand side management (DSM). Different approaches to calculate the energy demand value have been introduced in this paper. Various algorithms with time-driven and event-driven calculation mechanism have been proposed. They have been implemented and experiment with real data has been performed to verify this implementation. Results of experiment have been analysed and discussed, taking into account the accuracy and speed of computing the energy demand value. The algorithms proposed in the paper have been developed according the LonWorks – open, international building automation standard, providing full interoperability with other devices integrated in the BACS. They are ready to use in an Internet of Things networks as well.
Rocznik
Tom
Strony
85--90
Opis fizyczny
Bibliogr. 25 poz., wykr.
Twórcy
autor
- AGH Akademia Górniczo-Hutnicza, Wydział Elektrotechniki, Automatyki, Informatyki i Inżynierii Biomedycznej tel.:+48126175011
autor
- AGH Akademia Górniczo-Hutnicza, Wydział Elektrotechniki, Automatyki, Informatyki i Inżynierii Biomedycznej tel.:+48126175011
Bibliografia
- [1] R. Missaoui, H. Joumaa, S. Ploix, and S. Bacha, “Managing energy Smart Homes according to energy prices: Analysis of a Building Energy Management System,” Energy Build., vol. 71, pp. 155–167, Mar. 2014.
- [2] K. Amarasinghe, D. Wijayasekara, and M. Manic, “Neural Network based downscaling of Building Energy Management System data,” in 2014 IEEE 23rd International Symposium on Industrial Electronics (ISIE), 2014, pp. 2670–2675.
- [3] A. Ozadowicz and J. Grela, “Control application for Internet of Things energy meter - A key part of integrated building energy management system,” IEEE Int. Conf. Emerg. Technol. Fact. Autom. ETFA, vol. 2015-Octob, pp. 1–4, 2015.
- [4] G. Bettinazzi, A. A. Nacci, and D. Sciuto, “Methods and Algorithms for the Interaction of Residential Smart Buildings with Smart Grids,” 2015 IEEE 13th Int. Conf. Embed. Ubiquitous Comput., pp. 178–182, 2015.
- [5] W. Kastner, M. Jung, and L. Krammer, “Future Trends in Smart Homes and Buildings,” in Industrial Communication Technology Handbook, Second Edition, R. Zurawski, Ed. CRC Press Taylor & Francis Group, 2015, pp. 59–1 – 59–20.
- [6] P. Augustyniak and E. Kantoch, “Turning Domestic Appliances Into a Sensor Network for Monitoring of Activities of Daily Living,” J. Med. Imaging Heal. Informatics, vol. 5, no. 8, pp. 1662–1667, Dec. 2015.
- [7] M. Jachimski, Z. Mikos, G. Wrobel, G. Hayduk, and P. Kwasnowski, “Event-based and time-triggered energy consumption data acquisition in building automation,” in 2015 International Conference on Event-based Control, Communication, and Signal Processing (EBCCSP), 2015, pp. 1–6.
- [8] M. Simonov and G. Zanetto, “Event-based hybrid metering feeding AMI and SCADA,” in 2015 International Conference on Event-based Control, Communication, and Signal Processing (EBCCSP), 2015, pp. 1–8.
- [9] M. Moreno, B. Úbeda, A. Skarmeta, and M. Zamora, “How can We Tackle Energy Efficiency in IoT BasedSmart Buildings?,” Sensors, vol. 14, no. 6, pp. 9582–9614, May 2014.
- [10] M. Noga, A. Ozadowicz, J. Grela, and G. Hayduk, “Active consumers in smart grid systems - Applications of the building automation technologies,” Electr. Rev., vol. 89, pp. 227–233, 2013.
- [11] J. Young, “BIoT BUILDING Internet of Things,” AutomatedBuildings.com. [Online]. Available: http://www.automatedbuildings.com/news/mar14/articles/realcomm/140219043909realcomm.html.
- [12] K. Aduda, W. Zeiler, and G. Boxem, “Smart Grid - BEMS: The Art of Optimizing the Connection between Comfort Demand and Energy Supply,” in 2013 Fourth International Conference on Intelligent Systems Design and Engineering Applications, 2013, vol. 2050, pp. 565–569.
- [13] A. Kavousian, R. Rajagopal, and M. Fischer, “Ranking appliance energy efficiency in households: Utilizing smart meter data and energy efficiency frontiers to estimate and identify the determinants of appliance energy efficiency in residential buildings,” Energy Build., vol. 99, pp. 220–230, Jul. 2015.
- [14] P. Palensky, D. Dietrich, S. Member, D. Dietrich, S. Member, and D. Dietrich, “Demand Side Management : Demand Response , Intelligent Energy Systems , and Smart Loads,” IEEE Trans. Ind. Informatics, vol. 7, no. 3, pp. 381–388, 2011.
- [15] A. Ozadowicz and J. Grela, “The street lighting control system application and case study,” in 2015 International Conference on Event-based Control, Communication, and Signal Processing (EBCCSP), 2015, pp. 1–8.
- [16] A. Di Giorgio, L. Pimpinella, A. Quaresima, and S. Curti, “An event driven Smart Home Controller enabling cost effective use of electric energy and automated Demand Side Management,” in 2011 19th Mediterranean Conference on Control & Automation (MED), 2011, vol. 96, pp. 358–364.
- [17] M. Babar, P. H. Nyugen, V. Cuk, I. G. R. Kamphuis, M. Bongaerts, and Z. Hanzelka, “The rise of AGILE demand response: Enabler and foundation for change,” Renew. Sustain. Energy Rev., vol. 56, pp. 686–693, Apr. 2016.
- [18] Echelon Corp., “The Industrial Internet of Things is Really Control Networking 2.0,” 2014.
- [19] D. Picault, O. Cottet, and T. Ruez, “Demand response: A solution to manage loads in the smart grid,” in 2015 IEEE 15th International Conference on Environment and Electrical Engineering (EEEIC), 2015, pp. 352– 356.
- [20] Echelon Corp., “IzoTTM Platform Info,” WWW page, 2014.
- [21] Y. Chen and A. Jaekel, “Energy-Aware Scheduling and Resource Allocation for Periodic Traffic Demands,” J. Opt. Commun. Netw., vol. 5, no. 4, p. 261, Apr. 2013.
- [22] M. Frincu, C. Chelmis, M. U. Noor, and V. Prasanna, “Accurate and efficient selection of the best consumption prediction method in smart grids,” in 2014 IEEE International Conference on Big Data (Big Data), 2014, pp. 721–729.
- [23] L. Continental Control Systems, “WattNode Plus for LONWORKS Installation and Operation Manual,” no. WNC-FT10–3.37b, 2011.
- [24] T. Fang and R. Lahdelma, “Optimization of combined heat and power production with heat storage based on sliding time window method,” Appl. Energy, vol. 162, pp. 723–732, 2016.
- [25] M. Noga, A. Ożadowicz, and J. Grela, “Modern, certified building automation laboratories AutBudNet – put ‘learning by doing’ idea into practice,” Electr. Rev., no. 11, pp. 137–141, 2012.
Uwagi
PL
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-1e13e954-0aee-4096-85a9-9849b9a7c2e2