Demand-Side Energy Management in Smart Buildings: A Case Study

Year 2021, Volume: 11 Issue: 2, 239 - 247, 30.12.2021

Nazlı Hasanova , Seçil Varbak Neşe

https://doi.org/10.36222/ejt.969881

Abstract

Electrical energy is indispensable in our daily life with the developing technology. The most important feature is reliable and sustainable transmission of electrical energy to consumers is to provide the supply-demand balance in real-time. The ever-increasing demand for electrical energy and gradual depletion of traditional resources used to meet demand and increasing dependence on foreign sources resulted in diverse electricity generation plants. As a result of this diversity, with the increase in importance of electricity storage systems and awareness of energy-saving, Demand Side Management (DSM) gains great importance in ensuring supply-demand balance. DSM reduces costs by scheduling consumption instead of increasing generation to balance supply and demand. Residences constitute a large part of energy consumption worldwide so DSM applications in buildings increase efficient usage of energy. Various management strategies can be used to save energy depending on the building type. In this article, firstly, an overview of Energy Management System (EMS) strategies to increase energy efficiency is presented. Then a case study is carried out in a residential model in Matlab/Simulink environment. The electrical devices were controlled with a Fuzzy Logic Controller (FLC) taking into account comfort, cost, and Demand Response (DR). In addition, Renewable Energy Resources (RES) to demonstrate their contribution were modeled and integrated into the system. Case studies were conducted and a comparative analysis of obtained results was carried out.

Keywords

Smart buildings, Energy management system, Demand side management, Fuzzy logic controller

References

• [1] L. Hurtado, P. Nguyen, W. Kling, W. Zeiler, "Building energy management systems—Optimization of comfort and energy use," presented at the 48th International Universities' Power Engineering Conference, Dublin, Ireland, Sept. 2-5, 2013.
• [2] M. P. Fanti, A. M. Mangini, M. Roccotelli, "A simulation and control model for building energy management," Control Engineering Practice, vol. 72, pp. 192-205, 2018.
• [3] P. H. Shaikh, N. B. M. Nor, P. Nallagownden, I. Elamvazuthi, "Intelligent multi-objective optimization for building energy and comfort management," Journal of King Saud University-Engineering Sciences, vol. 30, no. 2, pp. 195-204, 2018.
• [4] F. Wang, L. Zhou, H. Ren, X. Liu, S. Talari, M. Shafie-khah, J. P. Catalao, "Multi-objective optimization model of source–load–storage synergetic dispatch for a building energy management system based on TOU price demand response," IEEE Transactions on Industry Applications, vol. 54, no. 2, pp. 1017-1028, 2017.
• [5] O. Akar, U. K.Terzi, T. Sonmezocak and B.K. Tuncalp. “Determination of the optimum Hybrid renewable power system: a case study of Istanbul Gedik University Gedik Vocational School”, Balkan journal of electrical&computer engineering, Vol.7, No.4, pp.456-463, 2019.
• [6] U. Civan, "Akıllı binaların çevresel sürdürülebilirlik açısından değerlendirilmesi," Master Thesis, İstanbul Teknik University, Istanbul, Turkey, 2006.
• [7] F. M. Bhutta, "Application of smart energy technologies in building sector—future prospects," IEEE International Conference on Energy Conservation and Efficiency, Pakistan, Lahore, Nov. 22-23 2017.
• [8] A. Altayeva, B. Omarov, Y. Im Cho, "Multi-objective optimization for smart building energy and comfort management as a case study of smart city platform," presented at the 19th International Conference on High Performance Computing and Communications; IEEE 15th International Conference on Smart City; IEEE 3rd International Conference on Data Science and Systems, Bangkok, Thailand, Dec.18-20, 2017.
• [9] S. Smitha, J. Savier, F. M. Chacko, "Intelligent control system for efficient energy management in commercial buildings," Annual International Conference on Emerging Research Areas and International Conference on Microelectronics, Communications and Renewable Energy, Kanjirapally, India , June 4-6, 2013.
• [10] D. Mariano-Hernández, L. Hernández-Callejo, A. Zorita-Lamadrid, O. Duque-Pérez, F. S. García, "A review of strategies for building energy management system: model predictive control, demand side management, optimization, and fault detect & diagnosis," Journal of Building Engineering, p. 101692, 2020.
• [11] I. T. Erhan, "Akıllı binalarda aydınlatma otomasyonunun enerji verimliliğine katkısının incelenmesi," Master Thesis, Istanbul University, Istanbul, Turkey, 2018.
• [12] H. Kılıç, "Akıllı binalar, kurulmaları ve işletilmeleri," Master Thesis, İstanbul Teknik University, Istanbul, Turkey, 2007.
• [13] H. M. Günaydın, and S. Zağpus. “Türkiye’de Bina Otomasyon Sistemlerinin Mimarlar Tarafından Algılanması, Akıllı Bina Tasarım Süreci ve Kalitesi”. VI. Ulusal Tesisat Mühendisliği Kongresi ve Sergisi. https://www1.mmo.org.tr/resimler/dosya_ekler/28aa8ac200e659d_ek.pdf
• [14] Ö. Karadişoğulları, "Akıllı binalarda kullanılan sistemlerin sürdürülebilirlik bağlamında irdelenmesi," Master Thesis, Haliç University, Istanbul,Turkey, 2013.
• [15] Bilgi Teknolojileri ve İletişim Kurumu, "Elektronik Haberleşme Sektöründe Teknolojik Gelişmeler ve Eğilimler Raporu," Ankara, 2014.
• [16] J. Smiciklas, F. Spitzer, .A. Hay, R. Delgado, S. Kondepudi "Intelligent sustainable buildings for smart sustainable cities"," ITU-T Focus Group on Smart Sustainable Cities, Focus Group Technical Report, 2015.
• [17] D. Minoli, K. Sohraby, B. Occhiogrosso, "IoT considerations, requirements, and architectures for smart buildings—Energy optimization and next-generation building management systems," IEEE Internet of Things Journal, vol. 4, no. 1, pp. 269-283, 2017.
• [18] P. Palensky, D. Dietrich, "Demand side management: Demand response, intelligent energy systems, and smart loads," IEEE transactions on industrial informatics, vol. 7, no. 3, pp. 381-388, 2011.
• [19] M. Batić, N. Tomašević, G. Beccuti, T. Demiray, S. Vraneš, "Combined energy hub optimisation and demand side management for buildings," Energy and Buildings, vol. 127, pp. 229-241, 2016.
• [20] M. Zehir, M. Bağrıyanık, "Akıllı Şebekelerde Gelişmiş Yerel Talep Yönetimi," V. Enerji verimliliği ve Kalitesi Sempozyumu, Istanbul, Turkey, pp. 14-18, 2013.
• [21] A. Khalid, N. Javaid, M. Guizani, M. Alhussein, K. Aurangzeb, and M. Ilahi, "Towards dynamic coordination among home appliances using multi-objective energy optimization for demand side management in smart buildings," IEEE access, vol. 6, pp. 19509-19529, 2018.
• [22] F. Alfaverh, M. Denai, Y. Sun. "Demand response strategy based on reinforcement learning and fuzzy reasoning for home energy management," IEEE Access, vol. 8, pp. 39310-39321, 2020.
• [23] R. Khalid, N. Javaid, M. H. Rahim, S. Aslam, A. Sher. "Fuzzy energy management controller and scheduler for smart homes," Sustainable Computing: Informatics and Systems, vol. 21, pp. 103-118, 2019.
• [24] V. Mpelogianni, K. Giannousakis, E. Kontouras, P. P. Groumpos, D. Tsipianitis. "Proactive Building Energy Management Methods Based On Fuzzy Logic And Expert Intelligence," IFAC-PapersOnLine, vol. 52, no. 25, pp. 519-522, 2019.
• [25] M. B. Rasheed, N. Javaid, M. Awais, Z. A. Khan, U. Qasim, N. Alrajeh, Q. Javaid. "Real time information based energy management using customer preferences and dynamic pricing in smart homes," Energies, vol. 9, no. 7, p. 542, 2016.
• [26] M. Waseem, Z. Lin, S. Liu, Z. Zhang, T. Aziz, D. Khan, "Fuzzy compromised solution-based novel home appliances scheduling and demand response with optimal dispatch of distributed energy resources," Applied Energy, vol. 290, p. 116761, 2021.
• [27] M. I. Ghiasi, A. Hajizadeh, M. A. Golkar, M. Marefati, "Demand and supply side management strategies for zero energy buildings," IEEE 17th International Conference on Ubiquitous Wireless Broadband (ICUWB), Salamanca, Spain, Sept. 2-15, 2017.
• [28] W. Li, T. Logenthiran, W. L. Woo, V.-T. Phan, D. Srinivasan. "Implementation of demand side management of a smart home using multi-agent system," IEEE Congress on Evolutionary Computation (CEC), Vancouver, BC, Canada, July 24-29, 2016.
• [29] A. Ożadowicz and J. Grela. "An event-driven building energy management system enabling active demand side management," Second İnternational Conference on Event-based Control, Communication, and Signal Processing, Krakow, Poland, June 13-15, 2016.
• [30] M. M. Rahman, S. Hettiwatte, S. Gyamfi, "An intelligent approach of achieving demand response by fuzzy logic based domestic load management," Australasian Universities Power Engineering Conference, Perth, WA, Australia, Sept. 28, 2014.
• [31] F. Sehar, M. Pipattanasomporn, S. Rahman, "An energy management model to study energy and peak power savings from PV and storage in demand responsive buildings," Applied Energy, vol. 173, pp. 406-417, 2016.
• [32] M. S. Ahmed, A. Mohamed, T. Khatib, H. Shareef, R. Z. Homod, J. Abd Ali, "Real time optimal schedule controller for home energy management system using new binary backtracking search algorithm," Energy and Buildings, vol. 138, pp. 215-227, 2017.
• [33] D. Zhang, S. Li, M. Sun, Z. O’Neill, "An optimal and learning-based demand response and home energy management system," IEEE Transactions on Smart Grid, vol. 7, no. 4, pp. 1790-1801, 2016.
• [34] Z. Nadeem, N. Javaid, A. W. Malik, S. Iqbal, "Scheduling appliances with GA, TLBO, FA, OSR and their hybrids using chance constrained optimization for smart homes," Energies, vol. 11, no. 4, p. 888, 2018.
• [35] V. Pradhan, V. M. Balijepalli, S. A. Khaparde, "An effective model for demand response management systems of residential electricity consumers," IEEE Systems Journal, vol. 10, no. 2, pp. 434-445, 2014.
• [36] A. Baldauf, "A smart home demand-side management system considering solar photovoltaic generation," present at the 5th International Youth Conference on Energy, Pisa ,Italy, May 27-30, 2015.
• [37] M. A. F. Ghazvini, J. Soares, O. Abrishambaf, R. Castro, Z. Vale, "Demand response implementation in smart households," Energy and Buildings, vol. 143, pp. 129-148, 2017.
• [38] J. K. Gruber, M. Prodanovic, "Two-stage optimization for building energy management," Energy Procedia, vol. 62, pp. 346–354, 2014.
• [39] Z. Xu, Q.-S. Jia, X. Guan, "Supply demand coordination for building energy saving: Explore the soft comfort," IEEE Transactions on Automation Science and Engineering, vol. 12, (2), pp. 656-665, 2014.
• [40] J. K. Gruber, F. Huerta, P. Matatagui, M. Prodanović, "Advanced building energy management based on a two-stage receding horizon optimization," Applied Energy, vol. 160, pp. 194-205, 2015.
• [41] M. Yilmaz, “Real measure of a transmission line data with load fore-cast model for the future”, Balkan Journal of Electrical and Computer Engineering 6 (2), 141-145.
• [42] I. Ullah, N. Javaid, Z. A. Khan, U. Qasim, Z. A. Khan, S. A. Mehmood, "An incentive-based optimal energy consumption scheduling algorithm for residential users," Procedia Computer Science, vol. 52, pp. 851-857, 2015.
• [43] H. A. Özkan, "A new real time home power management system," Energy and Buildings, vol. 97, pp. 56-64, 2015.
• [44] S. Javaid, M. Abdullah, N. Javaid, T. Sultana, J. Ahmed, N. A. Sattar, "Towards Buildings Energy Management: Using Seasonal Schedules Under Time of Use Pricing Tariff via Deep Neuro-Fuzzy Optimizer," presented at the15th International Wireless Communications & Mobile Computing Conference, Tangier, Morocco, June 24-28, 2019.
• [45] M. Z. Abbas, I. A. Sajjad, R. Liaqat, M. Abdullah, M. A. Shah, M. F. Nadeem, "Non-intrusive Load Monitoring for Residential Customers Using Adaptive-Neuro Fuzzy Interface System and Fine Tree Classifier," presented at the 23rd International Multitopic Conference, Bahawalpur, Pakistan, Nov. 5-7, 2020.
• [46] A. Pina, C. Silva, P. Ferrão, "The impact of demand side management strategies in the penetration of renewable electricity," Energy, vol. 41, no. 1, pp. 128-137, 2012.
• [47] D. Karna, A. Vikram, A. Kumar, M. Rizwan, "A Novel Fuzzy based Intelligent Demand Side Management for Automated Load Scheduling," presented at the 4th International Conference on Green Energy and Applications, Singapore, March 7-9, 2020.
• [48] K. P. Sharma, N. Baine, "Application of a Fuzzy Logic based Controller for Peak Load Shaving in a Typical Household," IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), New Orleans, LA, USA, June 23-26, 2019.
• [49] A. Saidi, A. Harrouz, I. Colak, K. Kayisli, R. Bayindir, "Performance Enhancement of Hybrid Solar PV-Wind System Based on Fuzzy Power Management Strategy: A Case Study," 7th International Conference on Smart Grid, Newcastle, NSW, Australia, Dec. 9-11, 2019.
• [50] M. Elsisi, N. Bazmohammadi, J. M. Guerrero, M. A. Ebrahim, "Energy management of controllable loads in multi-area power systems with wind power penetration based on new supervisor fuzzy nonlinear sliding mode control," Energy, vol. 221, p. 119867, 2021.
• [51] W. Dong, Q. Yang, X. Fang, W. Ruan, "Adaptive optimal fuzzy logic based energy management in multi-energy microgrid considering operational uncertainties," Applied Soft Computing, vol. 98, p. 106882, 2021.
• [52] K. Meje, L. Bokopane, K. Kusakana, M. Siti, "Optimal power dispatch in a multisource system using fuzzy logic control," Energy Reports, vol. 6, pp. 1443-1449, 2020.
• [53] H. Golpîra, S. A. R. Khan, "A multi-objective risk-based robust optimization approach to energy management in smart residential buildings under combined demand and supply uncertainty," Energy, vol. 170, pp. 1113-1129, 2019.
• [54] R. Hemmati, H. Saboori, "Stochastic optimal battery storage sizing and scheduling in home energy management systems equipped with solar photovoltaic panels," Energy and Buildings, vol. 152, pp. 290-300, 2017.
• [55] M. Shafie-Khah, P. Siano, "A stochastic home energy management system considering satisfaction cost and response fatigue," IEEE Transactions on Industrial Informatics, vol. 14, no. 2, pp. 629-638, 2017.
• [56] W. Fan, N. Liu, J. Zhang, "An event-triggered online energy management algorithm of smart home: Lyapunov optimization approach," Energies, vol. 9, no. 5, p. 381, 2016.
• [57] C. Wang, B. Jiao, L. Guo, Z. Tian, J. Niu, S. Li, "Robust scheduling of building energy system under uncertainty," Applied Energy, vol. 167, pp. 366-376, 2016.
• [58] D. Setlhaolo X. Xia, "Combined residential demand side management strategies with coordination and economic analysis," International Journal of Electrical Power & Energy Systems, vol. 79, pp. 150-160, 2016.
• [59] Y. Krim, D. Abbes, S. Krim, and M. F. Mimouni, "Intelligent droop control and power management of active generator for ancillary services under grid instability using fuzzy logic technology," Control Engineering Practice, vol. 81, pp. 215-230, 2018.
• [60] F. Y. Melhem, O. Grunder, Z. Hammoudan, and N. Moubayed, "Optimization and energy management in smart home considering photovoltaic, wind, and battery storage system with integration of electric vehicles," Canadian Journal of Electrical and Computer Engineering, vol. 40, no. 2, pp. 128-138, 2017.
• [61] A. Ahmad et al., "An optimized home energy management system with integrated renewable energy and storage resources," Energies, vol. 10, (4), p. 549, 2017.
• [62] A. Anvari-Moghaddam, A. Rahimi-Kian, M. S. Mirian, J. M. Guerrero, "A multi-agent based energy management solution for integrated buildings and microgrid system," Applied Energy, vol. 203, pp. 41-56, 2017.
• [63] W. El-Baz, P. Tzscheutschler, "Short-term smart learning electrical load prediction algorithm for home energy management systems," Applied Energy, vol. 147, pp. 10-19, 2015.
• [64] S. Arun, M. Selvan, "Intelligent residential energy management system for dynamic demand response in smart buildings," IEEE Systems Journal, vol. 12, (2), pp. 1329-1340, 2017.
• [65] L. Ma et al., "Multi-party energy management for smart building cluster with PV systems using automatic demand response," Energy and Buildings, vol. 121, pp. 11-21, 2016.
• [66] I.-Y. Joo D.-H. Choi, "Distributed optimization framework for energy management of multiple smart homes with distributed energy resources," IEEE Access, vol. 5, pp. 15551-15560, 2017.
• [67] Z. Wu, X. Xia, "Optimal switching renewable energy system for demand side management," Solar Energy, vol. 114, pp. 278-288, 2015.
• [68] Z. Wu, H. Tazvinga, X. Xia, "Demand side management of photovoltaic-battery hybrid system," Applied Energy, vol. 148, pp. 294-304, 2015.
• [69] L. Ciabattoni, F. Ferracuti, M. Grisostomi, G. Ippoliti, S. Longhi, "Fuzzy logic based economical analysis of photovoltaic energy management," Neurocomputing, vol. 170, pp. 296-305, 2015.
• [70] E. Aykut, UK. Terzi. Techno-economic and environmental analysis of grid connected hybrid wind/photovoltaic/biomass system for Marmara University Goztepe campus. International Journal of Green Energy. 2020 Dec 7;17(15):1036-43.
• [71] M Yilmaz, H Kilic "Smart grid road map and challenges for Turkey",IET Digital Library, Microgrids for Rural Areas: Research and case studies, 2020, pp. 389-420.
• [72] Ghorai, D. Majumdar, T. Jash, S. Ray, "PV assisted Fuzzy based EV charge scheduling for demand side energy management: a case study," IEEE Calcutta Conference (CALCON), Kolkata, India, Febr. 28-29, 2020. [73] MS Can, M Sam "Imitation of Fuzzy Logic Controller Based Artificial Neural Network, And Application Of Inverted Pendulum System Control" European Journal of Technique, Volume 9, Issue 2, 121 - 136, 30.12.2019
• [74] O. Erdinc, N. G. Paterakis, T. D. Mendes, A. G. Bakirtzis, J. P. Catalão, "Smart household operation considering bi-directional EV and ESS utilization by real-time pricing-based DR," IEEE Transactions on Smart Grid, vol. 6 (3), pp. 1281-1291, 2014.
• [75] UK. Terzi , HE Ilhan, H Kaymaz, H Erdal, H. Çalik. A Review of Commercial Electric Vehicle Charging Methods. Promet-Traffic&Transportation. 2020 Mar 15;32(2):291-307.