Energy Management Model of a Smart House with Solar Collector and Electric Water Heater Considering Demand Response

Abstract

In the modern world, there is an increase in energy consumption associated with population growth, acceleration of industrial production and the development of technology. The heterogeneous distribution of fossil fuels, which constitute the source of conventional systems that are used effectively in the field of energy production, and the decrease of the predicted lifetimes of the reserves, have led to the search for alternative sources. To keep in the balance of the increasing consumption profile, important steps have been taken regarding the integration of renewable energy systems into the network, and new mechanisms such as the management of the demand side, which is one of the most effective applications of the smart grid concept, have become necessary. Equilibration energy production and consumption has great importance in terms of proper operation of the network and due to its nature, the increase in the share of renewable energy systems with variable power generation profile in the production field paves the way for adding new studies to the literature, especially in terms of energy management systems and energy efficiency. In this study; consumption profile consisting of flexible loads whose thermostat can be controlled together with critical loads of a smart home has been taken into consideration. The energy management system has been developed for the smart home model that uses the solar collector and the electric water heater(EWH). In controlling the power demand of the air conditioner (AC) and EWH, it was ensured that the indoor and boiler water temperatures were kept within certain value ranges by paying attention to the user comfort constraints. It is one of the most important features of the study to be careful not to exceed the comfort limits of the user while applying the Demand Response Program (DRP). The minimizing the cost of electricity consumption of the smart house is determined as the purpose function in the developed model and the results have been evaluated in detail with an optimization-based approach. The mathematical formulation obtained by the mixed-integer linear programming(MILP) method was simulated in the PuLP 1.6.8 open source library of the Python 2.7 version using the CPLEX commercial optimization solver.

Keywords

• Smart grid,demand response,renewable energy systems

Güneş Kollektörlü ve Elektrikli Şofbenli Bir Akıllı Evin Talep Cevabı Programı Kapsamında Enerji Yönetimi

Öz

Modern dünyada enerji tüketiminin; dünya nüfusunun artması, bununla birlikte endüstriyel üretimin ivme kazanması ve teknolojinin gelişmesine bağlı olarak her geçen gün arttığı gözlemlenmektedir. Enerji üretim alanında etkin olarak faydalanılan konvansiyonel sistemlerin kaynağını oluşturan fosil yakıtların dünya üzerinde heterojen dağılımı ve rezervlerin öngörülen ömürlerinin azalması alternatif kaynak arayışına neden olmuştur. Bu arayış sonucunda artış gösteren tüketim profilinin dengelenmesinde, yenilenebilir enerji sistemlerinin şebekeye entegrasyonu konusunda önemli adımlar atılmakla birlikte akıllı şebeke (Smart Grid) konseptinin en etkin uygulamalarından olan talep tarafının yönetimi (demand side management) gibi yeni mekanizmaların da dikkate alınması zorunlu hale gelmiştir. Enerji üretimi ve tüketiminin dengelenmesi, şebekenin düzgün işletimi açısından büyük bir öneme sahiptir ve doğası gereği değişken güç üretim profiline sahip bu sistemlerin üretim alanındaki payının artışı literatüre özellikle enerji yönetim sistemleri ve enerji verimliliği minvalinde yeni çalışmaların eklenmesinin önünü açmaktadır. Bu çalışmada; akıllı bir evin kritik yükleriyle birlikte termostatı kontrol edilebilir esnek yüklerden oluşan tüketim profili dikkate alınmıştır. Güneş kollektörü ve elektrikli su ısıtıcısından yararlanılan akıllı ev modeli için enerji yönetim sistemi geliştirilmiştir. Klima ve elektrikli su ısıtıcısından talep edilen gücün kontrolünde, iç ortam ve kazan suyu sıcaklıklarının kullanıcı konfor kısıtları dikkate alınarak belirli değer aralıklarında tutulması sağlanmıştır. Özellikle talep cevabı (Demand Response) programı uygulanırken kullanıcının konfor sınırlarının aşılmamasına dikkat edilmesi çalışmanın en önemli özelliklerindendir. Geliştirilen model ile ilgili akıllı evin elektrik enerjisi tüketim maliyetinin minimize edilmesi amaç fonksiyonunu oluşturmuş ve elde edilen sonuçlar ile optimizasyon tabanlı yaklaşımın değerlendirilmesi detaylı olarak gerçekleştirilmiştir. Karmaşık tam sayılı lineer programlama yöntemi (Mixed Integer Linear Programming-MILP) ile geliştirilen matematiksel modelleme, Python 2.7 versiyonunun PuLP 1.6.8 açık kaynak kodlu kütüphanesi içinde CPLEX ticari optimizasyon çözücüsünden faydalanılarak benzetim çalışması gerçekleştirilmiştir.

Anahtar Kelimeler

• akıllı şebekeler,enerji yönetimi,talep cevabı,yenilenebilir enerji

Kaynakça

1. AccuWeather. (2020, 1 Ocak). İstanbul Hava Durumu. Erişim Adresi: https://www.accuweather.com/en/tr/istanbul/318251/hourly-weather-forecast/318251?day=2
2. Ahmed, M. S., Mohamed, A., Homod, R. Z., Shareef, H., & Khalid, K. (2016). Modeling of electric water heater and air conditioner for residential demand response strategy. International Journal of Applied Engineering Research, 11(16), 9037–9046.
3. Ali, S. M. H., Lenzen, M., & Tyedmers, E. (2019). Optimizing 100%-renewable grids through shifting residential water-heater load. International Journal of Energy Research, September 2018, 1–15. https://doi.org/10.1002/er.4416
4. Alvina, P., Bai, X., Chang, Y., Liang, D., & Lee, K. (2017). Smart Community Based Solution for Energy Management: An Experimental Setup for Encouraging Residential and Commercial Consumers Participation in Demand Response Program. Energy Procedia, 143, 635–640. https://doi.org/10.1016/j.egypro.2017.12.739
5. Behrangrad, M. (2015). A review of demand side management business models in the electricity market. Renewable and Sustainable Energy Reviews, 47, 270–283. https://doi.org/10.1016/j.rser.2015.03.033
6. Cai, M., Ramdaspalli, S., Pipattanasomporn, M., Rahman, S., Malekpour, A., & Kothandaraman, S. R. (2019). Impact of HVAC Set Point Adjustment on Energy Savings and Peak Load Reductions in Buildings. 2018 IEEE International Smart Cities Conference, ISC2 2018, 1–6. https://doi.org/10.1109/ISC2.2018.8656738
7. ComEd. Central Air Conditioning Cycling Program. Erişim Adresi: https://www.comed.com/WaysToSave/ForYourHome/Pages/CentralACCycling.aspx
8. Chiu, T. C., Shih, Y. Y., Pang, A. C., & Pai, C. W. (2017). Optimized Day-Ahead Pricing with Renewable Energy Demand-Side Management for Smart Grids. IEEE Internet of Things Journal, 4(2), 374–383. https://doi.org/10.1109/JIOT.2016.2556006

9. Czech Brewery System. HWT-600 Sıcak Su Deposu 600 Litre. Erişim Adresi: https://eshop.czechminibreweries.com/tr/product/hwt-600/
10. Enerji Piyasaları İşletme A.Ş. (2020, 1 Ocak). Piyasa Takas Fiyatı. Erişim Adresi: https://seffaflik.epias.com.tr/transparency/piyasalar/gop/ptf.xhtml
11. Erol-Kantarci, M., & Mouftah, H. T. (2011). Wireless multimedia sensor and actor networks for the next generation power grid. Ad Hoc Networks, 9(4), 542–551. https://doi.org/10.1016/j.adhoc.2010.08.005
12. Facão, J. (2016). Optimization of flow distribution in flat plate solar thermal collectors with riser and header arrangements. Solar Energy, 120, 104–112. https://doi.org/10.1016/j.solener.2015.07.034
13. Freegah, B., & Al-Tabbakh, A. A. (2019). Experimental and numerical analysis of a thermosyphon solar water heater for domestic applications. UPB Scientific Bulletin, Series D: Mechanical Engineering, 81(1), 117–132.
14. Gaur, G. (2016). A Review on Demand Side Management Solutions for Power Utilities. 9829–9834. https://doi.org/10.15680/IJIRSET.2015.0506043
15. Gautam, A., Chamoli, S., Kumar, A., & Singh, S. (2017). A review on technical improvements, economic feasibility and world scenario of solar water heating system. Renewable and Sustainable Energy Reviews, 68(August 2016), 541–562. https://doi.org/10.1016/j.rser.2016.09.104
16. Global Solar Atlas. (2020, Ocak). Bahcelievler İstanbul. Erişim Adresi: https://globalsolaratlas.info/detail?c=41.157978,28.506775,9&s=41.004775,28.825378&m=site&pv=small,180,31,1
17. Godina, R., Rodrigues, E. M. G., Pouresmaeil, E., & Catalão, J. P. S. (2017). Home HVAC energy management and optimization with model predictive control. Conference Proceedings - 2017 17th IEEE International Conference on Environment and Electrical Engineering and 2017 1st IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2017, 309048, 7–11. https://doi.org/10.1109/EEEIC.2017.7977766
18. Godina, R., Rodrigues, E. M. G., Pouresmaeil, E., Matias, J. C. O., & Catalão, J. P. S. (2018). Model Predictive Control home energy management and optimization strategy with demand response. Applied Sciences (Switzerland), 8(3). https://doi.org/10.3390/app8030408
19. Goh, C. H. K., & Apt, J. (2005). Consumer Strategies for Controlling Electric Water Heaters under Dynamic Pricing. Carnegie Mellon Electricity Industry Center Working Paper, 1–8.
20. Güngör, V. C., Sahin, D., Kocak, T., Ergüt, S., Buccella, C., Cecati, C., & Hancke, G. P. (2011). Smart grid technologies: Communication technologies and standards. IEEE Transactions on Industrial Informatics, 7(4), 529–539. https://doi.org/10.1109/TII.2011.2166794
21. Hungerford, Z., Bruce, A., & MacGill, I. (2016). Review of demand side management modelling for application to renewables integration in Australian power markets. Asia-Pacific Power and Energy Engineering Conference, APPEEC, 2016-January. https://doi.org/10.1109/APPEEC.2015.7381083
22. Jouhara, H., & Yang, J. (2018). Energy efficient HVAC systems. Energy and Buildings, 179, 83–85. https://doi.org/10.1016/j.enbuild.2018.09.001
23. Kalogirou, S. A. (2009). Solar Energy Collectors. 121–217. https://doi.org/10.1016/B978-0-12-374501-9.00003-0
24. Koltsaklis, N. E., Panapakidis, I. P., Christoforidis, G. C., & Parisses, C. E. (2019). An MILP model for the optimal energy management of a smart household. International Conference on the European Energy Market, EEM, 2019-September, 1–6. https://doi.org/10.1109/EEM.2019.8916426
25. Lokeshgupta, B., & Sivasubramani, S. (2019). Multi-objective home energy management with battery energy storage systems. Sustainable Cities and Society, 47(February), 101458. https://doi.org/10.1016/j.scs.2019.101458
26. Mahin, A. U., Sakib, M. A., Zaman, M. A., Chowdhury, M. S., & Shanto, S. A. (2017). Developing demand side management program for residential electricity consumers of Dhaka city. ECCE 2017 - International Conference on Electrical, Computer and Communication Engineering, 743–747. https://doi.org/10.1109/ECACE.2017.7913001
27. McKinsey & Company. (2019). Global Energy Perspective 2019 : Reference Case. Energy Insights, January, 31. Medeiros, M., Nogueira, C. E. C., Siqueira, J. A. C., Lawder, J. H., de Souza, S. N. M., & Fracaro, G. de P. M. (2013). Otimização de um sistema misto de aquecimento de água (solar e elétrico) para áreas rurais. Acta Scientiarum - Technology, 35(1), 69–74. https://doi.org/10.4025/actascitechnol.v35i1.11998
28. Meyabadi, A. F., & Deihimi, M. H. (2017). A review of demand-side management: Reconsidering theoretical framework. Renewable and Sustainable Energy Reviews, 80(May), 367–379. https://doi.org/10.1016/j.rser.2017.05.207
29. Mohsenian-Rad, A. H., & Leon-Garcia, A. (2010). Optimal residential load control with price prediction in real-time electricity pricing environments. IEEE Transactions on Smart Grid, 1(2), 120–133. https://doi.org/10.1109/TSG.2010.2055903
30. Ovoenergy. How much electricity does a home use?. Erişim Adresi: https://www.ovoenergy.com/guides/energy-guides/how-much-electricity-does-a-home-use.html
31. Paterakis, N. G., Medeiros, M. F., Catalao, J. P. S., Siaraka, A., Bakirtzis, A. G., & Erdinc, O. (2015). Optimal daily operation of a smart-household under dynamic pricing considering thermostatically and non-thermostatically controllable appliances. International Conference on Power Engineering, Energy and Electrical Drives, 2015-September, 389–393. https://doi.org/10.1109/PowerEng.2015.7266348
32. Perez, K. X., Baldea, M., & Edgar, T. F. (2016). Integrated HVAC management and optimal scheduling of smart appliances for community peak load reduction. Energy and Buildings, 123, 34–40. https://doi.org/10.1016/j.enbuild.2016.04.003
33. Pipattanasomporn, M., Member, S., Kuzlu, M., & Rahman, S. (2012). 2012-06_Tsg_Hem. 1–8.
34. REN21. (2019). Renewables 2019. October, 335.
35. Safdarian, A., Ali, M., Fotuhi-Firuzabad, M., & Lehtonen, M. (2016). Domestic EWH and HVAC management in smart grids: Potential benefits and realization. Electric Power Systems Research, 134, 38–46. https://doi.org/10.1016/j.epsr.2015.12.021
36. Santos, A. N., MacAbuhay, M. A. A., & De Leon, J. N. (2018). Smart household socket with power monitoring control using android application. 2017 9th IEEE-GCC Conference and Exhibition, GCCCE 2017, 1–9. https://doi.org/10.1109/IEEEGCC.2017.8448055
37. Sempra Energy Utility. AC Saver(Summer Saver) for Business Energy Management Program. Erişim Adresi: https://www.sdge.com/businesses/savings-center/energy-management-programs/demand-response/summer-saver-program
38. Schreiber, M., Wainstein, M. E., Hochloff, P., & Dargaville, R. (2015). Flexible electricity tariffs: Power and energy price signals designed for a smarter grid. Energy, 93, 2568–2581. https://doi.org/10.1016/j.energy.2015.10.067
39. Siano, P. (2014). Demand response and smart grids - A survey. Renewable and Sustainable Energy Reviews, 30, 461–478. https://doi.org/10.1016/j.rser.2013.10.022
40. Streicher, W. (2016). Solar Thermal Technologies for Domestic Hot Water Preparation and Space Heating. In Renewable Heating and Cooling: Technologies and Applications. Elsevier Ltd. https://doi.org/10.1016/B978-1-78242-213-6.00002-3
41. Triki, C., & Violi, A. (2009). Dynamic pricing of electricity in retail markets. 4or, 7(1), 21–36. https://doi.org/10.1007/s10288-007-0056-2
42. Tsui, K. M., & Chan, S. C. (2012). Demand response optimization for smart home scheduling under real-time pricing. IEEE Transactions on Smart Grid, 3(4), 1812–1821. https://doi.org/10.1109/TSG.2012.2218835
43. Wang, H., Meng, K., Luo, F., Dong, Z. Y., Verbič, G., Xu, Z., & Wong, K. P. (2013). Demand response through smart home energy management using thermal inertia. 2013 Australasian Universities Power Engineering Conference, AUPEC 2013, October. https://doi.org/10.1109/aupec.2013.6725442
44. Yao, L., Damiran, Z., & Lim, W. H. (2017). Energy management optimization scheme for smart home considering different types of appliances. Conference Proceedings - 2017 17th IEEE International Conference on Environment and Electrical Engineering and 2017 1st IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2017. https://doi.org/10.1109/EEEIC.2017.7977565
45. Yao, L., Shen, J. Y., & Lim, W. H. (2017). Real-Time Energy Management Optimization for Smart Household. Proceedings - 2016 IEEE International Conference on Internet of Things; IEEE Green Computing and Communications; IEEE Cyber, Physical, and Social Computing; IEEE Smart Data, IThings-GreenCom-CPSCom-Smart Data 2016, 20–26. https://doi.org/10.1109/iThings-GreenCom-CPSCom-SmartData.2016.31
46. Zhi, C., Lei, W., & Yong, F. (2012). Real-Time Price-Based Demand Response Management for Residential Appliances via Stochastic Optimization and Robust Optimization. IEEE Transactions on Smart Grid, 3(4), 1822–1831.
47. Zhou, L., Zhang, Y., Lin, X., Li, C., Cai, Z., & Yang, P. (2018). Optimal sizing of PV and bess for a smart household considering different price mechanisms. IEEE Access, 6(c), 41050–41059. https://doi.org/10.1109/ACCESS.2018.2845900