AUTHORS: Madis Uuemaa, Taaniel Uleksin, Heikki Tammoja
Download as PDF
ABSTRACT: In this article the potential of cost optimization for electrical floor heating systems is studied. Model Predictive Control (MPC) based optimization method is used to optimize energy costs of electric floor heating while taking into account user set temperature restrictions (comfort levels) and dynamic day ahead electricity spot prices. The aim of this paper is to find what effect does MPC optimization have to the expenditure costs of direct electric floor heating systems. MPC is optimized according to the dynamic electricity prices and user temperature restrictions. This means that optimization problem is solved by taking into account hourly electricity price fluctuations which can mean that power consumption is shifted off from peak price hours to low price hours while taking into account all the restrictions, including user comfort. Data collected from different heating systems is used to run simulations for heating scenarios, including base scenario and several optimized scenarios. Data sets from quarter one 2016 to quarter two 2017 are used for the simulations. The obtained results are compared and total cost savings in a year are calculated.
KEYWORDS: Electric floor heating expenditure cost optimization; Day ahead electricity spot prices
REFERENCES:
[1] J. P. Lopes, N. Hatziargyriou, J. Mutale, P. Djapic, and N. Jenkins, “Integrating distributed generation into electric power systems: A review of drivers, challenges and opportunities,” Electric Power Systems Research, vol. 77, no. 9, pp. 1189 – 1203, 2007. Distributed Generation.
[2] P. Uuemaa, J. Kilter, J. Valtin, I. Drovtar, A. Rosin, and A. Puusepp, “Cost-effective optimization of load shifting in the industry by using intermediate storages,” in IEEE PES ISGT Europe 2013, pp. 1–5, Oct 2013.
[3] V. S. K. M. Balijepalli, V. Pradhan, S. A. Khaparde, and R. M. Shereef, “Review of demand response under smart grid paradigm,” in ISGT2011-India, pp. 236–243, Dec 2011.
[4] R. Halvgaard, N. K. Poulsen, H. Madsen, and J. B. Jørgensen, “Economic model predictive control for building climate control in a smart grid,” in 2012 IEEE PES Innovative Smart Grid Technologies (ISGT), pp. 1– 6, Jan 2012.
[5] M. Ali, J. Jokisalo, K. Siren, and M. Lehtonen, “Combining the demand response of direct electric space heating and partial thermal storage using {LP} optimization,” Electric Power Systems Research, vol. 106, pp. 160 – 167, 2014. 9
[6] “www.stat.ee,” June 2017. The 2010 statistics about energy consumption in Estonian households.
[7] “www.nordpoolspot.com,” Apr. 2017. Electricity prices for Estonian region in 01.01.2017 - 31.03.2017.
[8] “www.elektrilevi.ee,” Apr. 2017. Electricity transmission tariff pricelist of Elektrilevi AS.
[9] P. Uuemaa, J. Valtin, J. Kilter, A. Puusepp, I. Drovtar, and A. Rosin, “Load control implementation in the energy intensive industry,” in MELECON 2014 - 2014 17th IEEE Mediterranean Electrotechnical Conference, pp. 213–218, April 2014.
[10] A. Anvari-Moghaddam, H. Monsef, and A. Rahimi-Kian, “Optimal smart home energy management considering energy saving and a comfortable lifestyle,” IEEE Transactions on Smart Grid, vol. 6, pp. 324–332, Jan 2015.
[11] C. Bartusch, M. Odlare, F. Wallin, and L. Wester, “Exploring variance in residential electricity consumption: Household features and building properties,” Applied Energy, vol. 92, pp. 637 – 643, 2012.
[12] “www.tameo.ee,” June 2017. Industry practice about recommended power for electric floor heating installations.
[13] “www.wunderground.com,” June 2017. Outside weather temperature in Tallinn in 2016, hourly intervals
