Abstract
The daily increment of electricity usage has led many efforts on the network operators to reduce the consumption in the demand side. The use of renewable energy resources in smart grid concepts became an irrefutable fact around the world. Therefore, real case studies should be developed to validate the business models performance before the massive production. This paper surveys the economic impact of an optimization-based Supervisory Control And Data Acquisition model for an office building by taking advantages of renewable resources for optimally managing the energy consumption. An optimization algorithm is developed for this model to minimize the electricity bill of the building considering day-ahead hourly market prices. In the case study, the proposed system is employed for demonstrating electricity cost reduction by using optimization capabilities based on user preferences and comfort level. The results proved by the performance of the system, which leads to having great economic benefits in the annual electricity cost.
The present work was done and funded in the scope of the project UID/EEA/00760/2013 funded by FEDER Funds through COMPETE program and by National Funds through FCT.
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References
Arun, S., Selvan, M.: Intelligent residential energy management system for dynamic demand response in smart buildings. IEEE Syst. J. 12(2), 1329–1340 (2018)
Khorram, M., Faria, P., Vale, Z.: Optimization-based home energy management system under different electricity pricing schemes. In: IEEE 16th International Conference of Industrial Informatics (IEEE INDIN) (2018)
Rotger-Griful, S., Welling, U., Jacobsen, R.: Implementation of a building energy management system for residential demand response. Microprocess. Microsyst. 55, 100–110 (2017)
Rehmani, M., Reisslein, M., Rachedi, A., Erol-Kantarci, M., Radenkovic, M.: Integrating renewable energy resources into the smart grid: recent developments in information and communication technologies. IEEE Trans. Indus. Inform. 14(7), 2814–2825 (2018)
Abrishambaf, O., Faria, P., Gomes, L., Spínola, J., Vale, Z., Corchado, J.: Implementation of a real-time microgrid simulation platform based on centralized and distributed management. Energies 10(6), 806–820 (2017)
Faria, P., Vale, Z.: Demand response in electrical energy supply: an optimal real time pricing approach. Energy 36(8), 5374–5384 (2011)
Yang, F., Guo, Q., Pan, Z., Sun, H.: Building energy management based on demand response strategy considering dynamic thermal characteristic. In: IEEE Power & Energy Society General Meeting (2017)
Yu, L., Xie, D., Jiang, T., Zou, Y., Wang, K.: Distributed real-time HVAC control for cost-efficient commercial buildings under smart grid environment. IEEE Internet Things J. 5(1), 44–55 (2018)
Tsui, K., Chan, S.: Demand response optimization for smart home scheduling under real-time pricing. IEEE Trans. Smart Grid 3(4), 1812–1821 (2012)
Khorram, M., Faria, P., Abrishambaf, O., Vale, Z.: Demand response implementation in an optimization based SCADA model under real-time pricing schemes. In: 15th International Conference on Distributed Computing and Artificial Intelligence (DCAI) (2018)
Faria, P., Pinto, A., Vale, Z., Khorram, M., de Lima Neto, F., Pinto, T.: Lighting consumption optimization using fish school search algorithm. In: 2017 IEEE Symposium Series on Computational Intelligence (SSCI) (2017)
Pan, X., Lee, B.: An approach of reinforcement learning based lighting control for demand response. In: PCIM Europe 2016, Nuremberg, Germany (2016)
Shi, J., Yu, N., Yao, W.: Energy efficient building HVAC control algorithm with real-time occupancy prediction. Energy Procedia 111, 267–276 (2017)
Yoon, J., Bladick, R., Novoselac, A.: Demand response for residential buildings based on dynamic price of electricity. Energy Build. 80, 531–541 (2016)
Kim, Y.: Optimal price-based demand response of HVAC systems in multi-zone office buildings considering thermal preferences of individual occupants. IEEE Trans. Indus. Inform. 1 (2018)
Abdulaal, A., Asfour, S.: A linear optimization based controller method for real-time load shifting in industrial and commercial buildings. Energy Build. 110, 269–283 (2016)
Figueiredo, J., Costa, J.: A SCADA system for energy management in intelligent buildings. Energy Build. 49, 85–98 (2012)
Khorram, M., Abrishambaf, O., Faria, P., Vale, Z.: Office building participation in demand response programs supported by intelligent lighting management. Energy Inform. 1(9), 1–14 (2018)
MIBEL Electricity Market. http://www.omip.pt. Accessed 08 Aug 2018
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Khorram, M., Faria, P., Abrishambaf, O., Vale, Z. (2020). Economic Impact of an Optimization-Based SCADA Model for an Office Building. In: Madureira, A., Abraham, A., Gandhi, N., Varela, M. (eds) Hybrid Intelligent Systems. HIS 2018. Advances in Intelligent Systems and Computing, vol 923. Springer, Cham. https://doi.org/10.1007/978-3-030-14347-3_17
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DOI: https://doi.org/10.1007/978-3-030-14347-3_17
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