Czasopismo | PRZEGLĄD ELEKTROTECHNICZNY | Rocznik 2021 - zeszyt 4

Modeling and Scheduling Home Appliances using Nature Inspired Algorithms for Demand Response Purpose

10.15199/48.2021.04.10

nr katalogowy: 131002
10.15199/48.2021.04.10

Streszczenie

Demand response (DR) refers to programs used in endeavors to reduce overall power consumption, manage consumption peak hour shifting, and reduce demand on service providers or utilities using different methods. This paper proposes a home appliance scheduler suitable for DR applications. In the proposed method, a controller controls thermal and shiftable loads, where thermal loads are empirical models that consider different factors. They produce the load profile of the home in consideration of different input parameters, e.g., setpoints and user tolerance ranges, and various factors, e.g., the room’s physical structure and the external environment. A scheduler uses the controller to implement load shifting using the whale optimization algorithm, particle swarm optimization, and gray wolf optimization (GWO) algorithms for three different occupancy and price schemes. Acceptable results were obtained by applying the models using various outer temperatures and user tolerance ranges. The results also demonstrate cost reduction of 38.59% with GWO for the first occupancy scheme

Abstract

Demand Response (DR) oznacza programy do redukcji poboru mocy, doboru czasu pracy, odbiorników energii elektrycznej. W artykule zaproponowano program użycia urządzeń domowych spełniający wymagania DR z uwzględnieniem termicznych warunków pracy. . Zaproponowano algorytmy optymalizacji. (Modelowanie i programy użycia domowych odbiorników energii elektrycznej z wykorzystaniem algorytmów optymalizacji)

Słowa kluczowe

Keywords

Bibliografia

[1] C. Goldman, M. Reid, R. Levy, and A. Silverstein, “Coordination of Energy Efficiency and Demand Response,” Ernest Orlando Lawrence Berkeley National Laboratory, LBNL-3044E, Jan. 2010.
[Online]. Available: https://eetd.lbl.gov/sites/all/files/publications/report-lbnl-3044e.pdf.
[2] P. Siano, “Demand response and smart grids—A survey,” Renew. Sustain. Energy Rev., vol. 30, pp. 461–478, Feb. 2014, doi: 10.1016/j.rser.2013.10.022.
[3] F. Amara, K. Agbossou, A. Cardenas, Y. Dubé, and S. Kelouwani, “Comparison and Simulation of Building Thermal Models for Effective Energy Management,” Smart Grid Renew. Energy, vol. 06, no. 04, pp. 95–112, 2015, doi: 10.4236/sgre.2015.64009.
[4] C. Verhelst, “Model Predictive Control of Ground Coupled Heat Pump Systems for Office Buildings,” Ph.D Dissertation, Katholieke Universiteit Leuven, Heverlee, Belgium, 2012.
[5] P. R. Novak, N. Mendes, and G. H. C. Oliveira, “SIMULATION OF HVAC PLANTS IN 2 BRAZILIAN CITIES USING MATLAB / SIMULINK,” presented at the Ninth International IBPSA Conference, Montreal, Canada, Aug. 2005.
[6] F. Shariatzadeh, P. Mandal, and A. K. Srivastava, “Demand response for sustainable energy systems: A review, application and implementation strategy,” Renew. Sustain. Energy Rev., vol. 45, pp. 343–350, May 2015, doi: 10.1016/j.rser.2015.01.062.
[7] Z. A. Khan, A. Khalid, N. Javaid, A. Haseeb, T. Saba, and M. Shafiq, “Exploiting Nature-Inspired-Based Artificial Intelligence Techniques for Coordinated Day-Ahead Scheduling to Efficiently Manage Energy in Smart Grid,” IEEE Access, vol. 7, pp. 140102– 140125, 2019, doi: 10.1109/ACCESS.2019.2942813.
[8] M. Abdulgader, S. Lakshminarayanan, and D. Kaur, “Efficient energy management for smart homes with grey wolf optimizer,” in 2017 IEEE International Conference on Electro Information Technology (EIT), May 2017, pp. 388–393, doi: 10.1109/EIT.2017.8053392.
[9] K. Nimma, M. Al-Falahi, H. D. Nguyen, S. D. G. Jayasinghe, T. Mahmoud, and M. Negnevitsky, “Grey Wolf Optimization-Based Optimum Energy-Management and Battery-Sizing Method for Grid-Connected Microgrids,” Energies, vol. 11, no. 4, p. 847, Apr. 2018, doi: 10.3390/en11040847.
[10] H. Swalehe, P. Chombo, and B. Marungsri, “Appliance Scheduling for Optimal Load Management in Smart Home integrated with Renewable Energy by Using Whale Optimization Algorithm,” GMSARN Int. J., vol. 12, pp. 65–75, 2018.
[11] A. K. Sharma and A. Saxena, “A demand side management control strategy using Whale optimization algorithm,” SN Appl. Sci., vol. 1, no. 8, p. 870, Jul. 2019, doi: 10.1007/s42452-019- 0899-0.
[12] A. Kumar, V. Bhalla, P. Kumar, T. Bhardwaj, and N. Jangir, “Whale Optimization Algorithm for Constrained Economic Load Dispatch Problems—A Cost Optimization,” in Ambient Communications and Computer Systems, 2018, pp. 353–366.
[13] “Thermal Model of a House - MATLAB & Simulink.” https://www.mathworks.com/help/simulink/examples/thermalmodel- of-a-house.html (accessed Jul. 30, 2018).
[14] “How Air Conditioners Work,” archtoolbox.com. https://www.archtoolbox.com/materials-systems/hvac/how-airconditioners- work.html (accessed Mar. 30, 2019).
[15] O. Laguerre, “Heat Transfer and Air Flow in a Domestic Refrigerator,” in Mathematical Modeling of Food Processing, vol. 20100770, M. Farid, Ed. CRC Press, 2010, pp. 453–482.
[16] M. A. Fayazbakhsh, F. Bagheri, and M. Bahrami, “A Resistance– Capacitance Model for Real-Time Calculation of Cooling Load in HVAC-R Systems,” J. Therm. Sci. Eng. Appl., vol. 7, no. 4, p. 041008, Dec. 2015, doi: 10.1115/1.4030640.
[17] J. Kennedy and R. Eberhart, “Particle swarm optimization,” in , IEEE International Conference on Neural Networks, 1995. Proceedings, Nov. 1995, vol. 4, pp. 1942–1948 vol.4, doi: 10.1109/ICNN.1995.488968.
[18] S. Mirjalili and A. Lewis, “The Whale Optimization Algorithm,” Adv. Eng. Softw., vol. 95, pp. 51–67, May 2016, doi: 10.1016/j.advengsoft.2016.01.008.
[19] S. Mirjalili, S. M. Mirjalili, and A. Lewis, “Grey Wolf Optimizer,” Adv. Eng. Softw., vol. 69, pp. 46–61, Mar. 2014, doi: 10.1016/j.advengsoft.2013.12.007.
[20] S. Mirjalili, “A simple implementation of Particle Swarm Optimization (PSO) Algorithm,” Mathworks.com. https://www.mathworks.com/matlabcentral/fileexchange/67429-asimple- implementation-of-particle-swarm-optimization-psoalgorithm (accessed Oct. 03, 2020).
[21] I. Haroun, “Home Appliance Modelling and Control for Demand Response Applications,” presented at the International Conference on Electrical and Computing Technologies and Applications, AURAK, Aug. 2019.

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