Efficient Energy Management System using Honey Badger Algorithm for Smart Agriculture
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Published:
Jul 1, 2024
Keywords:
Agricultural Appliances, Farm Energy Management System, Honey Badger Algorithm, Renewable Energy Resources, Scheduling, Smart Agriculture
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Abstract
Today, optimization is crucial to solving energy crises, especially in smart homes. However, the optimization-based methods for energy management in smart agriculture available globally need further improvement, which motivates this study. To resolve the problem, an efficient scheduling farm energy management system is required. Therefore, this study proposes a Farm Energy Management System (FEMS) for smart agriculture by adopting a honey-badger optimization algorithm. In the proposed system, a multi-objective optimization problem is formulated to find the best solutions for achieving the set of objectives, such as electricity cost, load minimization and peak-to-average ratio minimization, while considering the farmers' comfort. The proposed system considers commercialized agriculture with the integration of Renewable Energy Resources (RES). Also, the proposed system minimizes both load consumption and electricity costs via the scheduling of farm appliances in response to Real-Time Pricing (RTP) and Time-of-Use (ToU) pricing schemes in the electricity market. Extensive experiments are carried out in MATLAB 2018A to determine the efficacy of the proposed system. The proposed FEMS consists of sixteen farm appliances with their respective power ratings, inclusive of RES. The simulation results showed that a system without FEMS has a high electricity cost of 50.69% as compared to 43.04% for FEMS without RES and 6.27% for FEMS with RES when considering the ToU market price. For RTP market price, a system without FEMS has an electricity cost of 42.30%, as compared to 30.64% for FEMS without RES and 27.24% for FEMS with RES. Besides, the maximum load consumption for a system without FEMS is 246.80 kW, as compared to 151.40 kW for FEMS without RES and 18.85 kW for FEMS with RES when considering the ToU market price. Also, for the RTP market price, the maximum load consumption for a system without FEMS is 246.80 kW, as compared to 186.40 kW for FEMS without RES and 90.68 kW for FEMS with RES. The significance of the study is to propose a conceptualized FEMS based on the honey badger optimization algorithm. The proposed system provides scheduling of farm appliances that alleviates the burden of the electricity grid and is cost-effective for large and small-scale farmers.
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[1]
S. Omaji, G. N. Edegbe, J. T. Ogbiti, E. Enoyoze, and I. D. Acheme, “Efficient Energy Management System using Honey Badger Algorithm for Smart Agriculture”, AJERD, vol. 7, no. 2, pp. 1-15, Jul. 2024.
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References
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[38] Hashim, F. A., Houssein, E. H., Hussain, K., Mabrouk, M. S., & Al-Atabany, W. (2022). Honey Badger Algorithm: New metaheuristic algorithm for solving optimization problems. Mathematics and Computers in Simulation, 192(1), 84-110.
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[40] Jeet, S., Barua, A., Bagal, D. K., Pradhan, S., Panda, S. N., & Mahapatra, S. S. (2022). Parametric Appraisal of Plastic Injection Moulding for Low Density Polyethylene (LDPE): A Novel Taguchi Based Honey Badger Algorithm and Capuchin Search Algorithm. In Numerical Modelling and Optimization in Advanced Manufacturing Processes, Cham: Springer International Publishing, 1-17
[2] Dehghani, M., Trojovská, E., &Trojovský, P. (2022). A new human-based metaheuristic algorithm for solving optimization problems on the base of simulation of driving training process. Scientific reports, 12(1), 1-21.
[3] Shao, X., Yu, J., Li, Z., Yang, X., & Sundén, B. (2023). Energy-saving optimization of the parallel chillers system based on a multi-strategy improved sparrow search algorithm. Heliyon, 9(10), 1-19.
[3] Nayak, S. (2020). Fundamentals of optimization techniques with algorithms. Academic Press, 1-10.
[4] Wang, Y., Zhang, Y., Zhang, C., Zhou, J., Hu, D., Yi, F., & Zeng, T. (2023). Genetic algorithm-based fuzzy optimization of energy management strategy for fuel cell vehicles considering driving cycles recognition. Energy, 263(1), 1-26.
[5] Majeed, Y., Khan, M. U., Waseem, M., Zahid, U., Mahmood, F., Majeed, F., & Raza, A. (2023). Renewable energy as an alternative source for energy management in agriculture. Energy Reports, 10(1), 344-359.
[6] Nutakki, M., & Mandava, S. (2023). Review on optimization techniques and role of Artificial Intelligence in home energy management systems. Engineering Applications of Artificial Intelligence, 119(1), 1-23.
[7] Zafra-Cabeza, A., Marquez, J. J., Bordons, C., & Ridao, M. A. (2023). An online stochastic MPC-based fault-tolerant optimization for microgrids. Control Engineering Practice, 130(1), 1-21.
[8] McHenry, M. P. (2009). Agricultural bio-char production, renewable energy generation and farm carbon sequestration in Western Australia: Certainty, uncertainty and risk. Agriculture, Ecosystems & Environment, 129(1), 1-7.
[9] Daramola, A. S., Ahmadi, S. E., Marzband, M., & Ikpehai, A. (2023). A cost-effective and ecological stochastic optimization for integration of distributed energy resources in energy networks considering vehicle-to-grid and combined heat and power technologies. Journal of Energy Storage, 57(1), 1-19.
[10] Li, L., & Zhou, M. (2023). Energy Control and Optimization for Manufacturing Systems Utilizing Combined Heat and Power System. Wiley-IEEE Press, 1(1), 257-272.
[11] Weingartshofer, T., Bischof, B., Meiringer, M., Hartl-Nesic, C., & Kugi, A. (2023). Optimization-based path planning framework for industrial manufacturing processes with complex continuous paths. Robotics and Computer-Integrated Manufacturing, 82(1), 1-25.
[12] Chen, Y., Kotamarthy, L., Dan, A., Sampat, C., Bhalode, P., Singh, R., & Ierapetritou, M. (2023). Optimization of key energy and performance metrics for drug product manufacturing. International Journal of Pharmaceutics, 631(1), 1-24.
[13] Aghsami, A., Abazari, S. R., Bakhshi, A., Yazdani, M. A., Jolai, S., & Jolai, F. (2023). A meta-heuristic optimization for a novel mathematical model for minimizing costs and maximizing donor satisfaction in blood supply chains with finite capacity queueing systems. Healthcare Analytics, 1(1), 1-26.
[14] Valizadeh, J., Boloukifar, S., Soltani, S., Hookerd, E. J., Fouladi, F., Rushchtc, A. A., & Shen, J. (2023). Designing an optimization model for the vaccine supply chain during the COVID-19 pandemic. Expert Systems with Applications, 214(1), 1-19.
[15] Mosallanezhad, B., Arjomandi, M. A., Hashemi-Amiri, O., Gholian-Jouybari, F., Dibaj, M., Akrami, M., & Hajiaghaei-Keshteli, M. (2023). Metaheuristic optimizers to solve multi-echelon sustainable fresh seafood supply chain network design problem: A case of shrimp products. Alexandria Engineering Journal, 68(1), 491-515.
[16] Yang, X., Shu, L., Chen, J., Ferrag, M. A., Wu, J., Nurellari, E., & Huang, K. (2021). A survey on smart agriculture: Development modes, technologies, and security and privacy challenges. IEEE/CAA Journal of Automatica Sinica, 8(2), 273-302.
[17] Friha, O., Ferrag, M. A., Shu, L., Maglaras, L., & Wang, X. (2021). Internet of things for the future of smart agriculture: A comprehensive survey of emerging technologies. IEEE/CAA Journal of Automatica Sinica, 8(4), 718-752.
[18] Henderson, K., & Loreau, M. (2023). A model of Sustainable Development Goals: Challenges and opportunities in promoting human well-being and environmental sustainability. Ecological Modelling, 475(1), 1-10.
[19] Wang, X., Kang, M., Sun, H., de Reffye, P., & Wang, F. Y. (2022). DeCASA in agriverse: Parallel agriculture for smart villages in metaverses. IEEE/CAA Journal of Automatica Sinica, 9(12), 2055-2062.
[20] Dragomir, O. E., & Dragomir, F. (2023). Application of Scheduling Techniques for Load-Shifting in Smart Homes with Renewable-Energy-Sources Integration. Buildings, 13(1), 134.
[21] Enova Power Corp (2023) Accessed on 4 February, 2023, https://enovapower.com/my-account/electricity-rates/
[22] Kour, V. P., & Arora, S. (2020). Recent developments of the internet of things in agriculture: a survey. IEEE Access, 8(1), 129924-129957.
[23] Charania, I., & Li, X. (2020). Smart farming: Agriculture's shift from a labor intensive to technology native industry. Internet of Things, 9(1), 1-22.
[24] Boursianis, A. D., Papadopoulou, M. S., Diamantoulakis, P., Liopa-Tsakalidi, A., Barouchas, P., Salahas, G., & Goudos, S. K. (2022). Internet of things (IoT) and agricultural unmanned aerial vehicles (UAVs) in smart farming: a comprehensive review. Internet of Things, 18(1), 1-17.
[25] Hu, X., Sun, L., Zhou, Y., & Ruan, J. (2020). Review of operational management in intelligent agriculture based on the Internet of Things. Frontiers of Engineering Management, 7(3), 309-322.
[26] Keswani, B., Mohapatra, A. G., Mohanty, A., Khanna, A., Rodrigues, J. J., Gupta, D., & De Albuquerque, V. H. C. (2019). Adapting weather conditions based IoT enabled smart irrigation technique in precision agriculture mechanisms. Neural computing and applications, 31(1), 277-292.
[27] Pujar, P. M., Kenchannavar, H. H., Kulkarni, R. M., & Kulkarni, U. P. (2020). Real-time water quality monitoring through Internet of Things and ANOVA-based analysis: a case study on river Krishna. Applied Water Science, 10(1), 1-16.
[28] Shamshirband, S., Khoshnevisan, B., Yousefi, M., Bolandnazar, E., Anuar, N. B., Wahab, A. W. A., & Khan, S. U. R. (2015). A multi-objective evolutionary algorithm for energy management of agricultural systems—a case study in Iran. Renewable and Sustainable Energy Reviews, 44(1), 457-465.
[29] Golmohamadi, H. (2022). Demand-Side Flexibility in Power Systems: A Survey of Residential, Industrial, Commercial, and Agricultural Sectors. Sustainability, 14(13), 1-16.
[30] Xenarios, S., Laldjebaev, M., & Shenhav, R. (2021). Agricultural water and energy management in Tajikistan: a new opportunity. International Journal of Water Resources Development, 37(1), 118-136.
[31] Cai, Y. P., Huang, G. H., Yang, Z. F., & Tan, Q. (2009). Identification of optimal strategies for energy management systems planning under multiple uncertainties. Applied Energy, 86(4), 480-495.
[32] Tritschler, P. J., Bacha, S., Rullière, E., & Husson, G. (2010, September). Energy management strategies for an embedded fuel cell system on agricultural vehicles. In The XIX International Conference on Electrical Machines-ICEM 2010, 1-6.
[33] Wilcox, T., Jin, N., Flach, P., &Thumim, J. (2019). A Big Data platform for smart meter data analytics. Computers in Industry, 105(1), 250-259.
[34] Ali, A., Almutairi, K., Padmanaban, S., Tirth, V., Algarni, S., Irshad, K., & Malik, M. Z. (2020). Investigation of MPPT techniques under uniform and non-uniform solar irradiation condition–a retrospection. IEEE Access, 8(1), 127368-127392.
[35] Lu, Q., Zhang, Z., & Lü, S. (2020). Home energy management in smart households: Optimal appliance scheduling model with photovoltaic energy storage system. Energy Reports, 6(1), 2450-2462.
[36] Fitzpatrick, P., D’Ettorre, F., De Rosa, M., Yadack, M., Eicker, U., & Finn, D. P. (2020). Influence of electricity prices on energy flexibility of integrated hybrid heat pump and thermal storage systems in a residential building. Energy and Buildings, 223(1), 1-27.
[37] Anees, A., Dillon, T., Wallis, S., & Chen, Y. P. P. (2021). Optimization of day-ahead and real-time prices for smart home community. International Journal of Electrical Power & Energy Systems, 124(1), 1-9.
[38] Hashim, F. A., Houssein, E. H., Hussain, K., Mabrouk, M. S., & Al-Atabany, W. (2022). Honey Badger Algorithm: New metaheuristic algorithm for solving optimization problems. Mathematics and Computers in Simulation, 192(1), 84-110.
[39] Schläpfer, M., Dong, L., O’Keeffe, K., Santi, P., Szell, M., Salat, H., & West, G. B. (2021). The universal visitation law of human mobility. Nature, 593(7860), 522-527.
[40] Jeet, S., Barua, A., Bagal, D. K., Pradhan, S., Panda, S. N., & Mahapatra, S. S. (2022). Parametric Appraisal of Plastic Injection Moulding for Low Density Polyethylene (LDPE): A Novel Taguchi Based Honey Badger Algorithm and Capuchin Search Algorithm. In Numerical Modelling and Optimization in Advanced Manufacturing Processes, Cham: Springer International Publishing, 1-17