[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content

Advertisement

Log in

A Multiscale study of flexible customer’s energy demand under smart grid architecture: A modeling and simulation study

  • Original Article
  • Published:
Energy Efficiency Aims and scope Submit manuscript

Abstract

In the context of an energy crisis, efficient energy management has become an unavoidable issue for sustainability, regardless of the domain under consideration. Smart grids are no exception; they aim to motivate energy optimization according to billing strategies and users’ comfort. In this paper, two optimization problems (OP) are proposed involving billing strategies and users’ flexibility. A single-centralized OP aims to minimize the total energy provided by a company, while a distributed OP targets minimizing individual user costs independently, involving users’ flexibilities, different billing strategies, and a variable number of users, with random appliances assigned during simulations. The resolution was carried out using the Non-dominated Sorting Algorithm II and Multi-Criteria Analysis, with a Game-based algorithm also utilized. Additionally, simulations were performed under three billing mechanisms. The findings show that costs decrease exponentially with user participation. Similarly, both individual user costs and total costs at the energy provider level were minimized as users’ flexibilities increased. The Peak-to-Average-Ratio is minimized and exhibits a bimodal behavior when observed as a random variable. Regarding the interplay of billing mechanisms, simulation results demonstrate that the smart billing mechanism proposed by the authors outperforms other billing models proposed in the literature for both consumers and utility companies.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Algorithm 1
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22

Similar content being viewed by others

References

  • Abassi, A., Arid, A., & Benazza, H. (2023). Moroccan consumer energy consumption itemsets and inter-appliance associations using machine learning algorithms and data mining techniques. Journal of Engineering for Sustainable Buildings and Cities, 4(1), 011004.

    Article  Google Scholar 

  • Wood, A.J., Wollenberg, B.F., Sheblé, G.B. (2013) Power Generation, Operation, and Control. Wiley, Hoboken, New Jersey, USA. https://books.google.co.ma/books?id=JafyAAAAQBAJ

  • Adika, C. O., & Wang, L. (2014). Smart charging and appliance scheduling approaches to demand side management. International Journal of Electrical Power & Energy Systems, 57, 232–240.

    Article  Google Scholar 

  • Ahmad, A., Khan, A., Javaid, N., Hussain, H. M., Abdul, W., Almogren, A., Alamri, A., & Azim Niaz, I. (2017). An optimized home energy management system with integrated renewable energy and storage resources. Energies, 10(4), 549.

    Article  Google Scholar 

  • Ahmed, M. S., Mohamed, A., Homod, R. Z., & Shareef, H. (2016). Hybrid lsa-ann based home energy management scheduling controller for residential demand response strategy. Energies, 9(9), 716.

    Article  Google Scholar 

  • Akhrif, O., Benfaress, C., Jai, E. L., M., El Bouzekri El Idrissi, Y., & Hmina, N. (2022). Completeness based classification algorithm: a novel approach for educational semantic data completeness assessment. Interactive Technology and Smart Education, 19(1), 87–111.

  • 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.

    Article  Google Scholar 

  • Anupong, W., Azhagumurugan, R., Sahay, K. B., Dhabliya, D., Kumar, R., & Babu, D. V. (2022). Towards a high precision in ami-based smart meters and new technologies in the smart grid. Sustainable Computing: Informatics and Systems, 35, 100690.

    Google Scholar 

  • Dickison, M.E., Magnani, M., Rossi, L. (2016) Multilayer Social Networks. Cambridge University Press, Cambridge, United Kingdom. https://books.google.co.ma/books?id=blCJDAAAQBAJ

  • Asgari, S., Haghir, S., & Noorzai, E. (2023). Reducing energy consumption in operation and demolition phases by integrating multi-objective optimization with lca and bim. Energy Efficiency, 16(6), 54.

    Article  Google Scholar 

  • Raza, M., Rind, Y., Javed, I., Zubair, M., Mehmood, M.Q., Massoud, Y. Smart meters for smart energy: A review of business intelligence applications. IEEE Access PP, 1–1. https://doi.org/10.1109/ACCESS.2023.3326724

  • Babaei, M., Abazari, A., Soleymani, M. M., Ghafouri, M., Muyeen, S., & Beheshti, M. T. (2021). A data-mining based optimal demand response program for smart home with energy storages and electric vehicles. Journal of Energy Storage, 36, 102407.

    Article  Google Scholar 

  • Baharlouei, Z., Hashemi, M., Narimani, H., & Mohsenian-Rad, H. (2013). Achieving optimality and fairness in autonomous demand response: Benchmarks and billing mechanisms. IEEE Transactions on Smart Grid, 4(2), 968–975.

    Article  Google Scholar 

  • Gils, H. C. (2014). Assessment of the theoretical demand response potential in europe. Energy, 67, 1–18.

  • Benysek, G., Bojarski, J., Jarnut, M., & Smolenski, R. (2016). Decentralized active demand response (dadr) system for improvement of frequency stability in distribution network. Electric Power Systems Research, 134, 80–87.

    Article  Google Scholar 

  • Cakmak, R., & Altaş, İH. (2020). A novel billing approach for fair and effective demand side management: Appliance level billing (applebill). International Journal of Electrical Power & Energy Systems, 121, 106062.

    Article  Google Scholar 

  • Celik, B., Roche, R., Suryanarayanan, S., Bouquain, D., & Miraoui, A. (2017). Electric energy management in residential areas through coordination of multiple smart homes. Renewable and Sustainable Energy Reviews, 80, 260–275.

    Article  Google Scholar 

  • Chahar, V., Katoch, S., & Chauhan, S. (2021). A review on genetic algorithm: Past, present, and future. Multimedia Tools and Applications, 80, 8091–8126. https://doi.org/10.1007/s11042-020-10139-6

    Article  Google Scholar 

  • Chauhan, R. K., Chauhan, K., & Badar, A. Q. (2022). Optimization of electrical energy waste in house using smart appliances management system-a case study. Journal of Building Engineering, 46, 103595.

    Article  Google Scholar 

  • Deb, K. (2000). An efficient constraint handling method for genetic algorithms. Computer Methods in Applied Mechanics and Engineering, 186(2–4), 311–338.

    Article  Google Scholar 

  • Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE Transactions on Evolutionary Computation, 6(2), 182–197.

    Article  Google Scholar 

  • Dey, B., Basak, S., & Bhattacharyya, B. (2023). Demand-side-management-based bi-level intelligent optimal approach for cost-centric energy management of a microgrid system. Arabian Journal for Science and Engineering, 48(5), 6819–6830.

    Article  Google Scholar 

  • Ortega, J.G., Han, L., Whittacker, N., Bowring, N.: A machine-learning based approach to model user occupancy and activity patterns for energy saving in buildings. In: 2015 Science and Information Conference (SAI), pp. 474–482 (2015). IEEE

  • Ebrahimi, J., & Abedini, M. (2022). A two-stage framework for demand-side management and energy savings of various buildings in multi smart grid using robust optimization algorithms. Journal of Building Engineering, 53, 104486.

    Article  Google Scholar 

  • Abassi, A., Arid, A., & Benazza, H. (2023). Moroccan consumer energy consumption itemsets and inter-appliance associations using machine learning algorithms and data mining techniques. Journal of Engineering for Sustainable Buildings and Cities, 4(1), 011004.

  • El Khattabi, M.-Z., El Jai, M., Lahmadi, Y., Oughdir, L., & Rahhali, M. (2024). Understanding the interplay between metrics, normalization forms, and data distribution in k-means clustering: a comparative simulation study. Arabian Journal for Science and Engineering, 49(3), 2987–3007.

    Article  Google Scholar 

  • Elio, J., Phelan, P., Villalobos, R., & Milcarek, R. J. (2021). A review of energy storage technologies for demand-side management in industrial facilities. Journal of Cleaner Production, 307, 127322.

    Article  Google Scholar 

  • Fan, C., Xiao, F., & Zhao, Y. (2017). A short-term building cooling load prediction method using deep learning algorithms. Applied Energy, 195, 222–233.

    Article  Google Scholar 

  • Forrest, S. (1996). Genetic algorithms. ACM computing surveys (CSUR), 28(1), 77–80.

    Article  Google Scholar 

  • Zagare, F. C. (1984). Game Theory: Concepts and Applications. Game Theory, vol. no. 41. SAGE Publications, Thousand Oaks, California, USA. https://books.google.co.ma/books?id=YLuwr8HqbBEC

  • Gils, H. C. (2014). Assessment of the theoretical demand response potential in europe. Energy, 67, 1–18.

    Article  Google Scholar 

  • Güçyetmez, M., & Farhan, H. S. (2023). Enhancing smart grids with a new iot and cloud-based smart meter to predict the energy consumption with time series. Alexandria Engineering Journal, 79, 44–55. https://doi.org/10.1016/j.aej.2023.07.071

    Article  Google Scholar 

  • Hochba, D. S. (1997). Approximation algorithms for np-hard problems. ACM SIGACT News, 28(2), 40–52.

    Article  Google Scholar 

  • Malek, M. R. A., Aziz, N. A. A., Alelyani, S., Mohana, M., Baharudin, F. N. A., & Ibrahim, Z. (2022). Comfort and energy consumption optimization in smart homes using bat algorithm with inertia weight. Journal of Building Engineering, 47, 103848.

  • Katoch, S., Chauhan, S. S., & Kumar, V. (2021). A review on genetic algorithm: past, present, and future. Multimedia tools and applications, 80, 8091–8126.

    Article  Google Scholar 

  • Chauhan, R. K., Chauhan, K., & Badar, A. Q. (2022). Optimization of electrical energy waste in house using smart appliances management system-a case study. Journal of Building Engineering, 46, 103595.

  • Khan, M. A., Javaid, N., Mahmood, A., Khan, Z. A., & Alrajeh, N. (2015). A generic demand-side management model for smart grid. International Journal of Energy Research, 39(7), 954–964.

    Article  Google Scholar 

  • Cakmak, R., & Altaş, İH. (2020). A novel billing approach for fair and effective demand side management: Appliance level billing (applebill). International Journal of Electrical Power & Energy Systems, 121, 106062.

  • Konak, A., Coit, D. W., & Smith, A. E. (2006). Multi-objective optimization using genetic algorithms: A tutorial. Reliability engineering & system safety, 91(9), 992–1007.

    Article  Google Scholar 

  • Adika, C. O., & Wang, L. (2014). Smart charging and appliance scheduling approaches to demand side management. International Journal of Electrical Power & Energy Systems, 57, 232–240.

  • Elio, J., Phelan, P., Villalobos, R., & Milcarek, R. J. (2021). A review of energy storage technologies for demand-side management in industrial facilities. Journal of Cleaner Production, 307, 127322.

  • Liu, T., Gao, X., & Wang, L. (2015). Multi-objective optimization method using an improved nsga-ii algorithm for oil-gas production process. Journal of the Taiwan Institute of Chemical Engineers, 57, 42–53.

    Article  Google Scholar 

  • Mahela, O. P., Khosravy, M., Gupta, N., Khan, B., Alhelou, H. H., Mahla, R., Patel, N., & Siano, P. (2020). Comprehensive overview of multi-agent systems for controlling smart grids. CSEE Journal of Power and Energy Systems, 8(1), 115–131.

    Google Scholar 

  • Malek, M. R. A., Aziz, N. A. A., Alelyani, S., Mohana, M., Baharudin, F. N. A., & Ibrahim, Z. (2022). Comfort and energy consumption optimization in smart homes using bat algorithm with inertia weight. Journal of Building Engineering, 47, 103848.

    Article  Google Scholar 

  • Monfared, H. J., Ghasemi, A., Loni, A., & Marzband, M. (2019). A hybrid price-based demand response program for the residential micro-grid. Energy, 185, 274–285.

  • Michalewicz, Z., & Schoenauer, M. (1996). Evolutionary algorithms for constrained parameter optimization problems. Evolutionary Computation, 4(1), 1–32.

    Article  Google Scholar 

  • Yuce, B., Rezgui, Y., & Mourshed, M. (2016). Ann-ga smart appliance scheduling for optimised energy management in the domestic sector. Energy and Buildings, 111, 311–325.

  • Mocci, S., Natale, N., Pilo, F., & Ruggeri, S. (2015). Demand side integration in lv smart grids with multi-agent control system. Electric Power Systems Research, 125, 23–33.

    Article  Google Scholar 

  • Mohsenian-Rad, A.-H., Wong, V. W., Jatskevich, J., Schober, R., & Leon-Garcia, A. (2010). Autonomous demand-side management based on game-theoretic energy consumption scheduling for the future smart grid. IEEE transactions on Smart Grid, 1(3), 320–331.

    Article  Google Scholar 

  • Monfared, H. J., Ghasemi, A., Loni, A., & Marzband, M. (2019). A hybrid price-based demand response program for the residential micro-grid. Energy, 185, 274–285.

    Article  Google Scholar 

  • Rasheed, M. B., Javaid, N., Awais, M., Akbar, M., & Khan, Z. A. (2017). A novel pricing mechanism for demand side load management in smart grid. In: 2017 31st International Conference on Advanced Information Networking and Applications Workshops (WAINA), pp. 283–290. IEEE

  • Nicolson, M. L., Fell, M. J., & Huebner, G. M. (2018). Consumer demand for time of use electricity tariffs: A systematized review of the empirical evidence. Renewable and Sustainable Energy Reviews, 97, 276–289.

    Article  Google Scholar 

  • Niharika, & Mukherjee, V. (2018). Day-ahead demand side management using symbiotic organisms search algorithm. IET Generation, Transmission & Distribution, 12(14), 3487–3494.

  • Yaagoubi, N., & Mouftah, H. T. (2015). Fairness-aware game theoretic approach for demand response in microgrids. In: 2015 Seventh Annual IEEE Green Technologies Conference, pp. 125–131 . IEEE

  • Park, L., Jang, Y., Cho, S., & Kim, J. (2017). Residential demand response for renewable energy resources in smart grid systems. IEEE Transactions on Industrial Informatics, 13(6), 3165–3173.

    Article  Google Scholar 

  • Pathan, M. I., AlOwaifeer, M., AlMuhaini, M., & Djokic, S. Z. (2020). Reliability evaluation of smart distribution grids with renewable energy sources and demand side management. Arabian Journal for Science and Engineering, 45, 6347–6360.

    Article  Google Scholar 

  • Peng, Y., Rysanek, A., Nagy, Z., & Schlüter, A. (2018). Using machine learning techniques for occupancy-prediction-based cooling control in office buildings. Applied Energy, 211, 1343–1358.

    Article  Google Scholar 

  • Peng, Y., Rysanek, A., Nagy, Z., & Schlüter, A. (2018). Using machine learning techniques for occupancy-prediction-based cooling control in office buildings. Applied Energy, 211, 1343–1358.

    Article  Google Scholar 

  • Wu, C., Mohsenian-Rad, H., Huang, J., & Wang, A. Y. (2011). Demand side management for wind power integration in microgrid using dynamic potential game theory. In: 2011 IEEE GLOBECOM Workshops (GC Wkshps), pp. 1199–1204. IEEE

  • Ramanathan, B., & Vittal, V. (2008). A framework for evaluation of advanced direct load control with minimum disruption. IEEE Transactions on Power Systems, 23(4), 1681–1688.

    Article  Google Scholar 

  • Baharlouei, Z., Hashemi, M., Narimani, H., & Mohsenian-Rad, H. (2013). Achieving optimality and fairness in autonomous demand response: Benchmarks and billing mechanisms. IEEE Transactions on Smart Grid, 4(2), 968–975.

  • Abdelfattah, A., Ahmed, A., & Hussain, B. (2022). Optimality and par reduction in autonomous demand response: Evaluation and billing mechanisms. In: E3S Web of Conferences, vol. 351, p. 01052. EDP Sciences

  • Rastegar, S., Araújo, R., Malekzadeh, M., Gomes, A., & Jorge, H. (2023). A new nialm system design based on neural network architecture and adaptive springy particle swarm optimization algorithm. Energy Efficiency, 16, 52. https://doi.org/10.1007/s12053-023-10125-5

    Article  Google Scholar 

  • Nicolson, M. L., Fell, M. J., & Huebner, G. M. (2018). Consumer demand for time of use electricity tariffs: A systematized review of the empirical evidence. Renewable and Sustainable Energy Reviews, 97, 276–289.

  • Rehman, U. U. (2020). Robust optimization-based energy pricing and dispatching model using dsm for smart grid aggregators to tackle price uncertainty. Arabian Journal for Science and Engineering, 45, 6701–6714.

    Article  Google Scholar 

  • Rong, H., Zhang, H., Xiao, S., Li, C., & Hu, C. (2016). Optimizing energy consumption for data centers. Renewable and Sustainable Energy Reviews, 58, 674–691.

    Article  Google Scholar 

  • Sadeeq, M. A., & Zeebaree, S. (2021). Energy management for internet of things via distributed systems. Journal of Applied Science and Technology Trends, 2(02), 80–92.

    Article  Google Scholar 

  • Shah, A. S., Nasir, H., Fayaz, M., Lajis, A., & Shah, A. (2019). A review on energy consumption optimization techniques in iot based smart building environments. Information, 10(3), 108.

    Article  Google Scholar 

  • Sharda, S., Singh, M., & Sharma, K. (2021). Demand side management through load shifting in iot based hems: Overview, challenges and opportunities. Sustainable Cities and Society, 65, 102517.

    Article  Google Scholar 

  • Sharifi, A. H., & Maghouli, P. (2019). Energy management of smart homes equipped with energy storage systems considering the par index based on real-time pricing. Sustainable Cities and Society, 45, 579–587.

    Article  Google Scholar 

  • Shinwari, M., Youssef, A., & Hamouda, W. (2012). A water-filling based scheduling algorithm for the smart grid. IEEE Transactions on Smart Grid, 3(2), 710–719.

    Article  Google Scholar 

  • Silva, B. N., Khan, M., & Han, K. (2020). Futuristic sustainable energy management in smart environments: A review of peak load shaving and demand response strategies, challenges, and opportunities. Sustainability, 12(14), 5561.

    Article  Google Scholar 

  • Kodali, S. P., Kudikala, R., & Kalyanmoy, D. (2008). Multi-objective optimization of surface grinding process using nsga ii. In: 2008 First international conference on emerging trends in engineering and technology, pp. 763–767. IEEE

  • Ullah, A., Haydarov, K., Ul Haq, I., Muhammad, K., Rho, S., Lee, M., & Baik, S. W. (2020). Deep learning assisted buildings energy consumption profiling using smart meter data. Sensors, 20(3), 873.

    Article  Google Scholar 

  • Vardakas, J. S., Zorba, N., & Verikoukis, C. V. (2015). A survey on demand response programs in smart grids: Pricing methods and optimization algorithms. IEEE Communications Surveys Tutorials, 17(1), 152–178. https://doi.org/10.1109/COMST.2014.2341586

    Article  Google Scholar 

  • Veras, J. M., Silva, I. R. S., Pinheiro, P. R., Rabêlo, R. A., Veloso, A. F. S., Borges, F. A. S., & Rodrigues, J. J. (2018). A multi-objective demand response optimization model for scheduling loads in a home energy management system. Sensors, 18(10), 3207.

    Article  Google Scholar 

  • Mahela, O. P., Khosravy, M., Gupta, N., Khan, B., Alhelou, H. H., Mahla, R., Patel, N., & Siano, P. (2020). Comprehensive overview of multi-agent systems for controlling smart grids. CSEE Journal of Power and Energy Systems, 8(1), 115–131.

  • Pipattanasomporn, M., Feroze, H., Rahman, S. (2009) Multi-agent systems in a distributed smart grid: Design and implementation. In: 2009 IEEE/PES power systems conference and exposition, pp. 1–8. IEEE

  • Anupong, W., Azhagumurugan, R., Sahay, K. B., Dhabliya, D., Kumar, R., & Babu, D. V. (2022). Towards a high precision in ami-based smart meters and new technologies in the smart grid. Sustainable Computing: Informatics and Systems, 35, 100690.

  • Yang, P., Tang, G., & Nehorai, A. (2012). A game-theoretic approach for optimal time-of-use electricity pricing. IEEE Transactions on Power Systems, 28(2), 884–892.

    Article  Google Scholar 

  • Yuce, B., Rezgui, Y., & Mourshed, M. (2016). Ann-ga smart appliance scheduling for optimised energy management in the domestic sector. Energy and Buildings, 111, 311–325.

    Article  Google Scholar 

  • Yusoff, Y., Ngadiman, M. S., & Zain, A. M. (2011). Overview of nsga-ii for optimizing machining process parameters. Procedia Engineering, 15, 3978–3983.

    Article  Google Scholar 

  • Snyman, F., Helbig, M. (2017). Solving constrained multi-objective optimization problems with evolutionary algorithms. In: Advances in swarm intelligence: 8th international conference, ICSI 2017, Fukuoka, Japan, July 27–August 1, 2017, Proceedings, Part II 8, pp. 57–66. Springer

  • Deb, K. (2000). An efficient constraint handling method for genetic algorithms. Computer methods in applied mechanics and engineering, 186(2–4), 311–338.

Download references

Author information

Authors and Affiliations

Authors

Contributions

Abdelfattah Abassi: Conceptualization; Formal analysis; Methodology; Programming; Results analysis and discussion; Writing the original draft. Mostapha El Jai: Statistical analysis and inference; Results analysis and discussion; correcting and editing the final manuscript. Arid Ahmed and Hussain Benazza: Investigation, Methodology, Project Supervision; review and editing the manuscript.

Corresponding author

Correspondence to Mostapha El Jai.

Ethics declarations

Conflict of interest

The authors declare that they have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Abassi, A., El Jai, M., Arid, A. et al. A Multiscale study of flexible customer’s energy demand under smart grid architecture: A modeling and simulation study. Energy Efficiency 17, 54 (2024). https://doi.org/10.1007/s12053-024-10234-9

Download citation

  • Published:

  • DOI: https://doi.org/10.1007/s12053-024-10234-9

Keywords

Navigation