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FAlloc: A Fair Power Limit Allocation-Based Approach to Implement Brownout

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Abstract

Due to the significant gap between demand and supply of electricity, power utility companies employ rolling blackouts, in which the power supply to different regions is cut-off periodically for a specified duration. However, this causes increased user inconvenience as consumers are left without power; also, a large undesirable blackout may occur if the circuit breakers are not opened in time. To solve this problem, this paper has developed a more pragmatic approach in which a power threshold limit is imposed on households during times of deficit power supply. This paper presents five algorithms designed to allocate load thresholds to households in a fair manner. Some of these algorithms perform the worst (high violations) in some time slots and best (minimal violations) in other slots, but none guarantees minimal mean violation across all the time slots. In this regard, this paper has also developed a novel optimal algorithm that ensures a minimal violation percentage of the allotted thresholds across all time slots in all the households of a neighbourhood. It also employs multiple heuristics for threshold allocation, thereby preventing household starvation. The algorithms are evaluated on a real-world dataset. Its mean violation across all the houses goes as low as nearly 45% and is minimum across all the time slots. It performs all the algorithms used for power limit distribution. This result demonstrates the effectiveness of the developed technique in the implementation of the brownout scheme in practise.

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Data Availibility

The dataset (publicly available) used for evaluation of the developed algorithms can be found at https://traces.cs.umass.edu/index.php/smart/smart.

References

  • Alsalloum, H., Merghem-Boulahia, L., & Rahim, R. (2020). Hierarchical system model for the energy management in the smart grid: A game theoretic approach. Sustainable Energy, Grids and Networks, 21(100), 329. https://doi.org/10.1016/j.segan.2020.100329

    Article  Google Scholar 

  • Baba, M. F. (2012). Smart grid with adsl connection for solving peak blackouts in west bank. In 2012 First international conference on renewable energies and vehicular technology (pp. 270–273). https://doi.org/10.1109/REVET.2012.6195282

  • Barker, S., Mishra, A., Irwin, D., et al. (2012). Smart*: An open data set and tools for enabling research in sustainable homes. In Proceedings of the ACM SustKDD, August 12, 2012. Association for Computing Machinery, New York, USA

  • Bin-Halabi, A., Nouh, A., & Abouelela, M. (2018). Interactive energy management system to avoid rolling blackouts. In 2018 5th international conference on electric power and energy conversion Systems (EPECS) (pp. 1–7)

  • Blume, S., & Sons, J.W. (2016). electric power system basics: for the nonelectrical professional. In IEEE Press series on power engineering. Wiley-Interscience, https://books.google.co.in/books?id=lxFrnQAACAAJ

  • Cai, Y., Lu, Z., Pan, Y., et al. (2022). Optimal scheduling of a hybrid ac/dc multi-energy microgrid considering uncertainties and Stackelberg game-based integrated demand response. International Journal of Electrical Power & Energy Systems, 142(108), 341. https://doi.org/10.1016/j.ijepes.2022.108341

    Article  Google Scholar 

  • Chen, L., Tang, H., Wu, J., et al. (2022). A robust optimization framework for energy management of CCHP users with integrated demand response in electricity market. International Journal of Electrical Power & Energy Systems, 141(108), 181. https://doi.org/10.1016/j.ijepes.2022.108181

    Article  Google Scholar 

  • Ebrahimi, J., Abedini, M., & Rezaei, M. M. (2020). Optimal scheduling of distributed generations in microgrids for reducing system peak load based on load shifting. Sustainable Energy, Grids and Networks, 23(100), 368. https://doi.org/10.1016/j.segan.2020.100368

    Article  Google Scholar 

  • Eia.gov (2018) EIA - Electricity Data. https://www.eia.gov/electricity/monthly/epm_table_grapher.php?t=epmt_5_01

  • Feldman, M., Lai, K., & Zhang, L. (2009). The proportional-share allocation market for computational resources. IEEE Transactions on Parallel and Distributed Systems, 20(8), 1075–1088. https://doi.org/10.1109/TPDS.2008.168

    Article  Google Scholar 

  • Ferraz, B. P., Pereira, L. A., Lemos, F., & Haffner, S. (2020). Residential demand response based on weighted load shifting and reduction target. Journal of Control, Automation and Electrical Systems, 31, 422–435. https://doi.org/10.1007/s40313-019-00517-3

    Article  Google Scholar 

  • Gellings, C. W. (1981). Power/energy: Demand-side load management: The rising cost of peak-demand power means that utilities must encourage customers to manage power usage. IEEE Spectrum, 18(12), 49–52. https://doi.org/10.1109/MSPEC.1981.6369703

    Article  Google Scholar 

  • Gellings, C. W. (2009). The smart grid: Enabling energy efficiency and demand response. New York: Fairmont Press.

    Google Scholar 

  • Golmohamadi, K. R. H. (2017). Application of robust optimization approach to determine optimal retail electricity price in presence of intermittent and conventional distributed generation considering demand response. Journal of Control, Automation and Electrical Systems, 28, 664–678. https://doi.org/10.1007/s40313-017-0328-9

    Article  Google Scholar 

  • Jain, A., Smarra, F., Behl, M., et al. (2018). Data-driven model predictive control with regression trees-an application to building energy management. ACM Transactions on Cyber-Physical Systems. https://doi.org/10.1145/3127023

    Article  Google Scholar 

  • Kelkar, S., Kothari, N., & Ramamritham, K. (2015). Brownout energy distribution scheme for mitigating rolling blackouts. In Proceedings of the 2015 ACM sixth international conference on future energy systems. Association for computing machinery, New York, NY, USA, e-Energy ’15 (pp. 193–194). https://doi.org/10.1145/2768510.2770937

  • Kumar, G.K., Maniadarsh, S., Thungeshwaran, R., et al. (2020). Remotely controllable consumer perspective demand response using genetic algorithm. In: 2020 fourth international conference on I-SMAC (IoT in social, mobile, analytics and cloud) (I-SMAC) (pp. 904–9080. https://doi.org/10.1109/I-SMAC49090.2020.9243490

  • Laabid, A., Saad, A., & Mazouz, M. (2022). Integration of renewable energies in mobile employment promotion units for rural populations. Civil Engineering Journal, 8(7), 1406–1434. https://doi.org/10.28991/CEJ-2022-08-07-07

    Article  Google Scholar 

  • Lawton, P. (2012). Balancing the energy network. Ingenia, 53, 20–26.

    Google Scholar 

  • Lee, J.Y., Choi, S.G. (2014). Linear programming based hourly peak load shaving method at home area. In: 16th international conference on advanced communication technology (pp. 310–313). https://doi.org/10.1109/ICACT.2014.6778971

  • Levine, G. N. (1989). The control of starvation. International Journal of General Systems, 15(2), 113–127. https://doi.org/10.1080/03081078908935036

    Article  Google Scholar 

  • Mahmood, A., Ullah, M., Razzaq, S., et al. (2014). A new scheme for demand side management in future smart grid networks. Procedia Computer Science 32, 477–484. https://doi.org/10.1016/j.procs.2014.05.450, the 5th International Conference on Ambient Systems, Networks and Technologies (ANT-2014), the 4th International Conference on Sustainable Energy Information Technology (SEIT-2014)

  • Mansouri, S., Ahmarinejad, A., Sheidaei, F., et al. (2022). A multi-stage joint planning and operation model for energy hubs considering integrated demand response programs. International Journal of Electrical Power & Energy Systems, 140(108), 103. https://doi.org/10.1016/j.ijepes.2022.108103

    Article  Google Scholar 

  • Mishra, M. K., & Parida, S. K. (2020). A game theoretic approach for demand-side management using real-time variable peak pricing considering distributed energy resources. IEEE Systems Journal. https://doi.org/10.1109/JSYST.2020.3033128

    Article  Google Scholar 

  • Ndwali, N. J. W. E. P. K. (2020). Optimal operation control of microgrid connected photovoltaic-diesel generator backup system under time of use tariff. Journal of Control, Automation and Electrical Systems, 31, 1001–1014. https://doi.org/10.1007/s40313-020-00585-w

    Article  Google Scholar 

  • Onyeka, F. C., & Mama, B. O. (2021). Analytical Study of Bending Characteristics of an Elastic Rectangular Plate using Direct Variational Energy Approach with Trigonometric Function. Emerging Science Journal, 5(6), 2021. https://doi.org/10.28991/esj-2021-01320

    Article  Google Scholar 

  • Popa, F. (2007). On Pareto efficiency and equitable allocations of resources. Romanian Economic Journal, 10(23), 73–79.

    Google Scholar 

  • Raj, B. D., Kumar, S., Padhi, S., et al. (2018). Brownout based blackout avoidance strategies in smart grids. In: Proceedings of the ninth international conference on future energy systems. Association for computing machinery, New York, e-Energy ’18 (pp. 456–458). https://doi.org/10.1145/3208903.3212059

  • Rastegar, M., Fotuhi-Firuzabad, M., & Lehtonen, M. (2015). Home load management in a residential energy hub. Electric Power Systems Research, 119, 322–328. https://doi.org/10.1016/j.epsr.2014.10.011

    Article  Google Scholar 

  • Rocha, H. R., Soares, W. C., Silvestre, L. J., Celeste, W. C., Junior, L. O. R., Coura, D. J., & Silva, J. A. (2023). Identification of similar electrical loads in smart homes with 100% accuracy provided by a convolutional neural network with minimum parameters. Journal of Control, Automation and Electrical Systems, 34(1), 137–149.

    Article  Google Scholar 

  • Salimian, M. R., & Aghamohammadi, M. R. (2018). A three stages decision tree-based intelligent blackout predictor for power systems using brittleness indices. IEEE Transactions on Smart Grid, 9(5), 5123–5131.

  • Shafie-Khah, M., Talari, S., Wang, F., et al. (2020). Decentralised demand response market model based on reinforcement learning. IET Smart Grid, 3(5), 713–721. https://doi.org/10.1049/iet-stg.2019.0129

    Article  Google Scholar 

  • Silberschatz, A., Galvin, P. B., & Gagne, G. (2012). Operating System Concepts, 9th. edn. Wiley Publishing

  • 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. https://doi.org/10.3390/su12145561

    Article  Google Scholar 

  • Soundarabai, P., Thriveni, J., Venugopal, K. R., & Patnaik, L. M. (2012). Comparative study on load balancing techniques in distributed systems. International Journal of Information Technology and Knowledge Management, 6(1), 53–60.

    Google Scholar 

  • Stamatescu, G., Stamatescu, I., Arghira, N., et al. (2019). Data-driven modelling of smart building ventilation subsystem. Journal of Sensors, 2019, 1–14. https://doi.org/10.1155/2019/3572019

    Article  Google Scholar 

  • Strbac, G. (2008). Demand side management: Benefits and challenges. Energy Policy, 36(12), 4419–4426. https://doi.org/10.1016/j.enpol.2008.09.030

    Article  Google Scholar 

  • Subbiah, R., Pal, A., Nordberg, E., et al. (2017). Energy demand model for residential sector: A first principles approach. IEEE Transactions on Sustainable Energy, 8, 1215–1224.

    Article  ADS  Google Scholar 

  • Tahiri, F. E., Chikh, K., & Khafallah, M. (2021). Optimal management energy system and control strategies for isolated hybrid Solar–Wind–Battery–Diesel power system. Emerging Science Journal, 5(2), 111–124. https://doi.org/10.28991/esj-2021-01262

    Article  Google Scholar 

  • Thang, V., Ha, T., Li, Q., et al. (2022). Stochastic optimization in multi-energy hub system operation considering solar energy resource and demand response. International Journal of Electrical Power & Energy Systems, 141(108), 132. https://doi.org/10.1016/j.ijepes.2022.108132

    Article  Google Scholar 

  • Vyakaranam, B., Vallem, M., Nguyen, T., et al. (2017). A study of the impact of peak demand on increasing vulnerability of cascading failures to extreme contingency events. In: 2017 IEEE power & energy society general meeting (pp. 1–5). https://doi.org/10.1109/PESGM.2017.8274656

  • Wierzbicki, A. (2014). Trust and Fairness in Open. Distributed Systems. https://doi.org/10.1007/978-3-642-13451-7

    Article  Google Scholar 

  • Wyatt, A. (1986). Electric power: Challenges and choices. Toronto: The Book Press Limited.

    Google Scholar 

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Correspondence to Anshul Agarwal.

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Agarwal, A. FAlloc: A Fair Power Limit Allocation-Based Approach to Implement Brownout. J Control Autom Electr Syst 35, 361–375 (2024). https://doi.org/10.1007/s40313-024-01077-x

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