Smart Distribution Network with Focus on Demand Side Management

Authors

  • Ramin Fazeli * Department of Electrical Engineering, To.C., Islamic Azad University, Tonekabon, Iran.
  • Ali Bahadori Department of Electrical Engineering, To.C., Islamic Azad University, Tonekabon, Iran.
  • Mehdi Ahmadzadeh Department of Electrical Engineering, To.C., Islamic Azad University, Tonekabon, Iran.

https://doi.org/10.48314/imes.vi.34

Abstract

The transition toward Smart Distribution Networks (SDNs) is pivotal in enhancing energy efficiency, system reliability, and the effective integration of Distributed Energy Resources (DERs). Within this framework, Demand-Side Management (DSM) emerges as a critical mechanism for reshaping load profiles, mitigating peak demand, and optimizing the utilization of variable renewable energy sources.

This study proposes a comprehensive SDN architecture founded on DSM principles, integrating Advanced Metering Infrastructure (AMI), real-time data analytics, machine learning–based predictive modeling, and evolutionary multi-objective optimization to enable flexible and user-centric energy management.

The proposed framework employs artificial neural networks (ANNs) and support vector regression (SVR) to generate accurate short-term forecasts of both load and generation. An enhanced genetic sorting algorithm is then utilized to balance multiple conflicting objectives—namely peak reduction, cost minimization, voltage profile enhancement, and loss reduction—while adhering to power flow constraints, voltage limits, and consumer preferences.

Performance evaluation is conducted using the IEEE 33-bus radial distribution test system in a MATLAB/Simulink environment, incorporating solar generation units, controllable loads, and dynamic pricing mechanisms. The simulation outcomes demonstrate notable improvements, including:

  • An 18% reduction in peak load (from 4.2 MW to 3.444 MW);
  • A 28.6% decrease in active power losses (from 210 kW to 150 kW);
  • An improved minimum voltage level, rising from 0.913 Pu. to 0.98 Pu;
  • A 12% reduction in daily operational costs.

These results substantiate the proposed approach’s capability to enhance network resilience, increase renewable hosting capacity, and improve economic performance, offering a practical and scalable framework for next-generation integrated smart energy systems.

Keywords:

Smart distribution network, Demand side management, Advanced metering infrastructure, , Machine Learning Prediction, Multi-Objective Optimization, Renewable Incorporation, Cost Efficiency.

References

  1. [1] Gellings, C. W. (1985). The concept of demand-side management for electric utilities. Proceedings of the ieee, 73(10), 1468–1470. https://doi.org/10.1109/PROC.1985.13318
  2. [2] Albadi, M. H., & El-Saadany, E. F. (2008). A summary of demand response in electricity markets. Electric power systems research, 78(11), 1989–1996. https://doi.org/10.1016/j.epsr.2008.04.002
  3. [3] Department of energy. (2025). Demand response. https://www.energy.gov/oe/demand-response
  4. [4] Staff, F. E. R. C. (2006). Assessment of demand response and advanced metering. Federal energy regulatory commission, 32, 33–67. https://www.ferc.gov/sites/default/files/2020-04/dr-07-20-06_0.pdf
  5. [5] Khorasany, M., Mishra, Y., & Ledwich, G. (2020). A decentralized bilateral energy trading system for peer-to-peer electricity markets. IEEE transactions on industrial electronics, 67(6), 4646–4657. https://doi.org/10.1109/TIE.2019.2931229
  6. [6] Callaway, D. S., & Hiskens, I. A. (2011). Achieving controllability of electric loads. Proceedings of the IEEE, 99(1), 184–199. https://www.energy.gov/sites/default/files/2023-11/4.Kundu-WS_Kundu.pdf
  7. [7] Kundu, S. (2023). Deep learning for probabilistic net-load forecasting. https://www.energy.gov/sites/default/files/2023-11/4.Kundu-WS_Kundu.pdf
  8. [8] Mengelkamp, E., Notheisen, B., Beer, C., Dauer, D., & Weinhardt, C. (2018). A blockchain-based smart grid: Towards sustainable local energy markets. Computer science-research and development, 33(1), 207–214. https://doi.org/10.1007/s00450-017-0360-9
  9. [9] Seyedi, M., Taher, S. A., Ganji, B., & Guerrero, J. (2021). A hybrid islanding detection method based on the rates of changes in voltage and active power for the multi-inverter systems. IEEE transactions on smart grid, 12(4), 2800–2811. https://doi.org/10.1109/TSG.2021.3061567
  10. [10] Mancarella, P., & Chicco, G. (2013). Real-time demand response from energy shifting in distributed multi-generation. IEEE transactions on smart grid, 4(4), 1928–1938. https://doi.org/10.1109/TSG.2013.2258413
  11. [11] International-Standard. (2009). Communication networks and systems for power utility automation. International electrotechnical commission. https://webstore.ansi.org/preview-pages/IEC/preview_iec61850-7-420%7Bed1.0%7Den.pdf
  12. [12] OpenADR. (2023). OpenADR 2.0 profile specification B profile. https://www.openadr.org/specification
  13. [13] 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. https://doi.org/10.1109/4235.996017
  14. [14] Lopes, J. A. P., Hatziargyriou, N., Mutale, J., Djapic, P., & Jenkins, N. (2007). Integrating distributed generation into electric power systems: A review of drivers, challenges and opportunities. Electric power systems research, 77(9), 1189–1203. https://doi.org/10.1016/j.epsr.2006.08.016
  15. [15] Seyedi, M., Taher, S. A., Ganji, B., & Guerrero, J. M. (2019). A hybrid islanding detection technique for inverter-based distributed generator units. International transactions on electrical energy systems, 29(11), e12113. https://doi.org/10.1002/2050-7038.12113
  16. [16] Photovoltaics, D. G., & Storage, E. (2018). IEEE standard for interconnection and interoperability of distributed energy resources with associated electric power systems interfaces. IEEE Std 1547-2018 (Revision Of IEEE Std 1547-2003). https://doi.org/10.1109/IEEESTD.2018.8332112
  17. [17] Schneider, K. P., Mather, B. A., Pal, B. C., Ten, C. W., Shirek, G. J., Zhu, H., … & Kersting, W. (2018). Analytic considerations and design basis for the IEEE distribution test feeders. IEEE transactions on power systems, 33(3), 3181–3188. https://doi.org/10.1109/TPWRS.2017.2760011
  18. [18] Byrne, R. H., Nguyen, T. A., Copp, D. A., Chalamala, B. R., & Gyuk, I. (2018). Energy management and optimization methods for grid energy storage systems. IEEE access, 6, 13231–13260. https://doi.org/10.1109/ACCESS.2017.2741578
  19. [19] Conejo, A. J., Morales, J. M., & Baringo, L. (2010). Real-time demand response model. IEEE transactions on smart grid, 1(3), 236–242. https://doi.org/10.1109/TSG.2010.2078843
  20. [20] Wang, Z., Gu, C., Li, F., Bale, P., & Sun, H. (2013). Active demand response using shared energy storage for household energy management. IEEE transactions on smart grid, 4(4), 1888–1897. https://doi.org/10.1109/TSG.2013.2258046
  21. [21] Palensky, P., & Dietrich, D. (2011). Demand side management: Demand response, intelligent energy systems, and smart loads. IEEE transactions on industrial informatics, 7(3), 381–388. 10.1109/TII.2011.2158841
  22. [22] Pipattanasomporn, M., Kuzlu, M., & Rahman, S. (2012). An algorithm for intelligent home energy management and demand response analysis. IEEE transactions on smart grid, 3(4), 2166–2173. https://doi.org/10.1109/TSG.2012.2201182

Published

2025-02-22

Issue

Vol. 2 No. 1 (2025): Intelligence Modeling in Electromechanical Systems

Section

Articles

Categories

How to Cite

Fazeli , R., Bahadori, A., & Ahmadzadeh, M. (2025). Smart Distribution Network with Focus on Demand Side Management. Intelligence Modeling in Electromechanical Systems, 2(1), 65-76. https://doi.org/10.48314/imes.vi.34

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