Dynamic Load Management in Modern Grid Systems Using an Intelligent SDN-Based Framework

Khawaja Tahir Mehmood and Muhammad Majid Hussain ()
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Khawaja Tahir Mehmood: Department of Electrical Engineering, Bahauddin Zakariya University, Multan 60000, Pakistan
Muhammad Majid Hussain: School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh EH14 4AS, UK

Energies, 2025, vol. 18, issue 12, 1-24

Abstract: For modern power plants to be dependable, safe, sustainable, and provide the highest operational efficiency (i.e., enhance dynamic load distribution with a faster response time at reduced reactive losses), there must be an intelligent dynamic load management system based on modern computational techniques to prevent overloading of power devices (i.e., alternators, transformers, etc.) in grid systems. In this paper, a co-simulation framework (Panda-SDN Load Balancer) is designed to achieve maximum operational efficiency from the power grid with the prime objective of real-time intelligent load balancing of operational power devices (i.e., power transformers, etc.). This framework is based on the integration of two tools: (a) PandaPower (an open-source Python tool) used for real-time power data (voltage; current; real power, P Real ; apparent power, P Apparent ; reactive power, P Reactive ; power factor, PF; etc.) load flow analysis; (b) Mininet used for the designing of a Software-Defined Network (SDN) with a POX controller for managing the load patterns on power transformers after load flow analysis obtained through PandaPower via the synchronization tool Message Queuing Telemetry Transport (MQTT) and Intelligent Electrical Devices (IEDs). In this research article, the simulation is performed in three scenarios: (a) normal flow, (b) loaded flow without the proposed framework, and (c) loaded flow with the proposed framework. As per simulation results, the proposed framework offered intelligent substation automation with (a) balanced utilization of a transformer, (b) enhanced system power factor in extreme load conditions, and (c) significant gain in system operational efficiency as compared to legacy load management methods.

Keywords: transformer load management; Software-Defined Network (SDN); Message Queuing Telemetry Transport (MQTT); load flow analysis; Intelligent Electrical Devices (IEDs) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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