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

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 tech...

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Main Authors: Khawaja Tahir Mehmood, Muhammad Majid Hussain
Format: Article
Language:English
Published: MDPI AG 2025-06-01
Series:Energies
Subjects:
Online Access:https://www.mdpi.com/1996-1073/18/12/3001
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author Khawaja Tahir Mehmood
Muhammad Majid Hussain
author_facet Khawaja Tahir Mehmood
Muhammad Majid Hussain
author_sort Khawaja Tahir Mehmood
collection DOAJ
description 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<sub>Real</sub>; apparent power, P<sub>Apparent</sub>; reactive power, P<sub>Reactive</sub>; 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.
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spelling doaj-art-7b6d62c0d7ce44139290d48c3bee7e492025-08-20T02:24:25ZengMDPI AGEnergies1996-10732025-06-011812300110.3390/en18123001Dynamic Load Management in Modern Grid Systems Using an Intelligent SDN-Based FrameworkKhawaja Tahir Mehmood0Muhammad Majid Hussain1Department of Electrical Engineering, Bahauddin Zakariya University, Multan 60000, PakistanSchool of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh EH14 4AS, UKFor 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<sub>Real</sub>; apparent power, P<sub>Apparent</sub>; reactive power, P<sub>Reactive</sub>; 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.https://www.mdpi.com/1996-1073/18/12/3001transformer load managementSoftware-Defined Network (SDN)Message Queuing Telemetry Transport (MQTT)load flow analysisIntelligent Electrical Devices (IEDs)
spellingShingle Khawaja Tahir Mehmood
Muhammad Majid Hussain
Dynamic Load Management in Modern Grid Systems Using an Intelligent SDN-Based Framework
Energies
transformer load management
Software-Defined Network (SDN)
Message Queuing Telemetry Transport (MQTT)
load flow analysis
Intelligent Electrical Devices (IEDs)
title Dynamic Load Management in Modern Grid Systems Using an Intelligent SDN-Based Framework
title_full Dynamic Load Management in Modern Grid Systems Using an Intelligent SDN-Based Framework
title_fullStr Dynamic Load Management in Modern Grid Systems Using an Intelligent SDN-Based Framework
title_full_unstemmed Dynamic Load Management in Modern Grid Systems Using an Intelligent SDN-Based Framework
title_short Dynamic Load Management in Modern Grid Systems Using an Intelligent SDN-Based Framework
title_sort dynamic load management in modern grid systems using an intelligent sdn based framework
topic transformer load management
Software-Defined Network (SDN)
Message Queuing Telemetry Transport (MQTT)
load flow analysis
Intelligent Electrical Devices (IEDs)
url https://www.mdpi.com/1996-1073/18/12/3001
work_keys_str_mv AT khawajatahirmehmood dynamicloadmanagementinmoderngridsystemsusinganintelligentsdnbasedframework
AT muhammadmajidhussain dynamicloadmanagementinmoderngridsystemsusinganintelligentsdnbasedframework