State-of-the-art of controllers and soft computing techniques for regulated load frequency management of single/multi-area traditional and renewable energy based …

A Latif, SMS Hussain, DC Das, TS Ustun - Applied Energy, 2020 - Elsevier
Applied Energy, 2020Elsevier
Load frequency management (LFM) has become more significant in modern power systems
due to variation demand and generation profiles. Further, integration of renewable energy
resources (RSs) into the power systems makes LFM job more challenging. To this end, the
concept of secondary frequency control, or LFM, objective is introduced to single and multi-
area power systems to manage the power mismatch of the particular power system. This
helps regulate the system frequency for single/multi area systems and schedule tie-line …
Abstract
Load frequency management (LFM) has become more significant in modern power systems due to variation demand and generation profiles. Further, integration of renewable energy resources (RSs) into the power systems makes LFM job more challenging. To this end, the concept of secondary frequency control, or LFM, objective is introduced to single and multi-area power systems to manage the power mismatch of the particular power system. This helps regulate the system frequency for single/multi area systems and schedule tie-line power exchange for multi area systems. To control and reduce the system frequency deviation, load frequency controllers are introduced. However, to achieve optimal power management, intelligent soft computing techniques that take different controllers into account are utilized. This paper aims to provide a review of different controllers utilized in traditional as well as renewable energy-based power system for LFM such as; classical controllers, fractional order controllers, cascaded controllers, sliding mode controller (SMC), tilt-integral-derivative controllers, H-infinity controller and other recently developed controllers. Some popular and recently adopted soft-computing tools for power management such as; genetic algorithm, particle swarm optimization, firefly, cuckoo search techniques, fuzzy tuning tool, model predictive technique and other newer once have been explored. Finally, the paper concludes by highlighting some future scope in the field of LFM.
Elsevier
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