Advanced Computational Methods, State Estimation, and Control Strategies for Enhancing Efficiency in Modern Power Systems and Clean Energy Technologies

Ashok Kumar Chowdhury

doi:10.61424/ijans.v4i2.933

Authors

Ashok Kumar Chowdhury Department of Electrical and Electronics Engineering, Bangladesh Power Development Board, Chattogram, Bangladesh

DOI:

https://doi.org/10.61424/ijans.v4i2.933

Keywords:

Advanced computational methods, state estimation, model predictive control (MPC), volt/VAR optimization (VVO), modern power systems

Abstract

The power systems are changing quickly, with more and more incorporation of renewable energy resources, distributed energy resources, energy storage devices, and intelligent communication technology. The complexity of these developments has resulted in the need for sophisticated computational techniques, accurate state estimation and intelligent control strategies for reliable and efficient grid operation. This review discusses the latest developments of computation methods such as optimization algorithms, Model Predictive Control, Volt/Var Optimization, and data driven methods for enhancing power system monitoring and decision making. The role of state estimation in the context of EMS is explained as the backbone of the Energy Management Systems and how the integration of SCADA with PMS units can improve network observability, accuracy of estimation and real-time situational awareness. The advanced voltage regulation, reactive power management, renewable energy coordination and energy storage control strategies are also assessed. Additionally, applications, software tools, measurement technologies, benchmarking frameworks and industrial implementations are identified which support intelligent grid operation. The current measurement uncertainty, cybersecurity threats, communication delay, data fusion, scalability, distributed computation and uncertainty caused by renewable generation are thoroughly discussed concerning the emerging research directions. The results show the substantial gains in grid efficiency, reliability, power quality and renewable energy integration that can be achieved by integrating advanced computational techniques with advanced state estimation and predictive control. The next steps involve the implementation of AI, machine learning to create adaptive and sustainable electricity networks that can deliver the energy required to support global energy transitions and growth.

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