Increased innovation on finding new ways to generate energy from different sources to meet the gr... more Increased innovation on finding new ways to generate energy from different sources to meet the growing demand of consumers has led to various challenges in controlling the power network when it faces different disruptions. To address these challenges, a new approach has been proposed in this research paper, which combines a controller with a soft computing technique called Particle Swarm Optimization (PSO). The study considers a power system with four units, where three different energy sources are utilized and distributed across two areas. Each area has two power sources, with one area having a combination of thermal and gas power plants, and the other area consisting of a nuclear power plant and a gas power plant. Transmitting power from the nuclear power plant is particularly complex due to its high sensitivity to disturbances. Therefore, an intelligent and efficient controller is needed to ensure robust control in this type of power network that includes nuclear power. The paper also conducts a thorough analysis of the harmful emissions associated with electricity generation from the different power plants considered. The goal is to reduce the carbon footprint associated with power generation. The proposed work and analysis in the paper are implemented using the MATLAB/ SIMULINK environment. Conventional power emits enormous harmful emission and also fossil fuels are limited on the earth, so nonconventional power plant is required to address this environmental issue. Nuclear power plants have the potential to meet power needs but are highly sensitive to disturbances and sudden loading. This paper focuses on a power system with a nuclear power plant as the primary unit and emphasizes the need for an efficient controller to handle sudden loading or disturbances in the power network. The authors of 1 have provided mathematical models in the s-domain for different sources of energy, including thermal, hydro, wind, and nuclear power systems. These models serve as a foundation for understanding and analyzing the behavior of these energy sources. In 2 , the authors specifically address thermal energy systems and present the automatic voltage regulating loop and load frequency loop. These control loops help maintain stable voltage and frequency levels in thermal power systems. The authors in 3 discuss the tuning of controller parameters, such as TID, PID, and FOPID, using soft computing techniques. This approach helps optimize the performance of the controllers and improve the overall stability and response of the system. 4 focuses on multi-area power systems and highlights the successful control of power system frequency using hybrid controllers. Hybrid controllers combine different control strategies to achieve improved performance in regulating the frequency of the power system. Authors in 5, 6 discuss the design and optimization of fitness or cost functions. These functions play a crucial role in determining the optimal control strategies and parameters for power system operation. The authors employ a metaheuristic technique called Particle Swarm Optimization (PSO) to solve the optimization problems. In 7 , the authors present the implementation of a fractional-order controller for an inverted pendulum case. They also employ PSO for parameter tuning of the controller, which helps enhance the stability and control performance of the system. 8 investigates the effect of Super Magnetic Energy Storage (SMES) in a multi-area power system with multiple sources of power. The authors provide a detailed analysis of the system's frequency response, examining the impact of SMES on system stability and response to disturbances. Load frequency control in an interconnected power system with enhanced
Increased innovation on finding new ways to generate energy from different sources to meet the gr... more Increased innovation on finding new ways to generate energy from different sources to meet the growing demand of consumers has led to various challenges in controlling the power network when it faces different disruptions. To address these challenges, a new approach has been proposed in this research paper, which combines a controller with a soft computing technique called Particle Swarm Optimization (PSO). The study considers a power system with four units, where three different energy sources are utilized and distributed across two areas. Each area has two power sources, with one area having a combination of thermal and gas power plants, and the other area consisting of a nuclear power plant and a gas power plant. Transmitting power from the nuclear power plant is particularly complex due to its high sensitivity to disturbances. Therefore, an intelligent and efficient controller is needed to ensure robust control in this type of power network that includes nuclear power. The paper also conducts a thorough analysis of the harmful emissions associated with electricity generation from the different power plants considered. The goal is to reduce the carbon footprint associated with power generation. The proposed work and analysis in the paper are implemented using the MATLAB/ SIMULINK environment. Conventional power emits enormous harmful emission and also fossil fuels are limited on the earth, so nonconventional power plant is required to address this environmental issue. Nuclear power plants have the potential to meet power needs but are highly sensitive to disturbances and sudden loading. This paper focuses on a power system with a nuclear power plant as the primary unit and emphasizes the need for an efficient controller to handle sudden loading or disturbances in the power network. The authors of 1 have provided mathematical models in the s-domain for different sources of energy, including thermal, hydro, wind, and nuclear power systems. These models serve as a foundation for understanding and analyzing the behavior of these energy sources. In 2 , the authors specifically address thermal energy systems and present the automatic voltage regulating loop and load frequency loop. These control loops help maintain stable voltage and frequency levels in thermal power systems. The authors in 3 discuss the tuning of controller parameters, such as TID, PID, and FOPID, using soft computing techniques. This approach helps optimize the performance of the controllers and improve the overall stability and response of the system. 4 focuses on multi-area power systems and highlights the successful control of power system frequency using hybrid controllers. Hybrid controllers combine different control strategies to achieve improved performance in regulating the frequency of the power system. Authors in 5, 6 discuss the design and optimization of fitness or cost functions. These functions play a crucial role in determining the optimal control strategies and parameters for power system operation. The authors employ a metaheuristic technique called Particle Swarm Optimization (PSO) to solve the optimization problems. In 7 , the authors present the implementation of a fractional-order controller for an inverted pendulum case. They also employ PSO for parameter tuning of the controller, which helps enhance the stability and control performance of the system. 8 investigates the effect of Super Magnetic Energy Storage (SMES) in a multi-area power system with multiple sources of power. The authors provide a detailed analysis of the system's frequency response, examining the impact of SMES on system stability and response to disturbances. Load frequency control in an interconnected power system with enhanced
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