In the power system operation, the Load Frequency Control (LFC) is required for good quality reliable electric power supply. The main aim of the load frequency control is to maintain the frequency of each area and the tie-line power flow within the specified tolerance. This is achieved by adjusting the real power output of the generators for the corresponding changes in the load demand. Electric power industry is now an open market structure. This deregulated environment comprises of GENCOs, TRANSCOs and DISCOs, which are supervised by an Independent Service Operator (ISO). In this paper, the transaction between the GENCOs and DISCOs in the deregulated market structure based on the DISCO Participation Matrix (DPM) is designed and simulated. The Proportional-Integral (PI) controller is used to tune the LFC. In this tuning, an intelligent global Particle Swarm optimization (PSO) algorithm is used, which is a population based evolutionary algorithm. The dynamic response of the system is improved by minimizing the Integral Square Error (ISE). The response of the PSO tuned PI controller is compared with the response of the conventional PI controller for the system considered. It is found that, the response of the proposed PSO tuned PI controller is better than that of the conventional PI controller. The simulation is implemented in MATLAB-Simulink.
Load frequency control plays a vital role in power system operation and control. LFC regulates the frequency of larger interconnected power systems and keeps the net interchange of power between the pool members at predetermined values for the corresponding changes in load demand. In this paper, the two-area, hydrothermal deregulated power system is considered with Redox Flow Batteries (RFB) in both the areas. RFB is an energy storage device, which converts electrical energy into chemical energy, that is used to meet the sudden requirement of real power load and hence very effective in reducing the peak shoots. With conventional proportional-integral (PI) controller, it is difficult to get the optimum solution. Hence, intelligent techniques are used to tune the PI controller of the LFC to improve the dynamic response. In the family of intelligent techniques, a recent nature inspired algorithm called the Flower Pollination Algorithm (FPA) gives the global minima solution. The optimal value of the controller is determined by minimizing the ISE. The results show that the proposed FPA tuned PI controller improves the dynamic response of the deregulated system faster than the PI controller for different cases. The simulation is implemented in MATLAB environment.
Recently, there has been an increase in the growth and advancement of electric propulsion in marine electrical drives. A maximum amount of energy is utilized by ships for propulsion drives. To be aware of it and develop an optimized structure to improve the effectiveness of the propulsion system with power consumption is necessary. The proposed paper aims to develop a model and perform functional analysis as per the above understanding and requirements. The factors considered include greenhouse gas emissions, CO2 emissions, environmental aspects, and the availability of non-renewable resources, which leads to the introduction of renewable energy as a replacement method of power generation. For this work, two different renewable sources, such as solar and wind energy, were chosen. The combination of these two resources can manipulate the voltage and satisfy the load in a desirable way. For voltage improvement, a high gain converter with a minimal number of active and passive components is selected. This system adopts a storage system to meet the needs in the future. The inverter switches are controlled by the recommended control algorithm, which can balance and provide adequate power towards the drive by a feedback control loop. The speed of propulsion in the drive is adjusted by the induction motor coupled with the propeller. The analytical study of the proposed system is carried out in MATLAB software. The simulation study revealed the effectiveness of this modern optimization technique.
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