• Energy Research

The function of Automatic Generation Control (AGC) is to regulate the output power of the corresponding generator in response to changes in system frequency or tie line loading or in both cases within a prescribed limit. In this paper Differential Evolution (DE) based Proportional-Integral (PI) controller is designed and simulated to observe its performance for a two area Hydro-Thermal power system. The purpose of the DE is to find out the optimal parameter values of the PI controller (K p and K i ). The Optimal set of values is chosen based on eigenvalue of system matrix and objective function. The performance was evaluated based on the transient response (Settling time and peak overshoot) of the system while different step load changes were applied on both or either of the areas. The proposed controller has been found functioning properly for not only smaller (1%) but also larger (5%) load disturbances. All the simulations are done using MATLAB/SIMULINK software.

This work proposes the adoption of Enhanced Gradient-Based Optimizer (EGBO) as a new approach to the Load Frequency Control (LFC) problem in a two-area interconnected power system. The importance of determining the optimal parameters for the controllers for the LFC problem cannot be overstated, and the fact that estimating these parameters require complex and nonlinear computations makes the optimization procedure even more unique and challenging. Consequently, application of an efficient optimization algorithm to successfully attain optimal controller parameters is critical. To accomplish this task, the proposed EGBO algorithm is compared to the fundamental Gradient-Based Optimizer (GBO), Chimp Optimization Algorithm (ChOA), Sine Cosine Algorithm (SCA), Grey Wolf Optimization (GWO), and Particle Swarm Optimization (PSO) for optimizing an Integral-Time-multiplied-Absolute-Error (ITAE) based objective function. The relevant findings show that the EGBO algorithm is competitively superior in terms of resilience, precision, and latency when compared to other optimization methods. Lastly, the statistical comparison further strengthens the outcome of the study. To be published in Engineering Applications of Modern Metaheuristics under Studies in Computational Intelligence series

this paper, an evolutionary algorithm-Invasive Weed Optimization (IWO) based power system stabilizer (PSS) is proposed for multi-machine power system. IWO is a derivative-free real parameter optimization technique that mimics the ecological behavior of colonizing weeds. Owing to its superior performance in comparison with many other existing meta-heuristics, it has used to search for optimal settings of PSS parameters. Eigen-value based objective function is considered to enhance system damping of electromechanical mode. The performance of proposed IWO- based PSS is tested and demonstrated under different loading conditions and disturbances for a four machine example power system. The Eigen value analysis and non-linear simulation results prove the effectiveness of the proposed IWO-based PSS design. The robustness of the design method is confirmed by testing the IWO based PSS performance under varying load conditions.

Load Frequency Control (LFC) for any power generating station is a subject of great concern for power system researchers. With the changes of load demand, frequency starts fluctuating which results in deviation in tie line power flow and frequency deviation at consumer end. To overcome this problem, many control techniques have been adopted. In early days fixed value integral/proportional-integral control, Optimal Control, Quantitative feedback theory, pole placement etc. methods were applied. In recent times, neural network, fuzzy logic, genetic algorithm controllers are replacing the conventional techniques. All the control techniques are used to find the optimal values of the PID/PI controller gain parameters (Kp, Ki, Kd) for which system stability is confirmed with minimum of Area Control Error (ACE). Differential Evolution (DE) which is a newer branch of genetic algorithms has been successfully applied in this problem. In this paper DE based PI controller has been implemented for Hydro-Thermal power plants to find out the optimal value of gain parameters for system stability. Nonlinearity has been considered in governor part of the thermal area for practical scenario. 1% step load changes have been applied to both areas simultaneously and individually to confirm its performance. Desired set of controller gain parameters (Kp, Ki) are selected based on eigenvalue and minimum value of Objective Function. All simulations are done in the MATLAB/SIMULINK environment.

Load flow analysis is a significant tool for proper planning, operation, and dynamic analysis of a power system that provides the steady-state values of voltage magnitudes and angles at the fundamental frequency. However, due to the absence of a slack bus in an islanded microgrid, modified load flow algorithms should be adopted considering the system frequency as one of the solution variables. This paper proposes the application of nature-inspired hybrid optimization algorithms for solving the load flow problem of islanded microgrids. Several nature-inspired algorithms such as genetic algorithm (GA), differential evolution (DE), flower pollination algorithm (FPA), and grasshopper optimization algorithm (GOA) are separately merged with imperialistic competitive algorithm (ICA) to form four hybrid algorithms named as ICGA, ICDE, ICFPA, and ICGOA. Performances of these algorithms are tested on the 6-bus test system and the modified IEEE 37-bus test system. A comparison among the proposed algorithms is carried out in terms of statistical analysis conducted using SPSS statistics software. From the statistical analysis, it is identified that on an average, ICDE takes less number of iterations and consequently needs less execution time compared with other algorithms in solving the load flow problem of islanded microgrids.

An optimal energy mix of various renewable energy sources and storage devices is critical for a profitable and reliable hybrid microgrid system. This work proposes a hybrid optimization method to assess the optimal energy mix of wind, photovoltaic, and battery for a hybrid system development. This study considers the hybridization of a Non-dominant Sorting Genetic Algorithm II (NSGA II) and the Grey Wolf Optimizer (GWO). The objective function was formulated to simultaneously minimize the total energy cost and loss of power supply probability. A comparative study among the proposed hybrid optimization method, Non-dominant Sorting Genetic Algorithm II, and multi-objective Particle Swarm Optimization (PSO) was performed to examine the efficiency of the proposed optimization method. The analysis shows that the applied hybrid optimization method performs better than other multi-objective optimization algorithms alone in terms of convergence speed, reaching global minima, lower mean (for minimization objective), and a higher standard deviation. The analysis also reveals that by relaxing the loss of power supply probability from 0% to 4.7%, an additional cost reduction of approximately 12.12% can be achieved. The proposed method can provide improved flexibility to the stakeholders to select the optimum combination of generation mix from the offered solutions.

Abstract Low-frequency oscillations should be dealt with extreme care for secure electric networks. This paper tunes the critical parameters of power system stabilizers in three different electric networks in real-time, employing the neurogenetic approach to damp out the low-frequency oscillations. The first network is a single machine infinite bus power system equipped with a power system stabilizer. Besides, the second and third networks are coordinated with second-generation flexible alternating current transmission system devices, namely a unified power flow controller and static synchronous compensator in coordination with power system stabilizers, respectively. The investigation of eigenvalue and minimum damping ratio analyses for different loading conditions proves the efficiency of the proposed approach. Additionally, the time-domain simulation comparison shows the superiority of the proposed approach over the conventional method. Besides, the satisfactory values of the statistical performance measures give confidence to the proposed approach in predicting power system stabilizer parameters and thus mitigating the low-frequency oscillations in real-time.

The impact of High voltage direct current (HVDC) transmission on power system dynamic stability is mainly discussed in this paper and the system dynamic response is analyzed under small disturbance. The establishment of linearized model of a single machine infinite bus (SMIB) system equipped with Voltage source converter (VSC) based HVDC is also presented in this paper. Generally, power system dynamic stability is maintained by PSS which may adversely affect the voltage profile. HVDC based damping controller can be an effective solution to this problem which is designed using phase compensation technique to enhance damping of low frequency oscillations. The performance and effectiveness of the proposed controller is demonstrated through eigen value analysis and non linear time domain simulations. Moreover the performance of the controller with different control input signals is evaluated and most effective control input is determined. Simulation results carried by MATLAB show the effectiveness of proposed control mechanism to strengthen the system dynamic stability.