A power system’s nonlinearity and complexity increase from time to time due to increases of power demand. Therefore, properly designed power system controlsare required. Without these, system instability will cause equipment failures, and possibly even cascading events and blackouts. To cope with this, intelligent controllers using soft computing are necessary for real time operation. In this paper, the reheat type three-area thermal power system is considered, and the output scaling factors, gain parameters of fuzzy membership functions, and parameters of fuzzy-proportional integral derivative (FPID) controllers are optimized using a differential evolution (DE) optimization techniqueand integral time multiplied absolute error (ITAE) as objective functions. To improve the limitations of the controller and to enhance stability of the system, high voltage direct current (HVDC) technology is advantageous due to its quickresponse capabilities. In this paper, a HVDC is connected in parallel to the system, revealing that a FPID controller with a HVDC provides better and more accurate resultscompared to a system without a controller. The test results presented in this paper show the proposed controller’s suitability for managing random load changes.

The feasibility and potential assessment (PA) of solar PV energy is one of the key factors in identifying the most promising areas for the installation of solar PV stations. It determines the useful energy generated in the given area. This paper assesses the solar energy distribution and PA in the North Shewa administration zone. Based on the data collected and analysis made, it is found that more than 80% of the North Shewa areas are suitable for the solar energy generation for off-grid and on-grid systems. Hence, the solar potential of the North Shewa zone completely fulfills the standards of sunshine, solar radiation, and temperature. That is, most of the areas have solar radiation of 5.2 kWh/m2, and daily sunshine is greater than 7 h. The maximum energy production is found in December in Shewa Robit, Mehal Meda, Eneware, Debre Berhan, Alem Ketema, and Sela Dengay with 175.35 kWh, 188.18 kWh, 180.78 kWh, 189.54 kWh, 175.78 kWh, and 189.63 kWh, respectively. This is a good opportunity for investors to invest in solar PV electricity generation. It will solve the issue of electricity supply to the community, which currently relies on wood and fossil fuels. It also highlights the positive opportunities for the future implementation of solar energy.

Wind power is one of the most promising renewable energy resources and could become a solution to contribute to the present energy and global warming crisis of the world. The commonly used doubly fed induction generator (DFIG) wind turbines have a general trend of increasing oscillation damping. Unless properly controlled, the high penetration of wind energy will increase the oscillation and affect the control and dynamic interaction of the interconnected generators. This paper discusses power oscillation damping control in the automatic generation control (AGC) of two-area power systems with DFIG wind turbines and Matlab code/Simulink interfacing optimization methods. The differential evolution (DE) optimization technique is used to obtain the controller gain parameters. In the optimization process, a step load perturbation (SLP) of 1% has been considered in Area 1 only, and the integral of time weighted absolute error (ITAE) cost function is used. Three different test studies have been examined on the same power system model with non-reheat turbine thermal power plants. In the first case, the power system model is simulated without a controller. In Case Study 2, the system is simulated with the presence of DFIG and without a controller. In Case Study 3, the system is simulated with a PID controller and DFIG. Most of the studies available in the literature do not optimize the appropriate wind penetrating speed gain parameters for the system and do not consider the ITAE as an objective function to reduce area control error. In this regard, the main contribution and result of this paper is—with the proposed PID+DFIG optimized DE—the ITAE objective function error value in the case study without a controller being 6.7865, which is reduced to 1.6008 in the case study with PID+DFIG-optimized DE. In addition, with the proposed controller methods, the dynamic system time responses such as rise time, settling time, overshoot, and undershoot are improved for system tie-line power, change in frequency, and system area controller error. Similarly, with the proposed controller, fast system convergence and fast system oscillation damping are achieved. Generally, it is inferred that the incorporation of DFIG wind turbines in both areas has appreciably improved the dynamic performance and system stability under consideration.

In this study, a fuzzy proportional integral derivative controller (FPID) was adjusted using the differential evolution (DE) method to enhance the automated generation control (AGC) of a three-zone reheat-type power system. The objective function used in this study was an integral of the time-weighted absolute error (ITAE). In the optimization, the gain control parameters of the proportional integral (PI), the integral (I), and FPID were optimized and compared to improve the limitations drawn by the controller over a few parameters. To demonstrate that FPID controllers with IPFC produce better and more accurate optimization results than integral and PI controllers optimized by DE, the interline power flow control (IPFC) of a flexible AC transmission system (FACTS) device with suitable connections and control parameter optimization was used. Also, the particle swarm optimization (PSO) PID with IPFC was compared with the proposed DEFPID + IPFC, and better results were achieved by using the DE technique. Similarly, to demonstrate the suggested technology’s strong control capacity, random load changes were applied to the system in various conditions, and it was demonstrated that the suggested control unit easily tolerated random load perturbations and returned the system to a stable functioning state.

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