In recent decades, one of the main management's concerns of professional engineers is the optimal integration of various types of renewable energy to the grid. This paper discusses the optimal allocation of one type of renewable energy i.e. wind turbine to the grid for enhancing network's performance. A multi-objective function is used as indexes of the system's performance, such as increasing system loadability and minimizing the loss of real power transmission line by considering security and stability of systems' constraints viz.: voltage and line margins, and eigenvalues as well which is representing as small signal stability. To solve the optimization problems, a new method has been developed using a novel variant of the Genetic Algorithm (GA), specifically known as Non-dominated Sorting Genetic Algorithm II (NSGA-II). Whereas the Fuzzy-based mechanism is used to support the decision makers prefer the best compromise solution from the Pareto front. The effectiveness of the developed method has been established on a modified IEEE 14-bus system with wind turbine system, and their simulation results showed that the dynamic performance of the power system can be effectively improved by considering the stability and security of the system.

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Background: Modern power system operations often faced with very unsafe conditions caused by voltage stability problems. If the problem cannot be controlled with the right method, then a cascade system can occur. This condition can cause a voltage reduction drastically and leads to a power outage. Objective: The Fast Voltage Stability Index (FVSI) and Line Stability Factor (LQP) implemented in this study. Both indices can determine the optimal location and capacity of Wind Energy (WE) into the grid to anticipate a sustained increase in load. Method: One type of optimization method, a new variant of Genetic Algorithms, is used to solve the multi-objective optimization problem known as Genetic Algorithm Sorting Non-Domination Sorting II (NSGA-II). This algorithm can determine the optimal location and WE's capacity into the grid by minimizing line power loss (Ploss) of power system with a system load increase scenario. Bus voltage security, thermal line limits, and stability system are used as obstacles to maintain the system in a safe condition due to the increasing the maximum load. Results: The method suggested in this paper has been adequately tested on modification of the IEEE 14-bus standard test system connected to the WE. The WE integrated into the grid modeled using the Power System Analysis Toolbox (PSAT). Based on the multi-objective manner, the method developed can determine the best location and capacity of the WE simultaneously by minimizing Ploss with SLI and satisfied all the system's security and stability constraints. Conclusion: The technique provides well-distributed non-dominated solutions and well exploration of the research space.

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In this paper, a series FACTS controller namely Thyristor Controller Series Compensator (TCSC) has been suggested to enhance the power system loadability. The location of the controller and the setting of their control parameters are optimized by one type of Evolutionary Optimization Technique to improve the performance of the power network. The objective functions are to maximize the system loadability whereas maintaining system security and stability margins, e.g., small signal stability, voltage stability index, and line stability factor within limits by considering the investment costs of the controller and minimizing active power loss of the system. The series FACTS controller is modeled and incorporated in the Newton Raphson power flow problem. The effectiveness of the proposed methodology has been investigated on a practical Java-Bali 24-bus Indonesian grid system.

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