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.
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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