To enhance the distribution power quality and maintains the voltage stability is a challenging tasks in the complex distribution network. This can be achieved through the distributed generation (DG). In this paper, a hybrid technique has been proposed which improves the distribution stability by allocating the DG at the optimal place. This technique uses the artificial neural network (ANN) and particle swarm optimisation (PSO) to find the optimal location and optimal rating of DGs. In this technique ANN used in two stages, in the first stage, bus data is determined, then the PSO technique is used to find the best fitness of the input and the optimal location of the DGs, and in the second stage of ANN the optimal rating of DGs is determined. The ANN is trained with the bus data. The effectiveness of the proposed technique is tested and verified using MATLAB on IEEE 30 bus system.

In the deregulated power market, the competition for purchase of electric power is increasing in our daily life. In order to know how much of power is transferred, the calculation of ATC plays a major role. To calculate ATC there are different methods. In this paper, DCPTDF, ACPTDF and RPF methods are used. The ATC is calculated in order to know the maximum amount of power that is transmitted on the transmission lines from sending end region to receiving end region. These methods are applied on IEEE standard 6 bus system and 14 bus system and they are simulated by using MATLAB software and the load flow solution is performed by means of NR (Newton Raphson) method.

Profit maximization is critical in the control of power system networks for both power providers and users. Electrical energy is freely accessible in the electrical grid during off-peak hours, with storage units helping to store excess energy and assist the electrical grid during high-demand situations. Such techniques promote grid stability and ensure safe operation. Because renewable resources are intermittent, energy storage technologies are especially significant in renewable-associated power systems. Vehicle-to-grid (V2G) technology has recently acquired popularity in preserving power grid stability in the presence of renewable resources.V2G technology employs automobiles as mobile storage devices and focuses on the efficient utilization of extra power available during off-peak hours. The goal of this work is to improve the functioning of a V2G system in a power network to reduce energy production costs while increasing system profitability. This study for deregulated power environments also depicts the influence of V2G mixing on system voltage profile and locational marginal pricing (LMP), as well as the performance of the Unified Power Flow Controller (UPFC) on system economics. The MiPower simulation program is used in the study to find the best placement of the power storage unit for the modified IEEE 14-bus system.

In this paper, a hybrid approach is proposed for optimizing the location and size of distribution generations (DGs) in power distribution system. This hybrid approach is the combination of artificial bee colony algorithm (ABC) and artificial neural network (ANN). Here ABC computes the optimal location of the DG systems and ANN is sized the capacity of DGs. The optimal location is evaluated as per the voltage deviation, power loss and real power variation of load buses. Then, exact capacity of DGs to improve the voltage stability and reduce the power losses of the load buses is done by using ANN. The Newton-Raphson load flow study is used for analyzing the power flow of the system. From the power flow analysis, the bus voltages, active power, reactive power, power losses and voltage deviation of the system is determined. The proposed method is implemented in MATLAB working platform.

In this chapter, an amalgamation of artificial bee colony (ABC) algorithm and artificial neural network (ANN) approach is recommended for optimizing the location and capacity of distribution generations (DGs) in distribution network. The best doable place in the network has been approximated using ABC algorithm by means of the voltage deviation, power loss, and real power deviation of load buses and the DG capacity is approximated by using ANN. In this, single DG and two DGs have been considered for calculation of doable place in the network and capacity of the DGs to progress the voltage stability and reduce the power loss of the system. The power flow of the system is analyzed using iterative method (The Newton-Raphson load flow study) from which the bus voltages, active power, reactive power, power loss, and voltage deviations of the system have been achieved. The proposed method is tested in MATLAB, and the results are compared with particle swarm optimization (PSO) algorithm, ANN, and hybrid PSO and ANN methods for effectiveness of the proposed system.