Optimal Allocation of Distributed Generation with Reconfiguration by Genetic Algorithm Using Both Newton Raphson and Gauss Seidel Methods for Power Losses Minimizing

Abstract

The power loss in electrical networks is considered unavoidable, because of its inherent resistance, for effective and economical operation; network loss should be reduced to maximum extent. There are two goals for this study, the first one is extracting the optimal size and location of distribution generators (DG) and the optimal reconfiguration with the aim of decreasing power loss and enhance voltage profile, while the second objective is to prove the success of the methods of Newton Raphson (NR) and guess sidle(GS) in the study and analysis of radial distribution networks. In order to reduce power loss, the genetic algorithm (GA) is used to address the network distribution problem. Software package MATPOWER-7 and MATLAB are utilized to simulate a 33-bus testing system for achieving four cases of various Distribution generator (DG) numbers. A comparison with another previous studies done and the conclusion indicated a positive impact on the efficiency of the system, this work support loss reduction and voltage improvement with the lowest Distribution generator (DG) size and prove that the increase of Distribution generator (DG) number not always can give better result though system cost increase, maintenance, along with the units' distance for gas supplying increases. There are 4 cases with distinctive Distribution generator (DG) number applied in this paper and a compassion is done of power losses and voltage profile with references where case4 show better result with voltage profile 0.96 and power losses 59.09. As load flow method the Gauss-Seidel (GS) method used because it is a simple iterative method for solving n number of load flow equations by using iterative method. Where partial derivatives not require. And a Newton-Raphson (NR) method used where it is based on Taylor's series and partial derivatives. The Newton-Raphson (NR) method advantages are less number of iterations needs to reach convergence, takes less computer time when computation cost is less and the convergence is definite. Also it is more accurate, and is insensitive to factors like slack bus selection, regulating transformers. and the number of iterations required in this method is almost independent of the system size. Genetic algorithm (GA) has a good ability to making a global search and explore the search space using their different kind of crossover. Also you can combine it with local search method to increase the exploitation search of the Genetic algorithm. In this paper the introduction part introduced where it is the general part of the paper then the research method proposed in section 3 with details then a Genetic algorithm proposed finally the result with conclusions are proposed.

Self-Adaptive Dragonfly Algorithm for Optimal Allocation of Multiple Energy Storage Systems with VAr Support for Islanded Microgrid Operation

“…On the other hand, network reconfiguration along with DGs and CBs can further improve the network performance. [35], mixed-integer particle swarm optimization (MIPSO) [36], genetic algorithm [37], self-adaptive butterfly algorithm (SABOA) [38], and honey badger algorithm [HBA] [39], and many other algorithms as seen in [40], have been used for simultaneous DGs/CBs allocation and network reconfiguration. In this connection, the current work can be further extended for reconfiguration.…”

On Integrating and Operating Distributed Energy Resources in Distribution Networks: A Review of Current Solution Methods, Challenges, and Opportunities

“…The above makes this method suitable for determining the power flow in small EDS [101]. On the other hand, in the Newton-Raphson method, the variables are not updated sequentially, and each iteration involves a recalculation through the use of a Jacobian matrix that includes their partial derivatives, leading to a faster convergence in comparison with the Gauss-Seidel method [102]. This technique is suitable for solving the power flow of large EDS.…”

Optimal placement of distributed generation in power distribution system and evaluating the losses and voltage using machine learning algorithms

“…This distinction provides operators with explicit data, enabling them to undertake corrective measures effectively. The Gauss-Seidel (GS) method is applied for load flow analysis, offering a simple iterative approach to solving load flow equations when partial derivatives are not required Alnabi et al (2022). A completely distributed energy trading system based on machine learning was proposed.…”