Reham A. Osama

Distribution Systems Reconfiguration Using Ant Colony Optimization and Harmony Search Algorithms

Abstract One objective of the feeder reconfiguration problem in distribution systems is to minimize the distribution network total power loss for a specific load. For this problem, mathematical modeling is a non-linear mixed integer problem that is generally hard to solve. This article proposes two heuristic algorithms inspired from natural phenomena to solve the network reconfiguration problem: (1) “real ant-behavior-inspired” ant colony optimization implemented in the hyper cube framework and (2) the “musician behavior-inspired” harmony search algorithm. The optimization problem is formulated taking into account the operational constraints of distribution systems. A 32-bus system and a 118-bus distribution were selected for optimizing the configuration to minimize the losses. The results of reconfiguration using the proposed algorithms show that both of them yield the optimum configuration with minimum power loss for each case study; however, the harmony search required shorter simulation time but more practice of the iterative process than the hyper cube–ant colony optimization. Implementing the ant colony optimization in the hyper cube framework resulted in a more robust and easier handling of pheromone trails than the standard ant colony optimization.

Distribution systems reconfiguration using the hyper-cube ant colony optimization algorithm

This paper introduces the Ant Colony Optimization algorithm (ACO) implemented in the Hyper-Cube (HC) framework to solve the distribution network minimum loss reconfiguration problem. The ACO is a relatively new and powerful intelligence evolution method inspired from natural behavior of real ant colonies for solving optimization problems. In contrast to the usual ways of implementing ACO algorithms, the HC framework limits the pheromone values by introducing changes in the pheromone updating rules resulting in a more robust and easier to implement version of the ACO procedure. The optimization problem is formulated taking into account the operational constraints of the distribution systems. Results of numerical tests carried out on two test systems from literature are presented to show the effectiveness of the proposed approach.

Impact of distribution system reconfiguration on optimal placement of phasor measurement units

Phasor measurement units (PMUs) become a necessity in the modern distribution system especially with the direction towards smart grid. The PMUs should be placed appropriately in the distribution network. The problem of optimal PMU placement for full observability is analyzed in literature. Distribution network reconfiguration for loss reduction is also analyzed in literature. This paper studies the placement problem of PMUs in distribution system considering the system reconfiguration. System reconfiguration is achieved using the ant colony optimization (ACO) algorithm to solve the minimum losses problem. Greedy algorithm is used as an optimization tool to obtain the minimal number of PMUs and their corresponding locations. The 33-node distribution system is tested for optimally locating the PMUs with different network reconfiguration.

Effect of Photovoltaic and Wind Power Variations in Distribution System Reconfiguration for Loss Reduction Using Ant Colony Algorithm

Intermittent characteristic of renewable power resources like photovoltaic PV power and wind power makes it very important to include power production at various times when evaluating the distribution system performance. This paper presents the effect of the intermittent renewable energy resources in the distribution system reconfiguration for loss reduction. The loss minimization problem is solved using the Ant Colony Optimization ACO algorithm implemented in the Hyper Cube HC framework. The 32-bus distribution network is studied for optimizing the configuration with and without the intermittent generations. The results of reconfiguration using the ACO algorithm show the improvement in the buses voltage profile with installing of PV and wind power sources with different values of solar irradiance and wind speed.

Distribution networks reconfiguration for loss reduction using the Hyper Cube Ant Colony Optimization

This paper introduces the Ant Colony Optimization algorithm (ACO) implemented in the Hyper-Cube (HC) framework to solve the distribution network minimum loss reconfiguration problem. The ACO is a relatively new and powerful intelligence evolution method inspired from natural behavior of real ant colonies for solving optimization problems. In contrast to the usual ways of implementing ACO algorithms, the HC framework limits the pheromone values by introducing changes in the pheromone updating rules resulting in a more robust and easier to implement version of the ACO procedure. The optimization problem is formulated taking into account the operational constraints of the distribution systems. Results of numerical tests carried out on two test systems from literature are presented to show the effectiveness of the proposed approach.

Optimum Clustering of Active Distribution Networks Using Back Tracking Search Algorithm

A microgrid has become the main building block of future distribution networks, demanding a systematic procedure for its optimal construction. Large distribution systems can be divided into clusters of distributed energy resources serving a group of distributed loads, known as microgrids to facilitate powerful control and operation. This paper compares the clustering of smart distribution systems into a set of microgrids based on two different objective functions. The probabilistic nature of intermittent distributed generators and loads is considered by performing a probabilistic Backward-Forward sweep power flow. The probabilistic approach aims to determine the optimal virtual cut set lines that split the system into self sufficient microgrids. The Back Tracking Search optimization algorithm is used to cluster a 69-bus distribution system with optimally allocated distributed generation units. The design concept, problem formulation, solution algorithms, probabilistic model and other graph related theories are presented in this paper.

Microgrid self adequacy optimization using back tracking search algorithm

This paper presents optimized approaches for microgrid design and allocation of energy storage units to enhance self adequacy of microgrids. Microgrids are clusters of distributed energy resources serving clusters of distributed loads either in grid connected or isolated grid modes. The self adequacy of microgrids is maximized by the optimal installment of energy storage units that can store the spilled energy generated by renewable based resources during off-peak hours and release it during on-peak hours. The optimal allocation problem is solved with 3 different methodologies to determine the best location for the storage resources and the cut sets foundries. Back tracking search algorithm is used as the solution algorithm. The probabilistic nature of intermittent generation and loads is considered by performing a probabilistic load flow. The well known PG&E 69-bus distribution system is selected for the study. The problem formulation and solution algorithms are presented in the paper.