This paper aims to study distribution system operations by the ant colony search algorithm (ACSA). The objective of this study is to present new algorithms for solving the optimal feeder reconfiguration problem, the optimal capacitor placement problem, and the problem of a combination of the two. The ACSA is a relatively new and powerful swarm intelligence method for solving optimization problems. It is a population-based approach that uses exploration of positive feedback as well as ldquogreedyrdquo search. The ACSA was inspired from the natural behavior of ants in locating food sources and bring them back to their colony by the formation of unique trails. Therefore, through a collection of cooperative agents called ldquoants,rdquo the near-optimal solution to the feeder reconfiguration and capacitor placement problems can be effectively achieved. In addition, the ACSA applies the state transition, local pheromone-updating, and global pheromone-updating rules to facilitate the computation. Through simultaneous operation of population agents, process stagnation can be effectively prevented. Optimization capability can be significantly enhanced. The proposed approach is demonstrated using two example systems from the literature. Computational results show that simultaneously taking into account both feeder reconfiguration and capacitor placement is more effective than considering them separately.

Reconfiguration and Capacitor Placement for Loss Reduction of Distribution Systems by Ant Colony Search Algorithm

As a direct consequence of power systems restructuring on one hand and unprecedented renewable energy utilization on the other, the uncertainties of power systems are getting more and more attention. This fact intensifies the difficulty of decision making in the power system context; therefore, the uncertainty analysis of the system performance seems necessary. Generally, uncertainties in any engineering system study can be represented probabilistically or possibilistically. When sufficient historical data of the system variables is not available, a probability density function (PDF) might not be defined, they must be represented in another manner i.e. using possibilistic theory. When some of the system uncertain variables are probabilistic and some are possibilistic, neither the conventional pure probabilistic nor pure possibilistic methods can be implemented. Hence, a combined solution is needed. This paper gives a complete review on uncertainty modeling approaches for power system studies making sense about the strengths and weakness of these methods. This work may be used in order to select the most appropriate method for each application.

A comprehensive review on uncertainty modeling techniques in power system studies

A power distribution system reconfiguration methodology considering the reliability and the power loss is developed in this paper. Probabilistic reliability models are used in order to evaluate the reliability at the load points. An algorithm for finding the minimal cut sets is utilized to find the minimal set of components appearing between the feeder and any particular load point. The optimal status of the switches in order to maximize the reliability and minimize the real power loss is found by a binary particle swarm optimization-based search algorithm. The effectiveness of the proposed methodology is demonstrated on a 33-bus and a 123-bus radial distribution system.

Reconfiguration of Power Distribution Systems Considering Reliability and Power Loss

A new power system reconfiguration scheme for power loss minimization and voltage profile enhancement using Fireworks Algorithm

During system restoration, it is critical to utilize the available black-start (BS) units to provide cranking power to non-black-start (NBS) units in such a way that the overall system generation capability will be maximized. The corresponding optimization problem is combinatorial with complex practical constraints that can vary with time. This paper provides a new formulation of generator start-up sequencing as a mixed integer linear programming (MILP) problem. The linear formulation leads to an optimal solution to this important problem that clearly outperforms heuristic or enumerative techniques in quality of solutions or computational speed. The proposed generator start-up strategy is intended to provide an initial starting sequence of all BS or NBS units. The method can provide updates on the system MW generation capability as the restoration process progresses. The IEEE 39-Bus system, American Electric Power (AEP), and Entergy test cases are used for validation of the generation capability optimization. Simulation results demonstrate that the proposed MILP-based generator start-up sequencing algorithm is highly efficient.

Optimal Generator Start-Up Strategy for Bulk Power System Restoration

A new method based on a fuzzy mutated genetic algorithm for optimal reconfiguration of radial distribution systems (RDS) is presented. The proposed algorithm overcomes the combinatorial nature of the reconfiguration problem and deals with noncontinuous multi-objective optimization. The attractive features of the algorithm are: preservation of radial property of the network without islanding any load point by an elegant coding scheme and an efficient convergence characteristic attributed to a controlled mutation using fuzzy logic.

Optimal reconfiguration of radial distribution systems using a fuzzy mutated genetic algorithm

In this letter, the probabilistic distribution-based interval arithmetic approach is used to incorporate uncertainty in load demand. The variation of load is represented as Gaussian distribution function. It is suggested that the proposed power flow could be useful for planning and expansion planning of radial distribution systems.

Use of interval arithmetic to incorporate the uncertainty of load demand for radial distribution system analysis

The purpose of this paper is to expose students to a simple and efficient load flow algorithm for a radial distribution system (RDS). The basic structure and properties of the RDS are discussed, followed by an illustrative description of the load flow algorithm. The proposed load flow algorithm exploits the tree-type structure of the RDS and uses data structures efficiently. The flowchart and pseudo-codes of the algorithm have been included to help the students in writing their own program. The time and space complexity of the algorithm have also been discussed. The treatment and analysis will be useful for students' understanding of RDS, use of data structures in programming, and approximate complexity analysis of algorithms. The instructors can effectively use the contents of the paper for a basic lecture on RDS (PDF files of the Microsoft PowerPoint slides and C language program code can be downloaded from the website http://www1.mmu.edu.my/similar to nirod/).

A Simple Approach for Branch Current Computation in Load Flow Analysis of Radial Distribution Systems

A large amount of power can be saved by optimal reconfiguration of radial distribution systems (RDS). Moreover, by transferring loads from the strongly loaded feeders to the lightly loaded ones, the reconfiguration of RDS can also achieve balancing of feeder loads and alleviate overload conditions. This paper presents a new approach for optimal reconfiguration of RDS based on a fuzzy-tuned genetic algorithm by minimizing real power loss and improving loading pattern of the feeder. The proposed algorithm overcomes the combinatorial nature of the reconfiguration problem and deals with the optimization of a non-linear, non-continuous objective function. The main features of the proposed genetic approach for network reconfiguration are: preserving radial property of the network without islanding any load point by an elegant coding scheme and an efficient convergence characteristic attributed to tuned mutation using fuzzy logic. The proposed algorithm is tested for seven case studies on a 69-bus RDS. Copyright (c) 2006 John Wiley & Sons, Ltd.

A fuzzy‐tuned genetic algorithm for optimal reconfigurations of radial distribution network

A new voltage stability index is proposed to identify nodes that are on the verge of voltage collapse. The value of the proposed voltage stability index is calculated at each node of the network. The results are compared with the other known methods of voltage sensitivity indices and found to be satisfactory. The authors present a comprehensive comparison with the recently proposed voltage stability index by Chakravorty and Das and carry out a detailed analysis of effects of different load models on voltage computation and voltage stability.

R. Ranjan

Papers

Optimal reconfiguration of radial distribution systems using a fuzzy mutated genetic algorithm

Use of interval arithmetic to incorporate the uncertainty of load demand for radial distribution system analysis

A Simple Approach for Branch Current Computation in Load Flow Analysis of Radial Distribution Systems

A fuzzy‐tuned genetic algorithm for optimal reconfigurations of radial distribution network

A new voltage stability index for radial distribution network