Walid El-Khattam

Optimal Placement and Sizing of Distributed Generators in Unbalanced Distribution Systems Using Supervised Big Bang-Big Crunch Method

This paper presents an efficient and fast-converging optimization technique based on a modification of the traditional big bang-big crunch method for optimal placement and sizing of voltage controlled distributed generators. The proposed algorithm deals with the optimization problems incorporating multiple distributed generators for the sake of power as well as energy loss minimization in balanced/unbalanced distribution systems. The proposed algorithm is implemented in MATLAB environment and tested on the 33-bus feeder system and the IEEE 37-node feeder. Validation of the proposed method is done via comparing the results with published results obtained from other competitive methods.

Optimal Planning of Distributed Generators in Distribution Networks Using Modified Firefly Method

Abstract—This article presents a novel algorithm for optimal planning of a dispatchable distributed generator connected to the distribution networks. The proposed algorithm modifies the traditional firefly method to be able to deal with the practically constrained optimization problems by proposing formulas for tuning the algorithm parameters and updating equations. The proposed algorithm rigidly determines the optimal location and size of the distributed generation units in order to minimize the system power loss without violating the system practical constraints. Moreover, the optimal distributed generator location and minimum size for achieving a certain specified power loss are determined using the proposed method and compared to the results of a proposed heuristic technique. The distributed generation units in the proposed algorithms are modeled as voltage controlled nodes with the flexibility to be converted to constant power nodes in the case of reactive power limit violation. The proposed algorithms are implemented in MATLAB and tested on the IEEE 33-bus and the IEEE 37-nodes feeder. The results that are via comparison with published results obtained from other competing methods show the effectiveness, accuracy, and speed of the proposed method.

A Multi-objective Optimization for Sizing and Placement of Voltage-controlled Distributed Generation Using Supervised Big Bang–Big Crunch Method

Abstract—This article presents an efficient multi-objective optimization approach based on the supervised big bang–big crunch method for optimal planning of dispatchable distributed generator. The proposed approach aims to enhance the system performance indices by optimal sizing and placement of distributed generators connected to balanced/unbalanced distribution networks. The distributed generation units in the proposed algorithms are modeled as a voltage-controlled node with the flexibility to be converted to a constant power node in the case of reactive power limit violation. The proposed algorithm is implemented in the MATLAB (The MathWorks, Natick, Massachusetts, USA) environment, and the simulation studies are performed on IEEE 69-bus and IEEE 123-node distribution test systems. Validation of the proposed method is done by comparing the results with published results obtained from other competing methods, and the consequent discussions prove the effectiveness of the proposed approach.

Optimal sizing and siting of distributed generators using Big Bang Big Crunch method

The concept of integrating small generating units in the power system attracted the attention in the last few decades. Distributed generator (DG) reinforces the main generating station in covering the growing power demand. DG can be connected or disconnected easily from the network unlike the main power stations, thus providing higher flexibility. Good planned and operated DG has many benefits as economic savings, decrement of power losses, greater reliability and higher power quality. Optimal location and capacity of DGs plays a pivotal rule in achievement of gaining the maximum benefits from DGs, on the other side improper placement or sizing of DGs may cause undesirable effects. This paper applies the Big Bang Big Crunch optimization algorithm on balanced/ unbalanced distribution networks for optimal placement and sizing of distributed generators. The proposed algorithm deals with the optimization problems incorporating voltage controlled distributed generators for the sake of power loss minimization. The proposed algorithm is implemented in MATLAB environment and tested on the 69-bus feeder system and the IEEE 37-node feeder. Validation of the proposed method is done via comparing the results with published results obtained from other competing methods.

Self-healing restoration of a distribution system using hybrid Fuzzy Control/Ant-Colony Optimization Algorithm

The restoration process for distribution system grid needs the operation of line switching to restore as many loads as possible for the faulted area. In this paper a multi objective, multi constraint combinatorial optimization problem is formulated to solve the self-healing restoration problem. A hybrid Fuzzy Control (FC)-Ant Colony Optimization Algorithm (ACOA) is proposed using values for P, Q from a SCADA system. Two case studies without/with distributed generation are carried out to evaluate the effectiveness and speed of the proposed algorithm. The obtained results are compared with the conventional ACO to illustrate the accuracy of the proposed hybrid algorithm. Finally, conclusions are discussed.

A novel fuzzy cause-and-effect-networks based methodology for a distribution system's fault diagnosis

Distribution systems gained importance due to their proximity to huge numbers of consumers. Power distribution system operation is essential for consumers and equipment safety. In case of abnormal events, accurate and fast fault diagnosis (identifying faults location and type) is a vital issue to retrieve a sound distribution systems operation. Thus, in this paper, a novel on-line distribution systems fault diagnosis methodology using fuzzy-cause-and-effect-networks (FCE-Nets) is presented. Both, a faults location and type are identified using simple matrix operations, Fuzzy logic data base, and if-then-rules. Two case studies are carried out to evaluate the proposed methodology. The obtained results are compared with two different methods: Expert System (ES) with Artificial Neural Networks (ANNs) and cause-and-effect-network (CE-NETs) to evaluate the proposed method. The comparison outcomes are discussed and conclusions are reported.

Fault section estimation in power systems Based on improved honey-bee mating optimization

This paper introduces an improved version of Honey Bee Mating Optimization (IHBMO) algorithm to solve the fault section estimation (FSE) problem. FSE is introduced as an optimization problem, where the objective function includes the status of protective relays and circuit breakers. The main problem of standard HBMO is that the inappropriate selection of the parameters may affect the local search. Also, this affects the computation time. So, an improvement is introduced to the standard HBMO to enhance the local search capability in order to reach the best solution with better computation efficiency. To ensure the validity of the IHBMO algorithm, two sample systems are examined with different test cases. Furthermore, the results obtained by the proposed algorithm are compared with those obtained via other several methods. The results show the accuracy and high computation efficiency of IHBMO.

Economic impact of Capacity Credit evaluation for Wind Energy Conversion Systems projects in Egypt

This paper investigates the impact of the Capacity Credit (CC) values on the economics of individual Wind Energy Conversion System (WECS) projects in the light of the current Egyptian Renewable Energy (RE) regulatory incentive schemes. The methodology proposed in this paper is to assigns different CC values for WECS projects under study while keeping all other parameters whether technical or financial fixed for all projects to calculate the total annual projects revenue. The impact of both the regulatory incentive schemes and the latest updated structure of the electricity consumption tariff have been taken into account in the assessment. The outcomes of this study have been illustrated and the significance of the CC evaluation on economics of WECS projects has been highlighted, discussed, and concluded.