Mahmoud M. Othman

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.

Invasive weed optimization algorithm for solving economic load dispatch

The economic load dispatch problem is an online optimization problem to calculate the minimal fuel cost for each power generator under load constraints. At first, the classical methods such as lambda iteration and non-linear programming were used to solve the economic load dispatch problem. Although of their accuracy, these methods are time consuming. Meta-heuristic algorithms such as Particle swarm optimization, Ant colony and Genetic algorithms give better solution while their running time is longer. The hybrid models, by combining two different algorithms, were experimented to get benefits of both mentioned methods. In this paper, a novel algorithm based on invasive weed optimization method is used to solve the economic load dispatch problem under generation and load constraints in order to overcome drawbacks of previous techniques. The proposed algorithm is implemented in MATLAB environment and tested on a 3-units power system. Different test cases are proposed and the results show the efficiency and accuracy of the proposed algorithm in solving the economic dispatch problem.

Optimal Operation of Virtual Power Plant in Unbalanced Distribution Networks

Abstract The work presented in this article aims to enhance electrical energy trading in unbalanced distribution networks using the virtual power plant concept. This article proposes different developed optimization algorithms based on modification of the big bang–big crunch method for virtual power plant realization. The proposed algorithms aim to manage the electrical energy in unbalanced distribution networks to minimize the cost of energy purchased from the grid. This goal is achieved through the optimal placement of renewable-based distributed generators, optimal scheduling of the controllable loads, and optimal sizing of energy storage elements. The proposed algorithms are implemented in a MATLAB environment (The MathWorks, Natick, Massachusetts, USA) and tested on the IEEE 37-node feeder. The results show great reduction in the cost of energy purchased from the grid, and the subsequent discussions emphasize the significance of using the virtual power plant concept in managing the electrical energy.

Integrating tidal energy to solve dynamic economic load dispatch problem using IWO

Integrating a renewable energy source to electric power systems has a great impact on the environment, the economy and the performance of the systems. Tidal energy has a future potential to supply bulk amounts of energy in different forms and significant consideration as a fuel-free energy and reliable energy source. In this paper, a novel approach to solve dynamic economic load dispatch problem integrating a tidal-in stream energy source is proposed. A meta-heuristic algorithm based on invasive weed optimization method is considered to solve the dynamic economic load dispatch problem under generation and load constraints. The proposed algorithm is implemented in MATLAB environment and tested on IEEE-30 bus system. Four days representing different seasons are proposed and the results obtained show impact of tidal energy on reducing fuel costs, the emission costs and emissions.

A novel Monte Carlo based modeling strategy for wind based renewable energy sources

This paper introduces a new algorithm strategy in order to model wind based renewable energy sources which are used for planning purposes in distribution systems. Initially, the available data of the wind speeds are divided into seasonal data (i.e. the available data of each season is separated) then the available separated data is divided into hourly data (i.e. 24-hours for each season). This algorithm is based on Monte Carlo Simulation Method which considers the stochastic nature of the wind power through the correct determination of the appropriate cumulative distribution function. Monte Carlo Simulation technique is utilized for obtaining the most likelihood wind turbine output power at each hour at each season. The results of the proposed strategy is compared with another probabilistic model to show the effectiveness of the proposed algorithm. The proposed algorithm is tested using MATLAB environment and the results and comparisons show that the proposed modeling algorithm gives accurate results.

A Novel Probabilistic Technique for Optimal Allocation of Photovoltaic Based Distributed Generators to Decrease System Losses

This paper presents a novel algorithm for modeling photovoltaic based distributed generators for the purpose of optimal planning of unbalanced distribution networks. The proposed algorithm utilizes sequential Monte Carlo method in order to accurately consider the stochastic nature of photovoltaic based distributed generators. An efficient algorithm based on Firefly optimization method is proposed for optimal placement of photovoltaic based distributed generators in unbalanced distribution network. The proposed optimization algorithm aims to minimize the annual energy loss by determining the optimal locations of photovoltaic distributed generators. The proposed algorithms are implemented in MATLAB environment and tested on the IEEE 37-node feeder. Several case studies are conducted to prove the effectiveness of the proposed algorithms. The results obtained are presented and discussed.