Yasser G. Hegazy

Optimal investment planning for distributed generation in a competitive electricity market

This paper proposes a new heuristic approach for distributed generation (DG) capacity investment planning from the perspective of a distribution company (disco). Optimal sizing and siting decisions for DG capacity is obtained through a cost-benefit analysis approach based on a new optimization model. The model aims to minimize the discos investment and operating costs as well as payment toward loss compensation. Bus-wise cost-benefit analysis is carried out on an hourly basis for different forecasted peak demand and market price scenarios. This approach arrives at the optimal feasible DG capacity investment plan under competitive electricity market auction as well as fixed bilateral contract scenarios. The proposed heuristic method helps alleviate the use of binary variables in the optimization model thus easing the computational burden substantially.

Adequacy assessment of distributed generation systems using Monte Carlo Simulation

This paper presents a Monte Carlo-based method for the adequacy assessment of distributed generation systems. The state duration sampling approach is employed in this paper to model the operating histories of the installed distributed generators. A general procedure to assess the ability of the system power capacity to meet the total demand is presented and implemented in a typical case study where several distributed generation units are running in parallel within a sample distribution system and the system margins and the average amount of unsupplied loads are estimated using Monte Carlo simulation. The results obtained are presented and a new perspective to the power management of distribution systems is discussed.

Partial discharge pulse pattern recognition using Hidden Markov Models

An approach for the classification of cavity sizes based on their maximum charge transfer characteristics, applied voltage partial discharge pattern using Hidden Markov Models, is described. In these models, the partial discharge patterns for different cavity sizes are represented by a sequence of events rather than by the actual curves. In the training phase, each cavity size represents a unique class, which emits its own eigen sequence. Vector Quantization is deployed to assign labels for this particular sequence of events. A Hidden Markov Model is trained for each class, using a set of training patterns consisting of the labels produced by Vector Quantization. During testing, the sequence of events to be recognized is quantized and then matched against all the developed models. The best-matched model pinpoints the cavity size class. Experimental results demonstrate the remarkable capability of the proposed algorithm.

Support vector machines (SVM) based short term electricity load-price forecasting

This paper presents a support vector machine based combined load — price short term forecasting algorithm. The algorithm is implemented as a classifier and predictor for both load and price values. The implicit relationship between price and load is modeled employing time series. A pre-classification technique is applied to reject the unwanted data before starting the process of the data using the proposed model. In the implemented model, support vector machine plays the role of a classifier and then acts as a forecasting model. Principle component analysis (PCA) and K nearest neighbor (Knn) points techniques are applied to reduce the number of entered data entry to the model. The model has been trained, tested and validated using data from, Pennsylvania-New Jersey-Maryland. The results obtained are presented and discussed.

Stochastic power flow analysis of electrical distributed generation systems

A Monte Carlo based power flow algorithm that integrates the deterministic and the stochastic natures of the new structured, electrical distributed generation systems is proposed. The uncertainties in both the locations and the states (on or off) of the distributed generation (DG) units constitute the random parameters of the studied systems. A novel algorithm to incorporate these parameters into the Newton-Raphson solution of the power flow equations is carefully designed and implemented in this paper. Monte Carlo simulation is employed to perform the analysis of all the possible operation scenarios of the system under study and thus ensure the validity of the results. The proposed algorithm is employed to obtain the power flow solution for a typical distributed generation system involving several DG units and the results obtained are presented and discussed.

A simulated annealing algorithm for multi-objective distributed generation planning

This paper presents a multi-objective optimization model to determine the optimal solutions for the problem of sizing and locating distributed generation facilities. Cost minimization is achieved through the minimization of system losses; complex power acquired from DG units and the number of connected DG units. A Simulated annealing technique is implemented to optimize the proposed multi-objective model. A typical case study is presented and the results obtained are discussed.

Fast match-based vector quantization partial discharge pulse pattern recognition

A novel approach for the classification of cavity size in terms of their apparent charge versus applied voltage (/spl Delta/Q-V) partial discharge pattern characteristics is described. The method makes use of the fast match-based vector quantization procedure, wherein a given partial discharge pattern is matched against a set of known partial discharge patterns in a database. The /spl Delta/Q-V partial discharge patterns for different cavity sizes are considered as a sequence of events rather than as /spl Delta/Q-V curve representations. In the training phase, each cavity size represents a unique class, which emits its own /spl Delta/Q-V sequence, and vector quantization (VQ) is used to assign labels for this sequence of events. In the testing phase, a fast match algorithm is proposed to determine the degree of similarity between the labels of the tested phenomena and the prestored labels for different partial discharge patterns previously stored during the training phase. The best-matched model pinpoints the cavity size class. The results demonstrate that while the implementation of such classifier is simple, it achieves high classification rates; this positions the method as a competitive alternative vis-a/spl grave/-vis other previously proposed classifiers, which suffer from both larger computational burdens and inherently more complicated structures.

An integrated distributed generation optimization model for distribution system planning

Summary form only given. This paper proposes a new integrated model for solving the distribution system planning problem by implementing distributed generation as an attractive option in distribution utilities territories. The proposed model integrates a comprehensive optimization model and planners experience to achieve optimal sizing and siting of distributed generation. This model aims to minimize distributed generations investment and operating costs, total payments towards compensating for system losses along the planning period as well as different costs according to the available alternative scenarios. These scenarios vary from, expanding of an existing substation and adding new feeders to purchasing power from an existing inter-tie to meet the load demand growth. Binary decision variables are employed in the proposed optimization model to provide accurate planning decisions. The present worth analysis of different scenarios are carried out to estimate the feasibility of introducing distributed generation as a key element in solving the distribution system planning problem.

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.

Improved Random Drift Particle Swarm Optimization With Self-Adaptive Mechanism for Solving the Power Economic Dispatch Problem

This paper proposes an improved version of the random drift particle swarm optimization algorithm for solving the economic dispatch problem. The improvement is achieved through adding a crossover operation followed by a greedy selection process while replacing the mean best position of the particles with the personal best position of each particle in the velocity updating equation. The improved algorithm is also augmented with a self-adaption mechanism that eliminates the need for tuning the algorithm parameters based on characteristics of the considered optimization problem. Practical features such as valve point effects, prohibited operating zones, multiple fuel options, and ramp rate limits are considered in the mathematical formulation of the economic dispatch problem. In order to demonstrate the efficacy of the proposed algorithm, five benchmark test systems are utilized. The obtained results showed that the improved random drift particle swarm optimization algorithm is capable of providing superior results compared to the original algorithm and the state of the art techniques proposed in previous literature.