S.A. Soliman

Short-term electric load forecasting based on Kalman filtering algorithm with moving window weather and load model

Abstract This paper presents a novel time-varying weather and load model for solving the short-term electric load-forecasting problem. The model utilizes moving window of current values of weather data as well as recent past history of load and weather data. The load forecasting is based on state space and Kalman filter approach. Time-varying state space model is used to model the load demand on hourly basis. Kalman filter is used recursively to estimate the optimal load forecast parameters for each hour of the day. The results indicate that the new forecasting model produces robust and accurate load forecasts compared to other approaches. Better results are obtained compared to other techniques published earlier in the literature.

Fuzzy short-term electric load forecasting

Load forecasting is necessary for economic generation of power, economic allocation between plants (unit commitment scheduling), maintenance scheduling, and for system security such as peak load shaving by power interchange with interconnected utilities. In this paper a fuzzy linear regression model for summer and winter seasons is developed. The estimation fuzzy problem for the model is turned out to linear optimization problem, fuzzy linear regression, and is solved using the linear programming based simplex method. Having obtained the fuzzy parameters, the fuzzy load for twenty-four hour ahead is predicted. Results of predication of 24 h load ahead for a utility company are presented. It has been found using such fuzzy model; a reliable operation for the electric power system could be obtained.

A new tabu search algorithm for the long-term hydro scheduling problem

A new efficient algorithm to solve the long-term hydro scheduling problem (LTHSP) is presented in this paper. The algorithm is based on using the short-term memory of the tabu search (TS) approach to solve the nonlinear optimization problem in continuous variables of the LTHSP. The paper introduces new rules for generating feasible solutions with an adaptive step vector adjustment. Moreover an approximated tabu list for the continuous variables has been designed. The proposed implementation contributes to the enhancement of speed and convergence of the original tabu search algorithm (TSA). A significant reduction in the objective function over previous classical optimization methods and a simulated annealing algorithm has been achieved. Moreover the proposed TS requires less iterations to converge than simulated annealing. The proposed algorithm has been applied successfully to solve a system with four series cascaded reservoirs. Numerical results show an improvement in the solution compared to previously obtained results.

State of the art in optimal capacitor allocation for reactive power compensation in distribution feeders

The application of capacitors in electric power systems is intended for the control of power flow, improvement of stability, voltage profile management, power factor correction, and loss minimization. This paper presents a review of different solution methods found in the literature and is intended as a guide for those interested in the problem or intending to do additional research in the area. The assumptions made and brief descriptions of the solution methods are presented.

The long-term hydro-scheduling problem - a new algorithm

Abstract A new efficient algorithm to solve the long-term hydro-scheduling problem (LTHSP) is presented in this paper. The algorithm is based on using the short-term memory of the Tabu search (TS) approach to solve the nonlinear optimization problem in continuous variables of the LTHSP. The paper introduces new rules for generating feasible solutions with an adaptive step vector adjustment. Moreover, a Tabu list for the continuous variables has been designed. The proposed implementation contributes to the enhancement of speed and convergence of the original Tabu search algorithm (TSA). A significant reduction in the objective function over previous classical optimization methods and a simulated annealing algorithm has been achieved. Moreover, the proposed TS requires less iterations to converge than simulated annealing. The proposed algorithm has been applied successfully to solve a system with four series cascaded reservoirs. Numerical results show an improvement in the solution compared with previously obtained results.

An innovative simulated annealing approach to the long-term hydroscheduling problem

This paper presents a new simulated annealing algorithm (SAA) to solve the long-term hydroscheduling problem. A new algorithm for randomly generating feasible trial solutions is introduced. The problem is a hard nonlinear optimization problem in continuous variables. An adaptive cooling schedule and a new method for variables discretization are implemented to enhance the speed and convergence of the original SAA. A significant reduction in the number of the objective function evaluations, and consequently less iteration are required to reach the optimal solution. The proposed algorithm has been applied successfully to solve a system with four series cascaded reservoirs. Numerical results show an improvement in the solutions compared to previously obtained results.

Simulated annealing optimization algorithm for power systems quality analysis

This article presents a new application of Simulated Annealing (SA) optimization algorithm for harmonics and frequency evaluation, for power system quality analysis and frequency relaying. An objective function based on the sum of the squares of the error is to be minimized to identify the amplitude and phase angle of each harmoic component as well as the fundamental frequency of the voltage signal. The problem formulated is a highly nonlinear optimization problem and does not need any approximation such as Taylors series expansion. The proposed algorithm uses the digitized samples of the voltage signal at the place where the power quality and frequency relaying are to be implemented. The proposed algorithm has an adaptive cooling schedule and a variable discretization to enhance the speed and convergence of the original SA algorithm. The proposed algorithm is tested on simulated data. Effects of different parameters on the estimated parameters are studied. It is shown that the proposed algorithm is able to identify harmonics spectrum in the signal.

Frequency and harmonics evaluation in power networks using fuzzy regression technique

Abstract A new technique for frequency and harmonic evaluation in power networks is proposed in this paper. This technique is based on fuzzy linear regression and uses the digitized voltage samples, which are fuzzy numbers, to estimate the frequency and harmonic contents of the voltage signal. In this technique we formulate a linear optimization problem, where the objective is to minimize the spread of the voltage samples at the relay location subject to satisfying two inequality constraints on each voltage sample. Effects of sampling frequency, data window size, and the degree of fuzziness on the estimated parameters are investigated. The performance of the proposed technique is illustrated using simulated data.

Effects of harmonic distortion on the active and reactive power measurements in the time domain: a single phase system

A new application of the least error squares (LES) estimation algorithm is given for identifying, in the time domain, the active and reactive power from available samples of voltage and current waveforms. The digital available instruments used to monitor active, reactive and apparent power use such samples. A new definition for the active reactive and apparent power for sinusoidal and nonsinusoidal waveforms is discussed in this paper. It is shown that misleading reactive power compensation could be obtained if the circuit power factor is not well defined. Simulated results are presented for different waveforms, where the authors compare the results obtained with other available techniques.

An innovative simulated annealing approach to the long-term hydro scheduling problem

This paper presents a new simulated annealing algorithm (SAA) to solve the long-term hydro scheduling problem (LTHSP). A new algorithm for randomly generating feasible trial solutions is introduced. The problem is a hard nonlinear optimization problem in continuous variables. An adaptive cooling schedule and a new method for variables discretization are implemented to enhance the speed and convergence of the original SAA. A significant reduction in the number of the objective function evaluations, and consequently less iterations are required to reach the optimal solution. The proposed algorithm has been applied successfully to solve a system with four series cascaded reservoirs. Numerical results show an improvement in the solutions compared to previously obtained results.