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

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 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 result

Fuzzy time domain and Z-transform for modeling of harmonic electric loads

This paper demonstrates the application of fuzzy systems and Z-transform for electric load modeling in the presence and/or absence of harmonics. The problem is formulated as one of linear optimization, where the objective is to minimize the spread of the model parameters. A triangular membership function is chosen for each load parameter. Having identified the fuzzy load parameters, the fuzzy admittance of the load is formulated in the frequency domain. The proposed algorithm is tested using simulated and actual recorded data for nonlinear loads having different characteristics. The results obtained are reported in the pape

Power system frequency estimation based on simulated annealing. Part I: A constant frequency study

In this paper a new algorithm based on Simulated Annealing (SA) method is used to estimate the signal parameters of a system steady power system, having a constant frequency during data window size. The proposed algorithm does not need any filter or model for the system frequency before and during the estimation process. The nonlinear optimization problem, which is the minimization of the sum of the squares of the errors, as a function of the signal amplitude, frequency and phase angle, is solved using the Simulated Annealing Algorithm (SAA). The problem is a nonlinear optimization problem in continuous variables. An adaptive cooling schedule and a new method for variable discretization are implemented to enhance the speed and convergence of the original SAA. The algorithm uses the samples of the voltage or current signal of one phase at the relay location. The proposed algorithm is tested using simulated and actual recorded data for noise free and harmonics contaminated signals. Effects of the critical parameters, such as sampling frequency and number of samples, on the estimated parameters are tested. It has been shown that the SAA is succeeded to estimate accurately the system frequency from a highly contaminated voltage signal

Long-term electric peak load forecasting for power system planning: A comparative study

A comparative study between two static estimation algorithms, namely least error squares (LES) and least absolute value (LAV) algorithm was presented. The proposed algorithm used the past history data for the load and the influence factors. Different models were developed and tested for long-term peak load power forecasting. It was shown that the LES algorithm produces better-predicted results than the LAV algorithm in the time-dependent model

Fuzzy linear parameter estimation algorithms: A new formulation

In this paper, we present a new formulation of a fuzzy linear estimation problem as one of the linear programming where the objective is to minimize the spread of the data points, subject to constraints on each measurement point to guarantee that the original membership is included in the estimated membership. Different models are developed based on fuzzy triangular membership as well as fuzzy numbers of LR-type, and applied to different examples in fuzzy linear regression and finally we apply these models for estimating the electrical load on a substation busbar. It is demonstrated that since the nature of the load is characterized by uncertainty, the proposed technique is well suited to apply for electrical load estimation.Scopu