Petros S. Dokopoulos

A fuzzy model for wind speed prediction and power generation in wind parks using spatial correlation

In this paper, a fuzzy model is suggested for the prediction of wind speed and the produced electrical power at a wind park. The model is trained using a genetic algorithm-based learning scheme. The training set includes wind speed and direction data, measured at neighboring sites up to 30 km away from the wind turbine clusters. Extensive simulation results are shown for two application cases, providing wind speed forecasts from 30 min to 2 h ahead. It is demonstrated that the suggested model achieves an adequate understanding of the problem while it exhibits significant improvement compared to the persistent method.

A solution to the unit-commitment problem using integer-coded genetic algorithm

This paper presents a new solution to the thermal unit-commitment (UC) problem based on an integer-coded genetic algorithm (GA). The GA chromosome consists of a sequence of alternating sign integer numbers representing the sequence of operation/reservation times of the generating units. The proposed coding achieves significant chromosome size reduction compared to the usual binary coding. As a result, algorithm robustness and execution time are improved. In addition, generating unit minimum up and minimum downtime constraints are directly coded in the chromosome, thus avoiding the use of many penalty functions that usually distort the search space. Test results with systems of up to 100 units and 24-h scheduling horizon are presented.

Long-term wind speed and power forecasting using local recurrent neural network models

This paper deals with the problem of long-term wind speed and power forecasting based on meteorological information. Hourly forecasts up to 72-h ahead are produced for a wind park on the Greek island of Crete. As inputs our models use the numerical forecasts of wind speed and direction provided by atmospheric modeling system SKIRON for four nearby positions up to 30 km away from the wind turbine cluster. Three types of local recurrent neural networks are employed as forecasting models, namely, the infinite impulse response multilayer perceptron (IIR-MLP), the local activation feedback multilayer network (LAF-MLN), and the diagonal recurrent neural network (RNN). These networks contain internal feedback paths, with the neuron connections implemented by means of IIR synaptic filters. Two novel and optimal on-line learning schemes are suggested for the update of the recurrent networks weights based on the recursive prediction error algorithm. The methods assure continuous stability of the network during the learning phase and exhibit improved performance compared to the conventional dynamic back propagation. Extensive experimentation is carried out where the three recurrent networks are additionally compared to two static models, a finite-impulse response NN (FIR-NN) and a conventional static-MLP network. Simulation results demonstrate that the recurrent models, trained by the suggested methods, outperform the static ones while they exhibit significant improvement over the persistent method.

Wind speed and power forecasting based on spatial correlation models

Wind energy conversion systems (WECS) cannot be dispatched like conventional generators. This can pose problems for power system schedulers and dispatchers, especially if the schedule of wind power availability is not known in advance. However, if the wind speed can be reliably forecasted up to several hours ahead, the generating schedule can efficiently accommodate the wind generation. This paper illustrates a technique for forecasting wind speed and power output up to several hours ahead, based on cross correlation at neighboring sites. The authors develop an artificial neural network (ANN) that significantly improves forecasting accuracy comparing to the persistence forecasting model. The method is tested at different sites over a year.

Network-constrained economic dispatch using real-coded genetic algorithm

A genetic algorithm (GA) solution to the network-constrained economic dispatch problem is presented. A real-coded GA has been implemented to minimize the dispatch cost while satisfying generating unit and branch power flow limits. A binary-coded GA was also developed to provide a means of comparison. GA solutions do not impose any convexity restrictions on the dispatch problem. The proposed method was applied on the electrical grid of Crete Island with satisfactory results. Various tests with both convex and nonconvex unit cost functions demonstrate that the proposed GA locates the optimum solution, while it is more efficient than the binary-coded GA.

Prediction and evaluation of the performance of wind-diesel energy systems

A Monte Carlo based method for predicting the economic performance and reliability of autonomous energy systems consisting of diesel generators and wind energy converters (WECs), is proposed. Several technical constraints are applied, among them the most significant are the limitation of wind power penetration due to both the load demand and a minimum permissible power of the diesel generators. Start up-shutdown costs, two-fuel diesel units and reliability are considered. The proposed method divides the total simulation period into time intervals and for every time interval uses dynamic programming techniques to determine the diesel unit commitment. Results are presented for two Greek islands. It is shown that proper central control of WECs in a system increases significantly the wind energy penetration, which is strongly affected by the way commitment is made.

Short term generation scheduling in a small autonomous system with unconventional energy sources

The authors present a method for problem solving the short-term generation-scheduling problem in a small autonomous system with both conventional and unconventional energy sources and a storage battery. The system generation consists of diesel generators, wind turbine generators, and photovoltaic panels. This is the generation mix of the power system of the Greek island of Kythons, and may be applied to other Greek islands. A dynamic programming algorithm together with a standard unit commitment, is used to determine the optimal short-term scheduling, which minimizes the fuel consumption for a certain scheduling horizon, e.g., for the next 24 h. >

Calculation of eddy current losses in nonlinear ferromagnetic materials

A complex analysis of the nonlinear diffusion problem in ferromagnetic materials under steady-state excitation is presented. The problem is solved by considering an equivalent fictitious material where the relative permeability is assumed to be constant in time but different from point to point and is related to the nonlinear B-H characteristic curve with the help of the stored magnetic co-energy density. Eddy current losses are calculated in a one-dimensional thick steel plate. A comparison made with results obtained from the classical step-by-step method shows a good agreement. >

A probabilistic method for the evaluation of the performance of wind-diesel energy systems

The method takes into account the constraint that the wind generation must not exceed a certain percentage of the system load, which is imposed for reliability reasons. The method efficiently computes the statistics of the wind generation and the diesel plant loading based on the statistics of the wind speed and the system load demand. The performance of the method is demonstrated with computational results. An example of obtaining the optimum wind penetration for an existing diesel system is presented. >

Inductive interference on pipelines buried in multilayer soil due to magnetic fields from nearby faulted power lines

The interference of power transmission lines to buried pipelines, sharing the same rights of way, has been a research subject for many years. Especially under fault conditions, large currents and voltages are induced on the pipelines, posing a threat to operating personnel, equipment, and the integrity of the pipeline. The soil structure is an important parameter that affects the level of this interference. In this study, the influence of a soil structure composed of layers with different resistivities, both horizontally and vertically, on the inductive part of this interference is investigated. The method used to determine the inductive interference comprises finite-element calculations and standard circuit analysis. The results show that good knowledge of the soil structure is necessary in order to estimate the above interference with minimum error. Therefore, it is desirable that soil resistivity measurements are made both at adequate depths and at locations far away from the rights-of-way.