Ηλεκτρικα Δικτυα Με Παραγωγη Απο Ντηζελομηχανες Και Ανεμογεννητριες

Abstract

Installation of Wind Energy Converters (WECs) as fuel savers is economically and environmentally attractive in windy regions. There numerous Greek islands with autonomous power systems consisting of diesel units that have high operational costs due to fuel used and having also high wind regimes.During installation of WECs in autonomous systems, technical constraints must be considered, since increasing wind penetration may disturb the operation of the system, leading to oscillation of voltage and frequency. Also in cases of high wind speeds the outage of WECs may damage conventional units. These effects determine at any instant a maximum permitted wind power penetration that depends on the load and on the diesel units.A probabilistic method for predicting the economic performance and reliability of autonomous energy systems, consisting of diesel generators and fixed pitch WECs is developed (Chapter 2).The method uses the duration curves of the load and the wind speed. Several constraints are applied among them the most significant are the following: load and wind speed are statistically independent and there is a limitation of wind power penetration due to load and minimum diesel plant output. The quantities that are computed are: wind and diesel energy production, diesel fuel consumption, loss of load probability (LOLP) and expected unserved energy (EUE). Results are presented for two Greek islands.Field results have shown that there are differences between calculated and real performance mainly due to the way diesel units are committed and operated. Therefore, it seemed necessary to consider the diesel generators more carefully, i.e. their commitment, upper and lower power limits, base load units, modelling of units consuming two types of fuel. A Monte Carlo based method is developed (Chapter 3). 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 the same Greek islands as in the probabilistic method and comparisons between the two methods are made. It is shown that proper central control of WECs in a system increases significantly wind energy penetration, which is strongly affected by the way diesel commitment is made. Also, a methodology for short term wind forecasting +15 min and +1 hour ahead is proposed (Chapter 4). The Neural Networks (NNs) concept is used and their predicting capability with respect to wind speed and wind power output is tested. Real wind speed time series are used for NNs training and testing. The results are very promising giving mean relative absolute error between 6.6% -11% for +15 min and +1 hour wind forecasts respectively.