A new expert system based control tool for power systems with large integration of PVs and wind power plants

Nogaret, Eric; Stavrakakis, G.; Bonin, J.C.; Kariniotakis, Georges; Papadopoulos, Michael; Hatziargyriou, N.; Papathanassiou, Stavros A.; Gatopoulos, J.; Karagounis, E.; Halliday, J.; Dutton, Geoff; Pecas-Lopes, J.; Androutsos, Aris; Pligoropoulos, P.

doi:10.1109/wcpec.1994.520142

Cited by 2 publications

(4 citation statements)

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“…The installed diesel capacity is 13.25 MW, while 2 wind parks of a total capacity of 1.14 MW are installed [8 WTs of 55 kW, 7 WTs of 100 kW]. The shorttime scheduling of the above power system is performed by an advanced control system described below, and necessitates forecasts of the wind power profile of each wind park for an horizon of 2 hours with a time-step of 10 minutes [13].…”

Section: Resultsmentioning

confidence: 99%

“…Alternatively, time-scale can be in the order of several minutes or even hours, when the objective is economic dispatch and power system planning. Such functions necessitate forecasts of the WECS power output profile [1,4,7,13,14]. (ii) the methodology.…”

Section: The Wind Power Forecasting Problemmentioning

confidence: 99%

“…An advanced control system (CS) for the optimal operation and management of wind-diesel power systems has been developed in the frame of the EU project JOU2-CT92-0053 [13]. The control system has been installed and is under evaluation in the wind-diesel power system of the island of Lemnos.…”

Section: Implementation In the Existing Control Systemmentioning

confidence: 99%

“…Wind Energy Conversion Systems (WECS) appear as an attractive alternative for electricity generation, especially when integrated to isolated power systems, like the systems of islands or of rural areas. Although the integration of WECS results in important environmental and economic benefits [2,4,14], the stochastic nature of the wind, imposes considerable difficulties on the optimal management of these power systems [7,13].…”

Section: Introductionmentioning

confidence: 99%

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Wind power forecasting using advanced neural networks models

Kariniotakis

Stavrakakis

Nogaret

1996

IEEE Trans. On energy Conversion

360

119

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International audienceIn this paper, an advanced model, based on recurrent high order neural networks, is developed for the prediction of the power output profile of a wind park. This model outperforms simple methods like persistence, as well as classical methods in the literature. The architecture of a forecasting model is optimised automatically by a new algorithm, that substitutes the usually applied trial-and-error method. Finally, the online implementation of the developed model into an advanced control system for the optimal operation and management of a real autonomous wind-diesel power system, is presented

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Section: Resultsmentioning

confidence: 99%