An advanced control system for the optimal operation and management of medium size power systems with a large penetration from renewable power sources

Nogaret, Eric; Stavrakakis, G.; Kariniotakis, Georges; Papadopoulos, Michael; Hatziargyriou, Nikos D.; Androutsos, Aris; Papathanassiou, Stavros A.; Lopes, João; Halliday, J.A.; Dutton, Geoff; Gatopoulos, J.; Karagounis, V.

doi:10.1016/s0960-1481(97)00028-1

Cited by 15 publications

(3 citation statements)

References 4 publications

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“…In Ref. [28], Lopes et al presented a control system for a smallsized win-diesel power system. In this work, short-term load forecasting was carried out using the ANN model.…”

Section: Department Of Electrical and Electronics Engineering Univermentioning

confidence: 99%

Modified meta heuristics and improved backpropagation neural network‐based electrical load demand prediction technique for smart grid

Islam

Baharudin

Nallagownden

2017

IEEJ Transactions Elec Engng

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For the assessment of 1‐h‐ahead electrical energy demand, this paper presents an improved backpropagation neural network that has been integrated with a simulated annealing algorithm and a chaos search genetic algorithm. A self‐adaptive learning rate and modified momentum factor are suggested to enhance the performance of the traditionally used backpropagation algorithm. For the combination scheme, the initial parameters of the improved backpropagation neural network have been modified through the utilization of the global search capability of the genetic algorithm. This has been enhanced by cat chaotic mapping to increase the genetic algorithm's optimization ability. The strong local search feature of the simulated annealing algorithm has been used to further enhance the solution set that was created by the optimized genetic algorithm. The model is tested for small and large‐sized grid data, integrated with renewable sources. The performance of the model is also verified for fluctuating load demand conditions. Furthermore, the proposed model performance is tested for all the four seasons of the year to validate its efficacy during seasonal variations. The results of the proposed technique in all these scenarios show higher prediction accuracy and fast convergence than the existing methods. The acceptable precision of 1‐h‐ahead load forecast and its adaptation in different load demand conditions determine the usefulness of the proposed model in the modern deregulated power industry. In particular, the model can be effectively implemented for the enhancement of demand response and other dynamic features of the smart grid. © 2017 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

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“…In Ref. [28], Lopes et al presented a control system for a smallsized win-diesel power system. In this work, short-term load forecasting was carried out using the ANN model.…”

Section: Department Of Electrical and Electronics Engineering Univermentioning

confidence: 99%

Modified meta heuristics and improved backpropagation neural network‐based electrical load demand prediction technique for smart grid

Islam

Baharudin

Nallagownden

2017

IEEJ Transactions Elec Engng

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“…the most economic and secure way, advanced energy management systems (EMS) are needed, [Nogaret, E. et al (1997)]. Moreover, in order to operate optimally within the new market conditions, the price of providing a given level of security has to be accounted for.…”

mentioning

confidence: 99%

Wind Power Impact on Power System Dynamic Performance

Karapidakis¹

2010

Wind Power

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“…The situation could be different if we deal with the technical constraints of the electrical system. Advanced control techniques, including wind and load forecast[2,9] combined with the use of pitch control and variable speed wind turbines, could be used to increase the instantaneous wind penetration, without reducing the safety of the electrical system. Using a mean hub-height wind speed of 8.1m/s as a reference value for the wind potential and 54$/b for the oil price, the sensitivity of the results on the allowed instantaneous wind penetration is examined.…”

mentioning

confidence: 99%

Analysis of Wind Power Penetration in Autonomous Greek Islands

Caralis

Zervos

2007

Wind Engineering

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Greek islands are characterized by excellent wind-energy potential for isolated island grid power, but face curtailment by the system operator of surplus wind-generated power during periods of low demand. The ability to balance demand with wind power, defines the wind capacity to be installed and the wind power to be absorbed by the grid. In this paper, isolated island electrical systems are simulated and the impacts of large penetration of wind power are evaluated using a parametrical approach. The simulation is based on the non-dynamic analysis of the system, in order to calculate the annual wind energy which can be absorbed by the electrical system. The conclusions are focused on the prospects of wind power decreasing the electrical system's cost. The results show that a proportionately large penetration of wind power in autonomous islands may decrease the system's electricity production cost, provided that a larger instantaneous wind penetration can be allowed.

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An advanced control system for the optimal operation and management of medium size power systems with a large penetration from renewable power sources

Cited by 15 publications

References 4 publications

Modified meta heuristics and improved backpropagation neural network‐based electrical load demand prediction technique for smart grid

Modified meta heuristics and improved backpropagation neural network‐based electrical load demand prediction technique for smart grid

Wind Power Impact on Power System Dynamic Performance

Analysis of Wind Power Penetration in Autonomous Greek Islands

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