A new spinning reserve requirement forecast method for deregulated electricity markets

Ancillary services are necessary for maintaining the security and reliability of power systems and constitute an important part of trade in competitive electricity markets. Spinning Reserve (SR) is one of the most important ancillary services for saving power system stability and integrity in respon...

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Veröffentlicht in:Applied energy Jg. 87; H. 6; S. 1870 - 1879
Hauptverfasser: Amjady, Nima, Keynia, Farshid
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Kidlington Elsevier Ltd 01.06.2010
Elsevier
Schriftenreihe:Applied Energy
Schlagworte:
Applied sciences
Economic data
Electric energy
Electricity market
Electricity market Spinning reserve requirement Hybrid forecast engine LM learning algorithm RCGA
Energy
Energy economics
Exact sciences and technology
General, economic and professional studies
Hybrid forecast engine
LM learning algorithm
Methodology. Modelling
RCGA
Spinning reserve requirement
LM learning algorithm
Hybrid forecast engine
Spinning reserve requirement
RCGA
Electricity market
Methodology
Forecast model
Forecasting
Learning
Energy requirement
Electricity
Levenberg Marquardt algorithm
Genetic algorithm
Reserve power
Hybrid model
Open market
ISSN:0306-2619, 1872-9118
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Beschreibung
Zusammenfassung:Ancillary services are necessary for maintaining the security and reliability of power systems and constitute an important part of trade in competitive electricity markets. Spinning Reserve (SR) is one of the most important ancillary services for saving power system stability and integrity in response to contingencies and disturbances that continuously occur in the power systems. Hence, an accurate day-ahead forecast of SR requirement helps the Independent System Operator (ISO) to conduct a reliable and economic operation of the power system. However, SR signal has complex, non-stationary and volatile behavior along the time domain and depends greatly on system load. In this paper, a new hybrid forecast engine is proposed for SR requirement prediction. The proposed forecast engine has an iterative training mechanism composed of Levenberg–Marquadt (LM) learning algorithm and Real Coded Genetic Algorithm (RCGA), implemented on the Multi-Layer Perceptron (MLP) neural network. The proposed forecast methodology is examined by means of real data of Pennsylvania–New Jersey–Maryland (PJM) electricity market and the California ISO (CAISO) controlled grid. The obtained forecast results are presented and compared with those of the other SR forecast methods.
ISSN:0306-2619
1872-9118
DOI:10.1016/j.apenergy.2009.10.026