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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Published in:	Applied energy Vol. 87; no. 6; pp. 1870 - 1879
Main Authors:	Amjady, Nima, Keynia, Farshid
Format:	Journal Article
Language:	English
Published:	Kidlington Elsevier Ltd 01.06.2010 Elsevier
Series:	Applied Energy
Subjects:	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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Abstract	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.
AbstractList	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.
Author	Keynia, Farshid Amjady, Nima
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Keywords	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
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SubjectTerms	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
Title	A new spinning reserve requirement forecast method for deregulated electricity markets
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