Optimal Spinning Reserve Indentification in Competitive Electricity Market Using Adaptive Neuro-Fuzzy Inference System

W. Ongsakul and K. Chayakulkheeree (Thailand)


Ancillary services, Adaptive neuro-fuzzy inference system.


This paper proposes an adaptive neuro-fuzzy inference system (ANFIS) based optimal identification for spinning reserve in competitive electricity market. The probability of generation outage or forced outage rate (FOR), the value of lost load (VOLL), the daily load forecast uncertainty (LFU) and the spinning reserve price are used as the input values. The spinning reserve requirement is the output of the proposed ANFIS. The method is tested on the IEEE 24-bus reliability test system. The ANFIS can evaluate several certain and uncertain factors affecting spinning reserve requirements in a soft computing manner. In addition, the obtained solutions are close to the minimum expected reliability cost based reserves.

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