Affiliations: Division of Instrumentation and control Engineering, Netaji Subhas Institute of Technology, University of Delhi, New Delhi, India
Correspondence:
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Corresponding author. Nandan Kumar Navin, Division of Instrumentation and control Engineering, Netaji Subhas Institute of Technology, University of Delhi, New Delhi, India. E-mail: nandan.nsit@gmail.com.
Abstract: We propose a fuzzy Reinforcement learning (FRL) framework for an efficient solution to the Economic thermal power dispatch (ETPD) considering multiple fuel options along with valve point loading effect concerning with thermal power generating units. The objective of ETPD is optimizing operating cost for specified power demand meet and to satisfy the generation capacity limits of each unit. In the presented work, We cast the ETPD as a multi agent FRL (MAFRL) problem wherein individual thermal generators act as players for minimizing operational cost and also satisfying the generation limits of each units to obtain a specified power demand. To prove supremacy and validity of proposed multi agent fuzzy reinforcement learning technique, two benchmark test systems involving 10 and 40 units integrated using numerous fuel systems with valve point loading effect have been simulated. Simulation results and comparison against several other existing solution approaches showcases the efficacy of MAFRL technique in solving the ETPD problem.
Keywords: Economic thermal power dispatch, fuzzy reinforcement learning, multiple fuel system, valve point loading
