Affiliations: [a] Department of Electrical Engineering, IET, Lucknow, UP, India
| [b] Department of Electrical Engineering, SNU, Dadri, Gautam Buddha Nagar, UP, India
Correspondence:
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Corresponding author. Shubham Tiwari, Department of Electrical Engineering, IET, Sitapur Road, Lucknow, UP, 226021, India. E-mail: [email protected].
Abstract: Inclusion of renewable energy resources with existing conventional generation resources summons revisit to optimization methods used in the field of generation scheduling. The Unit Commitment problem in itself is a highly convoluted problem governed by complex time varying constraints. It gets even more complicated when additional constraints are added due to inclusion of renewable generation backed up by battery storage system. An effort has been made in this paper to improve the model for solving the Unit Commitment problem of conventional thermal generation in conjunction with renewable energy based generation system with storage. A hybrid artificial intelligence based multiple stage solution methodology is envisaged to provide a techno-economical optimal solution to the problem. The proposed methodology provides economically better solution to the Unit Commitment problem of ten thermal generators when integrated with battery supported wind and solar generation. The overall operational cost gets reduced due to integration of renewable resources which gets further reduced by incorporating battery with a novel optimized charge/discharge scheduling technique.
Keywords: Unit commitment problem (UCP), priority list method (PLM), particle swarm optimization technique with time varying acceleration coefficients (PS0_TVAC), renewable energy resources (RERs), battery energy storage (BES)
