Binary fish swarm algorithm for profit-based unit commitment problem in competitive electricity market with ramp rate constraints

摘要

This study presents a new approach based on a binary fish swarm algorithm (BFSA) and dynamic economic dispatch (DED) method for generation companies (GENCOs) profit (PF)-based unit commitment (PBUC) problem considering power and reserve generations simultaneously in a day-ahead competitive electricity markets. BFSA is used to decide the units on/off status, whereas the optimum dispatch solution is determined using DED method with modified unit generation limits because of ramp rate constraints over the complete scheduled time horizon. To avoid the search to get trapped at a local optimal solution, swap move-based local search and cyclic re-initialisation operators are embedded in BFSA. Moreover, two strategies for selling power and reserve are considered in problem formulation and implementation phase. Its effectiveness is validated on 10 and 100 thermal units in a day-ahead electricity market in terms of GENCOs PF and computation time. The results obtained for PBUC problem with BFSA method have been compared with those obtained with priority list, dynamic programming, Lagrange relaxation, genetic algorithm and binary artificial bee colony algorithm, and BFSA has been found effective to achieve quality solutions in reasonable computation time.