Multi–dimensional firefly algorithm based on local search for solving unit commitment problem

Abstract

The Unit Commitment problem (UC) is a complex mixed-integer nonlinear programming problem, so the main challenge faced by many researchers is obtaining the optimal solution. Therefore, this dissertation proposes a new methodology combining the multi-dimensional firefly algorithm with local search called LS-MFA and utilizes it to solve the UC problem. In addition, adaptive adjustment, tolerance mechanism, and pit-jumping random strategy help to improve the optimal path and simplify the redundant solutions. The experimental work of unit commitment with the output of 10–100 machines in the 24-hour period is carried out in this paper. And it shows that compared with the previous UC artificial intelligence algorithms, the total cost obtained by LS-MFA is less and the results are excellent.

Unit Commitment Considering Electric Vehicles and Renewable Energy Integration—A CMAES Approach

“…Under these conditions, the best cost value obtained in 30 trial runs is USD 1,624,873.6, which is less than the USD 1,636,381.0 that is achieved using ABSSA [34] and the USD 1,643,750.0 that is obtained using B&B-IPM [35]. The unit commitment status for the 54 thermal units is given in [31]. A comparison of the cost over 30 runs of a 54-unit system is given in Table 8.…”