An Exhaustive Solution of Power System Unit Commitment Problem Using Enhanced Binary Salp Swarm Optimization Algorithm

The Unit Commitment (UC) problem is a combinatorial optimization problem in power system operation with the key focus on achieving optimum commitment schedule of the generators for forecasted demand and spinning reserve. The computational complexity to determine a solution for the UC problem grows exponentially with the number of generators and system constraints. In this paper, the UC problem is formulated as a mixed-integer optimization problem and solved using novel Adaptive Binary Salp Swarm Algorithm by considering minimum up/down time limits, prohibited operating zones, spinning reserve, valve-point effect, and ramp rate limits. The proposed algorithm is tested for efficiency on the standard 10-unit system, 26-unit RTS system, 54-unit IEEE 118-bus system, 20, 40, 60, 80, and 100-unit systems. Additionally, an Adaptive Multi-Objective Binary Salp Swarm Optimization Algorithm is proposed for resolving the bi-objective emission constrained UC problem and tested using a 10-unit system. The obtained results are analyzed for positive differences against other algorithms from the literature. The statistical analysis exhibits the efficiency of the proposed method for large scale real-time systems.