Multiobjective Salp Swarm Algorithm Approach for Transmission Congestion Management

Table 1

Comparison of proposed CM approach with the reported methods.

Reported methodologies	Congestion management strategy	Advantages	Disadvantages	Reference
NSGA-II/DE	DG	Multiobjective	Transmission expansion	[1]
Fitness distance ratio-PSO and fuzzy adaptive-PSO	GR	CM in hybrid power market	Single objective	[4]
Gravitational search algorithm	ATC enhancement	CM with TCSC	Cost not included/single objective	[7]
GAMS software	DR	CM with renewable energy source	Cost not included/Single objective	[13]
CPLEX (MATLAB)	DR	CM with renewable energy source	Cost not included/single objective	[14]
Differential evolution	DG	CM with renewable energy source	Cost not included/single objective	[15]
Matpower toolbox	DG	CM based on flow gate marginal price	single objective	[16]
GA based	DG	LMP difference based CM	Generation cost not included	[17]
DE-PSO	DG	CM with renewable energy source	Multiobjective problem handled as single objective	[18]
Flower pollination	DG	CM with renewable energy source	Multiobjective problem handled as single objective	[19]
MOPSO	DRP	2-objective functions	High demand response	[23]
Jaya algorithm	Transmission switching/DR	Hybrid power system	All transmission lines cannot be switched off	[24]
GAMS software	Optimal energy storage system charging	Multiobjective	Cost and emission are in single objective/load shedding	[35]
Hybrid bacterial foraging and nelder–mead algorithm	TCSC placement	2-objective functions	Multiobjective problem handled as single objective	[38]
MATPOWER (MATLAB)	TCSC placement	CM with renewable energy source	Cost not included/single objective	[39]
MOSSA [proposed]	DR/RIPP	3-objective functions/CM with renewable energy source		[Proposed]